From Policy to Practice: A Comparative Topic Modeling Study of Smart Forestry in China

Abstract

The accelerated penetration of digital technology into natural ecosystems has led to the digital transformation of forest ecological spaces. Smart forestry, as a key pathway for digital-intelligence-enabled ecological governance, plays an important role in global sustainable development and multi-level governance. However, due to differences in functional positioning, resource capacity, and policy translation mechanisms, semantic shifts and disconnections arise between central policies, local policies, and practical implementation, thereby affecting policy execution and governance effectiveness. Fujian Province has been identified as a key pilot region for smart forestry practices in China, owing to its early adoption of informatization strategies and distinctive ecological conditions. This study employed the Latent Dirichlet Allocation (LDA) topic modeling method to construct a corpus of smart forestry texts, including central policies, local policies, and local media reports from 2010 to 2025. Seven potential themes were identified and categorized into three overarching dimensions: technological empowerment, governance mechanisms, and ecological goals. The results show that central policies emphasize macro strategy and ecological security, local policies focus on platform construction and governance coordination, and local practice features digital innovation and ecological value transformation. Three transmission paths are summarized to support smart forestry policy optimization and inform digital ecological governance globally.

1. Introduction

1.1. Background

Forests represent the terrestrial ecosystems with the widest coverage and the most complex structure on Earth, shouldering the important mission of maintaining the global ecological balance [1]. However, with the accelerating degradation of global forests, the rapid decline of biodiversity, and the worsening of ecological security, the conventional forestry governance system—relying on manual patrols and static management approaches—requires urgent transformation towards intelligent and efficient management. In the context of the accelerated integration of digital technologies into natural ecosystems, forests are undergoing a gradual transformation into highly digitalized ecological spaces [2].

The Forestry 4.0 revolution is driving a reconfiguration of the forestry governance system. This transformation is characterized by the emergence of “smart forestry”. Its core feature is the comprehensive integration of digital technologies—such as artificial intelligence, remote sensing monitoring, the Internet of Things (IoT), and big data—across the entire process of forest resource monitoring, management, and protection [3]. As the foundational practice of Forestry 4.0, smart forestry endeavors to expand the technological boundaries of ecological governance while promoting the integration of ecosystems into digital governance networks through the integration of digital technologies into forest management protection. The integration of digital technologies into forestry practices can play a pivotal role in addressing the global ecological crisis. It can also contribute to the achievement of the Sustainable Development Goals (SDGs) and accelerate progress toward the climate-neutral vision 2050 [4]. This trend opens up new pathways for the digital transformation of national forestry policies.

As a vast nation with substantial global forest resources, China has elevated forest ecological security to the level of a national strategy, actively promoting the digital and intelligent transformation of forestry governance. Central authorities have issued a series of policy guidelines to promote system-wide sensing, intelligent coordination, and data-driven management in the forestry sector. At the local level, Fujian Province has emerged as a pioneer. Fujian is one of the first batches of ecological civilization pilot zones in China, with high forest coverage and an extensive mountainous area. The province has accumulated rich experience in the reform of the collective forest rights system and ecological protection. Since the implementation of the “Digital Fujian” strategy at the beginning of this century, Fujian has continued to promote the construction of digital infrastructure in the field of forestry, introduced a number of local policies and measures, and explicitly proposed the construction of “a forestry big data center, two service platforms, and three business systems.” In 2023, Fujian established China’s inaugural one-stop forestry drone service platform, thereby achieving the digitalization of the entire forest supervision process. Despite these promising advances, a critical governance challenge remains insufficiently addressed. While central policies outline ambitious blueprints for smart forestry, their transmission across administrative levels is often marked by semantic divergence, fragmented coordination, and inconsistent execution. This disconnect raises a fundamental yet underexplored question: how are smart forestry policies semantically conveyed, transformed, or lost across multiple layers of governance? Addressing this question requires a closer examination of the semantic structures embedded in policy documents, and a better understanding of how institutional dynamics shape policy coherence and local adaptability.

1.2. Literature Review

Smart forestry represents a new paradigm for integrating digital governance into ecological management. The concept of Smart Earth was first proposed by IBM in 2008, and smart forestry emerged accordingly as a key subsystem of this broader vision [5,6]. Building upon the foundation of digital forestry, smart forestry leverages next-generation information technologies such as cloud computing, the Internet of Things (IoT), mobile internet, and big data to realize the digitalization, perception, interconnectivity, and intelligent management of forest resources [7,8]. This integration enables a multi-dimensional sensing system and highly coordinated governance processes to be developed, aligning with the intrinsic requirements of forestry modernization [9].

Smart forestry is often described through its enabling technologies or practical applications in forest operations [10]. According to the Guiding Opinions on the Development of Smart Forestry in China (NFGA, 2013), smart forestry refers to a new development model that fully utilizes cloud computing, IoT, big data, and mobile internet to build a forestry system that is perceptive, interconnected, and intelligent. This model aims to enable multi-dimensional ecological sensing, efficient collaborative governance, and the integration of internal and external forestry services, while highlighting ecological value [11].

Recent studies have expanded the concept from technical applications to include its implications for governance models and institutional transformation. Some scholars argue that smart forestry is not merely a technological upgrade, but also a potential reshaping of governance structures, actor relationships, and mechanisms of participation [2,3]. Others have emphasized the evolution from digital forestry to smart forestry as a process of embedding intelligence into decision-making systems and ecological feedback loops [4].

In terms of technical development, significant progress has been made in sensing and monitoring. For instance, IoT-based systems now integrate data collection, cloud computing, and real-time alerts to detect forest fires, illegal logging, and biodiversity changes [12]. Some researchers further incorporate enterprise blockchain with IoT to enhance transparency and traceability in forest monitoring systems [13]. From this perspective, many governments have incorporated digitalization into their forestry policies at the regional level. For example, the Italian National Forestry Strategy highlights digitalization as a key factor, supporting both sustainable forest management and collaborative governance [14,15,16].

Regional differences in program implementation are shaped by variations in technological capacity and local policy will [17]. In China, a study in Fujian Province examined how internet access affects forest farmers. It found that better access to information helps increase household income and improves life satisfaction. This finding reflects the socioeconomic spillover effects of smart forestry [18]. In Latin America, the concept of “smart green governance” has been introduced, highlighting the role of cooperation between local governments and the public, supported by remote sensing and data analytics, in forest monitoring and urban ecological governance [19]. These insights offer cross-national references for advancing smart forestry policy design.

Despite these developments, research on how smart forestry policies are transmitted across governance levels remains limited. Current research focuses on policy and institutional innovation [20], the transformation of ecological product value [21], and capacity development [22], which have gradually outlined the institutional landscape of the integrated governance of “wisdom + ecology.” However, information asymmetry, the ambiguous division of responsibilities, and inefficient communication between different levels of government are still key obstacles to the implementation of policies [23]; the participation of multiple actors in ecological governance is still uneven, and the roles of the public and enterprises in policy formulation and implementation need to be strengthened; and there is a cognitive gap between ecological policy design and public acceptance, which may lead to deviations in the implementation of policies [24]. This leads to the formation of a fault line with the characteristics of “hot at the top, warm in the middle, and cold at the bottom” [25].

To address structural challenges, scholars have begun employing the Latent Dirichlet Allocation (LDA) topic modeling tool to analyze policy texts, thereby addressing the limitations of previous policy analyses that relied heavily on qualitative induction [26]. The method has been widely used in the analysis of research integrity policies, open science strategies, and climate governance texts [27,28,29], and has demonstrated strong adaptability in China’s policy research in the field of ecology [30,31]. However, existing LDA-based research on smart forestry policies remains limited in scope, often focusing on single policy types or isolated administrative levels. There is a clear lack of comparative studies that systematically examine cross-level semantic divergence and implementation asymmetry in smart forestry governance.

1.3. Research Objectives

To fill the research gap, this study adopts a cross-level semantic modeling framework to investigate how China’s smart forestry policies are interpreted and disseminated at different governance levels. By constructing a corpus consisting of three parts: national-level policy texts, provincial-level regulations from Fujian, and practice-level reports from representative media outlets, this study applies the Latent Dirichlet Allocation (LDA) approach to extract and compare thematic structures. This approach enables a data-driven diagnosis of semantic divergence, the identification of policy–practice gaps, and the evaluation of the adaptive capacity of different policy themes. The findings will contribute empirical evidence for optimizing digital ecological governance frameworks and improving policy coherence across hierarchical levels.

The general objective of the study is to enhance the intelligent and effective development of smart forestry in China by improving the alignment between policy design and practical implementation, and by identifying mechanisms that promote institutional responsiveness and cross-level coordination. This study focuses on three specific objectives:

• Identifying semantic mismatches and transmission inconsistencies: Topic modeling is conducted to compare the similarities and differences in thematic structures across central-, provincial-, and grassroots-level documents and assess the extent of semantic misalignment between governance layers.
• Analyzing governance mechanisms that facilitate or hinder policy translation: The adaptability and responsiveness of different policy themes are analyzed based on their practical manifestations.
• Proposing actionable, theory-informed policy recommendations: Policy themes that demonstrate high adaptability or fragmentation in transmission across governance levels are identified. Policy content is optimized, semantic coherence is enhanced, and the link between top-level strategy and bottom-up innovation is strengthened.

This study makes three key contributions. First, it introduces a cross-level semantic analysis framework that bridges the gap between central policy and local implementation. Second, it empirically identifies the conditions that enable effective policy translation, providing insights for adaptive governance. Third, it offers practical and theoretically grounded policy recommendations to enhance institutional capacity for smart forestry and inform broader digital ecological governance.

2. Materials and Methods

2.1. Data Sources and Sample Construction

In this study, a text corpus is constructed to examine the formulation and implementation of smart forestry policies from three primary sources.

The first source consists of central-level policy documents on digital and smart forestry, including normative documents and guiding opinions on smart forestry, digital forestry, and forestry informatization. They were issued by the State Council, the National Forestry and Grassland Administration, and other relevant central agencies from 2010 to 2025. The second source is local policy documents from Fujian Province, including regional smart forestry plans, implementation programs, and pilot guidance documents issued by the Fujian Provincial Government and the Provincial Forestry Bureau. The third source includes practice-level materials, collected from major mainstream media outlets such as Fujian Daily, the Southeast China Network, People’s Daily Fujian Channel, and Guangming Online. These texts focus on local-level implementation practices, covering topics such as technology deployment, ecological governance, and grassroots participation. Rather than assessing outcome effectiveness, these reports are treated as a textual lens to capture how policies are interpreted, localized, and translated into action narratives at the practical level.

All texts were obtained from official websites and mainstream media to ensure basic authenticity and reliability. The corpus was curated to reflect both top-down policy design and bottom-up practical response. To enhance representativeness, we selected texts with a balanced distribution of document types, thematic focus, and functional intent.

To further ensure comparability across sources, we analyzed the textual structure of each sub-corpus. All character counts were computed after removing punctuation marks and whitespace, thereby reflecting the actual semantic content of each document. As shown in

Table 1. Document distribution and character counts across sources.

Category	Number of Documents	Total Characters	Avg. Characters per Document
Central Policies	21	243,436	11,592
Fujian Policies	13	173,323	13,333
Media Reports	66	113,571	1721

, a total of 100 documents were collected, including 21 national policies (243,436 characters), 13 Fujian provincial policies (173,323 characters), and 66 media reports (113,571 characters). While media texts are shorter on average (1721 characters), their greater quantity and coverage of concrete implementation scenarios provide complementary insight into policy responsiveness and local adaptation. All texts were converted into the .txt format, with original metadata such as titles, release dates, and sources retained for subsequent processing and modeling analyses.

2.2. Text Preprocessing

To improve the performance of the LDA topic model, we established a standardized text preprocessing workflow for Chinese-language documents within a Python (3.12.4)-based environment. The process includes the following steps:

(i)

Data cleansing and tokenization: Raw policy texts are segmented using the Jieba lexical tool. A custom dictionary containing more than 300 domain-specific terms related to smart forestry is applied, ensuring the accurate recognition of compound expressions such as “forestry informatization,” “ecological restoration,” and “forest management system”.

(ii)

Sub-word elimination: To avoid semantic overlap caused by redundant stems such as “smart” and “smart forestry”, sub-word elimination is applied, improving topic separability during modeling.

(iii)

Stop-word removal: A customized stop-word list was developed by combining generic Chinese stop-words with frequently occurring structural phrases commonly found in policy texts. Examples include “comprehensively promote,” “effectively strengthen,” “to this end,” “work task,” and “work plan.” These structural expressions, which offer limited semantic value, are filtered out to reduce noise and sharpen thematic focus.

(iv)

Text standardization: Texts are standardized by unifying the Simplified Chinese character format and removing non-informative elements, including special characters, English punctuation, full-width spaces, pure numbers, and meaningless words, while retaining keywords that are meaningful.

By implementing the above preprocessing steps, a cleaner and more semantically consistent corpus is constructed, thereby facilitating more effective LDA topic modeling. To ensure consistency in training and comparability of model results across different types of text (central government policies, local government policies, and media reports) in subsequent topic modeling, the above preprocessing steps were performed uniformly on all three types of text. Before model training, all texts are merged into a single corpus, uniformly applying the same tokenization rules, domain-specific dictionaries, stop-word lists, and sub-word elimination strategies, without any differentiated processing for specific text sources. This standardized processing strategy ensures consistency in the semantic structure of the corpus, enabling the model to accurately reflect discourse differences across different text sources rather than biases arising from data processing methods.

Additionally, we trained the LDA model uniformly based on the merged corpus, enabling the three text types to share the same topic space for the comparative analysis of topic assignments. This method enhances the semantic consistency within the model and provides a reliable foundation for effectively identifying topic similarities and differences across different text sources.

2.3. LDA Topic Modeling Approach

In this study, the Latent Dirichlet Allocation (LDA) model is employed to extract latent semantic structures from the smart forestry policy corpus. As one of the most widely adopted probabilistic topic modeling methods, LDA has demonstrated superior performance in large-scale semantic analysis tasks, particularly in the policy and governance domains. Compared with models such as latent semantic analysis (LSA) [32] and probabilistic latent semantic analysis (PLSA) [33], LDA provides key advantages: a clearer generative structure, better algorithmic scalability, unsupervised learning capacity, and resistance to overfitting [34,35]. These advantages make it especially suitable for processing extensive and complex policy texts [36,37].

Text mining technology has the capacity to identify latent associations, legal principles, and patterns within extensive textual corpora, subsequently converting these into structured knowledge. Topic modeling represents a significant technique within the domain of text mining. This approach, predicated on the notion of “letting the text speak,” has the capacity to mine potential topic distributions in large-scale texts under unsupervised conditions [37]. Furthermore, it has the ability to reveal probabilistic relationships between documents and topics [38,39]. As Yang Hui (2023) notes, policy documents are typically long, standardized, and rich in institutional expressions [40], making them well suited to topic-based semantic modeling. In this study, LDA assumes that each document is a mixture of topics, and each topic is defined by a probability distribution over words. Following the bag-of-words assumption, it evaluates the semantic relevance of words based on their frequency and co-occurrence patterns [41,42,43].

In order to ascertain a reasonable number of topics, previous studies have proposed various evaluation methods, including perplexity [34,44,45], topic coherence, and Jensen–Shannon scatter [46,47,48]. The objective of these methods is to enhance the stability and differentiation of topic delineation by preventing excessive clustering and topic overlap. Among these, perplexity reflects the model’s predictive capability, with lower values indicating better generalization performance [45,49]. Conversely, the coherence places greater emphasis on the semantic coherence between high-frequency words. It is evident that semantic coherence holds greater evaluative value in the analysis of policy texts, wherein the primary objective is interpretability, particularly within the context of multi-level policy language accompanied by implicit topic induction. In such scenarios, the interpretability of themes is directly associated with the validity of the research conclusions.

In addition to these conventional indicators, this study incorporates Kullback–Leibler (KL) divergence and Jensen–Shannon divergence (JSD) to assess the semantic deviation between adjacent topic models. KL divergence is a measure of divergence between two probability distributions, reflecting the directional change in the distribution of keywords. However, given the non-symmetric nature of KL and its potential sensitivity to outliers, JSD is a smooth, symmetric variant of KL. It is employed to quantify the average semantic distance between topic distributions. The divergence metrics in question provide further evidence to support the identification of structural tension in topic configurations, thereby contributing to the empirical validation of the chosen topic number.

Given the structured yet abstract nature of multi-level governance documents, topic coherence is prioritized in this study as the more meaningful criterion for semantic analysis. To ensure the robustness of topic selection, we conducted a systematic parameter tuning process prior to final model fitting. Specifically, the number of topics was varied from 2 to 10, and for each candidate value, the corresponding topic coherence score and perplexity were calculated. The LDA model was trained with 30 passes, 100 iterations, and alpha set to “auto” to allow for adaptive topic–document distribution smoothing. As shown in Figure 1, the trends of the two variables are not precisely congruent under varying numbers of topics. When the number of topics is seven, the coherence score reaches its peak, indicating that the model has stronger semantic cohesion and interpretability under this setting. As shown in Figure 2, this study further introduces KL divergence and JSD divergence to quantify the degree of semantic structural changes in the adjacent topic number model. KL divergence is a measure of the asymmetric difference between two probability distributions. In contrast, JSD divergence, a more suitable metric for measuring the average semantic distance between topics, is the symmetric improvement in KL divergence. The analysis results demonstrate that, as the number of topics is altered, both the KL and JSD values for the transition from 7 to 8 exhibit a significant decrease. This finding suggests that the structural changes in topic structure from 7 to 8 are relatively minor, with semantic boundaries becoming increasingly blurred. However, during the transitions from 6 to 7 and 8 to 9, the JSD divergence remains relatively high, suggesting that topic 7 still maintains clear structural boundaries, supporting its role as an ideal distinction point.

Therefore, based on a comprehensive evaluation considering multiple dimensions such as the highest consistency, acceptable confusion, and the KL/JSD divergence maintaining good discriminative capability, this paper ultimately selects 7 as the optimal number of themes for modeling multi-source policy texts in intelligent forestry. This was achieved by taking into account the explanatory power and stability of the model, with the objective being to ensure that the themes are well-structured and the semantic expressions are clear.

3. Results

3.1. Structural Characterization of LDA Results

After determining the optimal number of topics, the LDA model was applied to the full corpus of smart forestry texts, generating seven distinct themes. The results were interactively visualized using the pyLDAvis toolkit [50] (Figure 3), which is based on the Multi-dimensional Scaling (MDS) algorithm to map the topic distributions in the high-dimensional space to the two-dimensional plane.

In the inter-topic distance map, each circle represents a topic: its size reflects the importance of the topic and the proximity between circles reflects thematic similarity. The distribution map reveals that, while the topics exhibit moderate overlap, indicating interconnected policy narratives, they also maintain sufficient independence to allow for analytical differentiation. The design of the visualization was also inspired by the Termite interface [51], which emphasizes interpretability through intuitive topic-term relationships. This visualization provides an intuitive understanding of the topic landscape and facilitates a cross-document comparison. To further interpret the LDA output, the identified topics are organized into a structured framework. The high-frequency keywords of the seven themes are semantically summarized and integrated, dividing them into three dimensions: technological enablement (T1, T3, T5), governance mechanisms (T2, T6), and ecological goals (T4, T7). This classification not only facilitates comparative analysis at different governance levels, but also serves as the analytical foundation for tracing policy–practice alignment and semantic transmission patterns.

Table 2. Categorization of LDA topics and their representative keywords.

Theme Label	High-Frequency Keywords	Dimension
Theme 1: Platform and Data Integration	Internet, Data Collection, Data Integration, Data Convergence, Public Service	Technological Enablement Dimension
Theme 3: Information Regulation and System Support	Forestry Informatization, Institutional Support, Regulatory Framework, Data Convergence, Approval Processes
Theme 5: Digital Innovation and Value Transformation	New Quality, Smart Forestry, Ecological Product Value Realization, Empowerment, High Quality
Theme 2: Tenure and Grassroots Governance	Forestry and Grassland, Leadership, Forest Rights, Forest Farm, Alignment	Governance Mechanisms Dimension
Theme 6: Forest Governance and Local Response	Forest Farmers, Forest Manager System, Rural Areas, Nature Reserves, Forest Resources
Theme 4: Ecological Governance and Land Protection	Natural Forest, Governance, Wildlife, Ecological Protection, Vegetation	Ecological Goals Dimension
Theme 7: National Strategy and Ecological Security	National, Governance, Parks, Greening, Ecological Protection

presents the detailed composition of each theme, including its representative keywords and corresponding dimension.

The technology empowerment dimension focuses on the foundations of informatization and digital support in smart forestry, covering three main themes: T1 shows the importance of information flow, system integration, and building service platforms, with core keywords such as “internet,” “ data collection,” and “data integration”; T3 highlights the role of forestry informatization, platform supervision, and system coordination, based on keywords such as “forestry informatization,” “ institutional support,” and “regulatory framework”; T5 emphasizes the realization of ecological product value, empowerment, and the development of new quality productivity, shown by keywords such as “new quality”, “ecological product value realization,” and “empowerment”. Together, these three themes form the “digital foundation” of smart forestry development, highlighting the supportive role of technological platforms, information systems, and digital applications for better forestry governance.

The governance mechanism dimension relates to policy implementation, organizational cooperation, and the operation of multiple actors, covering two themes: T2 focuses on forestry policy execution and multi-actor cooperation, with keywords such as “leadership,” “forest rights,” and “alignment”; and T6 relates to collaborative governance in forest areas based on institutional innovation, with keywords such as “forest farmers,” “forest manager system,” and “rural areas.” This dimension highlights the dual driving forces of “technology and institutional mechanisms” in forestry governance.

The ecological goals dimension focuses on the final goals of smart forestry, such as ecosystem restoration, biodiversity protection, and sustainable forestland management, covering two themes: T4 reflects policy attention to forestland restoration and ecosystem management, with keywords like “natural forest,” “governance,” and “wildlife”; and T7 emphasizes national-level planning under the strategy of ecological security, with keywords such as “national,” “governance,” and “greening.” This dimension reflects the view that smart forestry is part of the national strategies for ecological protection and highlights the high priority placed on ecological security at the national level.

To complement the semantic coherence and perplexity metrics used in model selection, we further evaluated the semantic distinctiveness and interpretability of the extracted topics through a pairwise keyword overlap analysis. Specifically, for each topic, the top 10 high-frequency keywords were retrieved and compared with all other topics to calculate the number of shared words. The results are visualized in the heatmap shown in Figure 4.

Diagonal values are fixed at 10, indicating complete self-overlap, while off-diagonal values predominantly range from 0 to 4. This low degree of lexical repetition suggests that the identified topics are semantically well separated, with minimal redundancy. Notably, only a few topic pairs (e.g., T1–T3 and T4–T6) exhibit mild overlap, likely due to partially shared vocabulary in broader thematic dimensions such as governance or digital infrastructure.

These findings reinforce the model’s internal consistency and justify the selection of seven topics, as the observed inter-topic dissimilarity aligns well with the high coherence score obtained during the parameter-tuning stage.

3.2. Comparative Analysis of Central and Fujian Provincial Smart Forestry Policy Themes

In order to gain a deeper understanding of the thematic structural differences between central and local policies in smart forestry governance, this section provides a comparative analysis. The analysis focuses on three dimensions: thematic focus of attention, structural features, and degree of consistency by visualizing the results of LDA thematic modeling.

Firstly, an analysis of the thematic keywords reveals notable differences in the focus of attention across the three types of texts. As shown in Figure 5, a comparison of keyword word clouds highlights notable distinctions between the central policy, the Fujian policy, and media reports.

Central policies place emphasis on terms such as “policy,” “governance,” “conservation,” “protection,” and “biodiversity.” This reflects a top-level strategy focused on ecological protection, resource management, and strategic security, with an emphasis on ecological resource management and national governance.

The word cloud for Fujian Province’s local policies features terms such as “platform,” “data,” “monitoring,” and “smart forestry.” This suggests a focus on platform building, data-driven approaches, and technological deployment, with an emphasis on technology-oriented features.

This discrepancy highlights the core issues in the discourse system of smart forestry policy between the central and local governments. The central government focuses more on establishing systems and frameworks to provide strategic guidance, while local governments emphasize the application of tools and practices.

Secondly, an analysis of the structure of theme distribution further reveals a significant difference between central and local policies in terms of topic focus and expression density. As shown in Figure 6, the central policy is predominantly focused on two themes, T4 and T7, which collectively account for over two-thirds of the total, thereby underscoring the central policy’s emphasis on macro-strategic layout, ecosystem security, and integrated resource management. The centralized theme structure reflects the policy logic of the central government’s overall strategic guidance and institutional framework for smart forestry.

In contrast, the thematic distribution of policies in Fujian Province is more diversified, covering areas such as T1, T3, and T6. This reflects a shift in the structural transformation of policy content, from strategy to implementation and from framework to tools.

The diversified distribution is indicative of a high degree of localization and optimization of technical paths. This emphasizes a high degree of attention to policy implementation, data connectivity, and collaborative governance mechanisms.

To complement the thematic distribution analysis, we further introduced two structural metrics to evaluate inter-topic distinctiveness and cross-source alignment. First, a keyword overlap heatmap (Figure 4, see Section 3.1) shows low redundancy between most topic pairs, indicating semantic separability and interpretive clarity. Second, a cosine similarity matrix (Figure 6) quantifies the thematic proximity among central policies, Fujian provincial policies, and media reports. As shown in Figure 7, the cosine similarity of the thematic distribution between the three types of texts is illustrated: central policies, Fujian provincial policies, and media reports.

The highest similarity, 0.77, is observed between Fujian provincial policies and media reports. This high degree of consistency suggests that local practices and media reports align with the direction of local policies, or both have focused on regional topics such as smart forestry implementation, ecological transformation, and technology pilots.

The similarity between the central policies and the Fujian provincial policies is 0.29, which is at a low level. This indicates that, while local policies are guided by the national top-level design, they also integrate local practical experience, needs, and adaptive adjustments. Furthermore, the local government plays the roles of “interpreter” and “redesigner” in the implementation of central policies.

The similarity between central policies and media reports is negligible, with almost no thematic overlap, suggesting a clear disconnection between the two in terms of thematic concerns. This highlights a notable structural divergence between macro-level strategies and localized implementation narratives.

3.3. Responsiveness and Gaps in the Local Implementation of Smart Forestry Policies in Fujian Province

3.3.1. Concerns Regarding Policies and Their Corresponding Responses in Practice

The thematic response between smart forestry policies and practices in Fujian Province reveals the structural interaction and mismatches between policy advocacy and local implementation. This study quantitatively compares the thematic consistency between policy texts and media reports in Fujian Province using LDA topic modeling. As shown in Figure 8 and Figure 9, the distribution and trend of each theme are visualized. The key findings are as follows.

First, T6 (Forest Governance and Local Response) is prominent in both policy and practice texts. This indicates the formation of a relatively stable policy–practice loop, reflecting strong grassroots responsiveness and effective policy transmission in the areas of forest patrol and collaborative governance.

Second, although T1 (Platform and Data Integration), T2 (Tenure and Grassroots Governance), T3 (Information-Based Supervision and System Support), and T7 (National Strategy and Ecological Security) occupy a large proportion in policy texts, they are largely absent from practice reports. This suggests a significant policy–practice gap. The differences illustrated in Figure 8 further confirm a time lag in translating institutional frameworks into concrete actions or behavioral changes.

Third, T5 (Digital Innovation and Value Transformation) is more active in practice than in policy, with local initiative and technological innovation capacity despite the limited space in the policy text. The “practice-first” phenomenon suggests that policymaking should be more fully informed by the technical accumulation and practical experience of the grassroots and that a bottom-up feedback mechanism should be established.

3.3.2. Typical Cases

To further explore the policy–practice response path of China’s smart forestry, we integrated the thematic response structures presented in Figure 6 and Figure 7 and selected three typical practice cases for in-depth analyses, corresponding to the key themes of T6, T5, and T3, respectively, with the relevant details provided in

Table 3. Typology of policy–practice response paths in smart forestry: evidence from Fujian.

Case Name	Time and Place	Content	Theme	Terms and Conditions	Media Reporting	Matching Type
Miscanthus management project in Xiapu County	2022–2023, Xiapu County	Drones + Video Sensing + Multi-Segment Management Processes	T6	“Fujian Province Intelligent Forestry “123” Project Construction” (2022): “Establish an integrated sky–earth monitoring system.”	“In this “battle” without smoke, digital scientific and technological means such as satellite remote sensing, drones, and video surveillance have been unveiled. The employment of drones is of particular significance in this context, as it enables the acquisition of enhanced flexibility, acuity of vision, and the establishment of a “sentinel.” reported by Fujian forestry.	Policy clarity–local response
AI-Based Wildlife Monitoring in Zhouning County	2023, Zhouning County Forestry Bureau	Infrared Cameras + AI Recognition for Biodiversity Database Construction	T5	14th Five-Year Plan for Forestry Informatization (2021): “Establish a wildlife monitoring system.” (no specific technical route specified)	“Network infrared cameras, through infrared sensing and AI recognition technology, have photographed Grade II national protected animals such as white pheasants and leopard cats, providing first-hand information for biodiversity protection” reported by Fujian forestry.	Policy generalization–locally initiated
Intelligent Forest Fire Early Warning Platform in Putian City	2023, Xitianwei Town, Putian	AI Algorithms + UAV Coordination for Fire Monitoring	T3 T6	14th Five-Year Plan for Forestry Informatization (2021): “Develop an intelligent forest fire early warning system.”	“The platform combines artificial intelligence, the Internet of Things, cloud computing, and other technologies to automatically monitor forest fires and issue early warnings” reported by Fujian forestry.	Policy framework–technical deepening

.

Case 1 is the Miscanthus management project in Xiapu County, which focuses on the ecological control of invasive species. The project employs a “sky–ground integration” platform that integrates UAV sensing, monitoring, and feedback in a closed loop, which focuses on the ecological control of invasive species. This case is associated with T6 (Forest Governance and Local Response).

Case 2 is a wildlife monitoring system based on infrared cameras and AI recognition in Zhouning County, which realizes the long-term monitoring of rare species through front-end sensing and intelligent identification. It aligns with T5 (Digital Innovation and Value Transformation).

Case 3 involves the construction of a forest fire early warning platform in Putian City. The system integrates AI algorithms, IoT devices, and a coordinated air–ground response mechanism to improve real-time forest fire response. It intersects with both T3 (Information Regulation and System Support) and T6.

These three cases demonstrate varying characteristics in terms of response content, technology path, and the degree of policy–practice alignment. The specific path types and transformation mechanisms will be further discussed in Section 4.

4. Discussion

4.1. Structural Differentiation of Themes in Multi-Level Policies

Prior to the interpretation of the results, it is imperative to elucidate the methodological positioning of this study. This study adopts a diagnostic and comparative approach, aiming to reveal how smart forestry policies are semantically structured and differentiated across governance levels, rather than to construct causal transmission models. While it is acknowledged that LDA, as an unsupervised learning method, does not support causal inference or mechanism identification, it remains a widely accepted tool in policy analysis for identifying textual misalignments, agenda divergence, and thematic emphasis shifts [34,52]. In this theoretical framework, the model’s outputs provide a foundation for empirical analysis, which can be used to assess semantic consistency and transmission tensions in multi-level ecological governance systems.

Based on the LDA modeling results, this study reveals a significant structural differentiation between the central and local smart forestry policy themes. The differentiation reflects not only the institutional division of policymaking functions, but also the functional translation path of the ecological governance paradigm within a multi-level institutional system.

On the one hand, the central policies exhibit a concentrated thematic structure, which mainly focuses on T7 (National Strategy and Ecological Security) and T4 (Ecological Governance and Forest Land Protection). These themes emphasize macro-level objectives such as the construction of ecological security barriers, the improvement in the national parks system, and the integration of natural resources. National strategies often prioritize top-down resource protection and ecosystem resilience [53,54]. However, central policies are typically characterized by high abstraction, limited operability, and unclear implementation pathways, serving more as directional guidance than task-specific instruction.

On the other hand, local policies in Fujian Province are diversified, engineered, operational, and technically oriented. They focus on themes such as T1 (Platform and Data Integration), T3 (Information Regulation and System Support), and T6 (Forest Governance and Local Response), which exhibit instrumental, systematic, and practical features. This is closely related to the dual role of local governments: they are both implementers of central strategies and active organizers of smart forestry initiatives. Local policy language frequently presents engineering terms such as “platform integration,” “data convergence,” and “unified supervision map,” emphasizing the feasibility and systemic nature of implementation. In addition, the frequent use of expressions such as “integration,” “sharing,” and “pilot” reflects the local ambition to build ecological collaborative governance networks. This aligns with research on subnational forest governance, which stresses the need for adaptive capacity and institutional experimentation [55,56]. Under the constraints of policy resources and technical conditions, local policies often focus on specific governance scenarios, such as pest monitoring and digital forest management, revealing localized adaptation and bottom-up innovation in the local translation of national strategies.

The structural differentiation of central–local policy themes reflects not only a strategic–technical divide, but also the structural tension between the division of responsibility allocation, functional positioning, and language system in the multi-level ecological governance system. To achieve the effective transmission and synergistic promotion of smart forestry, it is necessary to clarify the operational translation mechanisms of the central policy. Improving semantic alignment between national strategies and local policy responses will promote better goal coordination and discourse consistency across governance levels. While this structural differentiation underscores the fragmentation between national strategies and local implementation, not all themes are equally affected. In particular, T6 (Forest Governance and Local Response) demonstrates semantic presence across central-, local-, and practice-level texts, forming a relatively complete transmission loop. This indicates that, while structural disconnection exists at the system level, certain governance-oriented and adaptive themes may act as cross-level semantic bridges, helping to mediate alignment within fragmented discourse systems.

This divergence can be further explained by the functional attributes of the topics themselves. T6, categorized under the Governance Mechanisms dimension, focuses on local administrative practices and collaborative forest management systems. Its keywords, such as forest farmers, manager system, and rural areas, reflect governance mechanisms that are already embedded in the operational routines of forestry departments and rural collectives. These structures facilitate policy uptake and thematic convergence across levels. In contrast, T1 and T3 belong to the Technological Enablement dimension, which emphasizes infrastructure and digital regulation through terms such as data integration, forestry informatization, and approval systems. These topics require long-term investment, standardized platforms, and centralized technical capacity, making them less visible in local implementation or grassroots narratives. As such, T1 and T3 are more policy-driven but face bottlenecks in practical conversion, whereas T6 benefits from institutional proximity and existing governance networks. This explains why T6 forms a positive feedback loop while T1/T3 exhibit structural disconnect.

Specifically, institutional embeddedness, resource allocation, and incentive mechanisms collectively form the practical foundation that enables T6 to achieve a cross-level positive feedback loop. Firstly, T6 demonstrates a notable degree of institutional embeddedness. The forest rights reform, village-level monitoring, and collaborative governance mechanisms involved in T6 are not only explicitly outlined in central government policies but have also established stable operational practices in local forestry management and grassroots governance. Secondly, the resource threshold is relatively low. In contrast to T1 and T3, which depend on data platforms, system integration, and technical standards, T6 places greater emphasis on organizational collaboration and social supervision. The technical and financial investments required for implementation are lower, making it more suitable for local practice environments with limited resources. Thirdly, the existence of clearly defined incentive mechanisms is evident. The core components of the system, including collective forest rights and household contracts, are closely tied to the actual interests of grassroots governance entities. This, in turn, stimulates intrinsic motivation to actively align with policies and enhances policy responsiveness and semantic consistency. This phenomenon elucidates the heightened complexity of policy transmission in T1 and T3, attributable to their elevated technical barriers and comparatively weaker institutional foundations.

4.2. Practice Response Patterns and Policy Fallout Mechanisms

Fujian Province exhibits strong policy responsiveness and obvious thematic disparities in the implementation of smart forestry practices. This section focuses on the governance characteristics and structural mechanisms that underpin these differences.

First, T6 (Forest Governance and Local Response) focuses on the forest manager system, grassroots patrols, and local response. Its successful implementation is mainly attributed to three factors: (i) the institutional mechanism is relatively mature, with the operation path of policy, platform, and promotion; (ii) local governments possess a degree of autonomy and practical experience, enabling quick policy adaptation and adjustment; and (iii) the low threshold for technological inputs facilitates the rapid demonstration and replication of governance models. These characteristics are consistent with adaptive co-management literature, which emphasizes local empowerment and modular institutional design [57,58,59]. Clearly defined responsibilities and institutional continuity enhance implementation outcomes in forest governance [60]. To reinforce this process, policy designers can consider establishing inter-village cooperation mechanisms, performance-based incentives for local rangers, and lightweight digital logging tools that integrate seamlessly into existing workflows.

Second, T1 (Platform and Data Integration), T2 (Tenure and Grassroots Governance), and T3 (Information Regulation and System Support) represent a significant policy–practice gap. T1 and T3 focus on technical support and platform coordination, but the threshold for implementation is high, involving cross-system data governance and system docking, which local governments often lack the operational capacity to decompose and undertake. T2 concerns deep-rooted institutional reforms, including forest rights adjustment and multi-stakeholder governance. Studies have shown that difficulties in inter-agency coordination, unclear property rights, and insufficient stakeholder engagement often delay grassroots-level reforms [61,62]. Technological centralization without sufficient capacity building often results in uneven implementation outcomes across regions [54]. The implementation of these themes requires extensive financial investment, interdepartmental coordination, and long-term digital capability building, which poses challenges to local governments. In addition to the technical and coordination-related challenges, these disparities are also influenced by underlying structural constraints. The capacity of local forestry departments to sustain digital infrastructure is constrained by two factors: unequal fiscal transfers and fragmented budget authority. Moreover, the absence of unified performance incentives across departments, coupled with mismatched accountability systems, weakens cross-agency collaboration. Such institutional frictions frequently result in implementation inertia, particularly in under-resourced or administratively complex jurisdictions. This results in persistent policy–practice gaps and uneven uptake across regions. This reflects a high level of institutional adaptability, as local systems are well equipped to absorb and operationalize central policy directives. To improve practice-level uptake, policy designers might consider launching pilot programs in selected counties, forming multi-agency task forces to streamline administrative mandates, and allocating dedicated funding to facilitate digital transformation.

Third, T5 (Digital Innovation and Value Transformation) shows an inverse pattern: it is highly active in local practice but underrepresented in policy texts. It reflects the strong spontaneity and experimental drive of local actors in eco-product transformation and technology integration. The innovation process can trigger new institutional arrangements and policies (bottom-up) [63].

However, the lack of timely institutionalization and effective vertical feedback mechanisms limits the potential for scaling up these grassroots innovations into policy. To support bottom-up institutional learning, future policies should consider creating a provincial smart forestry innovation registry, encouraging horizontal exchange platforms among counties, and incorporating grassroots innovations into annual policy planning cycles. Similar issues have been reported in smart forestry cases in Europe [4]. Local innovation thrives where actors are empowered and supported, but often falters in the absence of formal mechanisms for policy uptake and institutional learning. From a governance perspective, this pattern reflects the dynamics of polycentric governance, wherein decentralized actors generate innovative responses but require interlinkages to achieve system-wide integration. These patterns reflect the local differences in resources, capacity, and path design. They also highlight the limitations of the current smart forestry policy expression system in terms of transmission efficiency and operational adaptability. Incorporating the lenses of institutional adaptability and polycentric governance helps illuminate why certain themes translate into actionable practices more effectively than others, and how future policy design can benefit from context-sensitive, multi-level coordination.

4.3. Policy Transmission Paths and Adaptive Mechanisms

China’s experience offers valuable lessons for countries aiming to develop or refine their smart forestry systems. The diversity of policy transmission pathways observed in Fujian reflect a flexible governance framework that combines an integrated top-down strategy with local implementation autonomy. For countries with decentralized forest management structures, this case exemplifies the importance of aligning national priorities with local innovation capacity. It is evident that policies adapted to regional experiments and that establish feedback loops are more likely to produce scalable and sustainable results.

In international settings marked by variation in ecological pressures and digital readiness, balancing directive and adaptive policy instruments becomes essential. In developing countries, where technological infrastructure may be limited, fostering grassroots-driven innovations, similar to theme T5 in this study, could catalyze bottom-up digital transformation in forest governance. Conversely, in high-capacity systems, structured systems for horizontal coordination and stakeholder engagement—as seen in theme T6—can foster integrated, cross-sectoral forest management. These findings affirm the broader global relevance of adaptive, multi-level governance and the role of semantic alignment and institutional learning in driving effective ecological transitions. Achieving the effective transformation from central directives to local actions remains a central challenge in advancing smart forestry governance. By analyzing typical cases in Fujian Province, as shown in Table 2, this study categorizes three types of policy–practice transmission paths, reflecting the transformation potential and adaptive mechanisms of different themes in the process of system development and local implementation.

First, the normative directive path, represented by T6, is characterized by clear policy objectives, specific implementation methods, well-defined pathways, and the explicit assignment of responsibilities at the local level. It reflects policy guidance and consistency and corresponds to the “policy clarity–local response” matching type summarized in Table 3. It suits themes with mature governance infrastructure and technological readiness [54]. The institutional adaptability perspective explains its effectiveness in policy–practice linkage and feedback refinement. Second is the flexible exploration-type path, represented by theme T5. In this path, policies typically offer directional principles without specifying concrete technical pathways or platform configurations. Local actors explore solutions by leveraging governance capacity, technological adaptability, and pilot resources. This reflects a bottom-up feedback model, enabling institutional learning and policy refinement [58,64]. It resonates with the polycentric governance theory, which emphasizes the role of autonomous local units in driving innovation and adaptation. Third, the integration and expansion path is represented by T3 and T6. In this path, policies provide basic principles and target directions. Local systems then adapt by integrating technologies and coordinating across departments, reflecting increasing policy complexity and horizontal collaboration [56]. This approach emphasizes cross-sectoral synergy and platform building and corresponds to the “policy framework–technical deepening” type described in Table 3. The application of polycentric governance here highlights the importance of coordination among diverse actors to achieve system-wide outcomes. The three paths constitute the conduction mechanism that drives the local implementation of smart forestry policies. The effectiveness of a policy theme depends not only on its emphasis in the policy text, but also on the clarity of expression, local resource mobilization capacity, and technological adaptability.

To promote stable and scalable smart forestry governance, future policy expression should accommodate directive, exploratory, and integrative logics. Specifically, (i) for the directive theme, a hierarchical implementation guide and platform support should be provided; (ii) for the exploratory theme, the system should be given room for error and a channel for absorbing the results; and (iii) for the integration theme, the platform co-construction and governance synergy mechanism should be strengthened so as to promote the steady progress of smart forestry.

Positioning China’s smart forestry experience within the global discourse on forest policy and governance enables this study to offer actionable insights for countries navigating their own digital ecological transformations. The findings emphasize that strategic coherence must be matched with localized operational capacity. As shown in the Fujian case, successful governance transitions depend on institutional responsiveness, iterative feedback loops, and adaptable implementation models. In other national contexts, whether developing or developed, this suggests that scalable and equitable smart forestry requires flexible governance architectures that integrate top-down mandates with bottom-up innovation. These observations are consistent with global calls for multi-level, adaptive governance capable of responding to both ecological uncertainty and sociopolitical diversity [65,66]. Strategic alignment, institutional learning, and context-sensitive implementation will remain critical for countries facing diverse ecological and administrative challenges [54].

5. Conclusions

5.1. Research Conclusion

This study involved the systematic analysis of the policy theme structure and its transmission paths within the smart forestry policy system, based on three types of texts—central policies, Fujian provincial policies, and local media reports—using the LDA topic modeling method. The following conclusions are drawn.

First, there is a significant distinction between national-level and local-level smart forestry policies in terms of thematic concerns and language styles. Central policies focus on national strategy and ecological security, with a high degree of concentration and directional guidance, while local policies focus on platform construction, information-based regulation, and collaborative governance, showing a stronger technical implementation orientation. This structural difference reflects the multi-level governance logic of strategic leadership and technical implementation in the policy system.

Second, there are variations in how local practices respond to different themes: T6 forms a more complete closed loop between policy and practice, while T1, T2, and T3 demonstrate a disconnection between policy emphasis and practice response. Additionally, exploratory themes such as T5 are more prominent in local practices, indicating an innovation-driven mechanism within local governance.

Third, three types of policy and practice transmission paths are summarized through typical cases: normative directive, flexible exploratory, and integrated expansion. These paths are suitable for different types of policy issues, revealing the combined influence of task clarity, local resource mobilization capacity, and technology suitability in the process of implementing smart forestry policies.

5.2. Marginal Contribution

This study provides new research perspectives and practical insights for the multi-level analysis of smart forestry policy systems and the transformation of global digital ecological governance, mainly with the following marginal contributions.

First, in terms of research methodology, this study involved the innovative construction of a cross-level thematic mapping framework for the three types of texts: central policy, local policy, and local practice. We systematically revealed the thematic evolution characteristics and semantic translation mechanisms of smart forestry policies between different governance levels and expanded the perspective of multi-level comparative analysis of the smart forestry policy system. This research addresses the inadequacies of the existing literature on the evolution mechanisms of the digital ecological governance policy chain.

Second, theoretically, a differentiated model of the transmission path of smart forestry policies and practices is proposed in this study, with the findings deepening the understanding of the differentiated characteristics of the policy landing mechanism and providing new theoretical support for the structural analysis of the digital ecological governance policy system.

Third, in terms of practical insights and based on the empirical analysis of the evolution of themes and transmission paths of China’s smart forestry policy system, the findings of this study indicate that, in the process of the digital transformation of global ecological governance, the multi-level governance experience—which is centered on the combination of strategic leadership and local adaptation and the precise alignment of policy issues and local innovations—is of significant international reference value. In particular, it provides a reference path and inspiration for the transformation of global digital ecological governance, especially for the construction of ecological governance systems in developing countries.

5.3. Research Limitations and Future Prospects

While this study has made initial progress in analyzing the smart forestry policy system, there are still some limitations. First, the practice samples were obtained mainly from Fujian Province, with limited regional representativeness and diversity. Thus, future study may be extended to different regions for cross-provincial comparisons to test the differences in policy adaptability under different ecological and administrative contexts. Second, the case study is mainly based on media texts and lacks firsthand interviews and field data. Although media texts are not substitutes for firsthand field data, we deliberately prioritized reports featuring concrete implementation cases and technical descriptions to reduce narrative bias. In addition, the number and types of reports were proportionally controlled to ensure structural comparability with policy documents. In the future, combining field surveys and multi-source data could enhance empirical support for policy implementation and its practical effects.

Additionally, future research could explore the dynamic evolution of smart forestry policies, cross-level synergies, and multi-stakeholder governance networks, promoting the more scientific, systematic, and adaptive development of the smart forestry policy system and supporting the global digital transformation of eco-governance.