Forestry Resource Efficiency, Total Factor Productivity Change, and Regional Technological Heterogeneity in China

By inergency On Jan 11, 2024

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1. Introduction

Forests provide livelihoods, protect watersheds, sustain biodiversity, and play an essential role in carbon sequestration and oxygen production. Forests are interdependent elements of the global ecosystem, influencing the climate, weather, and environmental health [1]. Preserving and managing forests sustainably is essential in addressing local and global challenges, such as ensuring clean water and biodiversity and mitigating climate change [2]. Industrial growth’s positive and negative effects on the ecosystem and forests are diverse and far-reaching. The technological advancements brought about by industrialization have enhanced forest management practices. Due to industrial innovation, sustainable logging techniques, reforestation efforts, and eco-friendly materials have been developed [3]. However, industrial expansion has also posed a significant environmental challenge. Expanding factories, infrastructure, and urban areas frequently result in deforestation and habitat loss, which endangers forest biodiversity. Both plant and animal species within forest ecosystems are negatively impacted by industrial pollution, such as air and water pollution [4].

In addition, industrialization has contributed to climate change by emitting greenhouse gases, which contribute to destabilizing forest ecosystems by altering temperature and precipitation patterns. This can potentially affect forest species’ distribution and viability [5]. Furthermore, the demand for basic materials from industrial sectors can lead to the overexploitation of forest resources, resulting in the degradation and loss of vital ecosystems. Striking a balance between industrial development and environmental preservation is a difficult task that requires careful planning, sustainable practices, and a commitment to protecting our forests and the larger ecosystem [6]. The UN has promoted afforestation and reforestation for 20 years to combat climate change and preserve biodiversity, increasing global forest cover (see Figure 1). Programs like CDM and REDD+ in developing countries promote tree cultivation and sustainable forest management [7]. International agreements like the Bonn Challenge and the New York Declaration on Forests set ambitious global goals for the restoration of degraded landscapes and cessation of deforestation, emphasizing the crucial role of afforestation and reforestation in reducing greenhouse gas emissions and safeguarding ecosystems [8,9].

With the fifth largest forest area globally, China’s forests have experienced complex challenges in recent years. Significant efforts have been made to increase the forest cover through afforestation and reforestation initiatives, resulting in substantial gains in forested areas [10]. However, many of these new forests are monoculture plantations that lack the biodiversity and resilience of natural forest ecosystems. China also grapples with illegal forestry, urbanization, and infrastructure development, contributing to deforestation and habitat loss. Due to industrial activities, air pollution, water contamination, and soil degradation further strain forest ecosystems. In addition, the effects of climate change, such as increased temperatures and altered precipitation patterns, threaten the health of forests [11,12].

The Chinese government has taken several measures to address these issues, such as enforcing stricter logging restrictions, promoting sustainable forest management, and making significant investments in projects focused on ecological restoration. China faces a significant challenge in balancing economic growth with protecting and restoring its various forest ecosystems [13]. For numerous important reasons, forestry efficiency is of paramount importance. First, effective forestry practices contribute to the sustainable management of forest resources, thereby preventing overexploitation and environmental destruction. Second, it maximizes economic benefits by optimizing timber production, improving the livelihoods of forest-dependent communities, and bolstering forest-related industries. Thirdly, increased efficiency reduces the carbon footprint of forestry operations, thereby contributing to climate change mitigation. Furthermore, effective forestry practices play a role in biodiversity conservation, safeguarding clean water sources, and maintaining crucial ecosystems. In essence, the efficiency of forestry operations is vital in achieving a harmonious balance between environmental preservation, economic growth, and social well-being [14,15].

Moreover, optimizing the forest efficiency relies on considering regional variations in production technology. Climate, ecosystems, and infrastructure influence the choice of methods. The diversity in forest cover and resources across various regions of the country should be considered. Modifying technology to the local conditions maximizes resource utilization and supports sustainable forest management in different regions [16]. Dynamic growth in forestry resource efficiency is also a powerful indicator of consistent growth in resource-optimized utilization. The Chinese government has significantly improved the forestry efficiency and dynamic productivity in recent years. These initiatives acknowledge the importance of forests for ecological preservation, carbon sequestration, and sustainable resource management [17]. China has implemented modern forest monitoring and management technologies, including remote sensing and geographic information systems (GIS). To combat deforestation and enhance forest health, the government has promoted sustainable logging practices, reforestation, and afforestation projects [18]. In addition, stringent regulations have been implemented to combat illegal harvesting and encourage responsible forest management. The government’s focus on green finance and investments in ecological restoration initiatives further underscore its commitment to enhancing forestry efficiency and sustainability in China. These extensive efforts are consistent with global environmental objectives and contribute to China’s role as a custodian of its forest resources [19].

Nevertheless, despite these commendable efforts by the Chinese government, the extent of its success in achieving forestry efficiency, assessing the total factor productivity change and understanding the regional technological disparities in different regions of China, remains largely unexplored. It represents a crucial research gap and is worth investigating. To this end, our study contributes to the existing literature in several ways. In the first stage, we employ DEA-SBM on the data of China’s 31 mainland provinces and municipalities from 2001 to 2020 to gauge the forestry efficiency, providing insights into how effectively these provinces utilize their forest resources. We evaluate the forestry efficiency over the study period and investigate the growth or decline in forestry efficiency in China. We also explore the level of success in the Chinese government’s mission of forestry efficiency growth over an extensive period of 20 years. In the second stage, the study includes a meta-frontier analysis to assess production technology discrepancies across four distinct Chinese regions, namely Northeast, Northern, Southern, and Southwest China, exploring the regional heterogeneity in technological advancements within the forestry sector. Thirdly, the study uses the Malmquist productivity index to measure changes in total factor productivity (TFP) over time, allowing us to determine whether efficiency improvements or technological changes primarily drive shifts in productivity growth. We also explore the dynamic change (year to year) in forestry efficiency. This can help policymakers and forest management authorities on regional and national levels to identify potential opportunities to enhance forest resource utilization. Productivity growth could be achieved by either strengthening the technical efficiency in forest resource utilization or acquiring the advanced technological capabilities used in forest resource conversion. Finally, to strengthen the study’s results, the Kruskal–Wallis test is used to estimate the significant statistical differences among the forestry efficiency, total factor productivity change, and technology gap ratio in four different regions of China. The rest of the study is distributed as follows. Section 2 presents the comprehensive literature review. Section 3 and Section 4 contain the Materials and Methods and Results and Discussion sections. Section 5 presents the conclusions and policy implications of the study.

2. Literature Review

Data envelopment analysis (DEA) is a powerful tool to assess the efficiency of forestry operations in different countries and regions. Chen et al. [20] stress the necessity of evaluating the forestry eco-efficiency, considering ecological preservation and economic output conflicts. They employ a three-stage DEA model with the DEA-Malmquist approach, finding strong influences from internal practices and external factors. Environmental variables impact the input parameters differentially. The study identifies absolute and conditional convergence in Chinese forestry eco-efficiency, implying catch-up effects in less developed provinces. Młynarski et al. [21] assess the Southern Polish Forest district’s efficiency using conventional data envelopment analysis and the Tobit econometric model. They cover 113 forest districts in four Regional Directorates of State Forests from 2008 to 2012. The economic and financial resource efficiency vary across districts, with higher efficiency in lowland districts. External factors have significant impacts, such as the population density affecting financial efficiency in lowlands and the population density and forest complexes negatively affecting the economic resource efficiency. Financial effectiveness correlates positively with forest complexes in the highlands but adversely with nature reserve areas. These characteristics have limited effects on the highland economic resource efficiency. Forest-based enterprises embrace bio-based economic strategies for renewable resource utilization. Bont et al. [22] stress the need for efficient forest management for biodiversity and ecosystem services, including timber production and carbon sequestration. Modern harvesting technologies could make the forestry business in Switzerland more cost-effective, especially in difficult terrain, resulting in economic gains and enhanced harvesting feasibility. The spatial decision support system estimates the best harvesting methods based on productivity models and expert-defined decision trees. It shows a 12% increase in the economically viable harvested forest area if applied nationwide. Further, numerous studies have used DEA to evaluate the forestry efficiency in different regions and countries [23,24,25,26,27,28,29].

Similarly, Li et al. [30] argue that climate change mitigation becomes more important as forests are valued for their non-timber benefits and biomass carbon sequestration. In Changbai Mountain, Northeast China, the forest type, age class, and establishment mode considerably affect the net primary productivity (NPP), evapotranspiration (ET), and water use efficiency (WUE). Coniferous woods have higher NPP and WUE but lower ET than broadleaved forests, indicating the importance of planning and management for temperate carbon sequestration and water conservation. Moreover, Jin et al. [31] examine the changes in China’s forestry industry, stressing integrated development and productivity for sustainable growth. Research using many analytical methodologies shows that the integration of the forestry industry has increased, with most provinces at moderate to medium–high levels. The total factor productivity in the forestry industry has improved, primarily due to technology. According to a study, forestry industry integration boosts total factor production by improving the pure technical efficiency and spatial spillover effects on nearby regions. These findings emphasize the importance of integrated forestry industry development, regional collaboration, and successful regional forestry growth policies. Lin and Ge [32] emphasize forestry’s role in climate change mitigation and regional economic output. They analyze the static efficiency and dynamic changes in forestry productivity in thirty Chinese regions using the slacks-based measure (SBM) technique and the Malmquist–Luenberger index. Ecological and economic development through forest carbon sinks and production value are highlighted in this study. The research shows that environmental efficiency and productivity estimators outperform economic progress, with the southwest region of China leading the way. Zhang and Xu [33] use a novel hybrid approach, combining LCA and time-series DEA, to evaluate an enterprise’s eco-efficiency in the forestry industry. Efficient years experience 45.25% of the environmental impact of inefficient years, with fiber and energy consumption further reducing these percentages to 65.53% and 77.66%. The study offers practical recommendations for forestry firms to minimize their environmental impacts and optimize resource use in the industrial chain. Obi and Visser [34] report 1.7% annual productivity growth in New Zealand’s forest harvesting sector from 2009 to 2018, driven by technology and efficiency improvements. Their research emphasizes that optimizing the technology efficiency and reducing inputs can sustain productivity growth in the forestry industry. Yin et al. [35] argue that the carbon sequestration efficiency, ecological afforestation, temperature, GDP per capita, urbanization, population, and total imports and exports have spillover effects. Many studies use the Malmquist productivity index to gauge the dynamic change (TFP) in forest resource efficiency globally [36]. Further, DEA has been extensively used in forestry and environmental efficiency estimation [37,38,39,40].

Studies have proven that the forestry efficiency and sustainability depend on technological advancement. GIS, remote sensing, and drones provide real-time data on forest health, growth, and environmental variables, enabling more precise forest management. Mechanized harvesters and GPS-guided tractors speed up logging, minimizing waste and environmental effects. Analytics and predictive modeling improve forestry decision-making, maximizing resource allocation and minimizing ecological impacts. Technology boosts productivity, resource use, and carbon reduction in the forestry business, making it more efficient and sustainable [41,42]. Obi and Visser [43] evaluated the efficiency of New Zealand’s forest harvesting sector using categorical data envelopment analysis (DEA) to resolve the increasing competition in the global forestry market. They demonstrated the suitability of categorical DEA for this application and evaluated the impact of log extraction techniques and processing locations on efficiency scores. The analysis revealed that timber extraction techniques significantly affected the overall performance, with grapple skidders exhibiting the highest average efficiency of 58%. However, processing at the stump is associated with the most significant average efficiency score. To remain competitive and profitable in the ever-changing timber market, selecting efficient log extraction technologies is crucial. Ke et al. [44] also demonstrated the importance of technology in enhancing forestry efficiency. This regional heterogeneity of production technology utilizing forest resources is essential in efficiency and productivity evaluation [45,46]. This study evaluates the forestry efficiency and total factor productivity growth in different regions and provinces of China to gauge the level of success and further understand the production technology heterogeneity in four other regions.

3. Materials and Methods

The synergy between forecasting and optimization is crucial in decision-making. Forecasting, utilizing methodologies like time-series analysis and machine learning, predicts future trends based on past data, providing insights for informed decision-making, risk management, and strategic planning. Methods for optimization, such as linear and integer programming, focus on finding the most efficient solutions to maximize or minimize objectives. It aids in the improvement of resource allocation and processes. Precise forecasts serve as valuable inputs for optimization models, guiding production planning and inventory management decisions. Optimization models like DEA, with dynamic capabilities, adjust plans in response to real-time or updated forecasts, enhancing the adaptability in volatile contexts. Data envelopment analysis (DEA) is a commonly used mathematical method that utilizes linear programming to evaluate the efficiency of similar decision-making units (DMUs) [47,48]. The selection of data envelopment analysis (DEA) over stochastic frontier analysis (SFA) in our research was driven by the unique characteristics of our dataset. Our data did not conform to the normality assumption, making DEA a more suitable choice. DEA is a non-parametric method that does not rely on distributional assumptions, making it robust when dealing with data that may not follow a normal distribution. This flexibility allowed us to effectively assess the efficiency and productivity in the forestry sector, considering the specific nature of our dataset. The traditional DEA model, pioneered by Charnes et al. [49], assumes a constant return to scale (CSR). Based upon this, Banker et al. [50] modified the model to incorporate a variable return to scale (VSR). A preliminary [51] investigation by Tone introduced the slacks-based measure (SBM) model. Subsequently, Tone [52] devised a method of ranking the most effective DMUs. The selection of DEA-SBM to estimate the forestry resource efficiency is attributed to its capacity to evaluate systems with multiple inputs and outputs. Specifically, it compares forestry units (provinces) based on inputs (forest area, investment) and outputs (forest output value, timber output, forest stock volume). DEA-SBM uses the efficiency frontier as a measure to evaluate unit performance. Importantly, DEA-SBM is suitable when it is difficult to establish a specific functional form for the production frontier, distinguishing it from stochastic frontier analysis (SFA). Unlike SFA, which assesses inefficiency by comparing it to a particular form, DEA-SBM’s non-parametric approach offers flexibility in evaluating the efficiency of China’s varied forest resources. The study used Max-DEA for estimation.

3.1. DEA-SBM Model

The slacks-based measure (SBM) represents a non-radial approach to assessing the data envelopment analysis (DEA) efficiency. Its primary strength lies in its capacity to directly evaluate excess inputs and insufficient outputs. When determining efficiency, it considers the slack, which represents the difference between inputs and outputs at the production frontier. This method operates based on the following principles. Suppose that we have a study with n decision-making units (DMUs) referred to as “Provinces”. M input indicators and s output indicators characterize each DMU. Let

B_{j}

, represent the

j

-th DMU, where j ranges within

j = 1,2, \dots . ., n; [x_{i j}]

, represents the

m \times 1

input indicators of DMU

B_{j}

, with

i

ranging from 1 to

m; [y_{r j}]

represents the

s \times 1

output indicators of DMU

B_{j}

, with r ranging from 1 to

s

. The relative efficiency value of the j_0-th DMU is denoted as

h_{j 0}

. Now, let us discuss how the output-focused SBM-DEA model with variable returns to scale operates:

$\begin{matrix} M i n h_{j 0} = θ \\ s . t \{\begin{matrix} \sum_{j = 1}^{n} λ_{j} x_{i j} \leq θ x_{i j 0}, i = 1, \dots, m & (1) \\ \sum_{j = 1}^{n} λ_{j} y_{r j} \geq y_{i j}, r = 1, \dots, s & (2) \\ \sum_{j = 1}^{n} λ_{j} = 1, λ_{j} \geq 0, j = 1, \dots ., n & (3) \end{matrix} \end{matrix}$

The efficiency value at the j-th position is represented as $θ$ , where $λ_{j}$ is a nonnegative vector. A DMU is considered efficient if and only if $θ$ equals 1, indicating that it operates at maximum efficiency. If $θ$ is not equal to 1, the DMU is inefficient and has room for improvement.

3.2. DEA-Meta-Frontier Model

The utilization of the meta-frontier model yields enhanced precision in the evaluation of DMU efficiency across distinct groups. Consequently, given that all DMUs within a given group operate under equivalent technological conditions, the most prudent approach is to undertake efficiency comparisons within the confines of the same group. The technical gap ratio (TGR) metric proves instrumental in gauging the extent of divergence in technological progress between specific groups [53].

$T G R = \frac{M F R E}{G F R E i}$

(4)

The forestry resource efficiency (FRE) is computed for the complete set of decision-making units (DMUs) under consideration. In this context, GFREi represents the FRE applicable to DMUs categorized within a specific group. In contrast, MFRE pertains to the Meta-FRE associated with DMUs spanning the entire population, including all distinct groups. By juxtaposing the divergence between a meta-frontier technology and the frontier technology specific to a given group, the technology gap ratio (TGR) functions to numerically characterize the discrepancy between these two cohorts of DMUs [54]. An equivalent of TGR denotes the absence of a technological rift between the comprehensive population and the group frontier, thus establishing TGR as a prevalent instrument in evaluating territorial disparities.

3.3. DEA-Malmquist Productivity Index

The Malmquist productivity index (MPI) is a comprehensive analytical tool to assess temporal productivity variations in decision-making units (DMUs), such as organizations and businesses. Derived from production economics and building upon data envelopment analysis (DEA), the MPI comprises two vital elements for nuanced evaluation. Firstly, the technical efficiency change (TEC) measures shifts in a DMU’s distance to the production frontier, indicating changes in efficiency relative to top performers. A positive TEC signifies increased efficiency, while negative values denote a decrease. Secondly, the technological change (TC) assesses transformations in the production frontier, reflecting technological and managerial advances. The MPI, obtained by multiplying TEC and TC, represents the aggregate productivity change between two periods. A score above one indicates heightened productivity due to technological advances and improved efficiency, while a score below one signifies a decline. The MPI is valuable for decision-makers seeking insights into productivity shifts, offering applicability across industries and guiding strategic decisions based on a comprehensive understanding of technological dynamics and efficiency. Malmquist productivity indices provide a valuable tool for a decision-making unit (DMU) to track improvements in efficiency over time. To effectively utilize this index, it is assumed that a production function that accurately represents the current technological environment exists. DEA models precisely pinpoint the location of this production function’s threshold. The difference in output between periods t and t + 1 defines a specific DMU, referred to as (

D M U_{0}

) [55].

$M_{0} = \frac{D_{0}^{t + 1} (x_{0}^{t + 1}, y_{0}^{t + 1})}{D_{0}^{t} (x_{0}^{t} y_{0}^{t})} {[\frac{D_{0}^{t} (x_{0}^{t + 1}, y_{0}^{t + 1})}{D_{0}^{t} (x_{0}^{t}, y_{0}^{t})} \frac{D_{0}^{t + 1} (x_{0}^{t + 1}, y_{0}^{t + 1})}{D_{0}^{t + 1} (x_{0}^{t} y_{0}^{t})}]}^{1 / 2}$

(5)

where:

$D_{0}^{t} (x_{0}^{t}, y_{0}^{t})$ shows the TE estimation of the $D M U_{0}$ for period t;
$D_{0}^{t + 1} (x_{0}^{t + 1}, y_{0}^{t + 1})$ illustrates the TE estimation for period t + 1;
$D_{0}^{t} (x_{0}^{t + 1}, y_{0}^{t + 1})$ specifies the variation in TE from time t to t + 1;
$D_{0}^{t + 1} (x_{0}^{t}, y_{0}^{t})$ represents the technical efficiency of a specific $D M U_{0}$ . This efficiency is computed by replacing its data from period t with the corresponding data from period t + 1.

The initial segment of Equation (5) without parentheses denotes the variation in the technical efficiency of $D M U_{0}$ between time t and t + 1. The timeframe enclosed within the square brackets illustrates the advancement in technology for the same DMU. If the index value exceeds 1, it signifies that $D M U_{0}$ achieved a greater output during the second period compared to the first. Two hypotheses can be put forth to elucidate this substantial rise in output. Firstly, it is plausible that $D M U_{0}$ embraced state-of-the-art methodologies, enhancing its efficiency.

3.4. Kruskal–Wallis Test

The Kruskal–Wallis test is a non-parametric statistical method used to assess the presence of statistically significant differences between three or more independent groups or treatments [56]. The Kruskal–Wallis test differs from the Mann–Whitney U test, designed to compare two independent groups by not assuming a normal data distribution. Instead, it determines whether the medians of the groups are comparable by rating all values collectively and then determining whether these ranked values exhibit significant disparities across groups. When the Kruskal–Wallis test identifies a statistically significant difference, it indicates that at least one group differs concerning the investigated variable. This test proves invaluable when ordinal or continuous data fail to satisfy the requirements of parametric tests such as the analysis of variance (ANOVA). The Kruskal–Wallis test is frequently used in social sciences, healthcare, and environmental studies to compare groups with non-normally distributed data. In this study, the different regions’ forestry efficiency, productivity growth, and technology gap ratios should be heterogeneous. However, are these values statistically significantly different for other regions of China? To prove this, the Kruskal–Wallis test identifies the significant statistical differences among the four Chinese regions for the average FRE, MI, and TGR. The hypotheses are as follows:

H₁:

The FRE is the same in the four different Chinese regions.

H₂:

The TGR change is the same in the four different Chinese regions.

H₃:

The MI is the same in the four different Chinese regions.

6. Conclusions and Policy Implications

The optimization of forestry resource usage has been a longstanding and prominent issue, as the establishment of sustainable resource management is a fundamental requirement in achieving economic and societal sustainability. Further, assessing technological diversity across different regions to maximize the utilization of forest resources is of great importance for giant economies like China. It allows tailored strategies, ensuring efficient, sustainable management and equitable development while considering regional differences. Moreover, estimating the determinant of total factor productivity in forest resource utilization in China is important in evaluating the success of dynamic productivity growth over time. Using regulations, afforestation, and investments in technology, the Chinese government has made considerable effort to improve the efficiency of forest resources, decrease technological disparities, and boost productivity growth. These measures aim to reduce regional disparities, foster sustainability, and standardize procedures. The success of these government efforts in enhancing the forest resource efficiency, reducing the technological heterogeneity, and increasing productivity growth in forest resource utilization is a promising yet undiscovered area worth exploring.

This study assesses the forestry resource efficiency, technology gap ratios in different regions, and total factor productivity growth in China’s 31 provinces/municipalities (2001–2020). SBM-DEA is used to evaluate resource efficiency; meta-frontier analysis is used to gauge the TGR by forest region; and the Malmquist index estimates dynamic efficiency changes. Input indicators include the forestry investment, workforce, and forested area, while output indicators are the forestry output value, timber production, and forest reserves. Chinese provinces have 45.70% potential to enhance their forest resource utilization efficiency (average FRE: 0.5430). The FRE fluctuated over time, with peak levels in 2015, 2016, and 2018. Anhui, Tibet, Fujian, Shanghai, and Hainan excelled in forestry utilization. In contrast, Qinghai, Gansu, Ningxia, Shaanxi, and Xinjiang were the poorest performers in terms of forestry efficiency. Further elaborating on the regional results, the study found that the average FRE in the southern forest region of China was at an optimum level, with an efficiency score of 0.7505. Similarly, the southwest (0.6019) ranked second, and the northeastern (0.4394) and northern (0.3329) regions ranked third and fourth, respectively. This indicates that, on average, provinces in the southern region are more efficient in utilizing their forest resources.

In a frontier analysis of the technological efficiency in China’s forest regions, the southern forest region emerges as the leader in modern forest resource utilization, with an impressive average technology gap ratio (TGR) of 0.915, approaching the optimal value of 1. This indicates that the southern provinces excel in harnessing forest resources through contemporary production technologies. Within this region, Anhui and Jiangsu have already reached the pinnacle of forestry efficiency, each achieving a TGR of 1, underscoring their advanced production technologies. In the northern region, which records the lowest TGR among the four regions, with an average of 0.4197, Tianjin stands out as a notable exception, maintaining superior technology with a TGR value of 0.7329. In contrast, Xinjiang lags with a TGR of 0.2206, making it the least efficient performer among the northern provinces. The northeastern region displays significant growth potential, with an average TGR of 0.4492, signifying room for improvement. Jilin, in particular, exhibits promise, boasting a TGR of 0.794, indicating its proximity to maximum efficiency. However, Inner Mongolia lags, with a TGR of 0.2271, denoting substantial room for advancement. Lastly, the southwestern forest region of China demonstrates moderate production technology, with an average TGR of 0.6512, ranking second after the southern region. Notably, Tibet stands out as the top performer in this region, achieving a TGR value of 1, signifying its prowess in maximizing its technological frontiers.

The Malmquist productivity index (MI) averaged 0.9644 (2001–2020), reflecting a 3.56% decline in forestry resource productivity in Chinese provinces. Technological change (TC) was the primary factor behind the productivity decrease, reaching 5.2%, while the efficiency change (EC) increased by 1.74%. The highest MI score was in the Southern Chinese forest region (1.0306), indicating a 3.06% average total factor productivity increase, driven by 3.38% EC growth. The northern forest region had the second-highest MI (1.0186), with EC growing by 4.4%, offsetting a 2.43% TC decrease. In contrast, the southwestern forest region saw a 1.25% total factor productivity decline, mainly due to an 8.81% TC decrease. Conversely, technical efficiency grew by 0.85%. Lastly, the northeastern forest region of China exhibited the lowest performance with a mean MI score of 0.9644, signifying a 3.56% total factor productivity decline. Technological change is the primary factor behind this decline, with a TC value of 0.948. Similar to the southwestern region, there was 1.74% growth in technical efficiency. Finally, the results of the Kruskal–Wallis test proved the significant statistical differences among the four forest regions of China regarding the FRE and TFP changes.

The government’s commendable efforts through regulations, afforestation, and technology investments should continue to reduce regional disparities, foster sustainability, and standardize practices. The varying efficiency and productivity highlight the need for targeted interventions, particularly in less efficient provinces, promoting technological investments. High-efficiency provinces can serve as models for sustainable practices, with capacity-building and technology transfer programs bridging gaps in less efficient regions.

Moreover, encouraging research and development in forest management technologies is essential to complement China’s efficient and sustainable resource utilization efforts. In conclusion, this study’s findings offer valuable insights for policymakers to refine their strategies, promoting sustainable resource management and fostering economic and environmental sustainability in this vital sector. While some regions in China have excelled in their technological utilization of forest resources, others lag, indicating the need for technology transfer and knowledge sharing from more advanced areas. Provinces that would benefit from acquiring technology and expertise from their more proficient counterparts include Inner Mongolia and Xinjiang in the northern region, as they have shown lower efficiency and technology gaps. Similarly, the northeast region, with provinces like Jilin and Inner Mongolia, could benefit from adopting best practices from the southern and southwestern regions. These provinces could leverage the experiences of Anhui, Jiangsu, and Tibet, which have demonstrated advanced efficiency and technology utilization, to bridge the technological gap and improve their overall forest resource productivity. Effective technology transfer programs and capacity-building initiatives can facilitate this process, fostering the more equitable and efficient utilization of forest resources across all regions in China.

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