Impact of Biased Technological Change on High-Quality Economic Development of China’s Forestry: Based on Mediating Effect of Industrial Structure Upgrading

Abstract

The high-quality development of the forestry economy refers not only to the quantity of economic growth but also to the improvement of its quality, that is, driving forestry economic development through technological change and industrial structure upgrading. In this paper, a multi-angle indicator evaluation system and obstacle model were constructed to explore the current situation and obstacle factors of the high-quality development of the forestry economy in 31 Chinese provinces (municipalities and autonomous regions) from 2005 to 2020. At the same time, based on the mediating effect of industrial structure upgrading, a mediating effects model was constructed to analyze the direct and indirect effects of forestry’s biased technological change on high-quality economic development. The results showed three key findings: (1) There was regional heterogeneity in the high-quality development of the forestry economy in China, and the biggest obstacle factor was technological change. (2) Forestry’s biased technological change had a significant positive effect on high-quality economic development, with a total effect of 0.222; industrial structure upgrading played a partial mediating effect, and 16.7% of the effect was indirectly realized by promoting industrial structure upgrading. (3) The contribution of forestry’s biased technological change to high-quality economic development in the western and northeastern regions was greater than that in the eastern and central regions. Based on this, the promotion of the high-quality development of the forestry economy and the enhancement of industrial structure upgrading through optimizing biased technological change were suggested.

1. Introduction

At present, China’s forestry economy is developing rapidly, with the total output value rising from CNY 3059.67 billion in 2011 to CNY 81,719.14 billion in 2020. However, there are still some problems in forest protection and development, such as insufficient forest resources, poor quality of forests, and increasing pressure on forest land protection. China’s economy has shifted from a stage of high-speed growth to high-quality development. During this period of economic adjustment and transformation, the high-quality development of the forestry economy aims to solve the existing problems and to change the development mode from relying on high-input and high-consumption to relying on technological change and industrial structure upgrading. As the main driving force of economic growth, the importance of technological change has been emphasized by many scholars [1,2,3]. In the “Fourteenth Five-year Plan of Forestry Development”, it is pointed out that we should improve the quality of forest products and promote the development of the forestry industry by perfecting technological change. The impact of technological change on economic development is reflected not only in the technological growth rate but also in the technological direction, that is, biased technological change, and even the direction effect is greater than the rate effect [4]. Therefore, what is the current situation of the high-quality development of the forestry economy? Does biased technological change promote high-quality economic development? What role does industrial structure upgrading play? These are urgent questions that need to be answered.

Based on the mediating effect of industrial structure upgrading, this paper comprehensively analyzed the direct and indirect impacts of forestry’s biased technological change on the high-quality development of the forestry economy. It provides theoretical reference and empirical evidence for accelerating high-quality economic development. The rest of this article is as follows. The second part reviews the existing literature. The third part analyzes the direct and indirect influence mechanisms of forestry’s biased technological change on high-quality economic development, and the hypotheses are proposed. The fourth part constructs the mediating effects model, selects variables and processes the data. The fifth part describes the empirical tests, which mainly include mediating effect tests, regional heterogeneity tests and robustness tests. The last part provides the conclusion and future directions.

2. Literature Review

The relevant research on high-quality economic development is still in its initial stage and has not yet formed a perfect research system; it mainly focuses on the meaning and how to measure it. Regarding its meaning, scholars have studied the macro-perspectives of high-quality economic development based on the five development concepts of innovation, harmonization, green, openness and sharing [5,6]. The meso-perspective is mainly explored from the aspects of industrial structure upgrading and coordinated economic development [7]. Additionally, the micro-level is considered from the aspects of improving economic growth efficiency and promoting factor utilization [8]. In terms of the measurement of high-quality economic development, different scholars adopt different indices [9]. Some scholars have selected a single index to evaluate it, such as labor productivity [10,11] and total factor productivity [12,13]. Most scholars have constructed index systems to measure the level of high-quality economic development [14,15,16]. Zhang and Liao (2019) measured it from the two aspects of economic growth and social achievements [17]. Ou et al. (2020) measured the high-quality economic development level of Guangdong Province based on five development concepts [18].

The definition of biased technological change can be traced back to 1932. Hicks (1932) believed that when the ratio of capital-labor remains unchanged, if the relative marginal output of capital and labor increased (decreased) under the influence of technological change, then the technological change was biased to capital (labor). If there was no change, the technological change was neutral [19]. Subsequently, Kennedy (1964) conducted an in-depth study of induced innovation. He believed that the production sectors were more inclined to use factors with lower prices to produce and innovate, which supplemented Hicks’s idea [20]. However, the early research did not take the micro basis of biased technological change into account. A series of studies by Acemoglu (2002, 2007) comprehensively expounded the micro basis. On the one hand, he expanded the definition of biased technological change to any two elements and, on the other hand, explored its drivers. He believed that the direction of biased technological change was determined by technological decision makers according to the profit and was affected by price and the market scale effect [21,22]. Biased technological change is an important factor affecting high-quality economic development and industrial structure upgrading, which has been studied by some scholars.

In the linkages between biased technological change and high-quality economic development, Antonelli (2011, 2012) believed that biased technological change affects factor income share and factor technical efficiency under the joint effect of multiple reasons and, thus, affects the output efficiency of the economy [23,24]. Schulte (2021) studied the impact of biased technological change on the economy in 40 countries from 1995 to 2007 [25]. Jorgenson (2008) et al. found that there were phased differences in the influence of technological change on promoting economic growth [26]. Deng (2015) and Chen et al. (2017) investigated the impact of biased technological change on economic fluctuations [4,27]. Antonelli and Quatraro (2010), Chen and Yu (2014) found that both OECD and non-OECD countries should choose appropriate biased technological change to promote the growth of total factor productivity [28,29]. Feder (2019) also confirmed this point [30].

In the research on the relationship between biased technological change and industrial structure upgrading, some scholars found that biased technological change would affect the production efficiency of factors, which would cause the flow of factors between different departments, and then affect the industrial structure [31]. Sun (2017) and David (1965) took China’s county economy and the United States as research areas to analyze the impact of biased technological change on industrial structure upgrading [32,33]. However, relevant research results are consistent at present; some scholars believe that biased technological change has a significant impact on the upgrading of the industrial structure [34], but some other scholars believe that there is no obvious causal link [35,36].

In the research on the relationship between industrial structure upgrading and high-quality economic development, Peneder et al. (2003) analyzed the data of 28 OECD countries and found that the industrial structure played a decisive role in economic growth [37]. Some other scholars analyzed different research areas, such as India [38], Asia [39], China and Russia [40], Germany [41], and the United States [42], and found that the adjustment and upgrading of the industrial structure would promote economic growth by improving productivity. In forestry, many scholars have explored the impact of the forest industrial structure on the economy [43,44,45]. By analyzing the forest system in India and Philippines, Suh (2014) found that forestry development and the overall economic environment complement each other [46]. On the other hand, some scholars have explored the impact of industrial structure upgrading on economic stability. Shaffer (2009) found that the industrial structure would increase employment so as to ensure the smooth running of the economy [47]. Jiang et al. (2020) explored the forestry industry in Heilongjiang Province and found that the upgrading of the forestry industrial structure could ease the fluctuations in the forestry economy, and this effect became more stable over time [48].

In general, scholars have carried out valuable explorations of high-quality economic development, which provides a reference for this paper. However, there is little research on the forestry industry. Meantime, few scholars put biased technological change, industrial structure upgrading and high-quality economic development into the same analytical framework to comparatively analyze the mechanism and effects of them, and the impact of biased technological change on the upgrading of the industrial structure has not been elucidated. Therefore, this paper intends to supplement the existing research in the following three aspects: (1) establish the indicator evaluation system from multiple angles to analyze the current situation of the high-quality development of the forestry economy in China, and explore its obstacles; (2) the mediating effects model is constructed to put high-quality economic development, biased technological change and industrial structure upgrading into the same framework, and the effect mechanisms among them are systematically analyzed;(3) divide the study areas into four regions of the eastern, central, western and northeastern regions according to the geographical location, resource structure and economic development level. This paper explores the regional heterogeneity of the impact of forestry’s biased technological change on high-quality economic development and puts forward targeted opinions and suggestions, so as to provide theoretical support for the realization of sustainable development.

3. Theoretical Analysis and Research Hypotheses

In order to explore the impact of forestry’s biased technological change on high-quality economic development and the function of industrial structure upgrading, this paper analyzed the theoretical mechanisms (as shown in Figure 1) and put forward the following hypotheses.

3.1. Direct Impact of Forestry’s Biased Technological Change on High-Quality Economy Development

First, affected by the price effect, technological change tends to use factors with lower prices, which reduces production costs and increases product output [49] and thus improves economic quantity. Second, the biased technological change matching the factor endowment structure can stimulate economic vitality, promote the growth of forestry total factor productivity, and affect economic development through technological diffusion [25]. At the same time, high-quality economic development not only refers to the increase in economic output but also reflects ecological construction. Technological change that is biased towards reducing pollution emissions and energy consumption can improve the quality of the ecological environment [50], which plays a positive role in promoting high-quality economic development. Therefore, the following hypothesis is proposed:

Hypothesis 1 (H1).

Forestry’s biased technological change has a direct positive effect on high-quality economic development.

3.2. Indirect Impact of Forestry’s Biased Technological Change on High-Quality Economic Development: The Mediating Effect of Industrial Structure

Based on the mediating effect of industrial structure upgrading, the indirect influence mechanism of forestry’s biased technological change on high-quality economic development is mainly described from two aspects: (1) The role of forestry’s biased technological change in industrial structure upgrading: the essence of forestry’s industrial structure upgrading is the continuous optimization of factor allocation among three industries, while biased technological change promotes the upgrading of the industrial structure by affecting the flow of production factors. On the one hand, biased technological change affects factor productivity and substitution elasticity. Sectors with high productivity will attract more factors to flow in, while the sectors with low productivity will lead to factor outflow. The flow of factors changes the input ratio between sectors and thus changes the industrial structure. On the other hand, biased technological change is accompanied by mechanization and technological innovation, which changes the marginal output and income share of production factors. Under the influences of price and market scale effects, the demand for factors and factor allocation would change, which would lead to the upgrading of the industrial structure. (2) The role of industrial structure upgrading in the high-quality development of the forestry economy: the industrial structure would change the utilization efficiency of forestry resources and promote high-quality economic development. On the one hand, the resource allocation of factors is optimized in the flow, and economic efficiency is improved. In this process, structural dividends are generated, and their release promotes high-quality economic development [32,51]. On the other hand, with the continuous adjustment of the industrial structure, there are fluctuations in the economy, which affect economic growth. In addition, the adjustment and upgrading of the industrial structure can also alleviate the factor mismatch between regions [52] and narrow the income gap; that is, industrial structure upgrading is an important transmission path of biased technological change to high-quality economic development.

Therefore, the following hypotheses are proposed:

Hypothesis 2 (H2).

Forestry’s biased technological change has a positive effect on industrial structure upgrading.

Hypothesis 3 (H3).

Forestry industrial structure upgrading promotes the high-quality development of the forestry economy.

Hypothesis 4 (H4).

Forestry’s biased technological change can accelerate the high-quality development of the forestry economy by promoting the upgrading of the industrial structure.

4. Model Construction and Data Processing

4.1. Model Construction

In order to explore the mediating effect of industrial structure upgrading on the influence of forestry’s biased technological change on high-quality economic development, empirical tests were conducted based on the data in 31 provinces (municipalities and autonomous regions) in China from 2005 to 2020.

The mediating effect was used to study the mediating mechanism of the influence of the explanatory variable (X) on the explained variable (Y), as follows:

$$Y=cX+e_1$$

(1)

$$M=aX+e_2$$

(2)

$$Y=c^{\prime }X+bM+e_3$$

(3)

where c is the total effect of X on Y, a is the effect of X on the mediating variable M, c’ is the direct effect of X on Y when the mediating variable is added, and b is the effect of the mediating variable M on Y. The mediating effect is the product of coefficients a and b, i.e., ab, and the sum of the direct effect and mediating effect is equal to the total effect, i.e., c’ + ab = c. If c’ passes the significance test, the mediating variable plays a partial mediating role; otherwise, it plays a complete mediating effect.

In this paper, a benchmark regression model is established to test Hypothesis H1.

$$FH{Q}_{it}={\alpha }_0+{\alpha }_{1}IBT{C}_{it}+{\alpha }_2^{\prime }contro{l}_{it}+{\mu }_{it}+{\epsilon }_{it}$$

(4)

In order to test H2, H3 and H4, the following mediating effects models were established.

$$S{T}_{it}={\beta }_0+{\beta }_{1}IBT{C}_{it}+{\beta }_2^{\prime }contro{l}_{it}+{\mu }_{it}+{\epsilon }_{it}$$

(5)

$$FH{Q}_{it}={\gamma }_0+{\gamma }_{1}IBT{C}_{it}+{\gamma }_{2}S{T}_{it}+{\gamma }_3^{\prime }contro{l}_{it}+{\mu }_{it}+{\epsilon }_{it}$$

(6)

where $FH{Q}_{it}, {IBTC}_{it} \mathrm{and} {ST}_{it}$ represent high-quality development of the forestry economy, forestry’s biased technological change index and industrial structure upgrading in province i in period t, respectively, $contro{l}_{it}$ is a set of control variables, ${\mu }_{it}$ is the individual fixed effect, and ${\epsilon }_{it}$ is the random error term.

4.2. Variable’s Selection

4.2.1. Explained Variables

An accurate understanding of the high-quality development of the forestry economy is the basis for establishing the indicator evaluation system. Based on the former literature and the characteristics of the forestry industry, it is defined in this paper as follows: the high-quality development of the forestry economy is supported by technological change, which not only promotes the steady growth of the forestry economy but also drives the optimization of the ecological environment, so as to continuously improve the living standard of forestry practitioners. The meaning of high-quality economic development can be divided into four main aspects. First is the promotion of the steady growth of the forestry economy. Second, the main means is the technological change in the forestry industry. Third, the particularity of the forestry industry determines the importance of ecological environment construction; therefore, ecological construction should be taken into account in high-quality economic development. Fourth, the ultimate goal is to improve people’s living standards. Therefore, this paper established the indicator evaluation system from these four perspectives and finally selected 8 second-level indicators and 20 third-level indicators according to the principles of data availability, comparability and comprehensiveness, as shown in

Table 1. Indicator evaluation system of high-quality development of the forestry economy.

First-Level Indicators	Second-Level Indicators	Third-Level Indicators (Unit)	Calculation
Economic Growth (EG)	Growth rate	Proportion of forestry total output value (%)	Forestry total output value/National gross domestic product
		Output of forest products (10,000 cu.m)	Represented by timber yield
	Stability	Price index of the forest product (/)	The price of the forest product
		Growth rate of the forestry output value (/)	Growth rate of the forestry total output value (preceding year = 100))
Technological Change (TC)	Research and development level	Scientific and technological personnel (persons)	Number of employed persons of engineering planning, technological planning management, technological exchange and promotion services in forestry
		R&D intensity (%)	Share of R&D investment in growth domestic product
		Forestry technological change index (/)	Calculated by DEA—Malmquist index method
	Technological achievements	Forestland output rate (CNY/hectare)	Forestry output value/Forest land area
		Labor output rate (CNY 10,000/person)	Forestry output value/Number of employed persons at year-end by the forestry industry
		Coefficient of investment effect (/)	Forestry output value/Forestry investment in fixed assets
Ecological Construction (ECO)	Ecological environment	The forest coverage rate (%)	Forest area/Land area
		The hazard-free prevention rate of forest pests (%)	The hazard-free prevention area of forest pests/Total prevention area of forest pests
		Occurrence rate of forest fires (case/10,000 hectare)	Forest fires/Forest area
	Pollution emission and energy consumption	Water consumption per ten thousand output value (cubic meter/CNY 10,000)	Forestry water consumption/Forestry output value
		Electricity consumption per ten thousand output value (kw·h/CNY 1000)	Forestry electricity consumption/Forestry output value
		Exhaust emissions per ten thousand output value (kg/CNY 1000)	Total volume of sulfur dioxide emission/Forestry output value
Shared Development (SD)	Income level	Forestry income level (CNY)	Average wage of employed persons in forestry system
		Proportion of per capita forestry output value (%)	Per capita forestry output value/Per capita national growth domestic product
	Public service	Proportion of expenditure for public security (%)	Expenditure for public security/General public budget expenditure
		Proportion of expenditure for agriculture, forestry and water conservancy (%)	Expenditure for agriculture, forestry and water conservancy/General public budget expenditure

Note: / indicates that the indicator has no unit.

.

In the system of forestry economic growth (EG), it is measured from two aspects of growth efficiency and stability. The proportion of forestry output value in the national domestic product growth reflects the changing trend of the forestry economy. The output of forest products eliminates the influence of price. Regarding the measurement of stability, the price index of the forest product can reflect the inflation and stability of the forestry economy. The growth rate of the forestry output value can directly reflect economic prosperity.

In the system of forestry technological change (TC), human resources and capital are the basis to ensure the smooth development of scientific and technological research, which is the key method to promote technological change, so scientific and technological personnel and R&D (Research and Development) intensity are used to measure the level of technological change. The technological change index comprehensively reflects the driving effect of technological innovation on productivity. The achievement of technological change is finally reflected in the improvement of resource efficiency. The forestland output rate and labor output rate reflect the change in the forestry output value driven by forestland and labor force under the influence of technological change, respectively. When they increase, technological change drives the improvement of resource efficiency. In addition, the coefficient of investment effect measures whether a unit investment can create more forestry output.

The system of forestry ecological construction (ECO) mainly comprises the aspects of the ecological environment, pollution emission and energy consumption. The forest coverage rate reflects the abundance of forest resources. The occurrence rate of forest fires reflects the ability of forests to withstand natural disasters and prevent risks. The hazard-free treatment rate of forest pests reflects the treatment of forest pests without using chemicals that damage the forest’s ecological environment. Water resources are mainly consumed in the production of the forestry primary industry, while electricity resources are mainly consumed in the secondary and tertiary industries, and a large amount of greenhouse gases are generated in their production. Therefore, water consumption, electricity consumption and exhaust emissions per CNY ten thousand were used to measure pollution emission and energy consumption.

In the system of shared development (SD), since the goal of high-quality economic development is to improve people’s living standards and increase happiness, it was measured from two aspects of income level and public service. The forestry income level directly reflects the improvement of people’s living standards driven by the development of the forestry industry, while the share of per capita forestry output value in per capita national gross domestic product reflects the sharing level among regions. Public service is represented by the share of expenditure for public security in general public budget expenditure and the share of expenditure for agriculture, forestry and water conservancy in the general public budget expenditure. The continuous improvement of forestry infrastructure would contribute greatly to the active participation of forestry practitioners.

4.2.2. Explanatory Variables

Forestry’s biased technological change index (IBTC) is calculated based on the DEA–Malmquist index method. The forestry production decision-making unit of a region is defined as follows: in period t, the input vector is $x=\left(x_1, x_2,\dots ,x_n\right)\in R_{+}^N$, and the output vector is $y=\left(y_1, y_2,\dots ,y_m\right)\in R_{+}^M$. N and M represent the number of input and output variables in period t, respectively. The production technology $T^t=\left\{\left(x^t, y^t\right)\in R_{+}^{N+M}\right\}$ represents all production possibility sets in period t, which satisfies the assumptions of a closed set and of convexity. The reciprocal of the input-oriented distance function is equal to the maximum reduction ratio of $x^t$ when $y^t$ is determined, which takes this form:

$$D_i^t\left(x^t,y^t\right)=max\left\{\theta :(\frac{x^t}{\theta },y^t)\in T^t\right\}$$

(7)

According to the method proposed by fare et al. [53,54], the change index of total factor productivity (MI) is divided into technological change index (TC) and technical efficiency index (EC), i.e., MI = EC × TC. TC can be further decomposed into magnitude of technological change (MTC), input-biased technological change (IBTC) and output-biased technological change (OBTC).

$$MI=EC\times TC=\frac{D_i^{t+1}\left(x^{t+1},y^{t+1}\right)}{D_i^t\left(x^t,y^t\right)}\times {\left[\frac{D_i^t\left(x^t,y^t\right)}{D_i^{t+1}\left(x^t,y^t\right)}\times \frac{D_i^t\left(x^{t+1},y^{t+1}\right)}{D_i^{t+1}\left(x^{t+1},y^{t+1}\right)}\right]}^{1/2}$$

(8)

$$\begin{array}{l}TC=MTC\times IBTC\times OBTC\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=\frac{D_i^t\left(x^t,y^t\right)}{D_i^{t+1}\left(x^t,y^t\right)}\times {\left[\frac{D_i^{t+1}\left(x^t,y^t\right)}{D_i^t\left(x^t,y^t\right)}\times \frac{D_i^t\left(x^{t+1},y^t\right)}{D_i^{t+1}\left(x^{t+1},y^t\right)}\right]}^{\frac{1}{2}}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\times {\left[\frac{D_i^{t+1}\left(x^{t+1},y^t\right)}{D_i^t\left(x^{t+1},y^t\right)}\times \frac{D_i^t\left(x^{t+1},y^{t+1}\right)}{D_i^{t+1}\left(x^{t+1},y^{t+1}\right)}\right]}^{\frac{1}{2}}\end{array}$$

(9)

When calculating MI and IBTC indices, the specific analytical expression of the distance function in four directions is shown in

Table 2. The linear programming analytic expression of the distance function.

${\mathit{D}}_{\mathit{i}}^{\mathit{t}}\left({\mathit{x}}^{\mathit{t}},{\mathit{y}}^{\mathit{t}}\right)=\mathit{m}\mathit{a}\mathit{x} \mathit{\theta }$	${\mathit{D}}_{\mathit{i}}^{\mathit{t}+\mathbf{1}}\left({\mathit{x}}^{\mathit{t}},{\mathit{y}}^{\mathit{t}}\right)=\mathit{m}\mathit{a}\mathit{x} \mathit{\theta }$
$s.t.\{\begin{array}{c}{\displaystyle \sum }_{k=1}^K{\lambda }_i^t{y}_{ik}^t-y_{ik}^t\ge 0, i=1,\dots ,I k=1,\dots ,K\\ {\displaystyle \sum }_{k=1}^K{\lambda }_i^t{x}_{jk}^t-{\theta }_k{x}_{jk}^t\le 0, j=1,\dots ,J \\ \lambda ,\theta \ge 0 \end{array}$	$s.t.\{\begin{array}{c}{\displaystyle \sum }_{k=1}^K{\lambda }_i^t{y}_{ik}^{t+1}-y_{ik}^t\ge 0, i=1,\dots ,I k=1,\dots ,K\\ {\displaystyle \sum }_{k=1}^K{\lambda }_i^t{x}_{jk}^{t+1}-{\theta }_k{x}_{jk}^t\le 0, j=1,\dots ,J \\ \lambda ,\theta \ge 0 \end{array}$
${\mathit{D}}_{\mathit{i}}^{\mathit{t}}\left({\mathit{x}}^{\mathit{t}+\mathbf{1}},{\mathit{y}}^{\mathit{t}}\right)\mathit{m}\mathit{a}\mathit{x} \mathit{\theta }$	${\mathit{D}}_{\mathit{i}}^{\mathit{t}+\mathbf{1}}\left({\mathit{x}}^{\mathit{t}+\mathbf{1}},{\mathit{y}}^{\mathit{t}}\right)=\mathit{m}\mathit{a}\mathit{x} \mathit{\theta }$
$s.t.\{\begin{array}{c}{\displaystyle \sum }_{k=1}^K{\lambda }_i^t{y}_{ik}^t-y_{ik}^t\ge 0, i=1,\dots ,I k=1,\dots ,K\\ {\displaystyle \sum }_{k=1}^K{\lambda }_i^t{x}_{jk}^t-{\theta }_k{x}_{jk}^{t+1}\le 0, j=1,\dots ,J \\ \lambda ,\theta \ge 0 \end{array}$	$s.t.\{\begin{array}{c}{\displaystyle \sum }_{k=1}^K{\lambda }_i^t{y}_{ik}^{t+1}-y_{ik}^t\ge 0, i=1,\dots ,I k=1,\dots ,K\\ {\displaystyle \sum }_{k=1}^K{\lambda }_i^t{x}_{jk}^{t+1}-{\theta }_k{x}_{jk}^{t+1}\le 0, j=1,\dots ,J \\ \lambda ,\theta \ge 0 \end{array}$

.

This paper addressed three input variables as well as two output variables. The key factors affecting the development of the forestry industry were selected as input variables, that is, labor, capital and forest land. Labor (L) was represented by the number of employees at the end of the year in forestry. Due to the limitation of data availability, the educational time and working hours of the labor force could not be comprehensively considered. Capital (K) refers only to material capital. Land and human capital were considered separately. Forest land (T) was represented by the area of forest land in each region. Considering the particularities of the forestry industry, economics and ecology were included in the output variables. Economic output (Y) was represented by the total output value of forestry in each region. Ecological output (V) was represented by the forest stock volume.

4.2.3. Mediating Variables

Upgrading the forestry industrial structure (ST) is represented by the advanced index of the forestry industrial structure, which measures the industrial structure by the proportion of the three industries. The calculation method is as follows.

$$S{T}_{it}=1\times s_1+2\times s_2+3\times s_3$$

(10)

where $s_1$, $s_2$ and $s_3$represent the proportion of the forestry output value of the primary, secondary and tertiary industries in the total output value. Since the more advanced the industrial structure, the larger the proportion of the tertiary industry and the smaller the proportion of the primary industry, this paper adopted the most extensive weights method; that is, the weight of the primary industry is 1, the secondary industry is 2, and the tertiary industry is 3. Therefore, the minimum value of ST is 1 and the maximum value is 3.

4.2.4. Control Variables

Five control variables were selected in this paper. The national emphasis (NA) is represented by the share of national investment in total investment, which reflects the national emphasis on forestry construction. Investment in fixed assets (IF): economic development is inseparable from investment. It is represented by the investment in forestry fixed assets. Technological level (TL): high-quality economic development needs to be supported by the technological level. It is represented by the output of forest products per hectare. Resource environment (RE): environmental pollution and destruction will lead to natural disasters, and it is represented by the growth rate of afforestation. Human capital (HC): the influence of human capital on high-quality economic development is reflected not only in the quantity of labor but also in the educational level. In this paper, it is expressed by the ratio of staff quantities with a junior middle school education or above to that of long-term staff in forestry stations.

4.3. Data Sources and Processing

4.3.1. Data Sources

We selected 31 provinces (municipalities and autonomous regions) as the study areas (due to the lack of relevant data, Hong Kong, Macao, and Taiwan were not explored). The original data were obtained from the China Forestry and Grassland Statistical Yearbook, China Statistical Yearbook on Environment, China Statistical Yearbook on Science and Technology and China Statistical Yearbook from 2004 to 2020. A few missing data points were estimated by the linear interpolation method.

4.3.2. Data Processing

(1) The index treatment of the high-quality development of the forestry economy. The entropy method was adopted to calculate the weight of the indicator evaluation system for the high-quality development of the forestry economy.

First, in order to eliminate the influence of data dimension and enhance comparability, the standardized value of each index was calculated. The standardization of positive and negative indicators was conducted using Formulas (11) and (12), respectively.

$$x_{it}^{\prime }=\frac{x_{ij}-x_{j\left(min\right)}}{x_{j\left(max\right)}-x_{j\left(min\right)}}$$

(11)

$$x_{it}^{\prime }=\frac{x_{j\left(max\right)}-x_{ij}}{x_{j\left(max\right)}-x_{j\left(min\right)}}$$

(12)

where $x_{j\left(max\right)} \mathrm{and} x_{j\left(min\right)}$ represent the maximum and minimum values of j indicators, respectively; $x_{it}^{\prime }$ is the standardized index value.

Second, we determined the proportion and the entropy of each index, as shown in Formulas (13) and (14). The greater the entropy value, the higher the consistency, and the less important the index; on the contrary, the smaller the entropy value, the weaker the consistency and the more important the index. Finally, the weight of indicators could be determined, as shown in Formula (15).

$$C_{ij}=x_{ij}^{\prime }/{{\displaystyle \sum }}_{i=1}^m{x}_{ij}^{\prime }$$

(13)

$$e_j=-\frac{1}{lnm}{{\displaystyle \sum }}_{i=1}^m{C}_{ij}ln\left(C_{ij}\right)$$

(14)

$$W_j=\left(1-e_j\right)/{{\displaystyle \sum }}_{j=1}^n\left(1-e_j\right)$$

(15)

where m and n are the number of provinces (municipalities and autonomous regions) and indicators, respectively.

In order to better analyze the obstacles affecting the high-quality development of the forestry economy and formulate relevant policy recommendations, the obstacle degree model is conducted in this paper. The formulas for determining the deviation degree ($I_j$) and obstacle degree ($Q_j$) are as follows:

$$I_j=1-x_j^{\prime }$$

(16)

$$Q_j=\frac{I_j\times W_j}{{\sum }_{j=1}^n{I}_j\times W_j}$$

(17)

(2) Index treatment of forestry’s biased technological change index. As a result of objective influences, such as policy and economic conditions, the forestry investment in the same region fluctuates greatly in different years. The perpetual inventory method was applied to estimate the capital stock with total forestry investment in fixed assets.

The specific formulas for the calculation of forestry capital stock are as follows:

$$K_{it}=K_{i t-1}+\raisebox{1ex}{$I_{it}$}\!\left/ \!\raisebox{-1ex}{$P_{it}$}\right.-\delta K_{i t-1}$$

(18)

$$K_{i0}=I_{i0}/\left(g+\delta \right)$$

(19)

where $K_{it}$ and $K_{i t-1}$ represent the forestry capital stock for region i in period t and period t − 1, respectively. $K_{i0}$ is the capital stock in the base period (i.e., in 2004) for region i. $I_{it}$ and $I_{i0}$ represent the forestry investment in fixed assets for region i in period t and in the base period, respectively. $P_{it}$ is the investment price index for region i in period t, $\delta$ is the depreciation rate, and g is the growth rate of the forestry investment.

According to Formulas (18) and (19), the problems to be solved when using the perpetual inventory method are as follows. (1) Determination of the depreciation rate. There is little research on the capital depreciation rate in the existing literature. We adopted the capital depreciation rates of different regions in China deduced by Wu (2008) [55]. (2) Determination of the forestry investment price index. In order to keep the consistency of statistical caliber, investment in the fixed assets price index of the provinces (municipalities and autonomous regions) was considered to eliminate price impact, and all indices from 2005 to 2019 were converted into a constant price index based on 2004. (3) Determination of the forestry investment growth rate. This study adopted the method of calculating the average annual investment growth rate in five periods around the base period (i.e., 1999–2009).

(3) The total forestry output value of each region was used to represent the economic output; in order to eliminate the influence of price and maintain the comparability, it was converted to the constant price in 2004. The formulas are as follows:

First, we calculated the constant price index of GDP (gross domestic product) in each year based on 2004, then calculated the real GDP ($Y_t^{*}$) of each year according to the nominal GDP in 2004 and GDP constant price index.

$$R_t=r_1\times r_2\times \dots \times r_{t-1}\times r_t$$

(20)

$$Y_t^{*}=Y_0\times R_t$$

(21)

where $r_i$ is the GDP index, which was calculated according to the constant price GDP of the two periods. It measures the real growth rate of GDP in period t relative to period t − 1 based on prices in period t − 1. $R_t$ represents the real growth rate of GDP in period t relative to period 2004, which excludes the effect of price.

Second, the GDP deflator based on 2004 was calculated using the proportion of nominal GDP ($Y_t$) and real GDP ($Y_t^{*}$).

$$P_t=\raisebox{1ex}{$Y_t$}\!\left/ \!\raisebox{-1ex}{$Y_t^{*}$}\right.$$

(22)

Finally, the actual forestry production value of each year ($y_t^{*})$ based on 2004 was calculated using the nominal forestry production value ($y_t)$ and GDP deflator.

$$y_t^{*}=\raisebox{1ex}{$y_t$}\!\left/ \!\raisebox{-1ex}{$P_t$}\right.$$

(23)

4.3.3. Descriptive Statistics of Variables

It can be seen from Figure 2 that each criteria layer has different effects on the high-quality development of the forestry economy. During the whole research period, the obstacle degree of technological change (TC) repeatedly showed a “declining-rising” trend, with constant fluctuation. Generally speaking, it decreased from 62.76% in 2005 to 59.74% in 2020. However, the degree of obstacles still ranked first, which had the greatest impact on the high-quality development of the forestry economy. Apart from 2013, the obstacle degree of economic growth (EG) and shared development (SD) ranked second and third, respectively. Ecological construction (ECO) had a minor effect on the high-quality development of the forestry economy, with an obstacle degree of less than 10%. The forestry industry is a part of ecological construction. It could mitigate climate change and achieve sustainable development by absorbing carbon dioxide and other greenhouse gases, which improves the ecological environment quality. In order to promote the high-quality development of the forestry economy, we should constantly promote technological change, improve the transformation of research achievements and promote the utilization rate of resources.

In addition, the descriptive statistics of other variables are shown in Table 3. The differences between the maximum and minimum values of each index were great, indicating that there are great regional heterogeneities in the degree of high-quality development of the forestry economy, the degree of biased technological change and industrial structure upgrading.

Table 3. Descriptive statistics of variables.

Variable Symbol	Variable Name	Maximum Value	Minimum Value	Mean Value	Standard Deviation	Sample Size
FHQ	High-quality development of the forestry economy	0.583	0.071	0.223	0.104	496
IBTC	Forestry’s biased technological change	3.055	0.951	1.025	0.130	496
ST	Forestry industrial structure upgrading	2.355	1.005	1.600	0.301	496
NA	The national emphasis	1.000	0.002	0.530	0.312	496
FI	Investment in fixed assets	931.006	0.007	34.766	106.492	496
TL	Technological level	104.156	0.001	0.998	5.947	496
RE	Resource environment	6.120	−0.862	0.134	0.683	496
HC	Human capital	1.000	0.566	0.938	0.054	496

Table 3. Descriptive statistics of variables.

Variable Symbol	Variable Name	Maximum Value	Minimum Value	Mean Value	Standard Deviation	Sample Size
FHQ	High-quality development of the forestry economy	0.583	0.071	0.223	0.104	496
IBTC	Forestry’s biased technological change	3.055	0.951	1.025	0.130	496
ST	Forestry industrial structure upgrading	2.355	1.005	1.600	0.301	496
NA	The national emphasis	1.000	0.002	0.530	0.312	496
FI	Investment in fixed assets	931.006	0.007	34.766	106.492	496
TL	Technological level	104.156	0.001	0.998	5.947	496
RE	Resource environment	6.120	−0.862	0.134	0.683	496
HC	Human capital	1.000	0.566	0.938	0.054	496

5. Empirical Test on the Impact of Forestry’s Biased Technological Change on High-Quality Economic Development

5.1. Analysis of Mediating Effect

The fixed-effects model was adopted based on applying the Hausman test to the panel data, and the results are shown in Table 4. The coefficients of determination of the three models were greater than 0.8, and the models fit well. As can be seen from the benchmark model, the coefficient of forestry’s biased technological change on high-quality economic development was 0.222 (recorded as c), and passed the significance test at the 1% level, which validated hypothesis H1. That is, forestry’s biased technological change has a direct positive effect on high-quality economic development. It verifies the conclusion of Sun and Zhang (2020) [56]. On the one hand, under the impact of factor price, biased technological change would reduce production cost and increase economic aggregates by adjusting the use ratio of factors. On the other hand, biased technological change will also improve the quality of the ecological environment by reducing resource consumption [50], thus promoting high-quality economic development. The mediating effects model was used to verify the role of industrial structure upgrading. It can be seen that the coefficient of forestry’s biased technological change was 0.103 (recorded as a), and it passed the significance test at the 10% level, which validated H2—forestry’s biased technological change has a positive effect on industrial structure upgrading. After adding the mediating variable of industrial structure upgrading into the model, the coefficient of forestry’s biased technological change on high-quality economic development decreased from 0.222 to 0.185 (recorded as c’), and the driving effect was significantly reduced. The coefficient of industrial structure upgrading on high-quality economic development was 0.360 (recorded as b) and passed the significance test at the 10% level, which validated hypotheses H3 and H4. Forestry’s biased technological change can accelerate the high-quality development of the forestry economy by promoting the upgrading of the industrial structure. Biased technological change is accompanied by mechanization and technological innovation, which lead to the change in marginal output and proportion of factors. The flow of factors drives the upgrading of the industrial structure. Meanwhile, the upgrading of the industrial structure would optimize the allocation of resources and promote high-quality economic development. The results indicated that industrial structure upgrading played a partial mediating effect in the transmission mechanism of biased technological change to high-quality economic development. The total effect was 0.222, the direct effect was 0.185, and the mediating effect was ab/c, i.e., 0.167; that is, there were two paths for forestry’s biased technological change to promote high-quality economic development from 2005 to 2020: one was the direct promotion effect, and the other was the indirect promotion effect through industrial structure upgrading, with an indirect realization of 16.7%.

Table 4. Estimates of the impact of forestry’s biased technological change on high-quality economic development.

Variables	FHQ1	ST	FHQ2
_CONS	−1.951 *** (−19.83)	1.461 *** (22.68)	−2.476 *** (−17.61)
IBTC	0.222 *** (2.63)	0.103 * (1.86)	0.185 ** (2.24)
ST			0.360 *** (5.10)
NA	0.074 * (1.72)	−0.062 ** (−2.21)	0.096 ** (2.29)
FI	−0.076 (−1.30)	0.076** (1.98)	−0.103 * (−1.80)
TL	0.351 *** (5.70)	−0.004 (−0.10)	0.352 *** (5.89)
RE	0.070 ** (1.97)	0.017 (0.74)	0.064 * (1.84)
HC	−0.012 (−0.30)	0.058 ** (2.14)	−0.033 (−0.82)
R2	0.874	0.879	0.881
Adj-R2	0.860	0.866	0.867
F	60.465	63.517	63.141

Note: *, **, *** significant at 0.1, 0.05, 0.01 level; T-statistic is in parentheses.

Table 4. Estimates of the impact of forestry’s biased technological change on high-quality economic development.

Variables	FHQ1	ST	FHQ2
_CONS	−1.951 *** (−19.83)	1.461 *** (22.68)	−2.476 *** (−17.61)
IBTC	0.222 *** (2.63)	0.103 * (1.86)	0.185 ** (2.24)
ST			0.360 *** (5.10)
NA	0.074 * (1.72)	−0.062 ** (−2.21)	0.096 ** (2.29)
FI	−0.076 (−1.30)	0.076** (1.98)	−0.103 * (−1.80)
TL	0.351 *** (5.70)	−0.004 (−0.10)	0.352 *** (5.89)
RE	0.070 ** (1.97)	0.017 (0.74)	0.064 * (1.84)
HC	−0.012 (−0.30)	0.058 ** (2.14)	−0.033 (−0.82)
R2	0.874	0.879	0.881
Adj-R2	0.860	0.866	0.867
F	60.465	63.517	63.141

Note: *, **, *** significant at 0.1, 0.05, 0.01 level; T-statistic is in parentheses.

In the period of economic adjustment and transformation, it is important to solve the existing problems of low resource utilization rate and low forestland quality by relying on biased technological change and industrial structure upgrading. Compared with the primary industry, the secondary and tertiary industries have the advantages of low investment and high return; however, the adjustment of industrial structure is a long-term process, and at the present stage, it is advisable for China to vigorously develop the forestry secondary and tertiary industries while maintaining the stable growth of the primary industry. At the same time, we should introduce technological change matching with forestry factor endowment so as to promote the high-quality development of the forestry economy.

For the control variables, the national emphasis on forestry (NA) passed the significance test at the 5% level, and the coefficient was positive. The traditional way of logging destroyed the forest’s ecological environment. At the same time, the continuous development of industrialization increases the demand for land and begins to occupy the forestland, which decreases the forest land area. In order to promote the sustainable development of forestry, China proposed in the Outline of the National Plan for Forest Land Protection and Utilization (2010–2020) that the forest land should be used reasonably on the premise of protection-oriented. The forest coverage rate rose from 20.36% in 2010 to 22.96% in 2020, driving the increase in forest products. It can be seen that the national emphasis could lead to the policy inclination and thus promote the high-quality development of the forestry economy. The coefficient of fixed asset investment (FI) was significantly negative, indicating that it had a negative impact on the high-quality development of the forestry economy. As high-quality economic development is accompanied by the upgrading of the industrial structure and the flow of factors, it is a relatively long process and requires a perfect market operation mechanism. Inappropriate investment would affect the market structure and destroy the flow of factors among industries, so it is not conducive to the high-quality development of the forestry economy. The coefficient of the forestry technological level (TL) was significantly positive. The continuous technological innovation reduces the cost of forestry production and improves the forestry output; meanwhile, it also deepens the utilization efficiency of resources and thus promotes the high-quality development of the forestry economy. The effect of the resource environment (RE) on high-quality economic development was significantly positive. Since high-quality economic development also refers to the optimization of the ecological environment, the increase in the afforestation rate has strengthened ecological construction. The impact of forestry human capital (HC) on high-quality economic development was negative and did not pass the significance test. The main reason for this is that high-quality talents are often attracted by regions with better economic development, resulting in a shortage of high-quality talents in some regions but surplus in others, which has a restraining effect on high-quality economic development.

5.2. Analysis of Spatial-Temporal Heterogeneity

As there are differences in geographical locations, factor endowments and economic levels in regions, it is crucial to analyze the regional heterogeneity of the effects of forestry’s biased technological change in different regions on high-quality economic development. Referring to the division method of the National Bureau of statistics, the study areas were divided into the eastern, central, western and northeastern regions for the regression test, as shown in Table 5.

Table 5. The regional heterogeneity test for the impact of forestry’s biased technological change on high-quality economic development.

Variables	Eastern Region	Central Region	Western Region	Northeastern Region
_CONS	−3.124 *** (−8.57)	−3.050 ** (−2.14)	−2.579 *** (−14.66)	−12.872 ** (−2.48)
IBTC	0.012 (0.04)	0.017 (0.01)	0.304 *** (3.43)	9.517 * (1.77)
ST	1.079 *** (13.17)	0.715 *** (2.67)	0.243 ** (2.19)	1.215 * (2.05)
NA	0.357 *** (4.56)	0.150 ** (2.36)	0.065 (1.04)	−0.175 (−1.31)
FI	−0.451 ** (−2.28)	−0.148 (−0.38)	0.144 * (1.69)	0.133 (0.31)
TL	0.269 *** (3.40)	0.410 *** (2.85)	0.274 (1.37)	−1.096 ** (−3.22)
RE	0.085 (1.06)	−0.037 (−0.80)	0.042 (0.71)	−0.073 (−0.83)
HC	−0.232 *** (−2.83)	0.024 (0.23)	−0.083 (−1.39)	−0.003 (−0.03)
R2	0.599	0.918	0.862	0.918
Adj-R2	0.580	0.885	0.833	0.833
F	32.390	28.019	29.966	10.761

Note: *, **, *** significant at 0.1, 0.05, 0.01 level; T-statistic is in parentheses.

Table 5. The regional heterogeneity test for the impact of forestry’s biased technological change on high-quality economic development.

Variables	Eastern Region	Central Region	Western Region	Northeastern Region
_CONS	−3.124 *** (−8.57)	−3.050 ** (−2.14)	−2.579 *** (−14.66)	−12.872 ** (−2.48)
IBTC	0.012 (0.04)	0.017 (0.01)	0.304 *** (3.43)	9.517 * (1.77)
ST	1.079 *** (13.17)	0.715 *** (2.67)	0.243 ** (2.19)	1.215 * (2.05)
NA	0.357 *** (4.56)	0.150 ** (2.36)	0.065 (1.04)	−0.175 (−1.31)
FI	−0.451 ** (−2.28)	−0.148 (−0.38)	0.144 * (1.69)	0.133 (0.31)
TL	0.269 *** (3.40)	0.410 *** (2.85)	0.274 (1.37)	−1.096 ** (−3.22)
RE	0.085 (1.06)	−0.037 (−0.80)	0.042 (0.71)	−0.073 (−0.83)
HC	−0.232 *** (−2.83)	0.024 (0.23)	−0.083 (−1.39)	−0.003 (−0.03)
R2	0.599	0.918	0.862	0.918
Adj-R2	0.580	0.885	0.833	0.833
F	32.390	28.019	29.966	10.761

Note: *, **, *** significant at 0.1, 0.05, 0.01 level; T-statistic is in parentheses.

It can be seen that in the eastern and central regions, the coefficients of forestry’s biased technological change on high-quality economic development were positive, but they did not pass the significance test, and the coefficients of fixed asset investment were negative. Continuous capital investment destroys the price mechanism of factors in the market, resulting in the deviation of biased technological change against factor endowment. In comparison, the coefficients of forestry’s biased technological change in the western and northeastern regions were positive and passed the significance test at the 1% and 10% levels, respectively, which promoted the high-quality development of the forestry economy. This is related to the Western Development Strategy and the Revitalization Strategy of Northeast China. With the support of policies, the reasonable allocation of factors has been continuously guided, and the utilization rate of resources has been improved. Therefore, the western and northeastern regions obtained more technological dividends than the eastern and central regions. In the four models, the coefficients of industrial structure grading were significantly positive, which promoted high-quality economic development.

5.3. Robustness Tests

In order to ensure the validity of the conclusions, robustness tests were carried out from two aspects. On the one hand, in order to eliminate the influence of model endogeneity, the lagged period of forestry’s biased technological change index was adopted as the instrumental variable, and the model was tested based on the two-stage least square method. On the other hand, the total factor productivity of forestry (calculated in 4.2.2) was adopted to measure the high-quality development of the forestry economy instead of the comprehensive index system. The results of the robustness tests of the two cases are shown in Table 6. It can be seen that the regression coefficients of forestry’s biased technological change on high-quality economic development were 0.316 and 0.257, respectively; they were positive and passed the significance test at the 10% level, indicating that forestry’s biased technological change contributes to high-quality economic development. The results were basically consistent with the previous regression results, which further proved the robustness of the conclusions.

Table 6. Robustness tests for the impact of forestry’s biased technological change on high-quality economic development.

Variables	The Two-Stage Least Squares	Substitution Variable
_CONS	−2.217 *** (4.54)	−5.991 *** (−8.19)
IBTC	0.316 * (1.68)	3.293 *** (5.53)
ST	0.225 (1.02)	0.960 *** (3.81)
NA	−0.023 (−0.16)	−0.023 (−0.18)
FI	−0.425 (−1.61)	0.099 (0.41)
TL	0.339 * (1.67)	0.233 (1.09)
RE	0.071 (0.48)	0.176 (1.46)
HC	−0.008 (−0.04)	−0.232 (−1.51)
R2	0.876	0.707
Adj-R2	0.861	0.671
F	53.004	19.822

Note: *, *** significant at 0.1, 0.01 level; T-statistic is in parentheses.

Table 6. Robustness tests for the impact of forestry’s biased technological change on high-quality economic development.

Variables	The Two-Stage Least Squares	Substitution Variable
_CONS	−2.217 *** (4.54)	−5.991 *** (−8.19)
IBTC	0.316 * (1.68)	3.293 *** (5.53)
ST	0.225 (1.02)	0.960 *** (3.81)
NA	−0.023 (−0.16)	−0.023 (−0.18)
FI	−0.425 (−1.61)	0.099 (0.41)
TL	0.339 * (1.67)	0.233 (1.09)
RE	0.071 (0.48)	0.176 (1.46)
HC	−0.008 (−0.04)	−0.232 (−1.51)
R2	0.876	0.707
Adj-R2	0.861	0.671
F	53.004	19.822

Note: *, *** significant at 0.1, 0.01 level; T-statistic is in parentheses.

6. Conclusions and Suggestions

This paper investigated the high-quality development of the forestry economy based on the data in 31 provinces (municipalities and autonomous regions) in China from 2005 to 2020. A mediating effects model was constructed to explore the function of biased technological change in promoting high-quality economic development, and the role of industrial structure upgrading was analyzed. The conclusions were as follows:

(1)

There were great regional differences in the high-quality development of China’s forestry economy, and the biggest obstacle factor was technological change. Forestry’s biased technological change had a direct positive effect on high-quality economic development, and the conclusions were valid and passed the robustness tests.

(2)

Industrial structure upgrading played a partial mediating role in the mechanism of forestry’s biased technological change on high-quality economic development. The total effect was 0.222, and it decreased to 0.185 when industrial structure upgrading was added into the model as a mediating variable, among which 16.67% was realized by promoting the upgrading of the industrial structure.

(3)

There was regional heterogeneity in the effect of forestry’s biased technological change on high-quality economic development. Forestry’s biased technological change in the eastern and central regions was contrary to factor endowment and could not effectively promote high-quality economic development, while in the western and northeastern regions, it had a significant promotion effect, so they could obtain more technological dividends.

Based on the conclusions, policy recommendations were put forward:

(1)

The first is to give full play to the positive guiding role of forestry’s biased technological change; optimize the direction of technological change according to the structure of the factor market so as to improve the labor quality; stimulate the effective allocation of forestry investment in each region; and drive the intensive use of forest land. Regionally, the forestry technological change violates the principle of comparative advantage in the eastern and central regions; therefore, the two regions should actively introduce technological change and management experience matching with forestry factor endowment and economic level so as to improve the utilization rate of resources and achieve high-quality economic development. In the western and northeastern regions, we should constantly strengthen the infrastructure construction of forestry, accelerate the transformation of technological achievements, and improve the popularization of forestry technological change so as to enhance the economic value and ecological value of forestry.

(2)

The second is to promote the upgrading of the forestry industrial structure. Industrial structure upgrading had a positive effect on the promotion of high-quality economic development. Therefore, it is of great significance to develop advantageous industries so as to reduce the construction costs and improve the benefits of forestry, and then promote the upgrading of the industrial structure. At the same time, the proportion of factors among various industries should be rationally allocated, and the construction of ecological forest should be strengthened, which could guide the development of the industrial structure from the primary industry to the tertiary industry on the premise of meeting market demand.

(3)

Strengthen the construction of the factor market and ensure the normal operation of the factor price mechanism. At the same time, it is necessary to promote the flow of factors in the eastern, central, western and northeastern regions; cultivate the factor of market structure as consistent with the direction of the industrial structure; and realize the coordinated development of the high-quality economy among regions.