Can Investment in Forestry Resource Management Reduce Haze Pollution and Carbon Emissions? Evidence from China

Abstract

In the context of green development, it is very important to explore the impact of investment in forestry resource management (IFRM) on atmospheric haze pollution and carbon emissions. Based on long time series data of 30 provincial administrative regions in China from 2008 to 2019, this study used ArcGIS spatial analysis and spatial econometric models to investigate the impact of IFRM on haze pollution and carbon emissions and its potential spatial spillover effects. The results show that areas with higher haze pollution concentrations in China were mainly distributed in the Sichuan Basin and the North China Plain; areas with high carbon emission intensity were mainly distributed in Beijing, Shanghai, Tianjin, Shandong, Hebei, etc. For every 1% increase in IFRM, haze pollution and carbon emissions decreased by 0.0655% and 0.1169%, respectively, indicating that IFRM has significantly improved the ecological environment. In addition, IFRM not only significantly reduced haze pollution and carbon emissions in local areas, but also had a strong negative effect on haze pollution in neighboring areas. This study provides important strategies for promoting forestry resource management and regional green development.

1. Introduction

Since the industrial revolution, the extensive use of fossil fuels such as oil and coal and the sharp decline in forest resources have led to a continuous increase in atmospheric carbon dioxide concentrations and serious haze pollution [1,2]. In particular, with global warming and the deteriorating ecological environment, promoting sustainable economic, social and environmental development has become a focus of widespread concern in the international community. Scholars from all over the world have realized that the problem of ecological environment degradation is not just a scientific issue, but has risen to a global political, economic and social issue [3]. Similar to other developing countries, China is also facing serious problems of haze pollution and carbon emissions [4]. According to data from the International Energy Agency, China’s carbon emissions exceeded 13 billion tons in 2020, making it the world’s largest carbon emitter (https://www.iea.org/, accessed on 12 February 2024). In addition, relevant research shows that 7 of the world’s 10 cities with the most serious air pollution are in China [5]. Severe haze pollution affects residents’ transportation, threatens their health and lives, and causes extreme concern about the surrounding environment. Obviously, the above phenomenon has also attracted great attention from Chinese government departments.

In the context of green transformation and development, scholars have conducted extensive research on the spatiotemporal patterns and influencing factors of carbon emissions and haze pollution [6]. Based on the quadrant diagram method and the Theil index, Zhang et al. found that China’s rural carbon emissions have strong temporal and spatial heterogeneity, and the rural carbon emission intensity in central China is the highest [7]. Ye et al. combined air monitoring data with ArcGIS spatial analysis and found that China’s air pollution pattern has strong temporal and spatial differences [8]. With the application of multivariate data and econometric models, the formation mechanism and governance approaches of carbon emissions and haze pollution have received widespread attention from scholars [9,10,11]. Raihan and Tuspekova found that for every 1% increase in forest area, carbon emissions decreased by 3.17% [12]. Lin et al. combined a mixed regression panel model and found that for every 1% increase in China’s forest area, the average PM2.5 concentration decreased by 2.53%, and advocated improving the ecological environment through sustainable forest management [13].

China has large reserves of forest resources, ranking among the largest in the world, but the per capita possession is small, and the quality of forest resources is relatively poor. With the continuous development of urbanization and industrialization, forestry resources are decreasing, posing a serious threat to the ecological environment and the stability of forest ecosystems [14,15]. Forestry resources are regulators of carbon emissions, and they play the role of carbon sinks and storage through photosynthesis, biomass accumulation and forest product processing; local governments should accelerate the development of forestry to help alleviate global climate change [16,17]. China launched the Global Carbon Sink Project in 2001, aiming to foster a forest carbon sink market and maximize the ecological value of forests. However, due to the complex ownership of forest assets and difficulties in forest land management, the marketization of forest carbon sinks in China is still in its infancy.

Currently, relevant scholars have explored the impact of investment in forestry resource management (IFRM) on net carbon emissions from many aspects [18,19,20]. Specifically, forestry resources have strong carbon absorption and storage functions, which convert carbon dioxide into organic matter through photosynthesis. In the process, forests absorb large amounts of carbon dioxide and convert it into organic matter, which gradually decomposes in the soil, thereby increasing the soil’s carbon storage [21]. It can be seen that forests have obvious advantages over other ecosystems and play a vital role in addressing climate change. In addition, China will consolidate the carbon sequestration function of existing forests, continuously increase forest area and stock, and enhance the incremental carbon sink capacity of ecosystems as one of the important development paths to achieve carbon neutrality [22]. Especially with the rise of international carbon markets, it has become possible to realize the value of forest ecological benefits through market mechanisms [23,24].

Forestry resources are the “green purifier” of air pollution and play a key role in controlling haze pollution [25]. On one hand, forest resources can absorb and retain dust and particulate matter from the air, reducing the diffusion range and concentration of PM2.5 [26,27]. On the other hand, forests release negative ions as they grow, which can combine with pollutants in the air and improve air quality [28,29]. The water vapor released by forests can absorb dust from the air, allowing it to fall to the ground under the action of gravity, thereby removing microparticles in the air [30]. In recent years, local governments have vigorously promoted policies such as natural forest protection, returning farmland to forests, afforestation, and mountain closure and reforestation to improve the quality of forest development and enhance the stability and sustainability of such ecosystems.

In summary, although existing research has achieved fruitful results, it still faces some challenges. Most previous studies have only used forest area to evaluate the level of forestry resource development, which makes it difficult to comprehensively reflect the quality of forestry resource development. In addition, existing research mainly focuses on the spatiotemporal distribution and influencing factors of IFRM, but what is its impact on haze pollution and carbon emissions? Will the IFRM in local areas have an impact on haze pollution and net carbon emissions in neighboring areas? What are the details and characteristics of this potential spatial spillover effect? To explore this, this study first used ArcGIS spatial analysis and kernel density analysis to explore the spatiotemporal evolution of China’s haze pollution and carbon emissions. Secondly, this study constructs a forestry resource evaluation index system from three levels: forestry economy, forestry status and forestry governance, and constructs an appropriate spatial econometric model to explore the impact of IFRM on haze pollution and carbon emissions. Finally, we further examined the spatial spillover effects of IFRM on haze pollution and net regional carbon emissions through partial differential equations.

2. Materials and Methods

2.1. Theoretical Mechanism Analysis

Based on the above analysis, this study further constructed an analytical framework for the impact of IFRM on haze pollution and carbon emissions (Figure 1). First, IFRM can exert strong forestry ecological effects and have a strong positive effect on ecological environment restoration and forest renewal. Second, IFRM can promote the development of forestry resources and absorb harmful substances such as sulfur dioxide, nitrogen oxides, and particulate matter in the air [31]. Finally, IFRM also have a strong forestry carbon sink function. Specifically, forestry carbon sinks can enhance the enthusiasm of relevant stakeholders to participate in forest ecological protection and restoration and achieve sustainable utilization of forest resources. In addition, as the forestry carbon sink trading market gradually improves, it has promoted the development of related industries such as forest tourism, forest economy, and biomass energy, and enhanced the forest ecological benefits and regional green development [32]. Furthermore, we also explored the spillover effects of IFRM on haze pollution and carbon emissions.

2.2. Dependent Variable

(1) Haze pollution (Haze): Haze is a common state of air pollution, and PM2.5 is the “culprit” causing haze pollution [33,34]. Because PM2.5 has a small particle diameter, it is more harmful to human health and can easily induce respiratory diseases such as lung cancer and cardiovascular disease and has attracted widespread attention from all walks of life. Referring to the relevant literature [35], we use PM2.5 concentration to represent haze pollution. The accuracy of PM2.5 concentration raster data is 0.01° × 0.01° spatial resolution. We use ArcGIS 10.3 software to interpret it into provincial PM2.5 concentration to characterize haze pollution.

(2) Carbon emission (Carbon): The main source of carbon emissions is fossil fuels, which mainly include coal, oil, natural gas, etc. Fossil fuels are converted into carbon dioxide during the combustion process and enter the atmosphere, posing a serious threat to global climate change. We measure carbon intensity in the following way. Firstly, referring to the relevant literature [36,37], the carbon emission C_p of each province was calculated according to the carbon emission coefficient (K_m) of eight energy sources (

Table 1. Average low heat, folding standard coal coefficient and CO₂ emission coefficient of eight energy sources.

Energy Type	Average Low Heat	Folding Standard Coal Coefficient	CO2 Emission Coefficient
Coal	20,908 kJ/kg	0.7143 kgce/kg	1.9003 kg-CO2/kg
Coke	28,435 kJ/kg	0.9714 kgce/kg	2.8604 kg-CO2/kg
Crude oil	41,816 kJ/kg	1.4286 kgce/kg	3.0202 kg-CO2/kg
Gasoline	43,070 kJ/kg	1.4714 kgce/kg	2.9251 kg-CO2/kg
Kerosene	43,070 kJ/kg	1.4714 kgce/kg	3.0719 kg-CO2/kg
Diesel	42,652 kJ/kg	1.4571 kgce/kg	3.0959 kg-CO2/kg
Fuel oil	41,816 kJ/kg	1.4286 kgce/kg	3.1705 kg-CO2/kg
Natural gas	38,931 kJ/m3	1.3300 kgce/m3	2.1622 kg-CO2/m3

). Secondly, based on administrative region data, we used carbon emissions per unit area of each province/municipality to represent carbon emission intensity.

$$C_P={\displaystyle {\sum }_{m=1}^{M=8}{E}_{pm}}\times {\displaystyle \raisebox{1ex}{$K_m$}\!\left/ \!\raisebox{-1ex}{$A_P$}\right.}$$

(1)

where C_p represents carbon emissions; E_pm was the consumption of the mth energy in province/municipality p; K represents the carbon dioxide emission coefficient; A_p represents the land area of province/municipality p.

2.3. Independent Variable

Investment in forestry resource management (IFRM): forestry resources in the traditional sense refers to forests, trees and woodlands growing on forest land, as well as plants that depend on forests, trees and woodlands for survival [38]. These resources together constitute the forest ecosystem, which provides rich material and non-material support for human production and life. However, the traditional forestry resource evaluation indicators are difficult to reflect the quality of forestry resource development from multiple levels. The connotation of IFRM in this study is mainly that the development of forestry resources not only depends on the existing status of forestry resources (forest coverage rate, forest land area, forest area, etc.), but is also closely related to forestry economy and forestry governance. Therefore, this study mainly constructs the IFRM evaluation index system from three aspects: forestry economic, forestry status and forestry governance, and then comprehensively measures the development level of forest resources. Compared with the traditional indicators of forestry resources, the IFRM in this study is more comprehensive and can reflect the development quality of IFRM from multiple dimensions and levels. With reference to the relevant literature and combined with the connotation of forestry resource development, a forestry resource evaluation index system is constructed from three aspects: forestry economic, forestry status and forestry governance [39,40]. Meanwhile, the entropy method is used to assign weights to each indicator to comprehensively measure the level of China’s forestry resource development. Five indicators are used to describe the forestry economy, namely, annual growth rate of total forestry output value (%), proportion of forestry output value in GDP (%), forest land value per unit area (10⁴ m²), per capita forestry output value (10⁴ m²), and proportion of forestry investment in GDP (%). Forestry status includes five indicators, namely, forest coverage rate (%), per capita afforestation area (10⁴ m²), volume per unit area (10⁴ m²), forestry land area (10⁴ m²), and forest area (10⁴ m²). Forestry governance includes five indicators, namely, forestry pest control rate (%), forest fire area (10⁴ m²), total standing stock of living trees (10⁴ m²), forest pest area (10⁴ m²), and disaster-affected forest area (10⁴ m²). The components of the IFRM evaluation index system are defined in

Table 2. IFRM evaluation index system.

Primary Indicator	Primary Symbol	Secondary Indicator	Secondary Symbol	Indicator Type
Forestry economy	X1	annual growth rate of total forestry output value (%)	X11	+
		proportion of forestry output value in GDP (%)	X12	+
		forest land value per unit area (104 m2)	X13	+
		per capita forestry output value (104 m2)	X14	+
		proportion of forestry investment in GDP (%)	X15	+
Forestry status	X2	forest coverage rate (%)	X21	+
		per capita afforestation area (104 m2)	X22	+
		volume per unit area (104 m2)	X23	+
		forestry land area (104 m2)	X24	+
		forest area (104 m2)	X25	+
Forestry governance	X3	forestry pest control rate (%)	X31	+
		forest fire area (104 m2)	X32	−
		total standing stock of living trees (104 m2)	X33	+
		forest pest area (104 m2)	X34	−
		disaster-affected forest area (104 m2)	X35	−

Note: Forest coverage rate (%): forest coverage rate refers to the ratio of forest area to total land area. It is an important indicator reflecting the actual level of forest resources and forest land occupation in a country (or region) and is generally expressed as a percentage. Forestry land area (10⁴ m²): forestry land area refers to the land area where trees, bamboos, shrubs, coastal mangroves and other tree species grow. Forest area (10⁴ m²): forest area mainly refers to the land area covered by tree forests, bamboo forests and special shrub forests.

.

Referring to the relevant literature, we selected industrial structure (IS), urbanization rate (UR), foreign direct investment (FDI), economic development (ECO), population density (POP) and environmental regulation (ER) from 2008 to 2019 as control variables for this study [41,42]. (1) Industrial structure (IS): This study uses the ratio of the output value of the tertiary industry to the secondary industry to characterize the industrial structure; (2) Urbanization rate (UR): This study uses the proportion of urban permanent population to the total population to represent the urbanization rate; (3) Foreign direct investment (FDI): Referring to relevant studies, the proportion of foreign direct investment to GDP is used to represent FDI [43]; (4) Economic development (ECO): This study uses GDP per capita to represent the level of economic development; (5) Population density (POP): This study uses the ratio of resident population to administrative area to characterize population density; (6) Environmental regulation (ER): With reference to the relevant literature, the proportion of industrial pollution control investment to industrial added value (industrial enterprises have the remaining value after deducting the material products consumed or transferred in the production process) is used to represent environmental regulation [44].

Finally, we used R 4.4.0 language software to draw the correlation matrix between variables (Figure 2). We found that the distribution of Haze, Carbon, and ER variables was relatively even, while most of the other variables were skewed. In addition, there is a certain correlation between each variable and the explained variable, which also shows to a certain extent that the spatial econometric model selected in this study is more reasonable.

2.4. Data Source

This study takes China’s 30 provincial-level administrative regions as the research objects. The main data sources for this study are China Statistical Yearbook (https://data.cnki.net/yearBook/single?id=N2023110024, accessed on 10 January 2024), China Forestry Statistical Yearbook (https://www.forestry.gov.cn/c/www/tjnj.jhtml, accessed on 10 January 2024), China Energy Statistical Yearbook (https://data.cnki.net/yearBook/single?id=N2024050932, accessed on 10 January 2024), China Environment Statistical Yearbook (https://data.cnki.net/yearBook/single?id=N2024050561, accessed on 10 January 2024) and Forest Resources Inventory Bulletin (https://www.forestry.gov.cn/, accessed on 10 January 2024). Some missing data were supplemented by interpolation. PM2.5 concentration raster data from the Atmospheric Composition Analysis Group website (https://sites.wustl.edu/acag/, accessed on 10 January 2024).

2.5. Methods

2.5.1. Global Spatial Autocorrelation

Global spatial autocorrelation models are usually used to explore the spatial dependence and discrete trends of research objects. The basic idea of this model is that geographically adjacent objects may have certain spatial correlations, which weaken or disappear as the distance increases [45]. In this study, a global spatial autocorrelation model was used to examine the spatial autocorrelation characteristics of haze pollution and carbon emissions.

$$Moran\prime s I={\displaystyle \frac{n}{{\displaystyle {\sum }_i{\displaystyle \sum w_{ij}}}}}\times {\displaystyle \frac{{\displaystyle {\sum }_i{\displaystyle \sum w_{ij}}}(x_i-\overline{x})(x_j-\overline{x})}{{\displaystyle {\sum }_i{(x_i-\overline{x})}^2}}}$$

(2)

where x_i and x_j are the haze pollution concentration or carbon emission intensity of city i and j respectively; n is the number of cities; w_ij is the spatial weight matrix. The global Moran’s I value range is [−1, 1]. If Moran’s I is closer to 1, it means that the spatial autocorrelation of haze pollution or carbon emissions is stronger; if it is closer to −1, it indicates a higher degree of spatial dispersion; if it is equal to 0, it means there is no spatial correlation.

2.5.2. Spatial Econometric Model

Incorporating spatial elements into existing econometric models is both a continuation and a challenge to traditional econometrics. There are three main types of spatial econometric models. When the spatial dependence between the explanatory variables has a significant impact on the model, the spatial lag model (SLM) is used. When the error terms of the model are spatially correlated, the spatial error model (SEM) is used [46]. However, the spatial Durbin model (SDM) is an extension of SLM and SEM and can effectively solve the problem of spatial autocorrelation [47,48]. After a series of tests (

Table 3. Spatial econometric model test.

Test	Haze Pollution	Carbon Emission
LM-spatial lag	623.2468 ***	2311.2233 ***
Robust LM-spatial lag	421.1659 ***	261.6899 ***
LM-spatial error	269.6642 ***	2057.6678 ***
Robust LM-spatial error	67.5833 ***	8.1345 ***
Wald-spatial lag	69.4150 ***	34.8682 ***
LR-spatial lag	69.9755 ***	35.0248 ***
Wald-spatial error	74.5058 ***	37.7588 ***
LR-spatial error	75.2563 ***	38.4978 ***
Hausman	247.4248 ***	195.1548 ***

Note: *** indicates p < 0.001.

), this study used SDM to capture the spatial effects of IFRM on haze pollution and carbon emissions.

$$\begin{array}{c}Haz{e}_{it}=\delta {\displaystyle \sum _{j=1}^n{w}_{ij}Haz{e}_{it}}+{\beta }_{1}IFR{M}_{it}+{\beta }_{2}I{S}_{it}+{\beta }_{3}U{R}_{it}+{\beta }_{4}FD{I}_{it}+{\beta }_{5}EC{O}_{it}+{\beta }_{6}PO{P}_{it}+{\beta }_{7}E{R}_{it}+{\theta }_1{\displaystyle \sum _{j=1}^n{w}_{ij}IFR{M}_{it}}\\ \hspace{1em}\hspace{1em} +{\theta }_2{\displaystyle \sum _{j=1}^n{w}_{ij}I{S}_{it}^2}+{\theta }_3{\displaystyle \sum _{j=1}^n{w}_{ij}U{R}_{it}}+{\theta }_4{\displaystyle \sum _{j=1}^n{w}_{ij}FD{I}_{it}}+{\theta }_5{\displaystyle \sum _{j=1}^n{w}_{ij}EC{O}_{it}}+{\theta }_6{\displaystyle \sum _{j=1}^n{w}_{ij}PO{P}_{it}}+{\theta }_7{\displaystyle \sum _{j=1}^n{w}_{ij}E{R}_{it}}+{\mu }_{it}+{\epsilon }_{it}\end{array}$$

(3)

$$\begin{array}{c}Carbo{n}_{it}=\delta {\displaystyle \sum _{j=1}^n{w}_{ij}Carbo{n}_{it}}+{\beta }_{1}IFR{M}_{it}+{\beta }_{2}I{S}_{it}+{\beta }_{3}U{R}_{it}+{\beta }_{4}FD{I}_{it}+{\beta }_{5}EC{O}_{it}+{\beta }_{6}PO{P}_{it}+{\beta }_{7}E{R}_{it}+{\theta }_1{\displaystyle \sum _{j=1}^n{w}_{ij}IFR{M}_{it}}\\ \hspace{1em}\hspace{1em} +{\theta }_2{\displaystyle \sum _{j=1}^n{w}_{ij}I{S}_{it}^2}+{\theta }_3{\displaystyle \sum _{j=1}^n{w}_{ij}U{R}_{it}}+{\theta }_4{\displaystyle \sum _{j=1}^n{w}_{ij}FD{I}_{it}}+{\theta }_5{\displaystyle \sum _{j=1}^n{w}_{ij}EC{O}_{it}}+{\theta }_6{\displaystyle \sum _{j=1}^n{w}_{ij}PO{P}_{it}}+{\theta }_7{\displaystyle \sum _{j=1}^n{w}_{ij}E{R}_{it}}+{\mu }_{it}+{\epsilon }_{it}\end{array}$$

(4)

where Haze_it and Carbon_it represent the haze pollution concentration and carbon emission intensity of province i at time t, respectively; β₁… β₇ represent the coefficients of explanatory variables; θ₁…θ₇ represent the spatial autoregression coefficients of the exogenous variables; δ is the spatial autoregression coefficient; u_it and ε_it represent the individual fixed effects and random error terms, respectively.

2.5.3. Spatial Weight Matrix

We first need to construct a spatial weight matrix before we can import the matrix into the spatial econometric model to measure the spatial spillover effects. The first law of geography states that geographically adjacent things affect each other [49,50]. The spatial weight matrix embeds geographical and economic factors into the econometric model, which can fully reflect the spatial correlation between economy and distance. This study comprehensively considers geographical and economic factors as well as the asymmetric relationship of economic distance between cities to construct an economic distance matrix (W1) and an asymmetric economic distance matrix (W2). The economic distance matrix not only considers the straight-line distance in geographical space, but also incorporates economic factors (GDP between regions) to more comprehensively measure the connection between spatial elements. The basic idea of the asymmetric economic distance matrix is to assume that regions with stronger economic strength will have a stronger impact on surrounding regions, while regions with weaker economic strength will have a weaker impact on surrounding regions.

$$W1=\left\{\begin{array}{l}{\displaystyle \frac{GD{P}_i\times GD{P}_j}{d_{ij}^2}},(i\ne j)\\ 0,(i=j)\end{array}\right.$$

(5)

$$W2=\left\{\begin{array}{l}1/d_{ij}\times diag\left({\displaystyle \frac{GD{P}_i}{GD{P}_j}}\right),(i\ne j)\\ 0,(i=j)\end{array}\right.$$

(6)

where GDP_i and GDP_j represent the actual GDP of city i and j, respectively; d_ij is the distance between administrative centers. diag is a diagonal matrix, a special matrix in which all elements outside the main diagonal are 0.

3. Results

3.1. Temporal Evolution Characteristics

Overall, China’s average PM2.5 concentration dropped from 44.9 μg/m³ in 2008 to 30.8 μg/m³ in 2019 (Figure 3). Specifically, the average PM2.5 concentration in China showed an upward trend from 2008 to 2011, indicating that since the 2008 financial crisis, the extensive economic growth model has accelerated the development of high-energy-consuming and high-pollution industries. From 2015 to 2019, the average annual PM2.5 concentration decreased by about 12.11%, and the environmental quality in most regions of China improved significantly. In recent years, the Chinese government has advocated afforestation, reduced fossil fuel use, developed green energy and green transportation, and strengthened the control of various smoke and dust pollutants. In addition, in 2018, China promulgated the most stringent “Ambient Air Quality Standards”, added PM2.5 average concentration emission standards, and established an air pollution early warning system. China’s carbon emission intensity increased from 3729.6 Ton/m² in 2008 to 4548.2 Ton/m² in 2011, with an average annual increase of 7.31%. However, the growth rate of carbon emission intensity from 2011 to 2019 was relatively slow, indicating that the Chinese government has accelerated the green and low-carbon transformation through measures such as improving energy utilization efficiency and developing forestry carbon sinks.

Judging from the position of the kernel density of haze pollution, its kernel density peak showed an obvious left shift trend from 2008 to 2019, indicating that China’s haze pollution is on a downward trend. Judging from the peak degree of haze pollution, peaks were formed between 20 and 40 in 2017 and 2019, indicating that haze pollution was relatively low in most areas. Judging from the core density position of carbon emissions, its core density peak slowly shifted to the right from 2008 to 2019, indicating that the average carbon emission intensity in some regions has increased. In addition, the absolute difference in carbon emission intensity continues to expand, indicating that carbon emissions have strong temporal and spatial non-equilibrium distribution characteristics.

3.2. Spatial Evolution Characteristics

During the study period, the haze pollution in China showed obvious spatial aggregation effects (Figure 4). The areas with high haze concentration were mainly distributed in the Sichuan Basin, North China Plain and the middle and lower reaches of the Yangtze River Plain, while the areas with low haze concentration were mainly distributed in the northwest, southwest and northeast regions. On one hand, basins were prone to haze deposition due to their low-lying terrain, while plains lack forests and mountains to block haze, causing it to spread over a wider area; on the other hand, regions that are relatively rich in coal and mineral resources will find it difficult to escape the trap of the “resource curse” in the short term, exacerbating haze pollution. In addition, haze pollution showed a spatial pattern of spreading from provinces with higher haze pollution to surrounding provinces. However, the distribution range of China’s haze pollution continued to shrink over time, and the average concentration dropped from 44.9 µg/m³ in 2008 to 30.8 µg/m³ in 2019.

China’s carbon emission intensity showed a decreasing trend from the east to the west. In 2008, the carbon emission intensity in most areas along the eastern coast was relatively high, while the low-value areas were mainly concentrated in western regions such as Xinjiang, Gansu and Qinghai. From 2013 to 2019, the spatial scope of areas with high carbon emission intensity has significantly shrunk, and are mainly concentrated in Beijing, Tianjin, Shanghai and Shandong Province. This is mainly because Beijing, Shanghai and Tianjin have high population density and large energy consumption, which in turn produces more carbon emissions. In addition, the urbanization rate in some areas is high, the urban built-up area is large, while the proportion of green spaces such as forests and wetlands is relatively small, which is not conducive to carbon absorption and storage.

3.3. Spatial Correlation Characteristics

During the study period, the global Moran’s I index of China’s haze pollution and carbon emissions was significantly positive (p < 0.001), with strong spatial agglomeration and cross-regional spread characteristics (Figure 5). Among them, the global Moran’s I index of haze pollution and carbon emissions increased from 0.3528 and 0.148 in 2008 to 0.394 and 0.1661 in 2019, respectively. This phenomenon showed that haze pollution and carbon emissions have a wide diffusion range, strong mobility, and strong spatial spillover and correlation. It is difficult to fundamentally solve regional environmental pollution problems by relying solely on unilateral governance. Therefore, local governments need to improve their air pollution control capabilities, build a cross-regional collaborative governance mechanism, and effectively control pollution and reduce environmental governance costs. It is worth noting that the Moran’s I index of haze pollution rose from 0.3528 in 2008 to 0.4291 in 2013, and then fell to 0.3940 in 2019. This is mainly because with the implementation of energy-saving and emission-reduction policies, the spatial concentration of China’s carbon emission intensity has decreased, resulting in a decline in the global Moran’s I index in 2019.

3.4. Model Verification and Identification

Haze pollution and carbon emissions have a strong spatial correlation, and it is difficult to objectively evaluate the regression results of the traditional model. The spatial econometric model fully considers the spatial correlation between geographic spatial data and can effectively solve the “spatial missing” problem [46]. Before selecting a spatial econometric model, LM, Robust LM, Wald and LR tests should be performed. The results show that both LM and Robust LM tests significantly indicate that the spatial econometric model is suitable for this study. However, there are three types of spatial econometric models (spatial lag model, spatial error model, and spatial Durbin model). Therefore, Wald and LR tests are also used to determine which type of spatial econometric model is better. We found that Wald and LR rejected the null hypothesis that the spatial Durbin model can be transformed into a spatial lag model and a spatial error model at the 1% significance level, so SDM is more suitable for this study (Table 3).

3.5. Benchmark Regression Analysis

The results show that IFRM has a significant inhibitory effect on haze pollution and carbon emissions; that is, for every 1% increase in IFRM, haze pollution and carbon emissions decrease by 0.0655% and 0.1169%, respectively (

Table 4. Regression analysis of IFRM’s impact on haze pollution and carbon emissions.

EVs	Haze Pollution		Carbon Emissions
	OLS	SDM	OLS	SDM
Investment in forestry resource management	−0.1514 *** (−3.0260)	−0.0655 * (1.7335)	−0.1179 ** (−2.0348)	−0.1169 ** (−2.1956)
Industrial structure	0.0005 (0.0132)	−0.0555 (−1.3951)	−0.0364 (−0.9053)	−0.0408 (−0.7277)
Urbanization	−1.0124 *** (−6.5343)	0.0545 (0.3599)	0.2807 * (1.7172)	0.8419 *** (3.9438)
Foreign direct investment	0.0045 (0.3584)	−0.0267 ** (−2.9271)	−0.0189 (−1.4144)	−0.0231 * (−1.7916)
Economic development	0.1171 ** (2.1315)	−0.0163 (−0.3042)	0.2310 *** (3.9873)	−0.0087 (−0.1149)
Population density	−0.9975 *** (−6.3982)	−0.4136 ** (−3.0146)	0.0880 (0.5351)	0.3577 * (1.8479)
Environmental regulation	−0.0599 *** (−5.4668)	−0.0129 * (−1.7546)	−0.0155 (−1.3371)	−0.0220 * (−1.8642)
WX	YES	YES	YES	YES
ρ		0.6300 *** (9.6907)		0.2579 *** (2.5925)
R2	0.5268	0.9683	0.5183	0.9940
Log-L		446.6510		328.9292

Note: * indicates p < 0.1, ** indicates p < 0.05, *** indicates p < 0.001. The significance of all the tables below is consistent with this. WX represents the spatial lag of the control variables.

). First of all, IFRM can effectively reduce particulate matter in the atmosphere and play a positive role in improving air quality and reducing haze pollution. Secondly, as a major component of the terrestrial ecosystem, forest is the largest carbon reservoir, which absorbs atmospheric carbon dioxide through photosynthesis, and has strong carbon fixation and carbon storage functions. Finally, forest carbon sequestration is mainly based on natural processes, which means it is low-cost and easy to implement compared to industrial carbon capture.

As for control variables, industrial structure reduces haze pollution and carbon emissions, but both failed to pass the significance test. This finding shows that China’s industry is currently in a stage of transformation and upgrading, and some regions are still mainly resource and labor intensive and have not formed a strong negative spatial effect on haze pollution and carbon emissions. Urbanization has significantly increased carbon emissions, but its impact on haze pollution is relatively weak. Urbanization has accelerated economic and social development, but it has also brought serious negative impacts, such as accelerated climate change, environmental pollution, and ecological damage. For every 1% increase in foreign direct investment, haze pollution and carbon emissions decreased significantly by 0.0267% and 0.0231%, respectively, indicating that foreign-invested enterprises have brought advanced production technology, environmental protection equipment and management experience, and improved total factor productivity. Population density significantly increases carbon emissions but reduces haze pollution. This is mainly because human activities have increased energy consumption, leading to increased carbon emissions, but population agglomeration has promoted the development of the sharing economy, thereby reducing pollution control costs and improving control efficiency. Environmental regulations have significantly reduced haze pollution and carbon emissions, indicating that appropriate environmental regulations are conducive to technological innovation, forcing companies to seek more efficient and environmentally friendly production methods and reduce pollutant emissions.

3.6. Spatial Spillover Effect

The results showed that for every 1% increase in IFRM, haze pollution in the local and neighboring areas decreased by 0.0826% and 0.5767%, respectively, both passing the significance test (

Table 5. Spatial spillover effects of IFRM on haze pollution and carbon emissions.

Evs	Haze Pollution		Carbon Emissions
	Direct Effect	Spillover Effect	Direct Effect	Spillover Effect
Investment in forestry resource management	−0.0826 ** (−2.1359)	−0.5767 ** (−2.1099)	−0.1133 ** (−2.1059)	0.0748 (0.3946)
Industrial structure	−0.0504 (−1.2491)	0.1773 (0.7700)	−0.0381 (−0.6849)	0.1237 (0.7462)
Urbanization	−0.0332 (−0.2268)	−1.4033 *** (−3.4604)	0.8192 *** (3.8273)	−1.0580 * (−1.7938)
Foreign direct investment	−0.0305 *** (−2.8065)	−0.1382 (−0.8718)	−0.0193 (−1.5464)	0.2089 * (1.9730)
Economic development	0.0121 (0.2266)	1.0474 *** (2.7626)	−0.0019 (−0.0258)	0.3483* (1.6858)
Population density	−0.4443 *** (−3.4665)	−0.8881 (−0.5885)	0.3451 * (1.8652)	−0.6067 (−0.5727)
Environmental regulation	−0.0186 ** (−2.1575)	−0.1943 *** (−3.2154)	−0.0209 * (−1.7746)	0.0606 (1.4675)

Note: * indicates p < 0.1, ** indicates p < 0.05, *** indicates p < 0.001.

). The finding provides sufficient evidence that forests block and absorb haze. In addition, for every 1% increase in IFRM, the carbon emission of local area decreases by 0.1133%, and that of neighboring areas increases by 0.0748%, but the latter fails the significance test. This finding suggests that the impact of IFRM on carbon emissions has a certain distance attenuation effect, which significantly reduces carbon emissions in local areas but has relatively little impact on neighboring areas.

3.7. Robustness Test

Generally, if the empirical results of a model remain stable under parameter changes, it indicates good robustness. With reference to the relevant literature [51], this study conducted robustness tests using three methods (

Table 6. Robust regression analysis of IFRM’s impact on haze pollution and carbon emissions.

EVs	Haze Pollution			Carbon Emissions
	(1)	(2)	(3)	(4)	(5)	(6)
Investment in forestry resource management	−0.0854 ** (−2.4543)	−0.0654 * (−1.7335)	−0.0824 ** (−2.0047)	−0.1216 ** (−2.3512)	−0.1131 ** (−2.0836)	−0.1629 *** (−3.0501)
Industrial structure	−0.0575 (−1.5964)	−0.0555 (−1.3950)	−0.0347 (−0.8215)	−0.0597 (−1.1149)	−0.0251 (−0.4381)	−0.0035 (−0.0647)
Urbanization	0.0761 (0.5784)	0.0544 (0.3599)	0.1753 (0.9827)	0.8909 *** (4.5601)	0.9940 *** (4.5667)	0.5671 *** (2.4489)
Foreign direct investment	−0.0292 *** (−3.3013)	−0.0266 *** (−2.9270)	−0.0290 *** (−2.9737)	−0.0257 * (−1.9554)	−0.0253 * (−1.9335)	−0.0202 * (−1.7413)
Economic development	−0.0136 (−0.2651)	−0.0162 (−0.3042)	−0.0400 (−0.6790)	−0.0129 (−0.1699)	−0.0731 (−0.9505)	0.0267 (0.3522)
Population density	−0.3187 ** (−2.3182)	−0.4136 *** (−3.0145)	−0.5816 *** (−3.7540)	0.5177 ** (2.5371)	0.4609 ** (2.3357)	0.4351 ** (2.1802)
Environmental regulation	−0.0120 (−1.5386)	−0.0129 (−0.0129)	−0.0150 * (−1.6860)	−0.0206 * (−1.7758)	−0.0205 * (−1.7026)	−0.0217 * (−1.9512)
Lag(-1)	0.8422 * (18.4534)	0.7349 *** (22.5381)	0.7552 *** (24.6435)	0.4659 *** (28.5287)	0.5387 *** (17.6547)	0.8854 *** (26.4657)
WX	YES	YES	YES	YES	YES	YES
ρ	0.5564 *** (8.5343)	0.6450 *** (5.3437)	0.6869 *** (7.6759)	0.3469 *** (5.5634)	0.4536 *** (3.6745)	0.5541 *** (5.5645)
R2	0.9845	0.9833	0.9654	0.9866	0.9856	0.9827
Log-L	433.5778	364.4540	5643.3496	367.2478	676.4358	535.4269

Note: * indicates p < 0.1, ** indicates p < 0.05, *** indicates p < 0.001. WX represents the spatial lag of the control variables.

). (1) Columns 1 and 4 in Table 6 are the regression results of replacing the economic distance matrix in the original model with the asymmetric economic distance matrix. (2) We use the explained variable lagged by one period (2008–2018) as the explanatory variable of the model to eliminate the influence of the current period. Columns 2 and 5 of Table 6 are the regression results using the lagged terms of the explained variables as the SDM explanatory variables. (3) We use principal component analysis to replace the entropy method to remeasure the level of forestry resource development, which can not only reduce the number of indicators but also reflect the main information of the original indicators. The results show that the negative effects of forest resources on haze pollution and carbon emissions are still significant after replacing the spatial weight matrix. In addition, the lag term of the explained variable is significantly positive. Finally, the remeasured forest resource index coefficients and significance levels were basically consistent with the benchmark regression model, further verifying the reliability of the main conclusions of this study.

4. Discussion

As an important ecological barrier, exploring the impact of IFRM on haze pollution and carbon emissions not only provides a scientific basis for strengthening forestry resource management, but also has important significance for pollution prevention and control. First, we found that China’s haze pollution and carbon emissions have strong spatiotemporal heterogeneity and spatial dependence. This finding is similar to that of Hao et al., who found that China’s haze pollution has a strong non-balanced distribution characteristic based on a long-term remote sensing dataset [52]. In addition, we measured China’s carbon emission intensity based on carbon emission data from eight types of energy and found that carbon emission intensity shows a decreasing trend from the east to the central and western regions. During the study period, the Moran’s I index of haze pollution and carbon emissions was significantly positive, indicating that haze pollution and carbon emissions have strong spatial correlation characteristics, which brings some new insights to environmental governance. Local governments should adopt a regional joint prevention and control strategy and establish a cross-regional pollution control mechanism based on the spatial diffusion characteristics of haze pollution and carbon emissions. It is worth noting that haze concentrations in most regions decreased significantly during the study period, while carbon emission intensity continued to fluctuate. For a long time, haze pollution has led to deterioration of air quality, water quality and soil pollution, posing a serious threat to the survival and health of residents. The Chinese government has repeatedly promulgated laws and regulations emphasizing the comprehensive prevention and control of air pollution from coal burning, industry, motor vehicles and ships, dust, agriculture, etc., striving to strengthen pollution control from the policy level. As for carbon emissions, with the rapid increase in China’s energy demand and the continuous development of industrialization and urbanization, carbon emission intensity will remain at a relatively high level in the future.

Secondly, we used the SDM model to find that IFRM significantly reduced haze pollution and carbon emissions, which is consistent with existing research. Waheed et al. used time series data from 1990 to 2014 and an autoregressive distributed lag model to demonstrate that Pakistan’s forestry resources have a strong negative impact on carbon emissions and advocated reducing carbon emissions by expanding forest area [53]. Rahman et al. used time series data and econometric models to find that in the long run, for every 1% increase in forest resources, India’s carbon footprint was significantly reduced by 1.84% [54]. Nowak et al. used 86 Canadian cities in 2010 as case studies to demonstrate that urban trees can remove air pollution, sequester carbon, and reduce temperatures [55]. However, most previous studies only used forest area to evaluate the level of forestry resource development, which is difficult to comprehensively reflect the quality of forestry resource development. This study constructed a forestry resource evaluation index system from three levels: forestry economy, forestry status and forestry governance, and combined the spatial Durbin model to examine the impact of IFRM on haze pollution and carbon emissions, which improved the accuracy of the estimation results.

Third, we further explored the impact of the remaining control variables in this study on haze pollution and carbon emissions. Specifically, the industrial structure has not significantly reduced haze pollution and carbon emissions, which shows that China’s current industrial structure is still relatively low, and most regions are mainly concentrated with energy- and pollution-intensive enterprises. For every 1% increase in urbanization level, carbon emission intensity increases by 0.8419%, indicating that a large amount of energy is consumed in the urbanization process, thereby increasing carbon emissions. Similarly, Sun et al. also believed that China’s early urbanization increased carbon emission intensity [56]. Foreign direct investment has significantly reduced haze pollution and carbon emissions, indicating that foreign-invested enterprises have brought advanced technology and experience to reduce environmental pollution in the production process. Population density is significantly positively correlated with carbon emissions, indicating that large population concentration increases energy consumption and dependence on private transportation. For every 1% increase in environmental regulation, haze pollution and carbon emissions decreased by 0.0129% and 0.022%, respectively, indicating that environmental regulation has prompted some high-pollution and high-energy-consuming enterprises to transform and upgrade, thereby reducing haze pollution and carbon emissions. Similarly, Wu et al. demonstrated that environmental regulation effectively curbed the growth of carbon emissions in eastern and central China [57]. It is worth mentioning that we found that annual growth rate of total forestry output value, volume per unit area and forest pest area have large weights in the IFRM evaluation index system, which indicates that local governments should promote the development of forestry resources by implementing measures such as cultivating forestry resources, developing forestry industries, and strengthening forestry pest and disease control.

Finally, we found that IFRM significantly reduced haze pollution in neighboring areas, but had a relatively weak impact on carbon emissions, which is different from existing studies. Previous studies have mainly explored the impact of IFRM on haze pollution and carbon emissions, but often ignored the spatial spillover effects [58,59]. However, since haze pollution and carbon emissions have the characteristics of cross-regional diffusion, IFRM not only has a strong negative effect on haze pollution and carbon emissions in local areas, but also have a certain impact on neighboring areas. This study incorporates IFRM, haze pollution and carbon emissions into a unified analytical framework, and comprehensively explores the spatial spillover effects of IFRM on haze pollution and carbon emissions, which is more in line with reality.

The research results show that forest resources can effectively restrain haze pollution and carbon emissions, which is of great significance to the management of forest resources and environmental changes in China and the world. In terms of policy implications, our research results show that forestry resources play an important role in coping with climate change and improving air quality. First of all, the international community should strengthen cooperation and jointly promote the development of forestry carbon sequestration projects. Developed countries should help developing countries to carry out afforestation, forest restoration and other projects by providing financial and technical support. In addition, governments should strengthen policy coordination and jointly formulate and implement environmental protection laws and regulations to ensure the sustainable utilization and protection of forestry resources. Finally, exploring the spatial effects of forestry resources on carbon emissions and haze pollution in China will also help to promote green economy and sustainable development. Governments around the world can develop policies to encourage and support the development of green industries, such as biomass energy and forestry carbon sink trading.

In terms of practical application, the role of forestry resources as one of the largest carbon sinks on the earth is self-evident. Exploring the spatial effects of forestry resources on carbon emissions and haze pollution in China not only provides reference for ecological environment protection in China, but also provides reference for forestry resources management and ecological environment protection in other countries. For example, developing countries such as India and Pakistan have caused great damage to forestry resources while developing urbanization, and this study can provide important reference for scientific management of forestry resources.

The limitations of this study are that haze pollution and carbon emissions are the result of the interaction between human activities and other natural factors. The formation and dissipation of haze pollution and carbon emissions are not only related to IFRM, but are also difficult to separate from factors such as automobile exhaust, energy structure, and waste incineration. In the future, we should combine ground-based meteorological observation data, high-altitude detection data and environmental-pollutant observation data to further explore the impact of natural and climate factors such as precipitation, evaporation, wind direction and speed, temperature, air pressure, and relative humidity on haze pollution and carbon emissions. In addition, how to build a reasonable forestry resource development evaluation index system and accurately measure the dynamic evolution of IFRM requires further study.

The above research conclusions provide important support for policy makers and forestry resource management.

(1)

Strengthen the supervision and management of IFRM and promote the sustainable development of forestry. For a long time, due to the extensive and loose forestry development mode and the lack of awareness of forest resources protection, forest resources in some areas of China are in crisis, and ecological problems occur frequently in forestry. Local governments should formulate relevant laws and regulations, strengthen publicity on forest resource protection, prevent deforestation and illegal logging, and increase the cost for those who destroy forest resources. In addition, it is necessary to establish a forestry ecological evaluation and management mechanism, make timely and scientific judgments on the forestry ecological status, and improve the forest resource protection system.

(2)

Introduce market mechanisms to accelerate the development of forestry carbon sinks. The Chinese government has proposed that the national forest coverage rate will reach 24.1% in 2025 and about 25% in 2030, indicating that China has a huge forest carbon sink market. The key to constructing a forestry carbon sink market is to cultivate market demand and to encourage and guide multiple entities to participate in market supply. On the one hand, conduct forest ecosystem carbon sink surveys and carbon stock assessments, and establish an ecosystem carbon sink monitoring and accounting system. On the other hand, local governments should guide multiple entities to actively participate in forestry carbon sink projects and continuously improve the forestry carbon sink trading laws and regulations.

(3)

Establish a joint prevention and control mechanism and social participation mechanism for haze pollution and carbon emissions. Our research shows that haze pollution and carbon emissions have strong spatial autocorrelation and cross-regional diffusion characteristics. Therefore, it is necessary to break through administrative divisions, abandon the concept of “local protectionism” and form a joint force of pollution control. In addition, a diversified ecological compensation system should be established to coordinate the imbalance of interests among relevant stakeholders. Finally, through tax breaks and financial subsidies, enterprises and individuals are encouraged to participate in environmental protection to raise public awareness of environmental protection.

5. Conclusions

During the study period, haze pollution in China has strong regional heterogeneity and spatial agglomeration effect. Areas with higher haze pollution concentrations are mainly distributed in the Sichuan Basin and the North China Plain, while low-value areas are mainly distributed in the northwest, southwest and northeast regions. However, haze concentrations in most areas have shown a clear downward trend over time, indicating that China’s air quality is continuing to improve. China’s carbon emission intensity shows a decreasing trend from the east to the central and western regions and has a certain core-periphery spatial structure.

In addition, we found that IFRM significantly reduced haze pollution and carbon emissions, which provides an important reference for formulating reasonable forestry resource management policies. It is worth mentioning that we found that IFRM not only significantly reduced the haze pollution in the local area, but also had a strong negative effect on the haze pollution in the neighboring areas. Finally, we prove the robustness of the above main conclusions by replacing the spatial weight matrix and setting variable lag terms.