Climate Change Mitigation Through Forest Quality Enhancement and Socio-Ecological Symbiosis: Evidence from China

Abstract

This paper, based on an analysis of the environmental Kuznets curve (EKC) for forest quality and carbon emissions in economic systems, explores effective pathways for carbon emission reduction through the symbiosis between forest quality and economic growth. The findings suggest that, without considering forest quality, the overall EKC for China presents an inverted U shape. However, when forest quality is integrated into the model, the overall EKC demonstrates an upward trend, indicating a positive impact on reducing carbon emissions. Geographically, the EKCs in the northwest, northeast, and central-southern regions display an inverted U shape, while those in the north and southwest show a U shape, and the eastern regions exhibit an approximately linear upward curve, reflecting regional disparities in carbon emission trends and environmental management. The synergy between forest quality and economic development significantly contributes to climate change mitigation, with enhancing the carbon emission suppression coefficient of both forest quality and economic systems being the most effective pathway for carbon reduction. The main contribution of this paper lies in the evaluation for forest quality based on entropy weights, and the application of a symbiotic model to analyze the EKC of carbon emissions in relation to forest quality and climate resilience.

1. Introduction

Forests are the most extensively utilized natural heritage by humans, the largest and most widely distributed ecosystems on land, the most complex in texture composition, and the richest in biological and mineral resources. They are the backbone of terrestrial ecosystems. They can provide us with wood and wood-based products and are also the habitat of most animals. They play an unparalleled, decisive role in maintaining the balance and stability of terrestrial ecosystems.

Forests, the main body of terrestrial ecosystems, have three major benefits: economic, ecological, and social, and the sum of the benefits is forest quality [1]. Forest quality is related to national ecological security, and most studies on forest quality focus on the influence of natural factors [2]. Forest quality is reflected not only in natural resources but also in economic benefits. As a basic resource of the national economy, the forest has an important role in social development. The development of forest resources is an important part of resources [3,4]. Academics are paying more and more attention to the scarcity of forest resources [5]. The quantitative panel data test of the relationship between forest resources and growth [6,7] has laid a rich foundation for the study of forest quality. Current research mainly focuses on two topics: the impact of forest resources on economic development and the relationship between forest resources and low-carbon development.

1.1. Impact of Forest Land on Economic Development

Forest resources provide heterogeneous service values and economic values for ecosystems, which have a constraining effect on economic growth, and suitable methods are needed to make the resources sustainable. Species diversity of forest land has a significant effect on risk and return [8], and there is a need to improve forest biodiversity [9]. The ecosystem services and economic values derived from forest resources vary widely, and the ecosystem services of forest resources should be assessed holistically, thus helping to inform forest management [10]. Diversification of economic activities such as forestry and ecotourism to conserve forest resources is preferable to direct consumption of forest resources [11].

The influential path of forest resources on economics is reflected in the impact on growth, employment, and labor income. Resource curses also exist in forest land and economic systems, e.g., deforestation in the Solomon Islands, which brings about unbalanced economic development [12]. In terms of income and employment, forest resources maintain the level of well-being, food, and human and spiritual values [13]. Forests play a key role in food security and mitigate climate change risks [14]. Forests also have the function of absorbing particulate matter and providing recreation and entertainment [15]. In reality, it is difficult to find a generalized management strategy that meets all technical, environmental, social, and cultural constraints while providing forest-based economic development [16].

Existing studies have explored the relationship of forest land and the economy but have not paid enough attention to the quality of forest resources. Existing studies still lack attention to forest quality as the improvement of forest ecological function. For a long time, the economic growth model of China’s forestry industry has been characterized by a crude approach [17], and some scholars have even observed an inhibitory effect of forest resources on economic growth [18,19,20].

1.2. Forest Land and Low-Carbon Economic Development

Forest land provide social benefits along with economic growth drivers for social development. The development of sustainable forest resources and environmental policies can be beneficial in mitigating the damage caused by climate change [21]. Forestry outputs are often competitive; e.g., monoculture plantations favor intensive timber management while potentially reducing forest biodiversity values [22]. Forest products with different life cycles do not have the same level of impact on the environment [23]. Declining industrial production in forests can significantly reduce the climate mitigation benefits of policies concerning forest carbon sinks, and there may also be cross-sectoral carbon leakage; e.g., part of the wood consumption will be shifted to higher-emission fuels [24,25]. Empirical analyses from China suggest that forest dynamics and transitions are driven by natural disasters and economic development [26]. Research on the influential path of low carbon focuses on the aspects of forest carbon sinks, carbon trading, and economic and ecological benefits brought by forest ecological service systems [27,28].

Currently, the research on environmental and economic growth is mainly based on the environmental Kuznets curve (EKC) hypothesis [29]; i.e., the degree of environmental pollution will increase with economic growth in the early stage of economic development and will decrease when the economic growth crosses a certain threshold. That is, the EKC hypothesis is also applicable to the study of the relationship between forest quality and economic growth in the field of forest ecology, specifically. Environmental decline reaches a tipping point as further economic growth exceeds a certain level. At the same time, deforestation is closely linked to carbon dioxide; i.e., carbon dioxide can be mitigated by reforestation and replenishment of forest resources [30].

However, environmental Kuznets curve studies of forest resources are inconclusive. Some studies have found empirical EKC support for forests [31,32,33], while others have not found any evidence supporting the existence of forest EKC [34]. As the research on forest EKC has progressed, more factors have been added to the forest EKC model. There is a long-term dynamic relationship between forests and the economy [35,36,37].

From the above discussion, it can be seen that EKC on forest land is still controversial, which suggests the need for more empirical research. The research objective of this study is to explore the symbiotic relationship between forest quality and economic growth, as well as the impact of this symbiotic relationship on carbon emissions, by developing appropriate extended models to analyze the EKC form of forest quality. The main research hypothesis of this article is as follows: (1) There is a close relationship between forest quality and economic growth. Forest quality has a significant impact on economic growth, and economic growth also has a significant impact on forest quality. (2) The relationship between forest quality and economic growth can be explained by the symbiotic relationship of ecosystems. (3) The impact of the symbiotic system between forest quality and economic growth on carbon emissions follows the environmental Kuznets curve hypothesis.

The research structure of this paper is arranged as follows: (1) constructing an extended EKC analysis model, (2) conducting empirical analyses using Chinese provincial-level regional data, and (3) optimizing the measurement of regional carbon emission systems. The research highlights of this paper are (1) introducing forest quality indicators into EKC research, (2) constructing a forest quality evaluation index system and using the entropy weight method to evaluate the forest quality of provincial-level regions in China, and (3) constructing an extended EKC model from a symbiotic perspective.

2. Methods and Materials

In order to understand the impact mechanism of forestry quality factors on EKC in China and explore effective carbon-reduction pathways, this paper constructs an EKC model considering forestry quality. The Lotka–Volterra model was introduced into the analysis of the symbiotic mechanism between economic growth and forestry land quality, and the forestry quality EKC and symbiotic mechanism model were used to compensate for the shortcomings of existing research. The research process is shown in Figure 1:

2.1. EKC Modeling of Carbon Emissions Under the Perspective of Forest–Economy Symbiosis

This paper refers to the analytical thinking of the environmental Kuznets curve [38,39]. The basic form of the model is as follows:

$$C_t={\alpha }_0+{\alpha }_1{G}_t+{\alpha }_2{G}_t^2+{\epsilon }_t$$

(1)

$C_t$ is the CO₂ emissions in year $t$; $G_t$ represents the gross domestic product (GDP) in year $t$; ${\alpha }_i$ is the coefficient to be estimated; and ${\epsilon }_t$ is a random error term.

$$C_t={\alpha }_0+{\alpha }_1{G}_t+{\alpha }_2{G}_t^2+{\alpha }_3{F}_t+{\epsilon }_t$$

(2)

$F_t$ represents forest quality.

There is a complex relationship between economic growth and forest quality in the EKC framework. This complex relationship is a symbiotic relationship that cannot be expressed as a simple cooperative or competitive relationship and is also reflected in the EKC literature [31,36].

Symbiotic relationships between populations in natural ecosystems are mainly expressed by Lotka–Volterra models, which, with the expansion of research, have also been applied to socio-economic symbiotic systems [40,41], the study of nature–society symbiotic systems [42], and the analysis of the symbiosis of industrial populations [43]. The model of the symbiotic relationship between economic growth and forest resources can be expressed by the following Lotka–Volterra model:

$$\left\{\begin{array}{l}\Delta G_t={\lambda }_1{G}_{t-l}+{\gamma }_1{G}_{t-l}^2+{\beta }_{12}{G}_{t-l}{F}_{t-l}\\ \Delta F_t={\lambda }_2{F}_{t-l}+{\gamma }_2{F}_{t-l}^2+{\beta }_{21}{G}_{t-l}{F}_{t-l}\end{array}\right.$$

(3)

l (l = 0,1, 2, …) denotes the time lag period.

$$\begin{array}{l}\left\{\begin{array}{l}{G}_t-G_{t-1}={\lambda }_1{G}_{t-1}+{\gamma }_1{G}_{t-1}^2+{\beta }_{12}{G}_{t-1}{F}_{t-1}\\ F_t-F_{t-1}={\lambda }_2{F}_{t-1}+{\gamma }_2{F}_{t-1}^2+{\beta }_{21}{G}_{t-1}{F}_{t-1}\end{array}\right.\\ \Rightarrow \left\{\begin{array}{l}{G}_t=({\lambda }_1+1)G_{t-1}+{\gamma }_1{G}_{t-1}^2+{\beta }_{12}{G}_{t-1}{F}_{t-1}\\ F_t=({\lambda }_2+1)F_{t-1}+{\gamma }_2{F}_{t-1}^2+{\beta }_{21}{G}_{t-1}{F}_{t-1}\end{array}\right.\end{array}$$

Let ${\lambda }_1+1={\theta }_1$ nd ${\lambda }_2+1={\theta }_2$. You will obtain

$$\left\{\begin{array}{l}{G}_t={\theta }_1{G}_{t-1}+{\gamma }_1{G}_{t-1}^2+{\beta }_{12}{G}_{t-1}{F}_{t-1}\\ F_t={\theta }_2{F}_{t-1}+{\gamma }_2{F}_{t-1}^2+{\beta }_{21}{G}_{t-1}{F}_{t-1}\end{array}\right.$$

(4)

The EKC model of the symbiotic system can be obtained by associating the above system of equations with model 2 as follows:

$$\left\{\begin{array}{l}{C}_t={\alpha }_0+{\alpha }_1{G}_t+{\alpha }_2{G}_t^2+{\alpha }_3{F}_t+{\epsilon }_t\\ G_t={\theta }_1{G}_{t-1}+{\gamma }_1{G}_{t-1}^2+{\beta }_{12}{G}_{t-1}{F}_{t-1}\\ F_t={\theta }_2{F}_{t-1}+{\gamma }_2{F}_{t-1}^2+{\beta }_{21}{G}_{t-1}{F}_{t-1}\end{array}\right.$$

(5)

2.2. Forest Quality Evaluation Based on Entropy Weight Method

Forest quality, which contains three main factors, ecological, social, and economic factors [1,44], can be evaluated by constructing an indicator system [45]. The main economic indicators of forest quality are selected to construct the EKC model, as a complex indicator system is not suitable for constructing the EKC model. This paper uses the provincial panel data of CO₂ emissions from China Emission Accounts Datasets (CEADs) [46,47]. Data on forests land in provincial-level regions of China from 2012–2021 were selected from the China Statistical Yearbook.

Based on relevant research, this article constructs an evaluation index system for forest quality, which evaluates forests from the perspectives of forest economic value, ecology, and society. The constructed evaluation index system is shown in the following

Table 1. Index system of forest quality.

${\mathit{a}}_{\mathit{i}\mathit{j}}$ aij	Index	Index Units	Index Type
$a_{i1}$	forestry output value	100 million RMB yuan	economy
$a_{i2}$	forestry investment	100 million RMB yuan	economy, ecology, and society
$a_{i3}$	forestry land area	10,000 hectares	ecology
$a_{i4}$	forest coverage	10,000 hectares	ecology
$a_{i5}$	prevention and control of forestry pests	prevention and control rate (%)	ecology
$a_{i6}$	forest stock	10,000 cubic meters	economy, ecology, and society

:

As shown in Table 1, multiple indicators are selected to evaluate forest quality, some of which are comprehensive indicators such as forestry investment and forestry stock volume. Forestry investment includes national forestry investment, ecological restoration, forest product processing and manufacturing, forestry service guarantee, and public management. Forestry stock volume is the total volume of tree trunks that exist in a certain forest area, which is closely related to forestry economic output, ecological effects, and social value.

Forest quality was evaluated based on the index system and entropy method. The evaluation matrix is A. In the matrix, $a_{ij}$ represents the values of various indicators in different regions. In order to eliminate the influence of differences in raw data between regions, the logarithmic operation is first performed on the indicator values.

$$A={\left[\ln a_{ij}\right]}_{310\times 6}$$

(6)

Entropy weight is an objective weight method [48,49].

Step 1: Normalize the evaluation matrix A.

$$r_{ij}={\displaystyle \frac{\ln a_{ij}}{\sqrt{{\displaystyle {\sum }_{i=1}^m{(\ln a_{ij})}^2}}}}$$

(7)

Step 2: Compute entropy.

$$e_j=-{\displaystyle \frac{1}{\ln m}}{\displaystyle {\sum }_{i=1}^m{r}_{ij}\ln r_{ij},j=1,2,\cdots ,n}$$

(8)

Step 3: The weights of each criterion are calculated based on the data in the example.

$$w_j={\displaystyle \frac{1-e_j}{{\displaystyle {\sum }_{i=1}^n(1-e_j)}}},j=1,2,\cdots ,n$$

(9)

Table 2. Urbanization index weight based on entropy method.

Indicators	${\mathit{a}}_{\mathit{i}1}$	${\mathit{a}}_{\mathit{i}2}$	${\mathit{a}}_{\mathit{i}3}$	${\mathit{a}}_{\mathit{i}4}$	${\mathit{a}}_{\mathit{i}5}$	${\mathit{a}}_{\mathit{i}6}$
$w_j$	0.163	0.169	0.175	0.173	0.174	0.146

presents the calculated values of entropy weights. On the basis of entropy weight, the forest quality evaluation values of each provincial region can be calculated, and the evaluation results are shown in

Table 3. Provincial regional forest quality evaluation score.

Region	Year										Mean
	2022	2021	2020	2019	2018	2017	2016	2015	2014	2013
Beijing	5.031	5.075	5.189	5.198	5.170	4.939	4.893	4.892	5.033	4.950	5.037
Tianjin	3.347	3.376	3.614	3.843	3.531	3.552	3.337	3.372	3.264	3.159	3.439
Hebei	6.106	6.138	6.171	6.148	6.160	6.007	5.941	5.871	5.856	5.785	6.018
Shanxi	5.940	5.926	5.926	5.852	5.863	5.786	5.777	5.732	5.740	5.703	5.825
Neimenggu	6.903	6.892	6.893	6.989	6.900	6.869	6.852	6.846	6.796	6.798	6.874
Liaoning	5.986	5.933	5.931	5.923	5.995	5.931	5.935	6.064	6.112	6.136	5.994
Jilin	6.266	6.267	6.268	6.268	6.282	6.245	6.276	6.260	6.185	6.228	6.254
Heilongjiang	7.003	6.947	7.089	6.891	6.903	6.875	6.887	6.877	6.832	6.842	6.915
Shanghai	3.219	3.307	3.466	3.478	3.393	3.187	3.202	3.139	3.062	2.993	3.245
Jiangsu	5.305	5.433	5.454	5.429	5.436	5.495	5.437	5.453	5.486	5.427	5.435
Zhejiang	6.095	6.081	6.140	6.162	6.116	6.080	6.059	6.060	6.061	6.070	6.093
Anhui	6.068	6.219	6.126	6.088	6.082	6.028	5.986	6.007	5.987	5.945	6.054
Fujian	6.523	6.470	6.445	6.565	6.609	6.614	6.624	6.628	6.637	6.620	6.574
Jiangxi	6.586	6.566	6.578	6.625	6.536	6.528	6.452	6.314	6.389	6.339	6.491
Shandong	5.692	5.885	5.900	5.798	5.936	5.898	5.887	5.864	5.875	5.820	5.855
Henan	5.908	5.955	5.939	6.069	5.892	5.867	5.893	5.918	5.918	5.905	5.926
Hubei	6.318	6.466	6.412	6.513	6.395	6.356	6.294	6.225	6.122	6.036	6.314
Hunan	6.518	6.630	6.644	6.453	6.682	6.567	6.593	6.569	6.578	6.521	6.576
Guangdong	6.599	6.514	6.481	6.136	6.431	6.346	6.308	6.298	6.207	6.226	6.355
Guangxi	6.925	6.915	6.820	6.741	6.792	6.686	6.720	6.754	6.805	6.683	6.784
Hainan	5.015	5.088	5.073	5.686	5.068	5.034	4.976	4.952	5.006	4.979	5.088
Chongqing	5.915	5.916	5.856	5.832	5.825	5.666	5.463	5.563	5.446	5.422	5.690
Sichuan	7.117	7.130	7.090	7.052	7.079	7.043	6.970	6.930	6.930	6.787	7.013
Guizhou	6.592	6.518	6.580	6.562	6.524	6.331	6.089	6.048	5.965	5.883	6.309
Yunnan	7.121	7.106	7.090	7.056	7.091	7.016	6.959	6.977	6.920	6.923	7.026
Xizang	5.877	5.872	5.866	5.812	5.686	5.871	5.840	5.793	5.738	5.785	5.814
Shaanxi	6.252	6.289	6.344	6.311	6.343	6.290	6.249	6.223	6.217	6.172	6.269
Gansu	5.961	5.923	5.946	5.946	5.925	5.843	5.785	5.756	5.731	5.723	5.854
Qinghai	5.353	5.282	5.279	5.306	5.246	5.178	5.123	4.998	4.955	4.859	5.158
Ningxia	4.311	4.253	4.226	4.247	4.179	4.135	4.143	4.216	4.133	4.138	4.198
Xinjiang	6.133	6.230	6.172	6.195	6.220	6.103	6.053	6.058	5.911	5.881	6.096

.

The data in Table 3 indicate that Yunnan (7.026) and Sichuan (7.013) have the highest scores in China’s forest quality assessment at the provincial level. In addition, there are fourteen provincial-level regions with forest quality evaluations above 6 points. Shanghai (3.245) and Tianjin (3.439) are the two provincial-level regions with the lowest forest quality assessment scores. From the perspective of ecosystem services and benefits, there is a complex relationship between forest ecology and economic growth. The relationship between forest quality and economic growth is not linear or unidirectional, but rather a diverse symbiotic relationship. The services and benefits of ecosystems to economic and social development are comprehensively influenced by factors such as economic development stage, economic structure, and geographical environment, and the quantity relationship of the influence is not linear. The relationship between forest quality and economic growth is interdependent, and the economic system exerts an impact on changing the forestry ecology through measures such as forestry regeneration and development, forestry economy, and forestry technology. This symbiotic relationship is reflected in the data, which show that there are regions with high levels of forestry quality in economically developed areas, as well as regions with relatively low levels. The specific symbiotic mechanisms in different regions need to be further analyzed. The statistical characteristics of variables in the EKC model of this article are shown in

Table 4. Statistical characteristics of the sample data.

Variable	Description	Unit	Mean	SD
C	CO2emissions	ten thousand tons	342	223
G	GDP deflator	billion RMB yuan	14,956	11,390
F	forestry quality evaluation score	evaluation score	5.889	0.910

:

3. Results

3.1. Tests for the Econometric Analysis

The mean value of the variance inflation factor of the variables in the EKC1 model is 10.81, and the mean value of the variance inflation factor of the variables in the EKC2 model is 8.03. Variables in the EKC2 model meet the requirements for the variance inflation factor (VIF) better. To compare the heterogeneity of the EKC1 and EKC2 models, the results of the econometric analysis of the EKC1 model are retained in this study, and the results are shown in

Table 5. Variance inflation factor (VIF).

Variable	VIF	1/VIF
GDP	10.81	0.092538
GDP2	10.81	0.092538
Mean VIF	10.81
GDP	11.59	0.086289
GDP2	11.46	0.087269
Forest	1.06	0.947512
Mean VIF	8.03

.

When using panel data for regression analysis, the Hausman test fixed-effects model and random-effects model are used. The results of the Hausman test are shown in

Table 6. Hausman test results.

EKC Model	Area	Chi2	Prob > Chi2	Fe or Re
EKC 1	Nationwide	0.65	0.4202	RE
	North China	0.52	0.4726	RE
	Northeast China	32.15	0.0000	FE
	East China	0.54	0.4600	RE
	Central-South China	5.04	0.0247	FE
	Southwest China	2.66	0.1026	RE
	Northwest China	19.54	0.0000	FE
EKC 2	Nationwide	7.66	0.0210	FE
	North China	4.35	0.1130	RE
	Northeast China	34.31	0.0000	FE
	East China	9.13	0.0104	FE
	Central-South China	8.24	0.0162	FE
	Southwest China	21.98	0.0000	FE
	Northwest China	24.86	0.0000	FE

.

The data in Table 6 show the results of the Hausman test, and the EKC1 and EKC2 models are significantly different. The results of the Hausman test for the EKC1 model for the different regions show that four regions are suitable for the random effects model, and three regions are suitable for the fixed effects model. The results of the Hausman test for the EKC2 model show that six regions are suitable for the fixed effects model. The EKC2 model is also more suitable for the Hausman test than the EKC1.

3.2. Environmental Kuznets Curve (EKC) Analysis of Carbon Emissions

(1)

The Basic EKC Model for Carbon Emissions (fixed effects model)

From the data in

Table 7. Regression results of the basic EKC model for carbon emission.

Area	α0	α1	α2	Shape of EKC
North China	541.206 (2.49) ***	−8.93 × 10−4 (−4.386)	−4.61 × 10−7 (2.758)	linear decrease
Northeast China	401.864 (8.36) ***	−6.40 × 10−3 (−0.82)	4.65 × 10−9 (0.02)	U shape
East China	91.496 (1.20)	0.013 (3.58) ***	3.43 × 10−8 (0.60)	linear increase
Central-South China	182.901 (4.49) ***	5.80 × 10−3 (1.85) *	5.51 × 10−8 (1.32)	linear increase
Southwest China	161.608 (6.71) ***	5.27 × 10−3 (1.58)	9.88 × 10−8 (0.85)	linear increase
Northwest China	−465.630 (−7.25) ***	0.19 (9.24) ***	−7.20 × 10−6 (−6.82) ***	Inverted U shape
Nationwide	106.364 (5.06) ***	0.019 (8.118) ***	−1.29 × 10−7 (−2.71) ***	Inverted U shape

() t value,* p value < 0.1, ** p value < 0.05, *** p value < 0.01.

, it can be seen that the overall EKC of China nationwide shows an inverted U-shaped curve. From the perspective of geographic differences, the EKC in the northwest also shows an inverted U shape. The EKC shape indicates that it is possible for China to achieve carbon peaking nationwide when the economy grows. However, the EKC in most regions is not optimistic, such as the U shape in the northeast and the nearly linear increasing relationship in the eastern region, the south-central region, and the southwestern region. The value of the constant term (α₀) of the carbon emission EKC equation is greater in North China, which suggests that the initial value of carbon emission from economic growth in North China is higher, and it is a linear decreasing shape EKC running at a high level. This also explains the air pollution control problems in North China in recent years and its long-term expectation.

The EKCs of the eastern, south-central, and southwestern regions with an approximately linear increasing relationship show a concerning trend in carbon emissions. In particular, the eastern region encompasses China’s most developed industrial region and the Yangtze River Delta Economic Circle, while the eastern regions of Jiangsu, Zhejiang, and Shanghai have more optimized industrial structures and higher levels of innovation. Regions with higher levels of economic growth, science and technology, education, and social development are still not free from carbon constraints. Eastern China is also a region with a high dependence on foreign trade, and the growth in the eastern region confirms the pollution haven hypothesis.

An important assumption premise was set in the early EKC test: different regions are homogeneous when facing the EKC test [50]. The assumption of homogeneity implies that the determinants of environmental quality and development trajectories of different regions are convergent. However, it is difficult for scholars to fulfill this assumption when conducting the EKC test, which is the “Heterogeneity Difficulty” of EKC. The traditional EKC test is generally based on the assumption of homogeneity, which does not take into account the differences in resource endowment and competitive advantages of different sample individuals, and the research conclusions are often fragile and limited. In order to solve the “Heterogeneity Difficulty”, make the EKC test more reasonable, and analyze the differences in the shape of EKC between different categories of individuals, it is necessary to effectively divide the research object into regions according to the scientific method for different situations, so as to realize the group test based on a certain division standard. The results of the empirical analysis in this subsection also confirm the existence of EKC heterogeneity characteristics in different regions of China.

(2)

Carbon emissions EKC taking into account forestry impacts

The ultimate goal of studying EKC is to find a harmonious development path between pollution and economic growth. The EKC theory holds that pollution increases with economic growth, and the transformation of industrial structure creates an inverted U-shaped relationship between pollution and economic growth. Technological upgrading reduces pollution by improving production efficiency. In addition, to avoid the deterioration of carbon emissions during economic growth, carbon sinks can be utilized to reduce carbon dioxide emissions. Carbon sinks occur in places where plants grow, such as forests, farmland, grasslands, and green spaces. Forests are the largest carbon pool in terrestrial ecosystems. Therefore, when constructing the carbon emission EKC model, the impact of forests should be considered, especially the impact mechanism of forest quality on carbon emissions. This section will use model (2) to examine the carbon reduction effect of considering forestry quality in economic growth EKC. The results of the analysis are shown in

Table 8. Effect of forest quality on carbon emissions EKC.

Area	α0	α1	α2	α3	Shape of EKC
North China	511.409 (1.49) *	−0.203 (−3.703) ***	8.027×10−6 (3.828) ***	204.987 (9.167) ***	U shape
Northeast China	293.941 (0.775)	0.060 (2.421) **	−1.499×10−6 (−1.491) *	−59.632 (−1.225)	Inverted U shape
East China	−173.177 (−1.448)	0.015 (2.185) **	5.118×10−8 (0.414)	33.551 (2.425) **	Monotone increasing
Central-South China	102.405 (1.323)	0.026 (14.391) ***	−2.867×10−7 (−9.167) ***	−21.784 (−1.555)*	Inverted U shape
Southwest China	168.866 (1.961) **	−0.028 (−3.727) ***	1.243×10−6 (4.742) ***	31.499 (2.805) ***	U shape
Northwest China	302.298 (2.809) ***	0.133 (7.750) ***	−7.238×10−6 (−6.949) ***	−84.803 (−3.269)	Inverted U shape
Nationwide	−104.502 (−1.600) *	0.014 (4.969) ***	−5.624×10−8 (−0.976)	43.543 (3.920) ***	Monotone increasing

() t value,* p value < 0.1, ** p value < 0.05, *** p value < 0.01.

.

The data in Table 8 bring out two findings. On the one hand, the inclusion of forestry development quality indicators has the potential to change the shape of the EKC, and on the other hand, the effect of forestry output value indicators on carbon emissions shows regional differences. For example, for the whole country as a whole, the addition of forest variables transforms the EKC from an inverted U shape to an approximately linearly increasing curve. If the overall EKC of the country can be controlled in the downward segment of the inverted U shape, it can effectively control the peak of carbon emissions. The overall EKC of the country shows an increasing shape, mainly due to the negative impact factor of forestry on carbon emissions, which has not achieved the coordinated development of forestry quality and economic growth.

Comparing the regression results of EKC model 1 and model 2 shows that the addition of forest quality indicators can significantly change the shape of the EKC. The reason lies in the complexity of forest quality assessment itself. Human society inevitably interacts with forests in the process of development, and this interaction should be mutually beneficial and collaborative. The synergistic symbiosis between forest quality and economic growth is a prerequisite for forest systems to achieve effective carbon reduction. In real social life, some forestry activities make insufficient contributions to carbon reduction. For example, in economically developed areas, natural forests are harvested in order to achieve urbanization or industrialization. When the economy develops to a certain level, people begin to plant and breed artificial forests in large quantities. The process of alternating evolution between natural forests and artificial forests significantly affects the ecological capacity of forest systems. Therefore, carbon peak cannot be achieved simply by increasing forest area. In carbon reduction practice, it is necessary to compare and analyze different carbon reduction paths.

The data in

Table 9. Regional comparison of carbon emission modeling data.

Cycle	North China	Northeast China	East China	Central-South China	South West China	North West China	G1	Forestry Quality
1	309.0	445.9	149.7	224.8	170.7	484.5	10,000	5.0
2	327.8	445.8	159.5	227.6	174.6	467.6	10,300	5.2
3	348.6	445.4	169.7	230.4	178.8	448.8	10,609	5.3
4	371.6	444.8	180.2	233.3	183.4	428.0	10,927	5.5
5	397.0	443.8	190.9	236.1	188.4	405.1	11,255	5.6
6	425.0	442.4	202.1	239.0	193.9	379.9	11,593	5.8
7	455.8	440.6	213.5	241.9	199.8	352.1	11,941	6.0
8	489.5	438.4	225.4	244.8	206.2	321.7	12,299	6.1
9	526.3	435.8	237.6	247.8	213.1	288.5	12,668	6.3
10	566.6	432.6	250.1	250.7	220.6	252.2	13,048	6.5
11	610.5	428.9	263.1	253.7	228.7	212.6	13,439	6.7
12	658.2	424.5	276.5	256.6	237.5	169.5	13,842	6.9
13	710.2	419.6	290.3	259.5	246.9	122.7	14,258	7.1
14	766.5	413.9	304.5	262.4	257.0	71.8	14,685	7.3
15	827.7	407.5	319.2	265.3	268.0	16.7	15,126	7.6
16	893.9	400.4	334.3	268.2	279.7	−43.1	15,580	7.8
17	965.6	392.3	349.9	271.0	292.4	−107.7	16,047	8.0
18	1043.1	383.3	366.0	273.8	306.0	−177.6	16,528	8.3
19	1126.8	373.4	382.6	276.5	320.6	−253.1	17,024	8.5
20	1217.2	362.3	399.7	279.2	336.2	−334.6	17,535	8.8

provide a feasible way of comparative analysis of carbon emission EKC between regions. It is not suitable to use raw data for direct comparative analysis due to the significant disparities in economic growth and carbon emission scales across different geographical regions. In order to solve the obstacles of data heterogeneity in geographic areas, this paper utilizes simulated data for the comparative analysis of carbon emission differences between regions. The data simulation process will set the initial stage GDP as 10 trillion RMB and keep a growth rate of 3% in each cycle. Meanwhile, the forestry quality score is set to an initial value of 5, maintaining the same growth rate as GDP in each cycle.

The simulation data in Table 9 show that the northeast and northwest regions achieved a gradual reduction in carbon emissions during the simulation period, making them the regions with the best carbon neutrality effect among all geographical regions. The carbon emission reduction effect is highest in the northwest region, indicating that the unit emission reduction intensity of forest quality in the northwest region is relatively high. The carbon emissions levels in the central southern, southwestern, and northeastern regions are similar and all at relatively low levels. Figure 2 shows the trend of carbon emissions.

3.3. Symbiotic Mechanisms of Economic Growth and Forest Quality

There is a complex symbiotic relationship between economic growth and forest quality, with the most common symbiotic relationships being synergistic, mutually beneficial, and competitive. After the economic system develops well, more resources are invested in the protection and development of forestry resources. A high-quality forestry system will also have higher economic value, thus forming a coordinated development of economic growth and forest quality. If the resources of the economic system are limited, it will lead to a competitive relationship between the economic system and the forest system, resulting in a contradictory relationship.

The data in

Table 10. Symbiotic mechanisms of economic growth and forest quality.

Area	${\mathit{\theta }}_1$	${\mathit{\gamma }}_1$	${\mathit{\beta }}_{12}$	${\mathit{\theta }}_2$	${\mathit{\gamma }}_2$	${\mathit{\beta }}_{21}$	Symbiotic Mechanism
North China	1.001 (16.569) ***	−4.146 × 10−7 (−0.175)	−1.021 × 10−4 (−0.010)	1.006 (51.010) ***	−1.320 × 10−3 (−0.455)	4.203 × 10−7 (0.644)	Laterality
Northeast China	1.302 (3.347) ***	−6.815 × 10−6 (−1.531) *	−0.043 (−0.770)	1.020 (25.968) ***	−1.625 × 10−3 (−0.291)	−9.502 × 10−7 (−1.556) *	Competition
East China	1.039 (25.582) ***	−5.154 × 10−7 (−1.338)	−7.795 × 10−4 (−0.119)	1.025 (67.991) ***	−3.079 × 10−3 (−1.353)	−2.605 × 10−7 (−1.962) *	Competition
Central-South China	0.954 (10.876) ***	1.023 × 10−7 (0.459)	9.834 × 10−3 (0.703)	1.035 (23.831) ***	−5.779 × 10−3 (−0.821)	1.444 × 10−7 (0.574)	Synergy
Southwest China	0.984 (11.549) ***	−1.568 × 10−6 (−1.179)	0.010 (0.745)	1.055 (46.556) ***	−7.227 × 10−3 (−1.951) *	−3.049 × 10−8 (−0.081)	Laterality
Northwest China	0.877 (5.991) ***	−7.414 × 10−7 (−0.282)	0.024 (0.917)	1.012 (65.601) ***	−6.573 × 10−4 (−0.213)	−6.079 × 10−7 (−1.146)	Laterality
Nationwide	0.988 (44.230) ***	1.949 × 10−7 (1.172)	0.003 (0.940)	1.016 (118.637) ***	−1.961 × 10−3 (−1.424)	−1.026 × 10−7 (−1.191)	Laterality

() t value,* p value < 0.1, ** p value < 0.05, *** p value < 0.01.

show that the symbiotic relationship between forestry and overall economic growth in different regions exhibits heterogeneity. The regression results of the national overall panel data show a biased relationship, indicating that China’s overall forestry and overall economic growth have not achieved a benign symbiotic relationship. The symbiotic relationship between forestry and overall economic growth in Northeast and Eastern China is manifested as a competitive relationship, while the symbiotic relationship between forestry and overall economic growth in other regions is manifested as a biased relationship. The forestry quality and overall economic growth in the central and southern regions are interdependent. The heterogeneous symbiotic relationship between forestry and overall economic growth also indicates that the carbon reduction path based on forestry development needs further analysis.

This article is based on the perspective of forestry and economic symbiosis systems and designs multiple carbon reduction paths to compare the emission reduction effects of different paths. In the previous analysis, it was found that only the forestry quality and economic growth in the central and southern regions have a synergistic relationship. Therefore, a simulation analysis model was constructed based on the symbiotic relationship between forestry and economy in the central and southern regions. The comparison results of carbon emission reduction paths are shown in

Table 11. Comparison of carbon emission reduction pathways.

Cycle	GDP (G1)	Forestry Quality	Carbon Emission (Ct)
			BASIC Model	2 Times the Carbon Emission Inhibition Coefficient	4 Times the Carbon Emission Inhibition Coefficient	1.1 Times the Forestry Proportion	1.1 Times the Forestry Proportion and 4 Times the Carbon Emission Inhibition Coefficient
1	10,000.0	5.0	224.8	196.1	138.8	213.9	127.9
2	10,228.7	5.2	226.2	196.2	136.2	214.9	125.0
3	10,462.6	5.3	227.5	196.1	133.3	215.9	121.8
4	10,701.9	5.5	228.8	196.0	130.3	216.9	118.4
5	10,946.6	5.6	230.1	195.7	127.0	217.8	114.7
6	11,196.9	5.8	231.3	195.4	123.5	218.7	110.9
7	11,452.9	6.0	232.5	194.9	119.7	219.5	106.7
8	11,714.8	6.1	233.7	194.3	115.6	220.3	102.3
9	11,982.7	6.3	234.8	193.6	111.3	221.0	97.5
10	12,256.8	6.5	235.9	192.8	106.7	221.7	92.5
11	12,537.2	6.7	236.9	191.9	101.7	222.3	87.1
12	12,824.2	6.9	237.9	190.8	96.5	222.8	81.4
13	13,117.8	7.1	238.8	189.5	90.8	223.3	75.3
14	13,418.3	7.3	239.7	188.1	84.8	223.7	68.9
15	13,725.8	7.6	240.5	186.5	78.5	224.0	62.0
16	14,040.5	7.8	241.2	184.7	71.7	224.3	54.7
17	14,362.7	8.0	241.9	182.8	64.5	224.4	47.0
18	14,692.5	8.3	242.5	180.6	56.8	224.5	38.8
19	15,030.1	8.5	243.0	178.2	48.7	224.4	30.1
20	15,375.8	8.8	243.4	175.6	40.1	224.3	21.0

.

The simulated data in Table 11 show the disparities between different measures. Simultaneously increasing the share of forestry (by 1.1 times) and the carbon emission inhibition coefficient are the most effective mitigation measures. Increasing the proportion of forestry output in GDP is favorable to reducing carbon emissions. Increasing the carbon emission inhibition coefficient also helps reduce carbon emissions. Simultaneously increasing the proportion of forestry output value and carbon emission inhibition coefficient has the best carbon emission reduction effect.

3.4. Robustness Analysis of EKC Model

In order to meet the data requirements of the EKC model and symbiotic model, raw data were used for regression analysis. Considering the differences in data scale between different regions and to further validate the stability of the model, this section will use model (2) and the logarithmic form of the original data to examine the carbon emission reduction effect of forestry quality in the economic growth EKC. The analysis results are shown in

Table 12. Robustness analysis of EKC model (logarithmic data regression).

Area	α0	α1	α2	α3	Shape of EKC
North China	164.919 (2.132) **	−33.787 (−2.048) **	1.770 (2.014) **	0.488 (6.532) ***	U shape
Northeast China	30.175 (1.238)	−6.151 (−1.162)	0.374 (1.314)	0.085 (0.731)	U shape
East China	22.531 (2.206) **	−4.450 (−2.177) **	0.272 (2.667) ***	0.124 (5.961) ***	U shape
Central-South China	−19.056 (−16.663) ***	4.551 (17.956) ***	−0.196 (−14.233) ***	−0.165 (−5.465) ***	Inverted U shape
Southwest China	55.652 (7.354) ***	−11.261 (−6.883) ***	0.619 (7.039) ***	0.163 (5.081) ***	U shape
Northwest China	−22.869 (−3.088) ***	6.211 (3.523) ***	−0.351 (−3.479) ***	0.288 (1.346)	Inverted U shape
Nationwide	−3.671 (−1.730) *	1.368 (2.904) ***	−0.041 (−1.596) *	0.027 (1.087)	Inverted U shape

() t value,* p value < 0.1, ** p value < 0.05, *** p value < 0.01.

.

The data in Table 12 show that the regression performance of EKC model 2, based on logarithmic data, is good, which confirms the adaptability of the EKC model, considering forestry quality factors to different data types. In the logarithmic regression results of EKC model 2, the shape of the EKC in most regions did not change. Among the seven regions, only the EKC in the central southern and southwestern parts changed from monotonically rising to a U shape and an inverted U shape, indicating that the stability of the model is relatively good.

3.5. Discussion

The carbon EKC can be broadly categorized into the following groups: U curve [51,52,53,54], inverted U curve [55], other nonlinear shapes [56,57,58,59], linear relationship [60,61,62,63], weak inverted U curve [64], or no relationship [65]. The EKCs in specific industrial sectors were conducted [66,67,68,69]. High-income countries have a higher percentage of reaching the turning point of carbon emissions [70]. The EKCs of different countries exhibit heterogeneity [71,72,73]. In summary, existing studies have found that the EKCs of forest resources show a diversified trend. The main reasons are as follows: (1) The stages of economic and forest resource development vary in different regions, and relevant studies have found the influence of the life cycle of economic development on the EKC. (2) Forest land and economic growth, forest resources and carbon emissions, and economic growth and carbon emissions are all capable of constituting complex systems, and these symbiotic systems are difficult to describe by simple positive or negative relationships. (3) Most of the existing studies use forest area as the main variable in the research model, and the attention to forest quality needs to be strengthened. To better describe the EKC problem of forest land, this paper constructs an EKC analysis model based on the symbiotic system of forest quality, economic growth, and carbon emissions.

This study has some similar conclusions to the existing studies. The study in this paper reproduces the different shapes of EKCs, such as inverted U shape [51,52,53,54], U shape [55], and nearly linear curves [60,61,62,63] in the existing studies on EKCs. The heterogeneous characterization of EKCs in different regions is verified. The heterogeneity of EKCs in different regions lies in the fact that economic growth is at different stages. Economic growth and carbon emissions have diverse manifestations, which depend on the stage difference of economic growth. The economic growth of different regions in China is in different stages of development, which can better reflect the heterogeneity characteristics of the EKC model.

Some of the existing studies have put the influence factors of forestry on the left side of the equation [27,35], which will take forestry as the dependent variable in the EKC. In this paper, the influence factors of forestry are placed on the right side of the equation, and forestry is used as the independent variable factor in the EKC. Since the symbiotic relationship between economic growth and forest resources is also reflected in the EKC literature [31,36], this paper introduces the LV model into the symbiosis analysis and carbon emission reduction pathway research. The LV model can effectively and specifically express the ecological symbiotic relationship. In this paper, it is found that pure symbiosis cannot necessarily lead to carbon emission reduction.

3.6. Policy Implications

It is clear that China can realize economic growth and emission reduction after experiencing a stage of growth. Currently, due to issues related to industrial structure and energy consumption, carbon emissions are increasing. To reach the carbon emission inflection point, it is essential to reduce carbon intensity and increase carbon productivity. This requires optimizing the industrial structure and energy consumption framework, establishing a low-carbon industrial development model, and guiding China’s economy toward a path of coordinated economic growth and carbon emissions that is both rapid and sustainable. This will enhance China’s ecological civilization and its capacity to address climate change. In turn, it will provide a favorable international development environment foundation for China’s low-carbon transformation [39].

Appropriate regional policies that coordinate economic growth and carbon emissions based on the heterogeneous characteristics of economic development in different regions should be formulated [39,46]. The northeast, north, east, central south, southwest, and northwest regions of China have different economic development bases and initial conditions. Moreover, the relationship between growth and carbon emissions varies among these regions. Therefore, it is important to consider the heterogeneity of different areas based on the varying relationships between per capita GDP and per capita carbon emissions in each region. Overall, there is a monotonic growth curve in China nationwide, which means that measures have to be formulated so that the inflection point of per capita carbon emissions comes earlier. In the eastern, northeastern, and northern regions, there is a positive U-shaped curve in the relationship between economic growth and carbon emissions. Therefore, efforts should be made to achieve a relative decoupling of economic growth from carbon emissions in these areas. The eastern region has a strong foundation for economic development and a large consumption of carbon-based energy. During the period of “rapid development”, the eastern region should adjust its economic system, and regions with the conditions can first pilot the program to explore the mode and path of developing a green economy. The eastern region should endeavor to innovate its economic development model and select a path of internal development with low energy consumption, low material consumption, low emissions, and low pollution.

The formulation of policies and measures for a low-carbon economy in China and its regions should take into account the stage of economic development [44]. Policy research should focus on identifying when the carbon emission inflection point will occur, striving to enter a “win–win” phase where economic growth happens alongside a gradual reduction in carbon emissions. At different stages, policy measures need to take into account the characteristics of the EKCs.

Sustainable development of forestry requires the sustainable use of forest land. Forest land is the material basis of forestry production, and careful management and adoption of appropriate management models are crucial [45,46,47]. In practice, different management measures are formulated for different levels of forest quality, scientifically utilizing forest land and fully tapping into its productivity, promoting the transformation of forest land utilization from extensive and inefficient to intensive and efficient. The water and heat conditions are very good, suitable for the growth of various tree species, and the forest has great production potential, making it suitable for intensive management. Fully tap into its production potential, improve productivity per unit area, focus on enhancing forest carbon sequestration capacity, and increase social and ecological benefits. Forests with thin soil layers, slightly insufficient soil fertility, and poor water and heat conditions are mostly located in areas with frequent natural disasters and important ecological locations and are suitable for the development of public welfare forests that focus on soil and water conservation. Mainly, strengthen the construction of disaster prevention, mitigation, and mitigation capabilities in forest ecosystems, build regional ecological barriers, and provide guarantees for regional socio-economic development.

4. Conclusions

This research used an integrated model for empirical analysis, tested research hypotheses, and achieved research objectives. The main conclusions are as follows:

(1) There is a significant relationship between forest quality and economic growth, and the relationship between forest quality and economic growth can be explained by the symbiotic relationship of ecosystems. The impact of the symbiotic system between forest quality and economic growth on carbon emissions follows the environmental Kuznets curve hypothesis. The level of symbiosis and coordination between forests and economic growth affects the shape and trend of the environmental Kuznets curve. This discovery expands the theoretical boundaries of the EKC model, provides a perspective for analyzing symbiotic systems, and explains the sources of differences in EKC shapes in different regions within the same economy, which has certain theoretical value.

(2) Based on the analysis of the carbon emission mechanism between forestry quality and the economy, exploring effective carbon reduction paths from the perspective of the symbiotic relationship between forestry quality and economic growth has significant practical value. Simply increasing investment in forestry development in order to improve forestry quality or simply improving the quality of low-carbon economic growth while ignoring the carbon reduction role of forestry are not the optimal carbon reduction solutions. This once again demonstrates that the coordinated development of forestry quality and the economy is a systematic project.

(3) Empirical analysis based on data from different regions of China shows that the task of carbon reduction is arduous, and there are significant regional differences in economic development, forestry quality, and carbon reduction. The classical EKC theory has insufficient explanatory power for regional carbon emission heterogeneity, and the symbiotic theory to some extent compensates for the shortcomings of the classical theory.

The main shortcomings of this study lie in the lack of long-term predictions of carbon emission mechanisms and dynamic analysis of EKCs. In future research, the difference between long-term and short-term EKCs will be considered.