Green Finance Empowering Forestry New Quality Productivity: Mechanisms and Practical Paths

Abstract

The impact of green fiscal policies on forestry new quality productivity (FNQP) development and the mechanisms behind this are explored in this study. A scientific basis for the promotion of FNQP development through green fiscal policies is intended to be provided. In addition, novel perspectives on the high-quality development of forestry are offered. Panel data from 31 Chinese provinces between 2011 and 2022 are collected and discrepancies between green fiscal policies and the level of FNQP are examined. By constructing a two-way fixed-effects model and employing econometric methods, the significant promotion of FNQP by green fiscal policies is indicated. Indirect promotion of FNQP development through the upgrading of the forestry industrial structure and the intensification of environmental regulation by green fiscal policies is demonstrated by indirect impact analysis results. Furthermore, a positive spatial spillover effect of green finance on forestry new quality productivity is documented. For policy selection, regional economic development level heterogeneity characteristics should be taken into account. Based on the findings of this study, some recommendations have been given for responses to green fiscal policy for forestry new quality productivity in the future.

1. Introduction

The organic embedding of green finance into the forest development system has been identified as a critical strategy to reconcile the tension between ecological preservation and productivity. Against this backdrop, many related concepts have been proposed, such as green total factor productivity (GTFP) and forestry new quality productivity. The concept of forestry new quality productivity is one that has been proposed based on China’s national conditions and does not yet exist in other countries. The concept of “new quality productive forces” was first proposed during an inspection visit to Heilongjiang Province in September 2023. New quality productive forces are distinguished by innovation playing a leading role, deviation from outdated conventional economic growth models and paths of productivity development, and emphasis on high use of technology, high efficiency, and high quality. Forestry new quality productivity (FNPQ) was established under the guidance of new quality productive forces theory [1], representing a new source of development momentum and direction. Its core lies in leveraging disruptive technological innovations like biotechnology and the deep application of new factors (such as data and knowledge-based talent) to drive a qualitative leap in the forestry industry. It aims to break free from traditional dependencies and develop high-tech, high-value-added new business models and formats, serving as one of the core driving forces for leading the forestry sector toward high-quality development in the future [2]. On the other hand, green total factor productivity (GTFP) can be regarded as an evaluation system, with its core focus on measuring the comprehensive output efficiency of the forestry economy under the strict inclusion of resource consumption and environmental pollution costs. It addresses how to achieve optimal input–output efficiency under ecological and environmental protection constraints, aiming for sustainable and efficient development. In summary, one of the objectives of developing forestry new quality productivity as a new driver of development is to enhance green total factor productivity, while improvement in GTFP is one of the key performance indicators for measuring the effectiveness of the development of FNQP. Therefore, FNQP serves as one of the core driving forces for leading the forestry sector toward high-quality development in the future.

At a time when the global environmental crisis caused by climate change is becoming increasingly severe, policymakers and scholars are increasingly recognizing the role of financial instruments in promoting forestry new quality productivity—i.e., innovation-based, efficiency-oriented, and ecologically sustainable development. In China, where the coverage of forests has increased from 12.7% in the 1980s to 22.96% in recent decades, green finance instruments such as carbon markets, green bonds, and digital inclusive finance are being widely implemented to optimize the efficiency of resource allocation and curtail ecological degradation. However, the channels through which such financial instruments improve productivity in forests remain understudied in China and beyond, specifically in balancing near-term economic gains and far-term ecological resilience.

Green finance can exert a certain influence on forestry. Integration and collaboration between green finance and productivity in the forest sector is realized through multi-path synergistic channels. Gong and Wang demonstrated how China’s green credit policies increased green total factor productivity (GTFP) in the forest sector by 12.8% between 2015 and 2022 by enhancing energy efficiency and technological innovation [3]. With a spatial econometric model, it was found that provinces with good green finance systems achieved 23% higher levels of carbon sequestration compared to lagging provinces, and policy coherence was a success determinant. An empirical study of panel data of 30 Chinese provinces revealed that green bond and carbon market investment has a nonlinear promoting effect of 15%–18% on productivity, but with diminishing marginal benefits beyond threshold values due to the limitations of institutions [4].

Digitalization serves as a central lever that amplifies these effects. Li et al. documented a 9.7% increase in agricultural GTFP as a result of rural China’s digital financial inclusion, with spillover effects in favor of the forestry industries through integrated value chains [5]. Transaction costs fell by 35% as a result of mobile payment systems, enabling smallholder foresters’ use of precision forestry technology. However, the consequent problem of regional uneven development has gradually emerged. Xiang et al. documented a “green divide” where eastern Chinese cities made use of fintech to achieve 27% higher GTFP growth in comparison to western China, and highlighted the need establish a responsive policy framework to cope with this trend [6].

The success of green finance instruments largely depends on the support of regulatory frameworks. Dong and Tao evaluated China’s Green Finance Reform Pilot Zones and discovered that firms in those pilot zones reduced carbon intensity by 19% via preferential loans—twice the national average [7]. However, their study also revealed contradictions within the system: 42% of forestry firms prioritized compliance in the near term over systemic innovation due to misaligned incentive structures. These findings are in line with Kirey’s evaluation of Central Asian forest bonds, which found that the actual performance of related afforestation projects was more than 30% lower than expected due to a lack of risk-sharing mechanisms [8].

Zhang et al. complicate this narrative by examining sector-specific impacts [9]. They conclude that green finance heavily benefits large-scale forestry companies at the disadvantage of smallholders who receive only 14% of subsidized loans on the basis of collateral requirements. This bias confirms Ge and Bao’s evidence on carbon trading pilots where state-owned companies dominated carbon credit transactions by 78%, leaving out community-based forest management activities [10]. Without the intervention of institutional equity guarantee mechanisms, green finance may intensify the trend of concentration of resources and power in the hands of economic elites.

Research on forestry new quality productivity is currently concentrated on the definition of its connotations, measurement methods, and driving factors. Kong F.B., utilizing a benchmark regression model and a mechanism testing model, explored the heterogeneity mechanism of forestry new quality productivity and its path of influence on the value realization efficiency of ecological products from the point of view of four factors: dependent variables, core explanatory variables, mechanism variables, and control variables [11]. The conclusion indicates that the evolution of forestry new quality productivity is imperative to enhance the value of forest ecological products. Liao proposed that new quality productivity enabled by digitalization promotes the matching of production factors [12], human capital accumulation, and technological innovation for forestry, thereby realizing the optimization and upgrading of the forestry industrial structure, narrowing the gap in forestry industry development at the county level, and promoting an overall improvement in the quality of the forestry industry. Yuan Baolong highlighted five pathways—scientific and technological innovation, industrial transformation, talent cultivation, infrastructure construction, and institutional reform—as the core drivers of high-quality development in forestry [13]. Wu Songze and Chen Qian using a coupling coordination model, analyzed the coupling coordination relationship and spatiotemporal evolution pattern between high-quality forestry development and new quality productive forces across 30 provinces in China. They concluded that new quality productive forces are a key factor influencing the high-quality development of forestry, and proposed corresponding policy recommendations based on the research findings [14]. Cao Yukun et al., based on the theory of smart forestry, conducted research from both natural and social science perspectives, aiming to maximize the economic benefits of smart forestry through technological innovation and knowledge dissemination, interdisciplinary integration, policy mechanisms, and global cooperation [15]. Guan Zhijie and Zhang Wei analyzed the development status of China’s smart forestry, compared it with that of developed countries such as the United States, Canada, and Japan, summed up the gaps and experiences, and put forward targeted strategic suggestions [16].

A paucity of systematic research focuses on the connection between green finance and forestry new quality productivity. However, the existing literature preliminarily outlines its interactive context. Xing Li, taking green finance as the core of his research, analyzed its empowering role in new quality productive forces [17]. Through the penetration of green concepts, the empowerment of key industries, and the construction of a collaborative path among finance, industry, and institutions, green finance ensures the full stimulation of new quality productive forces. Hu Meiling pointed out that digital inclusive finance plays an important role in the promotion of high-quality forestry development, and suggested accelerating innovations in digital inclusive finance to promote this development [18]. Wang Huogen, using Tobit and mediation models, examined how digital finance influenced forestry green total factor productivity, analyzed and summarized the results, and proposed rational suggestions based on the findings [19]. Li Qiang used Shaoguan’s “Finance + Forestry” model to analyze the realistic difficulties and institutional obstacles in the process of financial empowerment, and subsequently proposed corresponding suggestions and measures. Shi Haonan conducted a review of the present circumstances and the theoretical foundation of forestry carbon sink finance through a comprehensive literature review, clarified the bottleneck problems it faces, and proposed building a green financial support structure through innovative financial products and a supporting policy system to stimulate the internal kinetic energy of financial institutions, so as to upgrade the entire forestry industry chain [20]. Geng [21] analyzed the bottlenecks in financial services supporting collective forest tenure reform and put forward strategies based on policy incentives, increased credit support for the forestry industry chain, and innovation in financial services to support the reform. Wang Weihua, through a comparative analysis of four forest tenure pledge models in Shunchang, Pujiang, Ninghai, and Yong’an, summarized their respective adaptation conditions and promotion strategies, and proposed application strategies based on factors such as forest resources, forest tenure ownership, the forestry development level, and the scale of mortgage loans [22].

To sum up, existing research offers substantial theoretical support clarifying the internal relationship between green finance and forestry new quality productivity, and has made valuable explorations in the selection of policy tools and the exploration of practical paths. However, there is still a gap in the dimension of theoretical deepening and empirical analysis, and the systematic mechanism and operational model of green finance enabling new quality productive forces has yet to be established. Future research needs to further deepen theoretical discussions and strengthen empirical studies, with the aim of offering more robust theoretical support and practical guidance to facilitate the high-quality development of forestry in China.

2. Theoretical Analysis and Research Hypotheses

Green finance, a fiscal concept and practice, adapts to ecological civilization and aims to cultivate a harmonious coexistence between humanity and nature. Fiscal policies, through their guidance and support, allow green finance to function as an integral component in the modernization of fiscal and national governance systems, thereby advancing green development and enhancing green productivity [23,24]. FNQP represents the specific application and advancement of quality productivity in the forestry sector. For the purposes of this study, FNQP is defined as a more efficient, high-quality, and sustainable form of productivity. Specifically, in terms of efficiency, the contribution of science and technology to total factor productivity has significantly increased, replacing the traditional model dominated by human and resource inputs. In terms of quality, the added value per unit of forest land has increased, and the proportion of ecological services has also increased. In terms of sustainable development, growth has been decoupled from degradation. For example, as timber production increases, carbon sequestration also increases synchronously. This form is achieved by enhancing the quality of forestry productivity’s three core elements—practitioners, labor materials, and labor objects—driven by innovative development concepts. Based on theoretical analysis, this study proposes a hypothesized pathway, the basic framework of which is shown in Figure 1.

2.1. The Direct Impact of Green Finance on Forestry New Quality Productivity

Several aspects illustrate the way in which green finance directly affects the advancement of FNQP. Fiscal support constitutes the first aspect. Through policy instruments such as dedicated fiscal funds, tax advantages, and subsidies, green finance directly underpins the progress of FNQP. These financial resources facilitate the adoption of cutting-edge equipment and the advancement and dissemination of green production technologies [25], thereby securing the foundation for FNQP. The ecological environment improvement represents the second aspect. Enhancing ecological compensation mechanisms—for instance, fiscal transfer payments and carbon trading subsidies—can offset the economic burdens associated with forestry environmental protection and ensure equilibrium between ecological integrity and economic gains. In addition, by leveraging industrial ecological effects [26], green finance can motivate the green and low-carbon transformation of sectors such as forestry, thereby cultivating an increase in green total factor productivity [27] and boosting the development of FNQP. The guidance of green consumption and market demand is the third aspect. Green finance, through tax incentives and subsidies, can stimulate market demand for green consumption and forestry ecological products. This demand, accordingly, promotes structural reform at the production stage and encourages green production [28] through green consumption patterns, finally cultivating the development of FNQP.

Hypothesis H1. 

Green finance can promote the development of FNQP.

2.2. The Indirect Impact of Green Finance on Forestry New Quality Productivity

2.2.1. The Intermediary Effect of Upgrading the Forestry Industrial Structure

Regular shifts in the industrial structure of the forestry sector during periods of economic development are indicative of an upgrade in the forestry industrial structure. Specifically, this process entails a systematic transition from industries that exhibit lower-value-added activities to those that demonstrate higher-value-added activities. This progression amounts to an increased proportion of the output value being contributed by the secondary forestry industry (e.g., wood processing and manufacturing of products from wood, bamboo, rattan, palm, and grass) and the tertiary industry (e.g., forestry tourism and leisure, and ecological services) [29]. By eliminating outdated forestry production capacities, green finance steers the industrial transformation of forestry toward activities with higher value-added and reduced environmental impact. Moreover, the upgrading of the forestry industrial structure is further facilitated by green finance through its support of green technology innovation and sustainable management practices [12]. Efficiency and sustainability in forestry production are significantly enhanced by the restructuring of the forestry industrial structure through mechanisms such as optimized resource allocation, promoted technological innovation, and improved product industry chains. Therefore, this structural shift offers essential momentum for the creation and advancement of FNQP [30]. Meanwhile, green fiscal policies have infused the forestry sector with crucial production factors, including funding, technology, and talent. The flexible deployment of these resources across primary, secondary, and tertiary forestry sectors is a key driver of the advancement of FNQP. Through its unique policy focus and resource allocation capabilities, green finance offers robust support and assurance for both the optimization and upgrading of the forestry industrial structure and the cultivation of FNPQ. Hypothesis H2 is formulated upon this basis.

Hypothesis H2. 

Green finance can indirectly promote the development of FNQP by promoting the upgrading of the forestry industrial structure.

2.2.2. The Intermediary Effect of Environmental Regulation Intensity

Environmental regulation is defined as the government’s use of laws, rules, and economic incentives to regulate actions related to ecological environment protection and to cultivate the harmonious integration of environmental protection with sustainable economic development. This form of regulation not only highlights the sensible use and protection of environmental resources, but also seeks to advance the coordination and unity between environmental protection and sustainable economic development, ensuring their mutual reinforcement and synchronized progress. Here, green fiscal policies assume a vital role. These policies effectively amplify the intensity of local environmental regulations through a spectrum of positive incentives, including ecological compensation systems and reductions in taxes and fees.

Furthermore, the increased intensity of environmental regulation can trigger cascading benefits, specifically enhanced green innovation efficiency [31]. By incentivizing and assisting enterprises in pursuing green technology research and implementation, environmental regulations stimulate a gradual shift in production methodologies towards digitization and low-carbon operations. This shift, accordingly, lessens environmental contamination and ecological harm while enhancing resource utilization efficiency. Particularly in the forestry domain, this transformation holds significant importance. With the ongoing optimization of production methods, an enhancement in the level of FNQP has occurred, thereby injecting renewed dynamism into the sustainable development of forestry. Hypothesis H3 is derived from this premise.

Hypothesis H3. 

Green finance can indirectly promote the development of FNQP through increasing the intensity of environmental regulations.

2.3. The Spatial Spillover Effects of Green Finance on Forestry New Quality Productivity

The dissemination of green technologies in regions where fiscal policies are conducive to the adoption of such technologies is expected to occur through various channels, including technology exchanges and other means of exchange [32]. This technological spillover may extend to the formulation of green finance in neighboring regions and the enhancement of forestry productivity. The proximity of spatial locations between regions suggests the potential for regional green fiscal efforts to also compensate for the ecological environment of neighboring regions through the ecological compensation mechanism. This, in turn, could promote ecological improvement in neighboring regions and thus foster forestry new quality productivity. Hypothesis H4 is proposed on this basis.

Hypothesis H4. 

Green finance can indirectly influence the development of FNQP via spatial spillover.

3. Research Methods and Data Sources

3.1. Variable Selection

3.1.1. Core Explained Variables

Forestry new quality productivity (FNQP) is the central explained variable in this study. At present, a standardized method for measuring FNQP remains absent. To address this, this paper establishes an FNQP evaluation index system (

Table 1. Index system for evaluating forestry new quality productivity.

Primary Index	Secondary Index	Tertiary Index	Interpretation of Indicators	Unit	Attributes
New quality forestry workforce	Educational level of workers	Professional quality	Number of professional and technical staff/number of employees at forestry stations	%	+
		Educational quality	Years of schooling per rural laborer	year	+
	Labor input	Number of researchers	Number of forestry R&D personnel in China	ten thousand people	+
		Average value of production of forest workers	Gross forestry output/number of forestry employees	%	+
New quality forestry labor target	Industrial structure	Rationalization of the forestry industry	Forestry output/primary output	%	+
		Advanced forestry industrial structure	Forestry tourism and recreation services output/forestry output	%	+
	Ecological environment	Forest quantity	Rate of forest cover	%	+
		Forest quality	Forest stock	m3	−
New quality forestry labor resources	Material production resources	Infrastructure	Fiber optic line length	km	+
		Energy consumption	Energy consumption/gross regional product	%	−
	Intangible production resources	Digital development	Rural digital inclusive finance development index	score	+
		Policy support	Financial expenditures on forestry	hundred million yuan	+

Note: + indicates positive indicator, − indicates negative indicator.

). This system, referencing established new quality productive force evaluation methods [33], is structured around three dimensions: laborers, labor objects, and labor materials [34]. Then, the entropy method is used to determine the development level of FNQP.

From the perspective of the forestry labor force, forestry new quality productivity is matched with intellectual workers. Intellectual workers possess high-quality innovative, knowledge-based, and labor capabilities, and are better able to utilize and transform nature [35]. Therefore, in this article, the number of researchers is used to reflect innovative literacy, professional quality and educational quality are used to reflect knowledge literacy, and the average value of production of forest workers is used to reflect labor capacities. Secondly, new quality forestry labor target serves as the foundation for developing forestry new quality productivity. Traditional forestry primarily treats timber as its target of labor, a production approach that has historically resulted in severe environmental degradation and resource inefficiency. In contrast, forestry new quality productivity embodies profound ecological awareness and industrial transformation principles. These forces prioritize reducing resource costs through technological innovation, aiming at achieving coordinated development between forestry’s ecological and economic dimensions. This study employs the indicators of rationalization of the forestry industry and advanced forestry industrial structure to measure the upgrading of the forestry industrial structure, and utilizes the indicators of forest quantity and forest quality to reflect the quality of the ecological environment. Finally, new quality forestry labor resources constitute the key to developing forestry new quality productivity. These means should encompass not only various material production resources, but also a range of intangible production resources capable of transforming forestry production models and driving industrial innovation. Within this framework, infrastructure and energy supply serve as the foundation for cultivating these new qualitative means of labor. Policy support provides essential impetus for industrial upgrading and innovation in the forestry sector. Digitalization of the forestry industry represents both the outcome and the manifestation of this transformation in the means of labor.

In the process of calculating the level of forestry new quality productivity, it is necessary to clearly define the weights of each indicator. At present, the academic community primarily uses two methods to determine the weights of relevant indicators: the subjective weighting method and the objective weighting method. The subjective weighting method primarily relies on subjective judgment to determine the weights of each indicator; this has a strong subjective nature and may significantly influence the measurement results, leading to inaccuracies. In contrast, the objective weighting method assigns weights to each indicator based on objective information. Therefore, to ensure that the measurement results are more aligned with reality, the entropy method within the objective weighting method is employed for weighting. The specific approach is outlined below:

Firstly, standardized processing.

(1)

Positive indicator

$$X_{ij}^{\prime }={\displaystyle \frac{X_{ij}-\min \left\{X_j\right\}}{\max \left\{X_j\right\}-\min \left\{X_j\right\}}}$$

(1)

(2)

Negative indicator

$$X_{ij}^{\prime }={\displaystyle \frac{\max \left\{X_j\right\}-X_{ij}}{\max \left\{X_j\right\}-\min \left\{X_j\right\}}}$$

(2)

In this context, $X_{ij}^{\prime }$ represents the standardized value of the indicator after processing; $X_{ij}$ denotes the original value of indicator $j$ for sample $i$; $\max \left\{X_j\right\}$ denotes the maximum value of indicator $j$; and $\min \left\{X_j\right\}$ denotes the minimum value of indicator $j$.

Secondly, calculate the weight of indicator $j$ in sample $i$. The calculation formula is as follows:

$$P_{ij}=\raisebox{1ex}{$X_{ij}^{\prime }$}\!\left/ \!\raisebox{-1ex}{${\displaystyle \sum _{i=1}^m}$}\right.X_{ij}^{\prime }$$

(3)

In this equation, $m$ denotes the sample size, and the meanings of the other variables are the same as in Equation (1).

Thirdly, calculate the information entropy of the indicators. The calculation formula is as follows:

$$e_j=-k{\displaystyle \sum _{i=1}^m\left(P_{ij}\ln P_{ij}\right)},\hspace{0.33em}k={\displaystyle \frac{1}{\ln m}},\hspace{0.33em}0\le e_j\le 1$$

(4)

Fourthly, calculate the information entropy redundancy. The formula is as follows:

$$d_j=1-e_j$$

(5)

Fifthly, calculate the weight of each indicator. The formula is as follows:

$$w_j={\displaystyle \raisebox{1ex}{$d_j$}\!\left/ \!\raisebox{-1ex}{${\displaystyle \sum _{j=1}^n{d}_j}$}\right.}$$

(6)

In this context, $n$ denotes the number of indicators in the evaluation indicator system.

Sixthly, calculate the score for indicator $j$ of sample $i$. The formula is as follows:

$$S_{ij}=w_j{X}_{ij}^{\prime }$$

(7)

Seventhly, after obtaining the specific scores for each indicator of the $i$ sample, calculate the comprehensive index of forestry new quality productivity by summing up the scores. The calculation formula is as follows:

$$S_i={\displaystyle \sum _{j=1}^n{S}_{ij}}$$

(8)

Among these, $S_i$ represents the level of forestry new quality productivity. The higher the value of $S_i$, the higher the level of forestry new quality productivity; conversely, the lower the value, the lower the level.

3.1.2. Core Explanatory Variables

Green fiscal expenditure (GF) constitutes the primary independent variable in this research. The “Environmental Protection” account emerged in 2007 from the separation of environmental protection from broader governmental expenditure accounts. This account was redesignated as “Energy Conservation and Environmental Protection Expenditure” in 2011. Environmental pollution control, energy and resource conservation and utilization, and ecological construction and protection are the three primary categories that govern energy conservation and environmental protection expenditure. Following the methodology of Du Juntao et al. [36], this study employs the proportion of total energy conservation and environmental protection expenditure relative to local general public finance expenditure as a representation of government green finance expenditure.

3.1.3. Intermediary Variables

The theoretical analysis outlined earlier indicates that green finance influences the advancement of FNQP through two pathways: the upgrading of the forestry industrial structure and the intensification of environmental regulation. Therefore, these two factors are designated as intermediary variables. (1) Forestry industrial structure upgrading: This advancement is capable of cultivating high-value-added industries and propelling the growth of FNQP. Adopting the approach utilized for agricultural industrial structure upgrading [37], this paper calculates the output value that the forestry service industry has in total. This calculation involves multiplying the total forestry output value by the ratio of (the output value of agriculture, forestry, animal husbandry, and fishery specialties and auxiliary activities/the total output value of agriculture, forestry, animal husbandry, and fishery). The calculated value represents a proxy variable to upgrade the forestry industrial structure. The implementation of exclusive forestry alongside auxiliary activities, including forest resource cultivation and pest control, has been identified as a pivotal factor in the process of upgrading the forestry industrial structure. (2) Environmental regulation intensity: This intensity can stimulate environmentally friendly and efficient production by driving technological innovation and factor restructuring. The ratio of environmental governance expenditure to regional GDP offers a measure for environmental regulation intensity.

3.1.4. Control Variables

In addition to green finance, other variables may affect FNQP. Therefore, drawing upon existing research, this study incorporates the following control variables. (1) Economic development level: Regions demonstrating higher economic development levels typically possess more abundant financial resources and more favorable policy environments, both of which facilitate the implementation and advancement of green fiscal policies. This is measured with the logarithm of per capita GDP [38]. (2) Foreign investment level: Foreign investment in forestry holds the potential to significantly accelerate the progress of high-value-added industries in the forestry sector [39]. In addition, regions attracting greater foreign investment are more inclined to cultivate emerging industries, draw in increased green financial investment, and further enhance FNQP. This is measured by the proportion of foreign investment contributing to regional GDP in a given year. (3) Regional openness level: Enhanced regional openness improves the efficiency of forestry production technologies, optimizes resource distribution, elevates output quality, and promotes the adoption of novel technologies and industrial upgrades, thereby collectively cultivating the development of FNQP [40]. This is measured utilizing the percentage of import and export volume in total connected with GDP. (4) Urbanization level: Urbanization is capable of facilitating the transformation and upgrading of the forestry industrial structure [25], encouraging population concentration, capital flow, and so on. These factors collectively contribute to the level of FNQP. The urban population as a percentage of the total population is utilized for its measurement.

3.2. Model Construction

3.2.1. Construction of a Benchmark Regression Model

Based on the preceding theoretical analysis and research hypotheses, a benchmark regression model is expressed as follows:

$$FNP{Q}_{it}={\alpha }_0+{\alpha }_{1}G{F}_{it}+{\displaystyle \sum _{j=1}^4{b}_j}{C}_{jit}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(9)

where $i$ represents the individual (region). $t$ represents time. $G{F}_{it}$ denotes green finance. $FNP{Q}_{it}$ denotes forestry new quality productivity. $C_{jit}$ conveys the $j$-th control variable, including economic development level, foreign investment level, regional openness level, and urbanization level. ${\mu }_i$ and ${\lambda }_t$ illustrate individual (region) and time fixed effects, respectively. ${\epsilon }_{it}$ explains a random disturbance term. ${\alpha }_0$ indicates a constant term. ${\alpha }_1$ depicts a coefficient to be estimated. $b_j$ refers to an estimated coefficient for the control variable.

3.2.2. Construction of Intermediary Effect Model

To further explore how green finance affects FNQP, this paper uses an intermediary effect model that builds upon the aforementioned benchmark model.

$$M_{it}={\beta }_0+{\beta }_{1}G{F}_{it}+{\displaystyle \sum _{j=1}^2{m}_j{C}_{jit}}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(10)

$$FNQ{P}_{it}={\theta }_0+{\theta }_{1}G{F}_{it}+{\theta }_2{M}_{it}+{\displaystyle \sum _{j=1}^2{n}_j}{C}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(11)

where $M_{it}$ represents the intermediary variable, namely the upgrading of forestry industry structure and the intensity of environmental regulations. ${\beta }_0,{\theta }_0$ denote constant terms, and ${\beta }_1,m_j,{\theta }_1,{\theta }_2,n_j$ convey the coefficient to be estimated.

3.3. Data Sources

For this study, panel data collected from 31 Chinese provinces (excluding Hong Kong, Macao, and Taiwan) between 2011 and 2022 were utilized as the research sample. Data relevant to the evaluation systems for both green finance and FNQP were sourced from several official publications. These include the “China Statistical Yearbook”, the “China Forestry and Grassland Statistical Yearbook”, the “China Environmental Statistical Yearbook”, the “China Rural Statistical Yearbook”, and data offered by the National Bureau of Statistics. Information for the Rural Digital HP Financial Index was obtained from both the “China Rural Financial Services Report” and the Digital Inclusive Finance Data, which are released by the Digital Finance Research Center of Peking University. To ensure the completeness of the datasets, any marginal instances of missing data were addressed through linear interpolation methods. Concurrently, the utilization of Stata16 software is predominant in the analysis of the data.

3.4. Variable Description

The descriptive statistical results of the main variables are displayed in

Table 2. Descriptive statistics of variables.

Variable Name	Sample Size	Min	Max	Mean	Standard Deviation
Forestry new quality productivity	372	0.201	0.770	0.52	0.131
Green finance	372	0.011	0.068	0.03	0.009
Level of foreign investment	372	0.001	0.121	0.02	0.018
Level of economic development	372	9.706	12.156	10.9	0.455
Opening-up degree	372	0.008	1.548	0.26	0.284
Level of urbanization	372	0.228	0.896	0.59	0.130

.

4. Results and Discussion

4.1. Characteristics of Changes in Green Finance and Forestry New Quality Productivity

Figure 2 and Figure 3 illustrate the average levels of FNQP and green finance across 31 provinces in China during the period from 2011 to 2022, respectively. Figure 2 indicates that, despite fluctuations, the level of FNQP across the 31 provinces generally exhibited an increasing trend, growing from 0.401 in 2011 to 0.476 in 2022. Specifically, the eastern region demonstrated the highest average level of FNQP, followed by the central region, with the western and northeastern regions indicating lower levels. Figure 3 indicates that the national average level of green finance displayed a fluctuating upward trend from 2011 to 2019, after which it experienced a decline following its peak in 2019. The trend of change in green finance was largely consistent across the regions studied. This pattern may be attributed to China’s explicit emphasis on strengthening ecological civilization construction and promoting green development, as expressed in both the 13th and 14th Five-Year Plans, alongside the introduction of various policies designed to strengthen green finance. The eastern region has been at the forefront of implementing numerous green development strategies, such as forest city construction and ecological compensation systems, to cultivate FNQP. This is due to its high level of economic development. The central region benefits from national policy support, which facilitates the progressive advancement of forestry industry upgrades and ecological restoration projects. Notwithstanding facing less favorable natural conditions and weaker economic foundations, the western and northeastern regions have witnessed a gradual improvement in their levels of FNQP. This improvement is attributable to the positive effects of national ecological poverty alleviation and regionally coordinated development policies. The decline in green finance observed after 2019, potentially influenced by factors such as policy adjustments and shifts in the economic environment, thus impacted the advancement of FNQP.

4.2. Benchmark Regression Analysis

As illustrated in

Table 3. Baseline regression results.

Variables	Dependent Variable: Forestry New Quality Productivity
	(1)	(2)
Green finance	0.703 ***	0.601 ***
	(0.209)	(0.205)
Level of foreign investment		−0.069
		(0.113)
Level of economic development		0.064 ***
		(0.014)
Opening-up degree		0.012
		(0.022)
Level of urbanization		−0.000
		(0.001)
Constant	0.381 ***	−0.281 **
	(0.007)	(0.128)
Regional fixed effect	Yes	Yes
Time fixed effect	Yes	Yes
Observations	372	372
R2	0.937	0.941

Note: The numbers in brackets are standard errors; ** and *** indicate significance at the 5% and 1% levels, respectively.

, Models (1) and (2) demonstrate the benchmark regression findings concerning the effect of green finance on FNQP, presented without and with the inclusion of control variables, respectively. The data in

Table 4. Robustness test results.

Variables	(1) Winsorization	(2) Excluding Municipalities
Green finance	0.602 *** (0.207)	0.597 ** (0.276)
Level of foreign investment	−0.068 (0.114)	0.056 (0.143)
Level of economic development	0.064 *** (0.014)	0.064 *** (0.017)
Opening-up degree	0.013 (0.022)	0.036 (0.030)
Level of urbanization	−0.000 (0.001)	−0.040 (0.162)
Regional fixed effect	Yes	Yes
Time fixed effect	Yes	Yes
Observations	372	324
R2	0.940	0.939

Note: The numbers in brackets are standard errors; ** and *** indicate significance at the 5% and 1% levels, respectively.

indicate a significantly positive effect of green finance on FNQP, observed at the 1% significance level. Specifically, after incorporating control variables into Model (2), each 1% increase in green fiscal investment corresponded to a 0.601% rise in the level of FNQP. Meanwhile, the R-squared value increased, suggesting an enhanced model fit. These results offer evidence that green finance effectively promotes FNQP, thus confirming Hypothesis H1. This finding carries implications for facilitating the green transformation of the forestry sector and establishing an ecological civilization. It highlights the importance of fully leveraging green finance to direct social capital towards environmentally friendly forestry projects and to accelerate the green economic transition in forestry. In addition, it highlights the necessity for national-level enhancements to the policy framework and optimization of fiscal expenditure structures. Such improvements have the potential to offer valuable Chinese insights and solutions for promoting high-quality forestry development in the contemporary era. These insights and solutions address climate challenges and cultivate a harmonious relationship between humanity and nature. Further analysis of the control variables indicates that the degree of economic development exerts a significantly positive effect on FNQP. This positive effect may be due to the capacity of economic development to channel greater financial resources into forestry, encourage technological advancements, and facilitate the deployment of sophisticated equipment, finally boosting production efficiency and the growth potential of FNQP.

4.3. Robustness Test

In order to guarantee the validity of the previously mentioned conclusions, two methodologies were employed for robustness testing. First, Winsorization was applied. To reduce any potential effects of outliers on the regression results, a bilateral 1% Winsorization procedure was performed on the entire sample datasets. Second, considering the advanced economic development and strong policy support characteristics of these municipalities, a new regression analysis was conducted, specifically excluding data from Beijing, Tianjin, Shanghai, and Chongqing. The results of these robustness tests are presented in Table 4. The test results confirm that the positive effect of green finance on FNQP remained robust and consistent, thereby verifying the reliability of the results.

4.4. Endogeneity Test

To address potential endogeneity concerns, such as bidirectional causality and omitted variable bias, that might exist between green finance and FNQP in this study, an instrumental variable approach was adopted. Drawing upon the methodology of Zheng et al., the core explanatory variable, “green finance lagged by one session,” was chosen as the instrumental variable [41]. Two-stage least squares (2SLS) estimation was then performed.

Table 5. Endogeneity test results.

Variables	Forestry New Quality Productivity
(L.) Green finance	1.082 ** (0.520)
Control variable	✓
Observations	341
R2	0.975

Note: The numbers in brackets are standard errors; ** indicates significance at the 5% level.

displays the estimated results from this analysis. The model estimation results largely align with the benchmark regression results previously discussed, indicating that the original research conclusion remains valid even after accounting for endogeneity.

4.5. Analysis of Intermediary Effect

Building upon the theoretical analysis presented earlier, the mechanism of forestry industrial structure upgrading was incorporated to analyze how green finance impacts the development level of FNQP.

Table 6. Analysis of intermediary effects.

Variables	Upgrading the Structure of the Forestry Industry	Forestry New Quality Productivity	Intensity of Environmental Regulation	Forestry New Quality Productivity
	(1)	(2)	(3)	(4)
Green finance	73.415 *** (25.996)		0.327 *** (0.016)
Upgrading the structure of the forestry industry		0.003 *** (0.0004)
Intensity of environmental regulation				0.899 * (0.472)
Control variable	Yes	Yes	Yes	Yes
Regional fixed effect	Yes	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes	Yes
N	372	372	372	372
R2	0.491	0.946	0.646	0.948

Note: The numbers in brackets are standard errors; * and *** indicate significance at the 10% and 1% levels, respectively.

summarizes the findings of this intermediary effect analysis.

When taking the upgrading of the forestry industry structure into account as an intermediate variable, Model (1) yielded a regression coefficient of 73.415 for green finance’s impact on forestry industrial structure upgrading. Green finance encourages both innovation and environmentally sound development in forestry. It achieves this promotion of industrial structure upgrading through mechanisms such as targeted fund allocation and tax incentives. Model (2) indicated a regression coefficient of 0.003 between FNQP and forestry industrial structure upgrading. This coefficient was also significantly positive at the 1% level, implying that green finance can indirectly aid in the growth of FNQP by aiding the upgrading of the forestry industry structure. Therefore, Hypothesis H2 is supported by these findings. Specifically, government bodies can steer forestry enterprises toward independent R&D, the phasing-out of energy-intensive and polluting industries, and project modernization. This can be achieved through increased investment in forestry-related scientific research, tax reductions for high-end industries, and the implementation of environmental taxes. Furthermore, the evolution of the forestry industrial structure facilitates its integration with advanced technologies such as biotechnology, IT, and the Internet of Things. Such integration fosters emerging sectors, including forest tourism and forest-based healthcare, which enhance both economic output and ecological performance—promoting both “industrial ecologization” and “ecological industrialization” within the sector. Additionally, transformation and upgrading within forestry firms help to advance the industrial chain, building collaborative innovation networks among upstream and downstream enterprises through strengthened cooperation. The resulting innovative dynamism, driven by green fiscal policies, circulates across a wider range of industries, amplifies the benefits of green finance, and provides significant momentum for enhancing forestry new quality productivity.

In Model (3), a 1% increase in the level of green fiscal expenditure led to a 0.327% increase in the intensity of environmental regulations. This result was significant at the 1% level, demonstrating that green finance can effectively enhance the stringency of environmental regulations. Model (4) demonstrated a regression coefficient of 0.899 between environmental regulation intensity and FNQP. Elevating regulatory intensity is conducive to establishing a linkage mechanism between environmental performance and production permits. It also accelerates the decommissioning of resource-intensive models with high energy consumption and propels the forestry sector towards ecological sustainability, digitalization, and higher-value-added operations, thereby boosting FNQP. These results verify Hypothesis H3. More specifically, the government has strengthened environmental regulations through measures such as enacting ecological protection laws, supporting carbon trading markets, and directing environmental oversight. However, it is crucial to acknowledge that the government must carefully manage the level of environmental regulation intensity to prevent placing undue burdens on forestry companies through excessive regulation.

4.6. Heterogeneity Analysis

4.6.1. Heterogeneity in the Development of Forestry New Quality Productivity Levels

The results of the heterogeneity analysis are presented in

Table 7. Analysis results of heterogeneity in development levels of different forestry new quality productivity.

Variables	Forestry New Quality Productivity
	0.10 Quantile	0.25 Quantile	0.50 Quantile	0.75 Quantile	0.90 Quantile
Green finance	−0.074	0.106	0.172	0.588 **	0.750 ***
	(0.466)	(0.484)	(0.376)	(0.280)	(0.228)
Constant	0.651	0.207	0.231	−0.036	−0.186
	(0.471)	(0.232)	(0.210)	(0.152)	(0.139)
N	38	93	186	279	334
R2	0.697	0.763	0.838	0.901	0.928

Note: The numbers in brackets are standard errors; ** and *** indicate significance at the 5% and 1% levels, respectively.

. This analysis evaluates green finance across varying levels of FNQP development. At the 0.10, 0.25, and 0.50 quantiles, green finance exhibits a weak impact on FNQP. Specifically, this impact is not significant at these quantiles. However, the effect of green finance becomes significant at higher quantiles. Specifically, significance is observed at the 5% level for the 0.75 quantile and is further strengthened to the 1% level at the 0.90 quantile. Green finance demonstrates a positive and significant effect on FNQP development. This positive impact is most significant at the 0.90 quantile, and suggests that as the development level increases, green finance’s role in driving FNQP becomes increasingly crucial. Such observations can be attributed to the characteristics of regions with advanced FNQP. These regions typically possess more sophisticated industrial structures and achieve greater efficiency in allocating and utilizing forestry resources. Therefore, these factors positively affect both the trend and pace of improvements in FNQP, whereas regions with lower levels of FNQP face challenges. Less developed infrastructure and novel technologies in these regions can hinder the effective implementation of green development principles. Therefore, government intervention is crucial, comprising accurate fund allocation, infrastructure enhancement, and support for research and development in key technologies. These interventions should be designed for the specific FNQP levels of different regions. Moreover, robust policy mechanisms are essential. This necessitates strengthening policy implementation and evaluation. Regular evaluations of green fiscal policy effectiveness are necessary to ensure optimal outcomes. Policy tools and funding directions should be dynamically adjusted based on real-world conditions and evaluation results.

4.6.2. Heterogeneity in Economic Development Levels

To further appraise the heterogeneous effects of green finance on FNQP, particularly across varying levels of economic development, this study employs per capita GDP as an indicator for the economic development level. For the purpose of this analysis, the research samples were categorized into three regional groups based on per capita GDP. These groups are defined as follows: high-level regions (Beijing, Shanghai, Jiangsu, Fujian, Tianjin, Zhejiang, Guangdong, Inner Mongolia, Hubei, and Chongqing); medium-level regions (Shandong, Shaanxi, Shanxi, Anhui, Hunan, Jiangxi, Ningxia, Liaoning, Xinjiang, Sichuan, and Hainan); and low-level regions (Henan, Yunnan, Qinghai, Xizang, Hebei, Jilin, Guizhou, Guangxi, Heilongjiang, and Gansu). The results of this comparative analysis across the regional groups are detailed in

Table 8. Analysis results of heterogeneity across different levels of economic development.

Variables	Forestry New Quality Productivity
	Regions with High Levels of Economic Development	Regions with Medium Levels of Economic Development	Regions with Low Levels of Economic Development
Green finance	0.553 ** (0.251)	0.121 (0.301)	0.209 (0.410)
Constant	−0.812 *** (0.198)	−0.547 *** (0.166)	−0.107 (0.210)
N	120	132	120
R2	0.970	0.978	0.925

Note: The numbers in brackets are standard errors; ** and *** indicate significance at the 5% and 1% levels, respectively.

. As shown in Table 8, green finance has a considerable beneficial influence on FNQP in regions with higher economic development levels; however, this impact is not significant in regions with medium or low economic development levels. This gap may be due to the greater fiscal capacity of governments in high-level economic regions. These governments typically have more substantive funds available for investment in green forestry projects. In addition, these regions often exhibit increased environmental awareness and more robust market mechanisms. Therefore, the effective implementation of green finance instruments in these regions can better facilitate the optimal allocation and utilization of forestry resources. In comparison, regions with medium and low economic development levels often face constraints in offering adequate funding for forestry projects. These regions also experience significant pressures related to industrial structural transformation. A significant number of these regions are still experiencing a transition from traditional to green industries. This ongoing transition can limit the immediate potential of green finance to drive improvements in forestry productivity. Therefore, for regions with high economic development levels, continued and increased fiscal support remains essential. Optimizing green finance tools, such as enhancing green subsidies, tax incentives, and specialized funds, should also be prioritized. For regions with low-to-medium economic development levels, different strategies are needed. Strengthening technical and talent support is crucial. This can be achieved through talent introduction policies and inter-provincial assistance programs aimed at raising the overall technological capacity of these regions. Moreover, promoting industrial structure transformation and upgrading is vital. Finally, optimizing financial resource allocation and ensuring accurate allocation of green fiscal funds are necessary to address the specific challenges hindering FNQP development in these regions.

4.7. Spatial Effects Analysis

4.7.1. Global Moran’s Index

In order to ascertain the existence of spatial autocorrelation between green finance and forestry new quality productivity, this paper undertakes a verification approach by calculating the global Moran’s Index of forestry new quality productivity and green finance in the period from 2011 to 2022. The outcomes of the global Moran’s Index measurement are presented in

Table 9. Global Moran’s Index of FNQP and green finance over several years.

Year	Forestry New Quality Productivity			Green Finance
	Moran’s I	Z	p-Value	Moran’s I	Z	p-Value
2011	0.1607	2.3922	0.02	0.0727	1.3117	0.19
2012	0.1345	2.0726	0.04	0.0764	1.3640	0.17
2013	0.1021	1.6829	0.09	0.1451	2.2570	0.02
2014	0.2056	2.9692	0.00	0.0537	1.0916	0.28
2015	0.2165	3.1230	0.00	0.0706	1.3093	0.19
2016	0.1349	2.1627	0.03	0.0135	0.6115	0.54
2017	0.1140	1.9018	0.06	0.0948	1.7235	0.08
2018	0.1268	2.0414	0.04	0.1093	1.8026	0.07
2019	0.1186	1.9424	0.05	0.1315	2.1184	0.03
2020	0.1093	1.8305	0.07	0.3724	5.2501	0.00
2021	0.1061	1.8130	0.07	0.2158	3.0762	0.00
2022	0.0525	1.0733	0.28	0.1893	2.7582	0.01

. The findings indicate that the global Moran’s Index of forestry new quality productivity are positive and statistically significant at the 5% level during the period of 2011–2021. A thorough examination of the global Moran’s Index of green finance reveals a predominantly positive value in certain years, while in other years, it is not significant. This finding suggests the presence of a substantial spatial clustering characteristic of forestry new quality productivity during the study period, with the exception of the year 2022. The presence of a certain degree of spatial autocorrelation in the data can be examined through the utilization of a spatial econometric model. The strength and significance of the spatial autocorrelation of green finance exhibits variation across different years. This variability is likely attributable to the implementation of green finance policies in various provinces, which are tailored to their respective circumstances and are subject to the influence of other provinces.

4.7.2. Spatial Durbin Model

The spatial spillover effect of green finance on forestry new quality productivity is investigated further using the spatial econometric modeling. Firstly, an exploratory spatial econometric model setting test was performed, with the results shown in

Table 10. Spatial econometric model setting tests.

Test Methods		Statistic	p-Value
LM test Spatial error	Lagrange multiplier	116.006	0.000
	Robust Lagrange multiplier	4.483	0.034
LM test Spatial lag	Lagrange multiplier	147.010	0.000
	Robust Lagrange multiplier	35.487	0.000
Wald test	Can SDM be converted to SAR	32.30	0.000
	Can SDM be converted to SEM	34.78	0.000
LR test	Can SDM be converted to SAR	31.48	0.000
	Can SDM be converted to SEM	33.82	0.000

. The LM test’s statistical value was significant at the 5% level, indicating that both the spatial error effect and the spatial lag effect had to be considered while developing the model. The results of the LR test and Wald test were significant at the 1% level, indicating that the SDM model could not be transformed into an SAR or SEM model. The SDM model can be expressed by the following equation.

$$y_{it}=\rho W{y}_{it}+\beta X_{it}+\theta W{X}_{it}+{\epsilon }_{it}$$

(12)

Among these, $y_{it}$ represents the explained variable of forestry new quality productivity, $X_{it}$ represents the explanatory variables (including green finance and control variables), $W$ represents the spatial weight matrix, and ${\epsilon }_{it}$ represents the error term.

Table 11. Parameter estimation results and effect decomposition results of SDM model.

Variables	Main	Wx	Direct Effect	Indirect Effect	Total Effects
Green finance	0.449 ** (0.186)	0.948 * (0.510)	0.500 ** (0.197)	1.499 ** (0.712)	1.999 ** (0.798)
Level of foreign investment	−0.0323 (0.107)	−0.843 ** (0.410)	−0.0705 (0.112)	−1.148 * (0.618)	−1.219 * (0.684)
Level of economic development	0.0535 *** (0.0124)	0.0348 (0.0369)	0.0568 *** (0.0120)	0.0671 (0.0506)	0.124 ** (0.0529)
Opening-up degree	0.0314 (0.0206)	−0.210 *** (0.0604)	0.0242 (0.0206)	−0.264 *** (0.0838)	−0.240 *** (0.0909)
Level of urbanization	0.174 (0.114)	0.206 (0.312)	0.180 * (0.108)	0.328 (0.429)	0.508 (0.453)
R2	0.452

Note: The numbers in brackets are standard errors; *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

illustrates the results of the parameter estimation and effect decomposition for the spatial Durbin model. The spatial autoregression coefficient is determined to be 0.277, which is significant at the 1% level. This finding suggests the presence of a positive spatial spillover effect of new forestry quality productivity. Several factors, including technology diffusion and ecological spillover, may have an impact on the phenomena. The implementation of novel forestry technologies in a specific region is likely to be adopted by neighboring regions through various mechanisms, including inter-regional collaboration and the demonstration effect. The improvement of forestry quality and efficiency in one region has the potential to raise forestry production in nearby regions due to the externalities of forest ecosystem services. This, in turn, can further stimulate the level of new forestry productivity in these neighboring regions.

The direct effect shows that a one-unit increase in the level of green finance is connected to a 0.5-unit increase in forestry new quality productivity in the region, which aligns with the results of the baseline regression. The indirect effect indicates that a 1% increase in the level of green finance is connected to an increase of 1.499% in the level of forestry new quality productivity in geographically neighboring and comparable regions, suggesting a significant positive spatial spillover effect. Consequently, Hypothesis H4 is verified.

5. Discussion

In this study, we found that positive green finance can significantly promote the development of forestry new quality productivity. The concept of forestry new quality productivity is a distinctive concept proposed based on China’s national conditions. While this concept does not yet exist in other countries, similar studies focusing on the integration of finance, innovation, and sustainability in the forestry sector are being studied worldwide.

Anas et al. found that green finance has a sustained positive impact on the sustainability of forest environments in emerging economies (such as Brazil and India) [42]. This finding suggests that green finance, by providing long-term financial support, can help emerging economies to break through the traditional trade-off between forest conservation and economic development, achieving a win–win situation for both ecological and economic benefits. Su et al. analyzed examples from countries such as the Netherlands and Brazil and proposed that adequate capital investment is key to the development of the forestry sector and the implementation of sustainable development concepts [43].

However, comparative analysis reveals significant differences between our research findings and those from other national contexts. International studies primarily emphasize the broader impact of macro-level financial support and other factors on forestry sustainability, while our research provides new empirical evidence explicitly linking green finance to forestry new quality productivity that aligns with the current era’s advocacy for green development. Additionally, our study conducts a more detailed analysis of forestry new quality productivity and establishes a new indicator system. Additionally, as studies in Indonesia have warned, green finance may fail to curb deforestation without a robust regulatory framework [44]. This provides new insights for our research, which emphasizes the role of combining policy guidance with financial tools in promoting productivity transformation and enhancement.

Therefore, although the concept of forestry new quality productivity remains in its infancy globally, this study offers a pioneering perspective for current international discussions by quantitatively linking green finance to productivity improvements that incorporate innovation and sustainability. It also provides an applicable framework for similar forestry economies.

Limitations

Two aspects of the study require some caution when interpreting the results. Firstly, due to limitations in data availability, it is difficult to obtain specific data on the application of fiscal expenditures in the forestry sector, which limits the current analysis of the impact of green finance on forestry development. In future research, we will strengthen communication and cooperation with relevant departments to conduct a more detailed and systematic study of the relationship between green finance and forestry new quality productivity.

Secondly, the effects of fiscal policy often have a long lag, so some long-term impacts may be underestimated or not captured. Therefore, in the future, a policy event timeline can be established to track long-term panel data for a more accurate analysis of the impact of green finance.

6. Conclusions

This study evaluated the influence of green finance on FNQP, and the mechanisms behind this, across 31 provinces (including autonomous regions and municipalities) in China. The research utilized empirical analysis of data from between 2011 and 2022. The primary findings are detailed below.

• Between 2011 and 2022, FNQP in China’s 31 provinces experienced an overall upward trend with fluctuations. Significant regional differences were observed, with eastern regions exhibiting higher productivity compared to western regions. In contrast, the level of green finance demonstrated an upward trend with fluctuations until 2019, followed by a decline. The general pattern of change in green finance was similar across these regions.
• Our analysis, incorporating various robustness and endogeneity tests, consistently verified the conclusion that green finance effectively cultivates the advancement of FNQP.
• The promotion of new forestry productivity by green finance is achieved indirectly through two key pathways: the enhancement of the forestry industry structure and the intensification of environmental regulation.
• The effect of green finance on the progression of FNQP is not uniform across different productivity levels. Specifically, as the levels of FNQP increase, the effect of green finance becomes progressively stronger and more significant.
• The influence of green finance on FNQP is also moderated by levels of economic development. Regions where economic development is at a high level demonstrate a significant positive correlation between green finance and productivity levels.

Policy Recommendations

The conclusions derived from this research offer several important policy inspirations.

• Strengthening investment in green finance is crucial. Efforts should focus on optimizing the green fiscal system and enhancing the guiding role of public financial resources. To facilitate the transition of the forestry industry from traditional models to FNQP paradigms, support should be directed towards high-end, intelligent, and green development in the sector. Mechanisms to achieve this include establishing dedicated forestry industry investment funds and implementing financial subsidies and tax incentives.
• Advancing FNQP requires several approaches. Regarding the workforce, improving the overall capabilities of forestry professionals is essential. This can be achieved by encouraging participation in relevant training programs and facilitating engagement in academic exchanges. Concerning labor objects, it is essential to broaden the scope and definition of new quality forestry labor objects. In addition, technological innovation and interdisciplinary integration are vital for realizing the value transformation of these novel labor objects. Regarding labor materials, the widespread adoption of efficient and modern forestry tools should be promoted to shift forestry production methods away from labor-intensive practices and towards capital-intensive and knowledge-intensive models.
• Structural improvements in the forestry industry are necessary. Green finance should primarily support the high-level and environmentally sound transformation of the forestry sector, including cultivating the growth of emerging industries, such as forestry ecotourism and the under-forest economy, thereby optimizing the industrial structure. Moreover, promoting forest products, building strong brands, and actively introducing and distributing advanced forestry technologies can further drive the evolution of the forestry industry.
• The establishment of more stringent and scientifically grounded forestry environmental standards is recommended. Enhanced oversight of corporate forestry practices is necessitated to encourage the adoption of environmental protection technologies and processes, finally minimizing environmental impact. Moreover, to ensure effective enforcement of environmental regulations, penalties for illegal pollution discharge should be increased.
• Adaptive policies and fiscal support for localized circumstances are essential for effectively promoting FNQP. The effect of green finance on enhancing FNQP exhibits significant heterogeneity depending on existing productivity and economic development levels. Therefore, differentiated policy implementation and accurate planning of the forestry industry, considering the specific developmental status of each region, are necessary.