Dynamic Impact of Digital Inclusive Finance and Financial Market Development on Forests and Timber in China: Economic and Social Perspective

Abstract

This study investigates how digital inclusive finance, financial development, and technology influenced forest and timber outputs across 31 provinces in China from 2011 to 2021. The findings, derived from panel quantile regression analysis, indicate that digital inclusive finance significantly enhances forest economic output, particularly in regions with lower economic activity, by improving access to critical financial resources such as credit and investment. However, the positive effects diminish at higher levels of economic activity, suggesting potential diminishing returns. Through the marketization of credit distribution and diverse financial instruments, financial development is essential for promoting sustainable forestry practices and adopting new technologies. Based on the findings, the study suggests expanding digital financial services in areas with low forest activity to help people access credit and investments, boosting forest productivity. It also recommends improving financial markets and investing in new forestry technologies to support better forest management and timber production.

1. Introduction

China holds significant forest resources, covering approximately 23% of its diverse landscapes, which are essential for the nation and the global community [1]. China’s forests extend through various climatic zones, from the tropical rainforests in the southern regions to the temperate and boreal forests in the northern areas [2]. This diversity in China’s forestry sector makes supporting biodiversity, providing raw materials, and maintaining ecological balance critical. However, despite this wealth of resources, the sector’s full potential, both economically and socially, remains unrealized due to challenges such as unsustainable management, inadequate investment, and limited access to financial resources in rural and forest-dependent areas. As a result, this study seeks to explore how innovative financial mechanisms, mainly digital inclusive finance, can contribute to sustainable forestry practices and improve economic productivity and social well-being across China’s provinces.

Forests have direct and indirect benefits for the economy. For instance, the economic significance of forests can be attributed to timber production, as China is one of the world’s largest producers and consumers of timber [3]. In 2022, the leading exporters were China (USD 20.4 billion), Canada (USD 20.1 billion), Germany (USD 13 billion), the United States (USD 10.6 billion), and Russia (USD 8.21 billion) (https://oec.world/en/profile/hs/wood-products accessed on 24 May 2024). China’s forestry sector plays a vital role in boosting the national GDP through timber, and wood production’s rapidly expanding economy, improved living standards, and an increasingly export-focused wood industry have driven up the demand for wood-based products [4]. However, beyond timber production, the broader value of forests, mainly non-market services like climate regulation, water purification, and biodiversity conservation, is often under-recognized. This oversight results in insufficient investment in the sector, hindering its ability to contribute to sustainable development fully.

On the other hand, China’s imports of wood-based forest products are increasing to meet the gap between domestic timber supply and demand. China now stands as the world’s largest importer of wood and the second-largest consumer [5]. It is said that the influence of China’s forestry sector on the economy is not limited to just its direct contribution to the country’s Gross Domestic Product (GDP). This sector also supports millions of jobs through wood product manufacturing and contributes to related industries such as construction and furniture manufacturing, which collectively contribute significantly to China’s economy [6]. Besides economic opportunities, forests in China also play a vital role in enhancing social well-being by supporting health and cultural values, mitigating natural disasters, promoting education and recreation, and conserving biodiversity (https://www.un.org/en/un-chronicle/healthy-forests-are-crucial-human-health-and-sustainable-development; https://doi.org/10.3390/f14101986 accessed on 24 May 2024). The forestry sector’s contribution to social well-being, particularly through timber production and non-timber forest products, is an area that warrants further exploration.

Further, forests support rural livelihoods through timber harvesting, collection of non-timber forest products (NTFPs) (e.g., mushrooms, herbs), and eco-tourism. These activities provide employment opportunities and income for millions of people, particularly in rural and forest-dependent communities. According to the UNFF (2015) (https://static.un.org/esa/forests/wp-content/uploads/2019/03/UNFF14-BkgdStudy-SDG8-March2019.pdf accessed on 24 May 2024), approximately 1.6 billion people depend on forests for subsistence, livelihood, employment, and income generation. The forest sectors have direct impacts, like forest output and timber production, and broader effects on the economy. Despite the vast array of economic goods and services that forests provide, their broader value is often overlooked in market assessments, resulting in insufficient resource allocation for sustainable management. Addressing this underinvestment is essential for maximizing the forest sector’s contribution to economic, social, and environmental well-being.

The forestry industry is inherently characterized by long production cycles and significant exposure to natural risks, which makes its development highly dependent on robust fiscal policies and targeted financial support [7]. According to the “Report on the Development of Forestry and Grassland in China 2018”, total investment in forestry ecological protection and construction in that year amounted to CNY 481.713 billion (approximately USD 11.24 billion), with CNY 243.249 billion (approximately USD 33.47 billion) sourced from national funds [7]. The intersection of financial development and digital inclusive finance within China’s forest sector is increasingly recognized as a critical area of study, as it reflects the evolving role of finance in promoting sustainable development and environmental stewardship [8]. Digital inclusive finance—through tools such as mobile banking, fintech solutions, and digital payments—can bridge the financial gap in rural areas, improving access to capital and fostering investment in sustainable forestry practices [9,10]. As China seeks to achieve balanced economic growth in the face of environmental challenges, the forestry sector emerges as a pivotal domain where financial innovations can significantly impact economic productivity and social well-being. Understanding and leveraging these financial mechanisms is essential for advancing sustainability within the sector.

Historically, financial development in China has played an essential role in supporting industrialization and infrastructure expansion [9]. Further, its impact on natural resource management, particularly in the forest sector, has garnered increasing attention [10]. The integration of digital inclusive finance—characterized by technologies such as mobile banking, digital payments, and fintech solutions—offers new avenues for financing and managing forest resources sustainably. Finance can accelerate forest landscape restoration efforts, enhance productivity in rural communities, and contribute to broader environmental goals such as climate change mitigation and biodiversity conservation [11,12,13]. These digital innovations can also improve the forestry sector’s transparency, accountability, and governance, reducing illegal logging and deforestation. Given China’s ambitious environmental policies, such as the Ecological Civilization Initiative and its commitments to carbon neutrality, understanding the synergies between financial development, digital inclusive finance, and the forestry sector is crucial for fostering inclusive growth and sustainability. Simultaneously, the social dimensions of financial and digital inclusion in the forest sector are profound. According to Ros-Tonen (2019) [14], financial services can empower marginalized communities, including smallholder indigenous groups, by providing them with financial tools to engage in value chains related to forest products.

Moreover, digital financial platforms can enhance transparency, accountability, and governance in forestry operations (Gabrys, 2020; Goldstein and Faxon, 2022) [15,16], promoting equitable distribution of benefits and reducing illegal logging and deforestation. Against the backdrop of China’s ambitious environmental policies, such as the Ecological Civilization Initiative and commitments to carbon neutrality, understanding the synergies between financial development, digital inclusive finance, and the forest sector is crucial. This intersection not only shapes the future of China’s economic landscape but also holds promise for fostering inclusive growth, resilience, and sustainable development practices that benefit both present and future generations.

Though China’s forest resources are vast and diverse, their full economic and social potential has not yet been fully realized. The forestry sector in China still faces several challenges, including the need for sustainable management, adequate investment, and effective integration of technology. This study aims to address these issues by exploring the dynamic impact of finance on the forest sector, offering insights into how strategic financial sources and technological advancements can drive positive economic and social outcomes while promoting sustainability in China. Specifically, the research seeks to assess the impact of digital inclusive finance on forest economic output, examine the role of timber production in improving social well-being, and analyze the broader effects of financial development on the forestry sector.

By addressing these objectives, the study contributes to a deeper understanding of how strategic financial policies and technological advancements can drive sustainable growth in China’s forestry sector, ultimately promoting economic productivity and social well-being. This paper is innovative in three ways. First, this study evaluates the impact of digital financial inclusion on forest output across Chinese provinces from 2011 to 2021, assessing forest output through economic productivity. Further, the study evaluates the two important dynamics of digital inclusive finance: (i) digital inclusive finance depth and (ii) digital inclusive breadth. Second, it incorporates timber output into the analysis of social well-being, highlighting its role in improving rural livelihoods. Third, the study evaluates the impact of financial market development, using a comprehensive market score to assess the financial sector’s influence on forestry. Additionally, essential control variables such as forest area, afforestation efforts, investment, and technology are included to ensure an all-inclusive analysis. This research offers significant insights for policymakers by demonstrating how digital financial tools can promote sustainable forestry, supporting China’s environmental goals like carbon neutrality and ecosystem preservation, while enhancing economic growth and improving the livelihoods of forest-dependent communities.

The rest of the study is arranged in the following way. Section 2 contains a brief literature review, and Section 3 a picture of the evolution of China’s forestry reforms and their impact on China’s forest sector. Section 4 describes the materials and methods used in the study. Section 5 contains the results and discussion, and Section 6 includes the conclusions.

2. Literature Review

In recent years, the relationship between financial development and the management of natural resources has gained considerable attention from academic scholars. However, the role of digital finance remains relatively underexplored. In this section, the study intends to cover the existing key studies on financial development and digital finance for sustainable forestry practices.

2.1. Digitalization of Finance and Forestry

We are living in a digital era in which the role of digital financial services—such as mobile banking, fintech platforms, and blockchain technology—is of the utmost importance for all sectors, including forestry. Scholars are working to explore its impact using different channels. Some scholars adopt digital inclusive finance indicators or alternative indicators to examine their impact. For instance, Zhang (2022) [17] examines the role of fintech in advancing green finance in China, with a focus on innovative models like Ant Forest, which utilizes big data and mobile payment systems. Through a qualitative case study and analysis of Ant Forest, the paper explores how financial technology can drive the development of green finance by launching innovative, widely accessible green financial products. The findings demonstrate that fintech innovations, such as Ant Forest, significantly reduce carbon emissions and improve environmental conditions, promoting green finance. The study highlights the effectiveness of the “FinTech plus Green Finance” model in supporting sustainable development during the era of technological innovation. Similarly, Zhang et al. (2021) [18] investigated Ant Forest, a land restoration initiative launched by China’s leading fintech company. Their findings suggest that Ant Forest holds significant potential for advancing various sustainable development goals, including land restoration and enhancing human health. They also highlighted that Ant Forest operates as an open and transparent platform, allowing users to first monitor their virtual tree-planting activities on the platform, which are then translated into real-world tree-planting efforts in desert areas by the company.

Ge et al. (2022) [19] used the expenditure of the “agriculture, forestry, and water affairs” factor as the input index to measure the efficiency of rural integration of tertiary industry. This research focused on the role of digital inclusive finance in the integration of rural industry. The findings highlighted that the development of digital financial inclusion creates a fit financial environment and enhances the efficiency of the industry. Shanin (2022) [20] addresses the issue of inadequate financing for innovative activities in Russian timber industry enterprises. Many companies in this sector require infrastructure upgrades, new energy-efficient technologies, and scientifically based innovations. The timber industry in Russia is transitioning toward digitalization and innovation, which is expected to influence its future development significantly. The study highlights the importance of integrating energy-efficient and advanced production methods that rely on scientific research and technological advancements. An economic-mathematical model for financing innovations was presented, with forecasting methods confirming the approach’s feasibility. The analysis found that pulp and paper enterprises receive the largest share of funding for science and innovation.

One study by Bareyko et al. (2022) [21] explores the role of digital technologies in enhancing the efficiency, accuracy, and transparency of Russia’s forestry sector, revealing that the adoption of such technologies improves forest resource management, reduces inefficiencies, and enhances business processes. The findings indicate that digitalization will drive the timber industry’s development and increase its investment attractiveness, supporting Russia’s broader digital economy objectives. Shabaeva and Shabaev (2023) [22] analyze the current state of digitalization in Russia’s timber industry, highlighting its strategic importance to the country’s economy due to vast forest resources and the extensive use of timber products domestically and internationally. The authors examine the external environment, focusing on ICT sector development in Russia and globally while addressing challenges and risks associated with full digitalization. The study also explores the impact of the current economic crisis on the industry. It offers recommendations, including a step-by-step approach for implementing digital platforms in wood processing enterprises to advance the sector’s digital transformation. Based on the literature reviewed, this study concludes that digital inclusive finance is a transformative tool that can be promising for enhancing economic productivity and social well-being in China’s forestry sector. By improving access to capital, especially in underserved rural areas, digital inclusive finance can bridge the investment gap necessary for advancing sustainable forestry. Thus, the study hypothesizes as follows:

Hypothesis 1.

Digital inclusive finance positively impacts forest economic output across Chinese provinces, contributing to sustainable forestry practices and increased timber production, thereby bringing back social well-being.

2.2. Financial Development and Forestry

Indeed, the development of financial markets is critical for supporting sustainable practices in sectors that rely heavily on natural resources. However, the forestry sector, with its long production cycles and exposure to natural risks such as fires and pests, requires tailored financial solutions that address its unique challenges. He and Deng’s (2023) [23] study finds that forest rents and global financial development have an asymmetric relation. Further, this study supports the concept of the natural resource curse, suggesting that reliance on forest rents may hinder financial development in the long run. The study implies that managing forest resources carefully, perhaps through government intervention and improved financial resource allocation, is essential to mitigate these negative effects and promote global financial development. The objective of the study by Rode et al. (2019) [24] was to explore and implement solutions to halt deforestation while benefiting climate, biodiversity, and ecosystem services. The findings reveal a mismatch between private investors seeking social and environmental impact (beyond financial returns) and the actual supply of sustainable land use investments. The study suggests that blended finance models, which combine commercial, public, and philanthropic funding, could help address this gap and support the financing of sustainable forest landscapes.

Zada et al. (2021) [25] examine the impact of financial management practices on the growth of small to medium size forest enterprises in Pakistan. This study focuses on five key practices: working capital management, financial reporting, accounting information systems, investment decisions, and financing. The findings reveal that the use of these financial management practices is positively associated with both financial performance and firm growth. Additionally, there is a strong positive correlation between effective financial management and firm success. The study highlights the importance of improving financial management to enhance the growth and sustainability of forest enterprises in Pakistan and offers valuable insights for business owners, managers, and regulators. Asif et al.’s (2020) [26] study examines the relationship between forest rents and financial development in Pakistan. In the short run, forest rents positively contribute to financial development, supporting the “natural resource abundance” hypothesis. However, in the long run, forest rents have a negative impact on domestic credit to the private sector, confirming the “natural resource curse” hypothesis. This suggests that while forest resources initially boost financial activities, over time, they hinder financial development, aligning with the broader concept of the resource curse. Based on the literature, the second hypothesis is as follows:

Hypothesis 2.

Financial market development can positively impact the forest sector’s economic and social outcomes.

Additionally, the study incorporates key control variables such as forest area, afforestation efforts, investment, and technology to provide a comprehensive analysis. Based on these considerations, the study’s last (third) hypothesis is as follows:

Hypothesis 3.

Do forest area, afforestation efforts, investment, and technology significantly affect forest and timber output?

3. Evolution of China’s Forestry Reforms on China’s Forest Sector

China is home to one of the largest global forest and wood manufacturing industries. Over time, China’s forest resource expansion has been reflected in the increase in forest area over the past 40 years [27,28]. Over the years, China has implemented numerous forest development reforms to enhance forest coverage, quality, and sustainability [29]. In 1981, the State Council issued the “Resolution on Several Issues Concerning Forest Protection and Development” to stabilize forest use rights, allocate self-retained mountainous land for villagers, and formalize family forest management and the Household Responsibility System (HRS) within collectives. A second round of forest reforms began in the mid-1990s, enhancing incentives for household forest investment and management [30]. These reforms, alongside reductions in forestry taxes and increased profits in the forestry industry, spurred capital investment in forestry and promoted the conversion of some marginal cropland to forest, contributing to significant improvements in forest conditions [31]. A major initiative, the Natural Forest Protection Program (NFPP), began in 1998 to safeguard natural forests by prohibiting commercial logging in critical areas and supporting afforestation and reforestation [32,33]. Following this, the Conversion of Cropland to Forest Program (CCFP), also known as Grain for Green, was introduced in 1999 to address soil erosion by transforming degraded farmland into forests and grasslands, offering farmers subsidies and grain to plant trees [34]. The early 2000s saw the start of the Collective Forest Tenure Reform, which sought to clarify land rights and stimulate private investment in forestry. These efforts collectively have significantly boosted forest cover. The positive impact of reforms initiated by the Chinese government over the years can be seen in Figure 1. It shows the increasing sequence of China’s forest area from 2011 to 2021. This shows that China’s forest area growth is positive. Figure 2 and Figure 3 show the timber and forest output trend from 2011 to 2021. The increasing trend shows its expansion over the years. However, in 2015, the timber output decreased somewhat.

Similarly, forestry output has been positive over the years except for 2013. Figure 4 describes the average forestry and timber output of each province from 2011 to 2021. Accordingly, Figure 5 shows that Shanghai, Tianjin, and Beijing have the lowest amount of forest area. However, Sichuan, Heilongjiang, Yunnan, and Inner Mongolia contain the highest amount of forest area.

4. Materials and Methods

4.1. Empirical Modeling

Given the significant role of the forestry sector in China’s economy and society, this study conducts a comprehensive analysis of the factors influencing forest and timber outputs. The empirical analysis focuses on 31 provinces in China from 2011 to 2021 (see Appendix A,

Table A1. List of Chinese provinces.

Shanghai	Henan	Xinjiang	Inner Mongolia
Tianjin	Qinghai	Shaanxi
Beijing	Hebei	Guangdong
Ningxia	Liaoning	Jiangxi
Jiangsu	Zhejiang	Hunan
Hainan	Gansu	Guangxi
Shandong	Hubei	Tibet
Chongqing	Guizhou	Sichuan
Shanxi	Jilin	Heilongjiang
Anhui	Fujian	Yunnan

). Forest output (FRST_O) and timber output (TMB_O) are the dependent variables in the models, while key independent variables include digital inclusive finance, financial development, forest area, afforestation efforts, and technology. This study builds upon previous research that has explored the economic and environmental impacts of finance on resource-based sectors. Studies such as Xiao et al. (2023) and Liu et al. (2023) [35,36] have previously examined how digital finance and financial development influence industrial outputs, including of the agricultural and forestry sectors, by improving access to financial resources, encouraging investment, and supporting innovation. Building on these frameworks, we extend the analysis to forestry outputs across Chinese provinces while incorporating newer elements such as digital finance depth and breadth. The empirical models are structured to capture the relationships between these variables as follows:

Baseline Model 1:

$$ln{FRST\_O}_{it}=f({lnDGF\_indx}_{it},{lnFAR}_{it}, {lnAFR}_{it}, {lnINV-FRST}_{it}, {lntech}_{it}, {\mu }_{it})$$

(1)

Baseline Model 2:

$$ln{FRST\_O}_{it}=f({lnFD}_{it},{lnFAR}_{it}, {lnAFR}_{it}, {lnINV-FRST}_{it}, {lntech}_{it}, {\mu }_{it})$$

(2)

Baseline Model 3:

$${lnTMB\_O}_{it}=f({lnDGF\_indx}_{it},{lnFAR}_{it}, {lnAFR}_{it}, {lnINV-FRST}_{it}, {lntech}_{it}, {\mu }_{it})$$

(3)

Baseline Model 4:

$${lnTMB\_O}_{it}=f({lnFD}_{it},{lnFAR}_{it}, {lnAFR}_{it}, {lnINV-FRST}_{it}, {lntech}_{it}, {\mu }_{it})$$

(4)

The study comprises four models to capture the study objectives and hypotheses. Model 1 is about the impact of digital inclusive finance and forest output.

Model 2 has been composed to analyze the effects of financial development on forest output. Model 2 focuses on the relationship between financial development (FD) and forest output (FRST_O). According to studies such as Xiao et al. (2023) [25] and Beck et al. (2000) [37], financial market development is critical in enhancing economic activities related to natural resources, including forests. The availability of financial resources facilitates investments in technological innovation, afforestation efforts, and improved forest management practices, all of which are expected to impact forest outputs positively. Model 3 and Model 4 have been composed to evaluate the impact of digital inclusive finance and financial development on timber output. Previous research by Bareyko et al. (2022) and Herdianti (2022) [21,38] highlights how financial inclusion through digital platforms can promote the responsible use of forest resources, improve timber yields, and ensure sustainable logging practices. Digital finance tools can help smallholder farmers and forest-dependent populations adopt better technologies, manage forests sustainably, and contribute to higher timber production.

${FRST\_O}_{it}$ and ${TMB\_O}_{it}$ (dependent variables) are the forest output and timber output, respectively. $DGF\_indx$ is the digital inclusive finance index, which includes measures of digital finance breadth and depth. $FD$ is the financial market development, representing the availability and diversity of financial resources, measured by two Factors of Market Development Scores (see

Table 1. Descriptive summary.

Variables	Acronyms	Units	Obs	Mean	Std Dev	Min	Max
Forest Output	$FRST\_O$	Forestry output value (100 million)	341	159.8716	168.8376	2.1	2289
Timber Output	$TMBR\_O$	Timber output (10,000 cubic meters)	341	279.2186	499.0603	0.04	3502.47
Digital Inclusive Finance Index	$DGF\_indx$	Peking University Digital Financial Inclusion Index of China (PKU-DFIIC)	341	230.4609	103.3629	16.22	458.97
Digital Inclusive Finance—breadth	$DGF\_breadth$		341	211.6459	103.9233	1.96	433.42
Digital Inclusive Finance—depth	$DGF\_depth$		341	225.9221	105.8761	6.76	510.69
1: Financial Market Development	$FMDC$	Factor Market Development Score: Financial Marketization Degree: Credit Fund Distribution Market	341	5.525557	2.65437	0.06	11.755
2: Financial Market Development	$FMM$	Factor Market Development Score: Financial Marketization Degree	341	6.413314	1.90947	0.12	11.185
Forest Area	$FAR$	Forest area (10,000 hectares)	341	25,245	134,620.9	5.97	852,720.9
Afforestation	$AFR$	Afforestation area (hectare)	341	199,493.3	160,866.2	710	805,156
Investment in Forest	$INV\_FRST$	Completed Investment in Forestry (10 thousand)	341	1,322,311	1,532,609	53,355	1.10 × 107
Technology	$tech$	High-tech expenditures on scientific research activities (Thousand)	341	5.03 × 107	8.06 × 107	138,650.8	5.80 × 108

). $FAR$ is the forest area of each province, and $AFR$ is afforestation. $INV-FRST{ \mathrm{a}\mathrm{n}\mathrm{d} tech}_{it}$ are the investment in forest and technology (measured by expenditures on scientific research activities). ${\mu }_{it}$ is the error term. A detailed description of the variables used in the study is given in Table 1.

4.2. Empirical Methods

The present study applies a series of econometric methods for appropriate evaluation. The roadmap of all econometric steps is given in Figure 6.

4.3. Cross-Dependence Testing

The study commenced with a cross-sectional dependence (CD) test to identify cross-dependence. This test is essential as the one-country policy modifications might result in consequential impacts, commonly known as “spillover effects”, in other countries. To avoid misleading findings, starting with a CD test will lead to a more precise finding [39]. Thus, the CD test developed by Pesaran (2004, 2015) [40,41] is considered most suitable for panel data. This test establishes the null hypothesis of cross-sectional independence against cross-sectional dependency within the dataset.

The cross-dependence test is formulated in the following manner:

$$CD=\sqrt{{\displaystyle \frac{2T}{N(N-1)}}}{\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N{\partial }_{ij}⟶N\left(\mathrm{0,1}\right)$$

(5)

${\partial }_{ij}$ is estimated as follows:

$${\partial }_{ij }={\displaystyle \frac{\sum _{t=1}^T{\in }_{it}{\in }_{jt}}{\left(\sqrt{\sum _{t=1}^T{{\in }^2}_{it}}\right)\sqrt{\sum _{t=1}^T{{\in }^2}_{jt}}}}$$

(6)

4.4. Panel Unit Root

With CD, the conventional unit root tests, such as the Phillip Perron, Levin, Lin, and Chu, are not preferable, as typical unit root tests cannot consider cross-sectional dependence [42]. Therefore, this research preferred the second-generation panel unit root CIPS (Cross-Sectionally Augmented Im, Pesaran, and Shin) test, proposed by Pesaran (2007) [43]. The CIPS test can account for cross-sectional dependency and enhance the accuracy of panel data measuring stationarity. The CIPS specification is as follows:

$$CIPS\left(N,T_m\right)={\displaystyle \frac{\sum _{i=1}^N{t}_i\left(N,T_m\right)}{N}}$$

(7)

4.5. Method of Moments Quantile Regression Model

This study utilizes the Panel Method of Moments Quantile Regression (PMMQR) technique, which was introduced by Machado and Silva (2019) [44]. Binder and Coad (2011) [45] noted the limitations of conventional regression techniques, which often focus on average effects. The concept of panel quantile regression was initially introduced by Koenker and Bassett (1978) [46], providing valuable insights into the intricate relationship between the conditional means of two variables. The PMMQR technique, which incorporates fixed factors, offers significant advantages in understanding the covariance effects critical for analyzing the dynamics of digital finance, fiscal decentralization, environmental expenditure, and solar renewable energy, especially under conditions of heterogeneity. The approach effectively examines the specific factors influencing the overall distribution, as noted by Koenker (2004) [47]. The PMMQR method is recognized for its versatility in estimating the conditional median across different quantiles, making it a reliable choice in various contexts. According to Firpo et al. (2022) [48], the comprehensive use of all available data and inclusion of parameter constraints in PMMQR enhance its efficiency, making it a valuable option. Our primary empirical methodology aims to determine whether the factors influencing overall change vary across contexts. Quantiles are formulated as follows:

$$Q_y\left(\tau \right|X_{it})$$

(8)

Conditional quantile:

$$Y_{it}={\alpha }_i+X_{it}^{\prime }\beta +({\theta }_i+Z_{it}^{\prime }\mathsf{\psi }){\mathbb{U}}_{it}$$

(9)

$Y_{it} \mathrm{a}\mathrm{n}\mathrm{d} X_{it}^{\prime }$ are dependent and endogenous factors, respectively. The factors to be examined are $(\alpha , \beta ,\theta , \mathsf{\psi })$, and the probability $P\{{\theta }_i+Z_{it}^{\prime }\mathsf{\psi }>0\}$ = 1. (${\alpha }_i, {\theta }_i), i=1, 2, ..n$, are the discrete $i$ fixed effects. The symbol $Z_{it}^{\prime }$ denotes the k-vector of variable $X$, and ${\mathbb{U}}_{it}$ represents the distribution of an unobserved random variable (Machado and Santos Silva, 2019) [44]. Therefore, Equation (7) implies the following:

$$Q_y\left(\tau |X_{it}\right)={(\alpha }_i+{\theta }_i q\left(\tau \right))+X_{it}^{\prime }\beta +Z_{it}^{\prime }\mathsf{\psi }q(\tau )$$

(10)

where $Q_y\left(\tau |X_{it}\right)$ denotes the quantile distribution of the dependent variables.

5. Results and Discussion

Table 1 shows a descriptive summary of the concerned parameters. The average for forest output is 159.87 million, showing high variation with a minimum of 2.1 and a maximum of 2289 million. The mean value of timber output is 279.22, with a substantial standard deviation of 499.06 and a range of 0.04 to 3502.47 cubic meters. The average forest area is 25,245 hectares, but it shows significant variation, with values spanning 5.97 to 852,720.9. For financial development, $FMM$ has a mean of 6.41 (Score) and a standard deviation of 1.91, while $FMDC$ has a mean of 5.53 and a standard deviation of 2.65. The digital inclusive finance index has an average of 230.46, with a standard deviation of 103.36 and values ranging from 16.22 to 458.97. Digital inclusive finance breadth and depth also show significant variation, averaging 211.65 and 225.92, respectively. The forest investment average is 1,322,311 thousand, with a high standard deviation of 1,532,609 and a range of 53,355 to 11,000,000. Lastly, the high-tech investment averages 50,300,000 thousand, with a substantial standard deviation of 80,600,000, and afforestation efforts average 199,493.3 units, also showing high variability with a standard deviation of 160,866.2. These statistics indicate significant variations across the parameters, highlighting differences in the forest’s economic contributions, finance, forest areas, technology, and investments.

The results in

Table 2. Cross-sectional dependence (CD) findings.

Variables	CD-Stat	p-Value	Average Joint T	Mean ρ	Mean abs ρ
$lnFRST\_O$	33.321	0.000	11.00	0.47	0.63
$lnTMBR\_O$	14.901	0.000	11.00	0.21	0.49
$lnDGF\_indx$	71.269	0.000	11.00	1.00	1.00
$lnDGF\_breadth$	70.676	0.000	11.00	0.99	0.99
$lnDGF\_depth$	70.935	0.000	11.00	0.99	0.99
$lnFMDC$	19.597	0.000	11.00	0.27	0.45
$lnFMM$	35.621	0.000	11.00	0.50	0.63
$lnFAR$	62.752	0.000	11.00	0.88	0.88
$lnAFR$	13.298	0.000	11.00	0.19	0.44
$lnINV\_FRST$	20.435	0.000	11.00	0.29	0.49
$lntech$	64.161	0.000	11.00	0.90	0.90

indicate significant cross-sectional dependence across all variables, as evidenced by the CD statistics and p-values, suggesting strong interrelationships between the series. The mean correlation coefficients further confirm the presence of significant dependencies.

Table 3. Panel CIPS unit root findings.

Variables	CIPS—Level		CIPS—First Difference
	Trend—Exclusive	Trend—Inclusive	Trend—Exclusive	Trend—Inclusive
$lnFRST\_O$	−1.630	−1.693	−3.239 ***	−3.360 ***
$lnTMBR\_O$	−1.481	−2.695	−3.193 **	−3.121 **
$lnDGF\_indx$	−3.930 ***	−2.799 **	−3.174 ***	−2.797 **
$lnDGF\_breadth$	−5.385 ***	−5.381 ***	−5.252 ***	−4.901 ***
$lnDGF\_depth$	−3.553 ***	−2.520	−2.920 ***	−2.883 *
$lnFMDC$	−0.613	−0.934	−3.670 ***	−4.109 ***
$lnFMM$	−0.919	−2.010	−4.473 ***	−4.022 ***
$lnFAR$	−1.402	−1.674	−4.832 ***	−6.053 ***
$lnAFR$	−1.328	−1.749	−4.209 ***	−3.276 ***
$lnINV\_FRST$	−2.332	−2.889	−3.832 ***	−3.756 ***
$lntech$	−2.317	−2.392	−3.197 ***	−3.565 ***

Note: *** p < 0.01, ** p < 0.05, * p < 0.1.

is about the Panel CIPS unit root findings, showing that most variables are non-stationary at the level but become stationary after taking the first difference, as indicated by the significant CIPS statistics. This suggests the variables are integrated into order one, I(1).

Table 4. Westerlund cointegration test.

Models	Variance Ratio	Statistics	p-Value
Forest_O		6.207	0.0000
Timber_O		6.8084	0.0000

demonstrates the Westerlund cointegration test results; the variables in the models for “Forests_O” and “Timber_O” are cointegrated, as the variance ratio statistics are significant. This implies a long-term equilibrium relationship among the variables within these models.

5.1. Forest, Timber, and Digital Inclusive Finance

This study applied the Method of Moments quantiles to estimate the long-run parameters on lower quantiles (10th), middle quantiles (50th), and high quantiles (95th). This quantile distribution will give an understanding of the impact of digital financial inclusion and financial development across the entire range of forest and timber outputs, not just for one instance.

Table 5. Digital inclusive finance and forest output.

$\mathit{l}\mathit{n}\mathit{F}\mathit{R}\mathit{S}\mathit{T}\_\mathit{O}$
Variables	Location	Scale	qtile_10	qtile_50	qtile_95
$lnDGF\_indx$	0.0757 *	−0.105 ***	0.233 ***	0.0748 *	−0.140 *
	(0.0409)	(0.0228)	(0.0501)	(0.0418)	(0.0839)
$lnFAR$	1.241 ***	0.0533	1.161 ***	1.242 ***	1.351 ***
	(0.306)	(0.158)	(0.399)	(0.305)	(0.436)
$lnAFR$	0.0567 **	0.0176	0.0304	0.0569 **	0.0928 *
	(0.0287)	(0.0172)	(0.0321)	(0.0288)	(0.0510)
$lnINV\_FRST$	0.105 **	0.0110	0.0886	0.105 **	0.128 ***
	(0.0493)	(0.0281)	(0.0820)	(0.0491)	(0.0468)
$lntech$	0.141 ***	0.0702 ***	0.0359	0.142 ***	0.285 ***
	(0.0348)	(0.0158)	(0.0423)	(0.0359)	(0.0557)
$Constant$	−8.266 ***	−1.160	−6.526 ***	−8.276 ***	−10.65 ***
	(1.838)	(0.958)	(2.428)	(1.833)	(2.628)
$Observ$	341	341	341	341	341

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the estimates of the impact of digital inclusive finance on forest economic output (lnFRST_O) at various quantiles (10th, 50th, and 95th) and under different statistical conditions (location and scale). Digital inclusive finance (lnDGF) shows a positive and marginally significant effect on the 10th (0.233) (lower quantiles) and 50th quantile (0.0748). However, a negative impact at the 95th quantile (−0.140) can be noticed. This means that increasing digital financial inclusion can significantly boost forest output or the economic performance of forests, even at lower levels. Access to digital financial services may provide necessary resources, such as credit, savings, and investment opportunities, [49,50] which can help these lower-performing regions and improve their productivity and output [51,52].

The lnFAR strongly impacts the lower, middle, and higher quantiles (1.161, 1.242, and 1.351, respectively). Larger forest areas provide a greater capacity for timber and non-timber forest products [53]. More extensive forest cover means more resources can be harvested sustainably, leading to a higher overall economic output for China’s economy. Further, as the forest area increases, more significant forest operations can benefit from economies of scale, reducing the per-unit cost of production and increasing profitability, boosting economic output [54,55]. Sustainable management practices are much more effective if a country has larger forest areas, ensuring a continuous supply of forest products without degrading the resource base [56,57]. This sustainability can eventually maintain and increase long-term economic output. The results can be further verified by (Kocak and Cavusoglu, 2024; Chen et al., 2015) [58,59]. The following important factor in China is afforestation. This factor contributes significantly and positively to the 50th (0.0569) and 95th quantiles (0.0928), as well as to the location model (0.0567). Afforestation, the process of planting trees in an area without previous tree cover, has significantly increased in China over the past few decades (Zhang, 2019) [60]. China has implemented several ambitious programs and policies to expand its forest cover, such as the “Great Green Wall” across northern China’s Grain for Green Program [61]. According to Gbadebo (2022) and Akomaning et al. (2023) [62,63], afforestation contributed to economic development by providing timber, non-timber forest products, and ecosystem services, as well as by supporting rural development. The forest investment indicates a positive and significant impact on the forest output at the 50th (0.105) and 95th quantiles (0.128). However, at lower quantiles, it is positive but insignificant. High technology has a strong positive impact, particularly significant across most models, with notable effects at the 50th (0.142) and 95th quantiles (0.285). The results can be verified in Häggström and Lindroos (2016) [64]. Technology is an efficient practice for forest management. Developed technology helps to maintain data on sector production and improve decision-making and resource allocation in the forest sector.

In the next estimation, the study adds digital inclusive finance—breadth in place of the index, showing in other words, how widely digital financial services are distributed and accessible to individuals and businesses within an economy. Adding this factor will make the judgment of digital finance impact on forest output extensive. The breadth of digital inclusive finance (

Table 6. Digital inclusive finance—breadth and forest output.

	$\mathit{l}\mathit{n}\mathit{F}\mathit{R}\mathit{S}\mathit{T}\_\mathit{O}$
Variables	Location	Scale	qtile_10	qtile_50	qtile_95
$lnDGF\_breadth$	0.0537 *	−0.0709 ***	0.162 ***	0.0521 *	−0.0986
	(0.0305)	(0.0189)	(0.0399)	(0.0309)	(0.0635)
$lnFAR$	1.288 ***	−0.0138	1.309 ***	1.288 ***	1.258 ***
	(0.306)	(0.156)	(0.414)	(0.306)	(0.420)
$lnAFR$	0.0563 *	0.0203	0.0252	0.0567 *	0.0999 *
	(0.0286)	(0.0172)	(0.0324)	(0.0289)	(0.0523)
$lnINV\_FRST$	0.105 **	0.00820	0.0920	0.105 **	0.122 **
	(0.0496)	(0.0292)	(0.0850)	(0.0492)	(0.0488)
$lntech$	0.148 ***	0.0561 ***	0.0623	0.149 ***	0.269 ***
	(0.0330)	(0.0163)	(0.0421)	(0.0337)	(0.0529)
$Constant$	−8.545 ***	−0.681	−7.502 ***	−8.559 ***	−10.01 ***
	(1.845)	(0.967)	(2.544)	(1.839)	(2.571)
$Observ$	341	341	341	341	341

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

) positively impacts forest output at lower (10th quantile, 0.162) and middle (50th quantile, 0.0521) levels of forest economic output, suggesting that in regions with lower to average levels of forest or its economic activity, increased access to and use of digital financial services helps improve output. It shows that at these levels, digital financial services enhance resource allocation efficiency, providing much-needed financial support for forest management and better access to credit, thereby enhancing productivity.

In contrast, the impact of lnDGF_breadth on forest output is negative at the higher quantile; additional financial services may not translate into proportional gains. Forest operations at these levels might already be optimized, and further financial influx could lead to inefficiencies or overinvestment in non-essential areas, negatively impacting economic output. At the scale level (−0.0709) and higher quantile (95th quantile, −0.0986), the effect of digital inclusive finance—breadth is negative, implying that in highly productive regions where forest output is already high, additional digital financial services might lead to diminishing returns or potential inefficiencies.

Table 7. Digital inclusive finance—depth and forest output.

	$\mathit{l}\mathit{n}\mathit{F}\mathit{R}\mathit{S}\mathit{T}\_\mathit{O}$
Variables	Location	Scale	qtile_10	qtile_50	qtile_95
$lnDGF\_depth$	0.0878 **	−0.0789 ***	0.213 ***	0.0870 **	−0.0737
	(0.0404)	(0.0200)	(0.0549)	(0.0406)	(0.0608)
$lnFAR$	1.220 ***	−0.0111	1.238 ***	1.220 ***	1.197 ***
	(0.298)	(0.151)	(0.404)	(0.298)	(0.404)
$lnAFR$	0.0572 **	0.0189	0.0272	0.0574 **	0.0960 *
	(0.0284)	(0.0172)	(0.0331)	(0.0286)	(0.0509)
$lnINV\_FRST$	0.107 **	0.00117	0.105	0.107 **	0.109 **
	(0.0493)	(0.0298)	(0.0866)	(0.0491)	(0.0500)
$lntech$	0.136 ***	0.0501 ***	0.0563	0.136 ***	0.238 ***
	(0.0356)	(0.0155)	(0.0444)	(0.0361)	(0.0483)
$Constant$	−8.133 ***	−0.435	−7.443 ***	−8.138 ***	−9.023 ***
	(1.795)	(0.914)	(2.441)	(1.792)	(2.432)
$Observ$	341	341	341	341	341

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the impact of digital financial inclusion—depth (the extent of access to digital financial services) on forest output (lnFRST_O) across different quantiles. lnDGF_depth shows a positive (0.0878) influence on increasing forest output. This suggests that, on average, greater depth of digital financial inclusion positively impacts forest economic output, implying that more people and businesses have access to a broader range of financial services, leading to better investment in forestry activities, improved management practices, and increased productivity.

Table 8. Digital inclusive finance and timber output.

	$\mathit{l}\mathit{n}\mathit{T}\mathit{M}\mathit{B}\_\mathit{O}$
Variables	Location	Scale	qtile_10	qtile_50	qtile_95
$lnDGF\_indx$	0.0234 *	−0.00946	0.0389 *	0.0235 *	0.00355
	(0.0134)	(0.00764)	(0.0214)	(0.0134)	(0.0178)
$lnFAR$	−3.954 ***	−0.539	−3.075 ***	−3.950 ***	−5.087 ***
	(0.596)	(0.337)	(0.733)	(0.599)	(1.052)
$lnAFR$	0.0346	0.0421	−0.0339	0.0343	0.123 *
	(0.0443)	(0.0279)	(0.0682)	(0.0450)	(0.0742)
$lnINV\_FRST$	−0.0538	−0.0149	−0.0295	−0.0537	−0.0852
	(0.0399)	(0.0273)	(0.0590)	(0.0399)	(0.0715)
$lntech$	0.110	0.113 *	−0.0745	0.109	0.348 *
	(0.111)	(0.0664)	(0.146)	(0.110)	(0.205)
$Constant$	26.63 ***	2.104	23.20 ***	26.61 ***	31.05 ***
	(2.964)	(1.562)	(3.779)	(2.984)	(4.606)
$Observ$	341	341	341	341	341

Note: Robust standard errors in parentheses. *** p < 0.01, * p < 0.1.

shows that digital finance’s impact on timber is positive and significant in the lower and middle quantiles. However, it is insignificant at higher quantiles. However,

Table 9. Digital inclusive finance—breadth and timber output.

	$\mathit{l}\mathit{n}\mathit{T}\mathit{M}\mathit{B}\_\mathit{O}$
Variables	Location	Scale	qtile_10	qtile_50	qtile_95
$lnDGF\_breadth$	0.000294	−0.000545	0.00117	0.000280	−0.000881
	(0.000768)	(0.000471)	(0.00129)	(0.000757)	(0.00117)
$lnFAR$	−3.915 ***	−0.404	−3.269 ***	−3.926 ***	−4.786 ***
	(0.645)	(0.366)	(0.946)	(0.646)	(1.115)
$lnAFR$	0.0264	0.0389	−0.0357	0.0274	0.110
	(0.0444)	(0.0278)	(0.0670)	(0.0448)	(0.0886)
$lnINV\_FRST$	−0.0330	−0.0115	−0.0147	−0.0333	−0.0578
	(0.0413)	(0.0272)	(0.0642)	(0.0412)	(0.0675)
$lntech$	0.203	0.130	−0.00444	0.206	0.482
	(0.140)	(0.0865)	(0.204)	(0.138)	(0.301)
$Constant$	25.96 ***	0.518	25.13 ***	25.97 ***	27.07 ***
	(4.133)	(2.418)	(6.880)	(4.111)	(4.888)
$Observ$	341	341	341	341	341

Note: Robust standard errors in parentheses. *** p < 0.01.

and

Table 10. Digital inclusive finance—depth and timber output.

	$\mathit{l}\mathit{n}\mathit{T}\mathit{M}\mathit{B}\_\mathit{O}$
Variables	Location	Scale	qtile_10	qtile_50	qtile_95
$lnDGF\_depth$	0.000533	−0.000338	0.00108	0.000521	−0.000201
	(0.000608)	(0.000398)	(0.00108)	(0.000598)	(0.000842)
$lnFAR$	−3.978 ***	−0.476	−3.208 ***	−3.995 ***	−5.012 ***
	(0.629)	(0.355)	(0.903)	(0.630)	(1.117)
$lnAFR$	0.0249	0.0369	−0.0348	0.0262	0.105
	(0.0445)	(0.0280)	(0.0678)	(0.0448)	(0.0835)
$lnINV\_FRST$	−0.0328	−0.0149	−0.00869	−0.0333	−0.0652
	(0.0424)	(0.0280)	(0.0686)	(0.0422)	(0.0683)
$lntech$	0.175	0.106	0.00417	0.179	0.405 *
	(0.121)	(0.0785)	(0.187)	(0.119)	(0.244)
$Constant$	26.78 ***	1.409	24.50 ***	26.82 ***	29.83 ***
	(3.579)	(2.129)	(6.056)	(3.561)	(4.490)
$Observ$	341	341	341	341	341

Note: Robust standard errors in parentheses. *** p < 0.01, * p < 0.1.

show that digital inclusive finance breadth and digital inclusive finance depth toward timber are positive but insignificant at lower and middle quantiles. The quantile coefficients of digital inclusive finance factors for forest and timber output are given in Figure 7a–c and Figure 8a–c. These figures show how digital inclusive finance affects forest and timber output across different productivity levels. In regions with lower output, digital finance positively impacts forest and timber production, suggesting that access to financial services helps improve outcomes. However, as productivity increases, the impact of digital finance decreases, and, in highly productive regions, it may even have a negative effect, indicating potential inefficiencies or diminishing returns.

5.2. Forest, Timber, and Financial Development

The study expands and incorporates the impact of financial development instead of digital inclusive finance. The study used two financial market development indicators for financial market development. One is Degree of Financial Marketization of Credit Fund Distribution, which focuses on allocating credit funds within the economy. This indicator helps assess how efficiently credit is distributed across various sectors, influencing access to capital for businesses and individuals. The second is the factor Market Development Score, which examines the extent of liberalization and competitiveness in the financial markets. In other words, it evaluates the overall efficiency of how the financial market is open and developed, including the availability of diverse financial instruments and services to sectors.

Table 11. Financial credit marketization and forest output.

	$\mathit{l}\mathit{n}\mathit{F}\mathit{R}\mathit{S}\mathit{T}\_\mathit{O}$
Variables	Location	Scale	qtile_10	qtile_50	qtile_95
$lnFMDC$	0.196 ***	−0.0707 **	0.311 ***	0.197 ***	0.0686
	(0.0577)	(0.0300)	(0.0819)	(0.0581)	(0.0714)
$lnFAR$	1.200 ***	−0.0152	1.225 ***	1.200 ***	1.172 ***
	(0.260)	(0.142)	(0.385)	(0.260)	(0.319)
$lnAFR$	0.0548 **	0.0223	0.0187	0.0546 *	0.0950 *
	(0.0277)	(0.0175)	(0.0324)	(0.0278)	(0.0492)
$lnINV\_FRST$	0.105 **	−0.00680	0.116	0.105 **	0.0923 **
	(0.0482)	(0.0319)	(0.0907)	(0.0483)	(0.0454)
$lntech$	0.181 ***	−0.00969	0.196 ***	0.181 ***	0.163 ***
	(0.0262)	(0.0146)	(0.0347)	(0.0261)	(0.0383)
$Constant$	−8.551 ***	0.359	−9.133 ***	−8.554 ***	−7.903 ***
	(1.459)	(0.810)	(2.161)	(1.462)	(1.811)
$Observ$	341	341	341	341	341

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

and

Table 12. Financial marketization degree and forest output.

	$\mathit{l}\mathit{n}\mathit{F}\mathit{R}\mathit{S}\mathit{T}\_\mathit{O}$
Variables	Location	Scale	qtile_10	qtile_50	qtile_95
$lnFMM$	0.0278 *	−0.0118	0.0462 **	0.0270 *	0.00415
	(0.0146)	(0.00883)	(0.0209)	(0.0146)	(0.0221)
$lnFAR$	1.396 ***	−0.189	1.691 ***	1.384 ***	1.019 ***
	(0.291)	(0.153)	(0.430)	(0.290)	(0.332)
$lnAFR$	0.0502 *	0.0322 *	5.72 × 10−6	0.0522 *	0.114 **
	(0.0282)	(0.0178)	(0.0352)	(0.0288)	(0.0514)
$lnINV\_FRST$	0.119 **	−0.0120	0.138	0.118 **	0.0951 *
	(0.0510)	(0.0359)	(0.0982)	(0.0495)	(0.0523)
$lntech$	0.200 ***	−0.00454	0.207 ***	0.199 ***	0.191 ***
	(0.0263)	(0.0139)	(0.0342)	(0.0262)	(0.0385)
$Constant$	−10.12 ***	1.299	−12.15 ***	−10.04 ***	−7.530 ***
	(1.676)	(0.862)	(2.356)	(1.673)	(2.033)
$Observ$	341	341	341	341	341

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

estimate lnFMDC and lnFMM for forest output, and

Table 13. Financial credit marketization and timber output.

	$\mathit{l}\mathit{n}\mathit{T}\mathit{M}\mathit{B}\_\mathit{O}$
Variables	Location	Scale	qtile_10	qtile_50	qtile_95
$lnFMDC$	0.121 ***	0.0222	0.0875	0.123 ***	0.168 **
	(0.0459)	(0.0251)	(0.0531)	(0.0465)	(0.0752)
$lnFAR$	−4.136 ***	−0.623 *	−3.190 ***	−4.192 ***	−5.449 ***
	(0.654)	(0.347)	(0.892)	(0.654)	(0.872)
$lnAFR$	0.0272	0.0125	0.00830	0.0284	0.0535
	(0.0478)	(0.0283)	(0.0724)	(0.0472)	(0.0658)
$lnINV\_FRST$	−0.0508	−0.0231	−0.0157	−0.0529	−0.0996
	(0.0457)	(0.0297)	(0.0750)	(0.0448)	(0.0639)
$lntech$	0.164 **	0.0586	0.0754	0.170 **	0.288 ***
	(0.0677)	(0.0481)	(0.115)	(0.0657)	(0.0974)
$Constant$	27.63 ***	3.334 *	22.57 ***	27.94 ***	34.67 ***
	(3.328)	(1.703)	(4.497)	(3.335)	(4.374)
$Observ$	341	341	341	341	341

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

and

Table 14. Financial marketization degree and timber output.

	$\mathit{l}\mathit{n}\mathit{T}\mathit{M}\mathit{B}\_\mathit{O}$
Variables	Location	Scale	qtile_10	qtile_50	qtile_95
$lnFMM$	0.150 **	0.0581 *	0.0654	0.160 **	0.267 ***
	(0.0598)	(0.0309)	(0.0594)	(0.0621)	(0.0965)
$lnFAR$	−3.904 ***	−0.476	−3.209 ***	−3.984 ***	−4.863 ***
	(0.591)	(0.312)	(0.812)	(0.585)	(0.739)
$lnAFR$	0.0136	0.0161	−0.00988	0.0163	0.0460
	(0.0451)	(0.0270)	(0.0683)	(0.0442)	(0.0598)
$lnINV\_FRST$	−0.0320	−0.0325	0.0155	−0.0374	−0.0974
	(0.0438)	(0.0282)	(0.0701)	(0.0425)	(0.0609)
$lntech$	0.289 ***	0.0783	0.174	0.302 ***	0.446 ***
	(0.0897)	(0.0524)	(0.108)	(0.0910)	(0.142)
$Constant$	23.69 ***	1.905	20.91 ***	24.01 ***	27.53 ***
	(2.632)	(1.313)	(3.742)	(2.583)	(3.024)
$Observ$	341	341	341	341	341

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

for timber outputs. The results show that the lnFMM factor influences the forest and timber output at lower and middle quantiles. However, it is positive but insignificant for forest output at higher quantiles.

In contrast, the lnFMDC and lnFMM are positive and significant in the middle and higher quantiles for forest and timber output. These results imply that financial development is crucial in providing better access to capital and allows forest owners to invest in more efficient production techniques and equipment. Further, funds such as green financing can benefit reforestation and conservation, boosting forest cover and sustainable timber production. Financial markets also provide risk management tools to help producers handle price fluctuations, thus stabilizing output. So, financial development also affects land use decisions, potentially increasing the land allocated for timber production. Finally, it supports innovation and technology adoption in forestry, further improving productivity and sustainability. The quantile coefficient of financial market development factors for forest and timber output are given in Figure 9a,b and Figure 10a,b. These figures demonstrate the relationship between financial market development and forest/timber output. Financial development has a positive impact across most output levels, but the impact’s strength varies. It is most effective in regions with moderate output, where better access to financial resources helps improve production efficiency and sustainability.

6. Conclusions

In this digital era, digital inclusive finance is important in empowering sectors by providing accessible financial services and fostering economic growth. Considering this, the study was conducted on the impact of digital inclusive finance, financial market development, and technology on forest and timber outputs across China’s 31 provinces from 2011 to 2021. Further, the study reveals several critical findings regarding digital inclusion breadth and depth. The analysis shows that digital inclusive finance significantly contributes to forest economic output, particularly in regions with lower levels of forest economic activity. It provides access to essential financial resources, such as credit, savings, and investment opportunities, crucial for sustainably improving productivity and managing forests. The positive effects of digital inclusive finance are more pronounced at lower and middle quantiles, indicating that it is especially beneficial for underperforming regions. However, the impact is less significant or negative at higher quantiles, suggesting that additional financial services may not lead to proportional gains beyond a certain level and might result in inefficiencies or overinvestment.

In contrast, financial development, particularly the degree of financial marketization and the availability of diverse financial funds, plays a significant and positive role in enhancing forest and timber outputs. It facilitates better access to capital, enabling forest owners and managers to invest in advanced technologies and efficient production techniques in China. Integrating technology (measured by expenditures on scientific research activities) in the forestry sector also strongly impacts different quantiles. Technological advancements enable better management of forest resources, data collection, and decision-making, improving productivity and sustainability. The study highlights the importance of technology in enhancing the efficiency and effectiveness of forest management practices. Afforestation significantly increases the forest’s output and timber at higher quantiles with digital inclusive finance. However, it is positive but insignificant to financial market development.

The findings suggest several policy implications for enhancing the sustainability and productivity of China’s forestry sector. The study findings indicate that the expansion of digital financial services is much more effective, especially in regions with lower levels of forest economic activity. Efficiency in these sectors can be enhanced by promoting mobile banking, digital payments, and fintech solutions that provide easier access to financial resources. Such services can empower rural and forest-dependent communities, enabling them to participate more actively in the forestry economy. Further, policymakers should promote financial instruments to support sustainable forestry practices. Green financing initiatives, such as subsidies for sustainable forest management, can incentivize conservation and sustainable timber production investment. Enhancing financial market development is crucial for providing the necessary capital and financial services to the forestry sector. It supports research and development in forestry technologies, training forest managers in the use of advanced tools and disseminating technological innovations across the sector. However, the success of all policies depends on continuous monitoring and evaluation, such as of how and to what extent digital inclusive finance and financial development work or progress in the forestry sector. This will help adjust policies and strategies to contribute effectively to sustainable forest management and economic growth. In conclusion, a multifaceted approach integrating financial development, digital finance, and technology is essential for promoting sustainable forestry practices in China. By addressing both economic and social dimensions, these policies can contribute to environmental conservation, economic productivity, and inclusive growth, aligning with China’s broader goals of ecological civilization and sustainable development.

While this study provides valuable insights into the impact of digital finance and financial development on China’s forestry sector, several avenues for future research remain. First, future studies could explore the long-term effects of digital inclusive finance on forest sustainability by incorporating more detailed environmental indicators, such as biodiversity conservation and carbon sequestration. Second, research could investigate the potential for digital finance to mitigate the risks associated with climate change in forestry, particularly in regions vulnerable to extreme weather events. Third, comparative studies between China and other countries with similar forestry challenges could provide a broader understanding of how digital and financial innovations can be tailored to different environmental and economic contexts. Finally, further research is needed to assess the social impacts of digital finance on forest-dependent communities, particularly in reducing inequality and enhancing social inclusion in the forestry economy. These future research directions will help deepen our understanding of the complex interactions between finance, technology, and sustainable forestry, ultimately contributing to more effective policies and practices.