The Impact and Mechanism of the Natural Forest Logging Ban Policy on Rural Residents’ Income: A Case Study of China

Abstract

The natural forest logging ban policy has substantially influenced rural residents’ production activities, daily lives, and income levels. Drawing on panel data from 30 provinces in China, this study examines both the overall effect of the policy on rural households’ income and the internal transmission mechanisms. The policy is regarded as an external shock, and its impact is identified through a multi-period difference-in-differences model combined with a mediation analysis. The results show three main findings: (1) the policy significantly raised rural households’ total income; the structural analysis indicates that its effects are notably positive on wage income and property income; in contrast, the impacts on operating income and transfer income are not statistically significant; (2) mechanism testing found that the policy significantly improved non-agricultural employment and increased ecological protection investment, indicating that the non-agricultural employment and ecological protection investment are important channels for the national natural forest logging ban policy to increase rural residents’ income; (3) heterogeneity analysis shows that the policy effect is more pronounced in areas with a higher distribution of state-owned forest areas, along with the policy effects being more pronounced in non-carbon trading market pilot areas. Therefore, this article proposes policy recommendations for continuously improving the natural forest protection policy system, ensuring effective employment of rural labor, and building coordinated development of forestry systems between regions.

1. Introduction

A key factor in improving livelihoods and quality of life, and a determinant of individual and household quality of life, is income [1]. Political and institutional factors influence the individual and household income levels [2]. Since 2014, the Chinese government has implemented a natural forest logging ban policy (NFLBP) to regulate human exploitation and manage the demand for these forest resources. When it comes to regional economic development and rural residents’ income, the major component of gifts from nature—natural forests—is a key booster. In particular, the NFLBP was implemented nationwide in 2017. There is no doubt that the implementation of the NFLBP protected the natural forests. At the same time, the implementation of the NFLBP has put the economic development and residents’ livelihoods in forest areas in a certain predicament, causing difficulties in the lives of forest farmers, the production and operation of various forestry management entities, the placement of personnel within the forestry system, and local fiscal revenue. Funds, talents, and infrastructure are difficult to adapt to the transformation and development of forestry in a short period of time. In particular, the changes in rural residents’ production and lifestyle have had a profound impact on their income as the main participants. From the current situation of the impact of natural forest logging ban (or strict logging restrictions) policies in developed countries on rural residents’ income, developed countries rarely see a “one size fits all” comprehensive cessation of logging. What is more common is a system that combines natural forest zoning protection, restricted logging of old age forests, pre-logging ecological assessment, and sustainable management certification. For example, after the implementation of aged forest conservation in the Pacific Northwest by the United States, timber harvesting revenue declined in the short term, but there was some buffering through career transitions, community development funds, and ecosystem service payments; Canada and EU member states (such as Finland and Sweden) mainly focus on private forests, and the cessation of logging is often embedded in “ecological networks” and compensation mechanisms; and Japan focuses on revitalizing mountain villages and providing subsidies for multifunctional forestry. In the short term, rural residents who rely on logging will experience a decrease in cash flow, and employment will shift towards the service industry and non-timber forest products; while in the medium to long term, compensation for protected areas, carbon credits, and tourism and leisure benefits can partially or fully compensate. The mechanism of action can be summarized as “quantity constraints–price and cost linkage–public transfer–industrial substitution”: stopping logging, compressing logging areas and quotas, and increasing compliance costs; the government reduces transitional friction through direct compensation, tax exemptions, and technology promotion; and ecotourism, conservation oriented agroforestry, and carbon markets provide new sources of income. For China, can the NFLBP bring about a synchronous increase in rural residents’ income while protecting forest resources and improving forest carbon sequestration capacity [3,4]? This is a major practical issue related to whether the NFLBP can be sustainably implemented, and it is also an important part of testing the NFLBP in promoting a harmonious coexistence between humans and nature.

Researchers have examined the effects of the NFLBP on rural residents’ income. After the implementation of the NFLBP, rural residents have borne various social and economic costs. At the income level, some rural residents face direct economic losses. As of 2015, there were about 150 million households in China which contracted forest land management. Rural residents’ incomes are significantly influenced by the NFLBP. In areas with good water and heat conditions, fast forest growth, and high forestry economic benefits in the south, rural residents’ annual forestry income far exceeds the ecological public welfare forest subsidy of 15 CNY per mu. The NFLBP has led to a sharp decrease in income. At the level of industrial transformation, rural residents need to bear the cost of transformation [5]. For rural residents who have long relied on forestry, they face difficulties in realizing investment and transitioning to development after NFLBP. On the one hand, they need to invest time and funds in learning new skills to adapt to the requirements of emerging industry positions; and on the other hand, in the early stages of transformation, new industries have not yet formed stable income, and rural residents’ income is unstable. At the local financial level, due to the NFLBP reducing fiscal revenue, forest areas with already poor economic foundations find it difficult to provide sufficient transformation support and compensation to rural residents [6], resulting in limited support funds for rural residents. In the short term, rural residents need to bear more economic pressure after the implementation of the policy alone. Specifically, studies conducted in major state-owned forest regions in China, such as Heilongjiang, Inner Mongolia, and Jilin, show that rural residents’ income comes from forest-related activities, such as timber sales and timber transportation. The NFLBP has significantly reduced forest economic activities [7]. As a consequence, livelihood opportunities for rural residents have decreased, leading to heightened social and psychological stress. Overall, their living conditions have deteriorated. Furthermore, the implementation of the NFLBP has severely impacted timber production [8] and restricted resource utilization by enterprises producing natural forest products [9,10], leading to increased unemployment and reduced income for rural residents in forest areas. Furthermore, human–wildlife conflicts come along with the protection of natural forests. Increased wildlife activity in forest areas has led to an increase in wildlife-related disasters, resulting in lower crop yields and reduced agricultural incomes [11,12], thus impacting the production and livelihoods of rural residents. On the other hand, some studies have shown that although the NFLBP restricts the timber usage of rural residents and businesses [13], Rural residents’ losses are partially compensated for by subsidies. Also, subsidies can help them transfer from forest-dependent residents to other types [14,15,16]. Furthermore, the ecological improvements resulting from the NFLBP have promoted the development of the forest economy and forest-based tourism. These developments, in turn, have contributed to higher incomes for rural residents [17]. Many forest areas, with their beautiful natural landscapes and abundant ecological resources, have developed forest health and rural tourism projects, attracting a large number of tourists and driving the development of local catering, accommodation, agricultural product sales, and other industries, and thereby injecting new vitality into rural economic development. In the long term, the NFLBP has enabled forest residents to gradually move away from sole reliance on forest production and management, diversify their livelihoods, and increase their incomes [18]. The internal structure of the forestry industry has also been continuously optimized, transforming from simple wood production to seedling cultivation, forest nurturing, ecological restoration, and other directions, thereby enhancing the sustainable development capacity of the forestry industry and promoting rural areas to embark on a green and sustainable development path. Existing studies have offered important perspectives on the link between the NFLBP and rural residents’ income. However, several gaps remain. First, the specific channels through which the NFLBP influences income have not been fully clarified, and past findings on this issue are often inconsistent. Second, regional differences in the effects of the policy are likely, yet current work provides only limited evidence on such heterogeneity. Third, some research has examined the dynamic influence of the NFLBP using cross-sectional or region-specific data, but these data are insufficient to capture its long-term and nationwide impact.

To address these limitations, this paper investigates how the NFLBP shapes rural residents’ income. We begin by building a logical framework to explain the policy–income relationship. Using provincial panel data from 2005 to 2022, the NFLBP is considered a quasi-natural experiment. A multi-period DID model is employed to assess its impact, regional differences, and transmission mechanisms. Compared with prior research, this study contributes in several ways. First, it uses long-term macro data to verify the income-raising role of the NFLBP. It further explores regional heterogeneity and highlights different patterns of income growth, which provide a reference for China’s forestry policy reforms aimed at supporting rural incomes. Second, it uncovers the mechanisms of impact, showing that non-agricultural employment and ecological conservation investment are key channels. Clarifying these pathways offers guidance for improving policy effectiveness.

This paper is structured as follows. Section 2 introduces the institutional background and theoretical assumptions concerning the impact of the NFLBP on rural residents’ income. Section 3 describes the research methodology, including model setup, variable construction, and data collection. Section 4 reports the empirical results and investigates the underlying channels of policy effects. Section 5 situates the findings within existing literature and policy practices. Section 6 provides the key conclusions.

2. Institutional Background and Theoretical Assumptions

2.1. Institutional Background

China’s natural forest protection entered a new phase of a complete logging ban. The State Forestry Administration launched a pilot program in Heilongjiang Province on 1 April 2014, requiring the Longjiang Forest Industry Group and the Daxing’anling Forest Industry Group to completely cease commercial logging of natural forests. In the year the policy was implemented, timber production by the Longjiang Group fell to 543,600 cubic meters, and by the Daxing’anling Group to 250,100 cubic meters, respectively, down 29.79% and 65.23% from 2013. In 2015, as the policy progressed, national commercial timber production shrank to 72.1821 million cubic meters, a year-on-year decrease of 12.33%. Longjiang Group’s output further declined to 40,100 cubic meters, while the Daxing’anling Group achieved zero logging. In April of the same year, the policy was expanded to state-owned forest areas and forestry farms in Inner Mongolia and Jilin Province, and by 2017, commercial logging of natural forests had been completely halted nationwide. This phased approach ensured the achievement of ecological protection goals while providing a buffer for local economic transformation.

By 2024, the NFLBP have been in place for 10 years. This article uses the period of 2005–2022 as a time window to examine the effects of the NFLBP.

Table 1. Implementation of natural forest logging ban policy in China.

Policy Implemented	Study Period (2005–2022)						Province	Ratio of Natural Forests (%)
	2005–2013	2014	2015	2016	2017	2018–2022
2014	Before	Post	Post	Post	Post	Post	Heilongjiang	15.81
2015	Before	Before	Post	Post	Post	Post	Inner Mongolia and Jilin	32.25
2016	Before	Before	Before	Post	Post	Post	Hebei, Fujian, Jiangxi, Hubei, Hunan, Guangxi, Yunnan	77.04
2017	Before	Before	Before	Before	Post	Post	Other provinces	100.00

shows the implementation of the NFLBP, the focus of this article. “Post” and “Before” represent the spatial and temporal distribution of the time period before and after policy intervention, respectively. Each province gradually implemented the NFLBP, following the three-step timetable outlined in the 13th Five-Year Plan (first piloting in key state-owned forest areas in Northeast China and Inner Mongolia, then expanding to other state-owned forest farms and forest areas, and ultimately full implementation). Table 1 shows the banned cumulative percentage of natural forest under the NFLBP, providing a visual reflection of policy progress over time. For example, the 100% rate at the end of 2017 indicates that the NFLBP was fully implemented in China.

2.2. Theoretical Assumptions

2.2.1. Direct Impact of the Natural Forest Logging Ban Policy on Rural Residents’ Income

The core goal of sustainable development theory is to regard the economy, society, and environment as the three pillars, pursue the true coordination and balanced development of the three, and ultimately achieve the unity of economic prosperity, social equity, and environmental sustainability. The NFLBP is a specific application case of this theory in practice. This policy aims to balance the needs of contemporary people for forest land utilization, economic development, and ecological environment, while reserving sufficient forest resources and high-quality ecological products for future generations, reflecting a balanced consideration of short-term and long-term benefits. The NFLBP is a mandatory ecological protection policy implemented by the government, which restricts rural residents’ forestry production activities and reduces their available natural resource donations. In the short term, rural residents in the main forest distribution areas need to face a break from traditional livelihood methods [19], which affects the income increase for rural residents. Based on the theory of farmers’ behavioral risk [20], when making production decisions, farmers will face various constraints. They tend to choose the best method to maximize production opportunities and resources. In the long run, the NFLBP poses challenges and impacts on the environment of “small-scale farmers”, forcing them to transform into “rational small-scale farmers”, gradually adjusting their livelihood strategies and expanding their livelihood paths, and implementing livelihood transformation, which is conducive to the growth of rural residents’ income. This is primarily manifested in the following ways: First, rural residents in forest areas can leverage their natural resource advantages to gradually explore new avenues for increasing income, shifting towards the ecological and economic development and management of non-timber forest products, such as developing new forestry businesses like forest health and wellness tourism, and transforming ecological advantages into industrial advantages [21]. While introducing new business entities to develop the forest economy on a large scale, rural residents also have various ways to expand their livelihoods, such as transferring forest land through leasing and equity, earning rental income and dividends, participating in forestry ecological protection projects, earning labor income and ecological compensation, etc., which have increased forest rural residents’ own property income, operating income, and wage income to a certain extent [22]. On the other hand, the state implements subsidy policies for the management of natural forests in logging-stopped areas, such as forest economy subsidies, logging suspension subsidies, forest ecological compensation, forest tending subsidies, and forest ranger appointments, which bring stable fiscal transfer income to rural residents in forest areas. In short, the direct impact of the NFLBP on rural residents’ income is mainly manifested in changing rural residents’ production and lifestyles, as well as providing rural residents with more income growth channels, thereby increasing rural residents’ cash income or material capital. In summary, the following research hypotheses are proposed:

Hypothesis H1.

The natural forest logging ban policy is conducive to increasing rural residents’ income.

2.2.2. Indirect Impact of the Natural Forest Logging Ban Policy on Rural Residents’ Income

The NFLBP has two main indirect impacts on rural residents’ incomes in forest areas. First, it increases rural residents’ income by promoting non-agricultural employment. Since the NFLBP prohibits forest rural residents from continuing to rely on natural forest resources for production, from the perspective of rural residents’ behavioral risk theory, rural residents will invest all kinds of production factors in more efficient departments after the implementation of the policy, thereby maximizing their income and the use value of factors [23]. In more concrete terms, rural residents will take the initiative to explore alternative livelihood strategies to adapt to the NFLBP [24]. Specifically, non-agricultural employment is frequently selected by rural residents as a means to reduce the negative impacts of this policy [25]. This process of labor migration can enhance access to information regarding non-agricultural employment and increase the availability of such opportunities [26]. This effect is particularly beneficial for low-income rural residents who lack pension support, as it reduces the costs and risks associated with non-agricultural work. Consequently, it contributes to more equitable income growth among rural residents across different income levels.

The government provides employment and technical training to rural residents in forest areas to mitigate the negative impact of the policy on rural residents’ livelihoods, which effectively enhances their human capital accumulation and employment skills and accelerates their transition to non-agricultural employment. Consequently, this shift to non-agricultural employment has broken the monotonous income structure of rural residents in forest areas from agricultural and forestry production [27], creating opportunities for increased income, effectively reducing household income uncertainty, and increasing rural residents’ income. Furthermore, the NFLBP promotes rural residents’ increased income through fiscal support. Funding shortages have led to insufficient input and endogenous development capacity in rural areas, becoming a major bottleneck restricting rural residents’ income growth [28]. As the NFLBP is put into practice, the government supports rural residents in shifting from conventional logging practices to more eco-friendly and cost-efficient forest management models. This support will be delivered through fiscal subsidy provisions, technical guidance services, and assistance in market development. For example, leveraging the natural resources and advantages of forest land, forest planting and animal husbandry are being developed, taking the path of forestry development that combines planting and animal husbandry with a green cycle, achieving both forest ecological friendliness and economic development in forest areas [29]. This has become one of the forestry management development directions actively led by the government after the NFLBP. The government supports the forest industries through local fiscal subsidies, helping rural residents in forest areas improve the comprehensive utilization ability of forest resources and the output of ecological products, which is conducive to increasing rural residents’ income. Based on the above analysis, this paper proposes the following hypothesis:

Hypothesis H2.

The natural forest logging ban policy can increase rural residents’ income through non-agricultural employment.

Hypothesis H3.

The natural forest logging ban policy can increase rural residents’ income through forest ecological protection investment.

In summary, the theoretical framework of this paper is shown in Figure 1.

3. Materials and Methods

3.1. Models

3.1.1. Baseline Regression Model

Because the timing of the NFLBP implementation varies across the sample, this study employs a multi-period difference-in-differences (DID) model to evaluate the effects of a complete ban on commercial logging of natural forests. The core concept of the multi-period DID approach is to define a treatment group, where the policy was enacted, and a control group, where it was not. By comparing the differences in rural residents’ income before and after the NFLBP, we can accurately assess the policy’s effectiveness. This study established the following regression model and utilized Stata15.0 to examine the impact of the NFLBP on rural residents’ income:

$$RR{I}_{it}={\beta }_0+{\beta }_{1}N{F}_{it}+{\beta }_2{X}_{it}+{\gamma }_t+{\lambda }_i+{\epsilon }_{it}$$

(1)

where the subscripts i and t represent province and year, respectively. Rural residents’ income is the dependent variable, and the NFLBP (NF_it) is set as the core independent variable. The NFLBP variable is treated as a binary variable indicating whether a province implemented the NFLBP in the year (1 for implementation and 0 for non-implementation). X represents a series of control variables that may affect rural residents’ income, including economic development level, transportation infrastructure, fiscal expenditure level, forest coverage, and forest damage severity. γ_t is the year fixed effect. λ_i is the province fixed effect. ε_it is the random disturbance term. The core coefficient of interest in this paper is β₁, which indicates whether rural residents’ income in the provinces with the NFLBP (treatment group) increased after the policy was implemented compared to the control group. A significantly positive β₁ indicates that the implementation of the NFLBP has helped increase rural residents’ income.

3.1.2. Mediation Effect Model

To verify the role of the NFLBP in indirectly increasing rural residents’ income by promoting non-agricultural employment and increasing forestry ecological subsidies, this paper draws on the approach of Montoya et al. [30] and constructs the following mediation effect model based on model (1):

$$M_{it}={\alpha }_0+{\alpha }_{1}N{F}_{it}+{\alpha }_2{X}_{it}+{\gamma }_t+{\lambda }_i+{\epsilon }_{it}$$

(2)

Among them, M_it represents the mediating variable, and the definitions and meanings of the other variables are the same as those in model (1).

3.2. Variable Selection

3.2.1. Explained Variable

This paper focuses on the income effect of the NFLBP. The income effect is measured by the disposable income of rural residents [31].

3.2.2. Core Explanatory Variable

The explanatory variable is the timing of each province’s NFLBP. To comprehensively and strictly protect natural forest resources, the “State Forestry Administration” implemented a pilot program in Heilongjiang Province in 2014 to completely halt commercial logging of natural forests. Because the timing of implementation of the NFLBP varied across provinces, a dummy variable was created based on whether the NFLBP was implemented in a specific year. This variable is assigned a value of 1 if the policy was implemented and a value of 0 if it was not.

3.2.3. Mechanism Variables

This article analyzes the mechanisms by which the NFLBP affects rural residents’ income from two perspectives: the effects of non-agricultural employment on rural residents and the effects of forest ecological protection investment. Specifically, drawing on the research of Callaway et al. [32], non-agricultural employment is measured by the proportion of employment in the secondary and tertiary industries to total industrial employment. The existing literature generally agrees that non-agricultural employment increases rural household income [33,34]. Wang et al. [35] found that starting in 2013, non-agricultural income for Chinese rural residents surpassed agricultural income, becoming the primary source of income. Non-agricultural employment plays a crucial role in raising rural households’ income. Forest ecological protection investment is captured by the expenditure on forestry ecological construction and conservation reported in the Yearbook. Such investment has a clear positive effect on the income of forest rural residents, which improves their living standards and strengthens their willingness to conserve forest resources. Beyond direct cash transfers, rural residents also benefit from in-kind support, including pest control services and technical training. This diversified investment model not only offers financial assistance but also enhances ecological assets and expands income sources for forest rural residents.

3.2.4. Control Variables

This paper references the existing literature on factors influencing rural residents’ income and controls for variables that may influence rural residents’ income. Specifically, these variables include the following:

Economic development level. Given the complex production cycle and low output efficiency of the forestry industry, rural residents’ income is undoubtedly affected by regional economic development. Higher economic development levels tend to stimulate higher production efficiency, and a region’s economic development level influences regional investment, ecological values, and other related factors. This variable is represented by the regional GDP growth rate.

Transportation infrastructure is typically measured by the density of transportation routes. In this paper, highways and railways are used as representatives of transportation infrastructure. The total operating mileage of highways and railways in each province and city is divided by the regional area to obtain the transportation infrastructure density (km/km²) for each province and city. On the one hand, the development of transportation infrastructure mitigates the negative impact of geographical location on rural residents’ income. On the other hand, transportation infrastructural development can also boost forestry development by broadening communication channels between forest areas and the outside world.

Forest coverage, defined as the proportion of forested land within the total land area, is an important factor influencing rural households’ income. As a core measure of regional forest resources and management conditions, it is widely applied as an indicator in forestry-related studies.

Forest damage is measured by the ratio of forest fires, pests, and rodents to the total area of forest land. Pests and diseases, among other issues, can have an immeasurable impact on forest growth, making the analysis of forest damage crucial for rural residents’ income.

Fiscal expenditure is defined as the ratio of government spending to GDP. Excessive government intervention may induce rent-seeking and corruption, which undermines market fairness [36]. It can also weaken the efficiency of resource allocation in the market. Hence, this variable is anticipated to exert a negative effect on rural households’ income.

3.3. Data Description

This study evaluates the income impact of the NFLBP using panel data from 30 mainland provinces, excluding Hong Kong, Macao, Taiwan, and Tibet. The analysis relies on the China Statistical Yearbook (2005–2022) and the China Forestry Statistical Yearbook, with economic indicators deflated to 2005 constant prices. Descriptive statistics in

Table 2. Descriptive statistics of variables.

Variable	Number of Samples	Mean	Standard Deviation	Minimum	Maximum
Rural residents’ income	540	10,960.08	6805.97	1971	39,729
NFLBP	540	0.34	0.47	0	1
Economic development level (%)	540	12.25	7.18	−5.34	66.92
Transportation infrastructure (%)	540	0.91	0.52	0.04	2.28
Forest coverage (%)	540	32.79	18.01	3.16	67.48
Forest damage (%)	540	9.52	10.87	0.36	80.89
Fiscal expenditure (%)	540	23.77	10.70	9.19	75.83

reveal marked variation across variables, highlighting regional heterogeneity and confirming the suitability of the dataset for assessing the policy’s effects.

Figure 2 illustrates the trend in the annual average growth rate of rural residents’ income in China from 2005 to 2022. Over this period, rural incomes across provinces increased rapidly, although the average annual growth rate exhibited a fluctuating downward pattern. In the treatment group, income rose from 3397 CNY in 2005 to 20,005 CNY in 2022, remaining below the control group’s levels. However, the average annual growth rate of the treatment group was 10.9933%, 0.9509 percentage points higher than that of the control group. Following the intersections with the control group in 2018 and 2019, incomes in the treatment group remained above those of the control group and displayed a scattered pattern. By 2022, the average annual growth rates were 6.0043% for the treatment group and 4.4456% for the control group, with a gap of 1.5587 percentage points.

4. Empirical Results and Analysis

4.1. Benchmark Regression Results

Baseline estimates are reported in

Table 3. Benchmark regression results.

Variable	(1)	(2)	(3)	(4)
	Rural Residents’ Income	Rural Residents’ Income	Rural Residents’ Income	Rural Residents’ Income
NFLBP	0.0286 *** (0.0098)	0.0292 *** (0.0097)	0.0295 *** (0.0096)	0.0293 *** (0.0096)
Economic development level		0.0007 (0.0004)	0.0008 * (0.0004)	0.0008 * (0.0004)
Transportation infrastructure		0.0555 *** (0.0157)	0.0446 *** (0.0163)	0.0565 *** (0.0167)
Forest coverage			0.0021 ** (0.0009)	0.0027 *** (0.0009)
Forest damage			0.0009 * (0.0005)	0.0009 * (0.0005)
Fiscal expenditure				0.0018 *** (0.0006)
Constant term	8.1221 *** (0.0072)	8.0848 *** (0.0127)	8.0232 *** (0.0281)	7.9722 *** (0.0331)
Time fixed effects	control	control	control	control
Province fixed effects	control	control	control	control
N	540	540	540	540
R2	0.995	0.996	0.996	0.996

Note: *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. The data in brackets are robust standard errors, and the same applies below.

. Column (1), which controls solely for province and year fixed effects, assesses the income effect of the NFLBP without additional covariates. Columns (2)–(4) add control variables on this basis to conduct an empirical analysis of the impact. This result shows that the implementation of the NFLBP has significantly increased rural residents’ income, passing the 1% significance test, and thereby verifying Hypothesis H1. In columns (2)–(4), this paper further introduces provincial-level control variables such as economic development level, transportation infrastructure, forest coverage, forest damage, and fiscal expenditure. Although the control variables of each regression equation are different, the estimated coefficients of the core explanatory variables are basically consistent in size and direction, and their significance remains unchanged, indicating the robustness of the research conclusions.

4.2. Test of Structural Effects

Recognizing that rural households’ earnings comprise four components, this paper further explores the distributional effects of the NFLBP. Regression estimates in

Table 4. Structural effect analysis.

Variable	(1)	(2)	(3)	(4)
	Wage Income	Operational Income	Property Income	Transfer Income
NFLBP	0.1037 *** (0.0341)	0.0145 (0.0294)	0.1471 ** (0.0733)	0.0537 (0.0660)
Economic development level	0.0017 (0.0015)	0.0038 *** (0.0013)	0.0023 (0.0032)	0.0012 (0.0029)
Transportation infrastructure	−0.1187 ** (0.0595)	0.1043 ** (0.0513)	0.4848 *** (0.1279)	0.5033 *** (0.1152)
Forest coverage	0.0011 (0.0034)	−0.0155 *** (0.0029)	0.0007 (0.0072)	0.0017 (0.0065)
Forest damage	0.0016 (0.0018)	−0.0002 (0.0016)	0.0050 (0.0039)	0.0163 *** (0.0035)
Fiscal expenditure	0.0100 *** (0.0022)	−0.0011 (0.0019)	0.0061 (0.0048)	0.0045 (0.0043)
Constant term	6.7598 *** (0.1179)	7.8114 *** (0.1017)	3.8548 *** (0.2534)	4.4660 *** (0.2283)
Time fixed effects	control	control	control	control
Province fixed effects	control	control	control	control
N	540	540	540	540
R2	0.958	0.928	0.793	0.951

Note: ** and *** indicate significance at the 5% and 1% levels, respectively. The data in brackets are robust standard errors, and the same applies below.

indicate its influence on wage, operating, property, and transfer income. The China Statistical Yearbook defines transfer income as regular payments from public institutions, organizations, or social bodies to households, as well as recurring transfers among households. Examples include pensions or retirement benefits, social assistance and subsidies, policy-driven production or living allowances, regular donations or compensation, reimbursement for medical expenses, support from other residents, and income remitted by non-resident household members. Transfer income does not encompass in-kind gifts exchanged between residents. It is found that rural residents’ wage income and property income are significantly positively impacted by the NFLBP, passing the 1% and 5% significance tests, respectively. However, the coefficients of the NFLBP on rural residents’ business income and transfer income, while positive, are not significant. The main reason for this is that the NFLBP has led to difficulties in the livelihoods of forest residents, especially for some workers engaged in timber harvesting and processing in forest areas, who have faced unemployment. This has forced these rural residents to engage in non-agricultural activities, which often offer better income returns than forestry production. Furthermore, forest residents engaged in non-agricultural activities may have led them to rent or sell their property after the implementation of the NFLBP, such as houses, land, and agricultural machinery, to generate rental or sales income, thereby increasing their property income. In contrast, the impact of the NFLBP on rural residents’ transfer income is not significant. The possible explanation is that the subsidies for the NFLBP are relatively small, and the compensation subsidy standards in most provinces are relatively low [37], which has affected the transfer income of rural residents.

4.3. Validity Test of the Model

4.3.1. Parallel Trend Test

A key prerequisite of DID estimation is the parallel trend assumption, implying no significant pre-policy differences in the evolution of income indicators between treatment and control provinces. The validity of this assumption determines the reliability of policy impact estimates. Testing for pre-treatment trend alignment reduces concerns of selection bias and temporal heterogeneity, ensuring group comparability. Furthermore, baseline regressions capture only the average impact of the NFLBP, without accounting for heterogeneous effects across post-policy years. To investigate dynamic responses, this paper employs the event-study framework of Jacobson et al. [38], embedding relative year indicators into the DID specification to expand the model into

$$F{E}_{it}={\beta }_0+{\beta }_n{\displaystyle \sum _{n=-9}^{6}N{F}_{it,policy+n}}+{\beta }_2{X}_{it}+{\gamma }_t+{\lambda }_i+{\epsilon }_{it}$$

(3)

where NF_it,policy+n represents the policy implementation window, taking the value 1 in the year of policy implementation and 0 in all other years; and X_it represents other control variables. It should be noted that due to the long study period, which can easily lead to biased estimates, we follow the approach of Li et al. [39] and combine years that occurred nine years or more before and six years or more after policy implementation. To avoid multicollinearity, the year before the NFLBP was used as the base period. This means that the equation excludes n = −1 and examines the dynamic trends of each province from nine years before to six years after the NFLBP.

Figure 3 reports the parallel trend test of the NFLBP’s impact on rural residents’ income. During the pre-policy period, all coefficients are insignificant, suggesting no systematic difference in income trajectories between treatment and control groups. This confirms that the parallel trend assumption holds.

The post-policy pattern shows a delayed effect. In the first four years after implementation, the estimated coefficients remain positive but not significant, indicating that the income effect of the NFLBP did not appear immediately. From the fifth year onward, however, the coefficients become significantly positive at the 5% and 10% levels. This demonstrates that rural households in the treatment group experienced notable income gains compared with the control group. Moreover, the persistence of significant coefficients suggests that the policy’s positive influence continues over time.

4.3.2. Placebo Test

To test the robustness of the baseline estimates, a placebo test is conducted to rule out random noise. Figure 4 presents the placebo results for the income effects of the NFLBP. The kernel density of the randomly generated coefficients follows an approximately normal distribution with a mean near zero. This distribution lies well below the actual baseline coefficients, represented by the dashed line in the figure. Such evidence supports the validity of the placebo test and suggests that the observed income effect of the NFLBP is unlikely to be driven by omitted variables.

4.4. Robustness Test

4.4.1. Excluding Concurrent Policies

Table 5. Robustness test excluding policies of the same period.

Variable	(1)	(2)	(3)
	Excluding the Collective Forest Tenure Reform	Excluding the Natural Forest Protection Project	Excluding the Forest Chief System Reform
NFLBP	0.0292 *** (0.0096)	0.0171 ** (0.0085)	0.0290 *** (0.0096)
Collective forest tenure reform	0.0107 (0.0101)
Natural forest protection project		0.0783 *** (0.0065)
Forest chief system reform			0.0057 (0.0083)
Economic development level	0.0008 * (0.0004)	0.0008 ** (0.0004)	0.0007 * (0.0004)
Transportation infrastructure	0.0564 *** (0.0167)	0.0677 *** (0.0147)	0.0578 *** (0.0168)
Forest coverage	0.0026 *** (0.0009)	0.0017 ** (0.0008)	0.0028 *** (0.0010)
Forest damage	0.0008 (0.0005)	0.0014 *** (0.0005)	0.0009 * (0.0005)
Fiscal expenditure	0.0017 *** (0.0006)	0.0001 (0.0006)	0.0018 *** (0.0006)
Constant term	7.9742 *** (0.0331)	8.0177 *** (0.0293)	7.9689 *** (0.0334)
Time fixed effects	control	control	control
Province fixed effects	control	control	control
N	540	540	540
R2	0.996	0.997	0.996

Note: *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. The data in brackets are robust standard errors, and the same applies below.

reports the results after accounting for concurrent policies.

Collective forest tenure reform. Even when this policy is included, the NFLBP still shows a significant positive effect on rural income at the 1% level, with a coefficient of 0.0292. This suggests that the collective forest tenure reform does not materially distort the estimation.

Natural forest protection project. After controlling for this program, the coefficient of the NFLBP on rural income is 0.171 and significant at the 5% level. Thus, the estimated effect is not driven by the protection project.

Forest chief system reform. With this reform considered, the NFLBP continues to show a coefficient of 0.029, significant at the 1% level.

Overall, these results confirm that the income-enhancing effect of the NFLBP is robust, regardless of whether other forest-related policies are taken into account.

4.4.2. Winsorization

To reduce the influence of extreme values on the baseline estimates, Equation (1) was re-estimated after trimming the sample at the 1% and 5% levels of rural residents’ income. The results are presented in

Table 6. Robustness test of Winsorization processing.

Variable	(1)	(2)	(3)	(4)
	1% Reduction	1% Reduction	5% Reduction	5% Reduction
NFLBP	0.0371 *** (0.0101)	0.0378 *** (0.0099)	0.1008 *** (0.0174)	0.0951 *** (0.0166)
Economic development level		0.0002 (0.0005)		0.0004 (0.0010)
Transportation infrastructure		0.0599 *** (0.0176)		0.2206 *** (0.0304)
Forest coverage		0.0018 * (0.0010)		−0.0065 *** (0.0015)
Forest damage		0.0012 ** (0.0006)		−0.0008 (0.0012)
Fiscal expenditure		0.0017 ** (0.0007)		−0.0007 (0.0014)
Constant term	8.1291 *** (0.0074)	8.0093 *** (0.0349)	8.2007 *** (0.0128)	8.2924 *** (0.0567)
Time fixed effects	control	control	control	control
Province fixed effects	control	control	control	control
N	540	540	540	540
R2	0.995	0.995	0.985	0.986

Note: *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. The data in brackets are robust standard errors, and the same applies below.

. In both cases, the NFLBP coefficient remains positive and statistically significant at the 1% level, consistent with the baseline findings. These results further confirm the robustness of the analysis and support the conclusion that the NFLBP effectively raises rural income.

4.4.3. PSM-DID Test

This study applies propensity score matching combined with difference-in-differences (PSM-DID) to test robustness. This approach helps mitigate systematic differences in NFLBP trends across provinces and reduces bias in the DID estimates. This paper uses a one-to-one nearest neighbor matching method for estimation, and it selects covariates including the economic development level, transportation infrastructure, fiscal expenditure level, forest coverage rate, and forest disaster severity to test whether the effect of the NFLBP in promoting rural residents’ income is robust. Figure 5 shows the covariate bias after PSM, and the results show that the covariate bias after matching is within 10%, indicating that the selection of covariates is effective.

In addition to nearest neighbor matching, this study also applies kernel matching and radius matching to pair individuals in the treatment and control groups using the same set of covariates. Using these matched samples, the effect of the NFLBP on rural residents’ income is re-estimated. The regression results are presented in

Table 7. Robustness test: PSM-DID.

Variable	(1)	(2)	(3)
	Nearest Neighbor Matching	Kernel Matching Method	Radius Matching Method
NFLBP	0.0176 * (0.0091)	0.0176 ** (0.0089)	0.0284 *** (0.0094)
Economic development level	−0.0005 (0.0006)	−0.0006 (0.0006)	0.0007 * (0.0004)
Transportation infrastructure	0.0464 ** (0.0182)	0.0455 ** (0.0179)	0.0484 *** (0.0171)
Forest coverage	0.0019 * (0.0010)	0.0020 ** (0.0010)	0.0019 * (0.0010)
Forest damage	0.0004 (0.0005)	0.0004 (0.0005)	0.0009 * (0.0005)
Fiscal expenditure	0.0010 (0.0006)	0.0008 (0.0006)	0.0019 *** (0.0006)
Constant term	8.0245 *** (0.0352)	8.0266 *** (0.0347)	7.9960 *** (0.0339)
Time fixed effects	control	control	control
Province fixed effects	control	control	control
N	476	473	528
R2	0.996	0.996	0.996

Note: *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. The data in brackets are robust standard errors, and the same applies below.

. Across all matching methods, the PSM-DID estimates are broadly consistent with the baseline regressions. These findings further confirm the robustness of the main results and indicate that the NFLBP contributes positively to rural household income.

4.5. Mechanism Analysis

4.5.1. Test Effect Results of Non-Agricultural Employment

This study posits that the NFLBP may influence rural residents’ income by affecting their participation in non-agricultural employment. Following the standard steps of the mediation effect model, Equation (2) is first estimated. The results, presented in

Table 8. Regression results of mechanism analysis.

Variable	(1)	(2)
	Non-Agricultural Employment	Forest Ecological Protection Investment
NFLBP	2.8444 *** (0.9362)	0.1744 ** (0.0749)
Economic development level	0.0405 (0.0415)	−0.0043 (0.0033)
Transportation infrastructure	7.4573 *** (1.6340)	−0.1055 (0.1308)
Forest coverage	0.1422 (0.0924)	0.0303 *** (0.0074)
Forest damage	−0.0643 (0.0502)	0.0034 (0.0040)
Fiscal expenditure	0.0383 (0.0609)	0.0091 * (0.0049)
Constant term	47.6793 *** (3.2377)	−0.7259 *** (0.2592)
Time fixed effects	control	control
Province fixed effects	control	control
N	540	540
R2	0.759	0.298

Note: *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. The data in brackets are robust standard errors, and the same applies below.

, show a regression coefficient that is positive and statistically significant at the 1% level, confirming that the NFLBP facilitates non-agricultural employment and supporting Hypothesis H2.

A possible explanation is that, in the short term, the NFLBP limits rural residents’ use of natural forest resources. Consequently, many rural residents shift to non-agricultural work to offset potential income losses. In response, the government provides employment opportunities and vocational training in forested areas, enhancing rural residents’ human capital and skill sets, which further promotes their transition to non-agricultural employment.

4.5.2. Results of Testing the Effect of Forest Ecological Protection Investment

This study proposes that the NFLBP may influence rural residents’ income by affecting investment in forest ecological protection. Following the steps of the mediation effect model, Equation (2) is first estimated. The results, shown in Table 8, indicate that the NFLBP has a positive and statistically significant effect on forest ecological investment at the 5% level, supporting Hypothesis H3.

Theoretically, the implementation of the NFLBP encourages greater investment in forest conservation and ecological construction. This is largely because the government aims to promote natural forest growth, improve forest quality, and enhance ecosystem functions. Accordingly, state investment in natural forest protection, afforestation, and tending has increased, motivating local governments to actively manage and reforest natural forests [40], especially following the NFLBP.

For instance, in September 2021, the General Office of the State Council issued the Opinions on Deepening the Reform of the Ecological Protection Compensation System, outlining key objectives and tasks. The policy primarily seeks to incentivize forest rural residents to shift away from traditional practices, such as deforestation and overfishing, through economic compensation, thereby reducing the over-exploitation and degradation of forest resources.

4.6. Heterogeneity Analysis

4.6.1. Impact of the Carbon Trading Market

The carbon trading market mechanism not only signals to forest rural residents that carbon sequestration has economic value [41] but also allows the external benefits of forest carbon sinks to be reflected in rural residents’ incomes [42]. This, in turn, enhances their motivation to engage in forestry operations and invest key resources. Forest carbon trading serves as a vital channel for converting ecological assets into economic gains, generating notable social and environmental benefits in rural areas. Agricultural carbon trading can directly raise the incomes of participating rural residents [43].

Given that the NFLBP has been shown to increase rural incomes, differences in the implementation of carbon emissions trading may lead to heterogeneous effects. To examine this, a triple-difference (DDD) model is employed. Using China’s carbon emissions trading pilot program, eight provinces—Beijing, Tianjin, Shanghai, Chongqing, Hubei, Guangdong, Sichuan, and Fujian—are designated as the treatment group, while the remaining 22 provinces constitute the control group. Regression results are reported in

Table 9. Heterogeneity analysis of carbon trading market.

Variable	(1)	(2)
	Rural Residents’ Income	Rural Residents’ Income
Triple difference term	−0.0257 *** (0.0089)	−0.0380 *** (0.0090)
Economic development level		0.0007 (0.0004)
Transportation infrastructure		0.0628 *** (0.0166)
Forest coverage		0.0035 *** (0.0010)
Forest damage		0.0010 ** (0.0005)
Fiscal expenditure		0.0017 *** (0.0006)
Constant term	8.1221 *** (0.0072)	7.9467 *** (0.0333)
Time fixed effects	control	control
Province fixed effects	control	control
N	540	540
R2	0.995	0.996

Note: ** and *** indicate significance at the 5% and 1% levels, respectively. The data in brackets are robust standard errors, and the same applies below.

. Regardless of whether control variables are included, the DDD coefficient is negative and significant at the 1% level, indicating that the NFLBP’s impact on rural income varies across regions. Specifically, rural residents in provinces not participating in the carbon trading pilot experience a stronger income effect from the NFLBP. This phenomenon can be explained from the following three aspects. First, non-carbon emission trading pilot provinces usually have well-equipped ecological compensation mechanisms. These areas have abundant forest resources, and the government compensates rural residents who have lost their sources of income due to policies directly or indirectly by providing financial subsidies, ecological compensation, and other means. Therefore, the improvement of the compensation mechanism is one of the key factors for rural residents’ income to maintain growth in non-carbon emission trading pilot provinces [44]. Second, the implementation of carbon emission trading pilot policies has brought additional economic benefits to rural residents in non-pilot provinces. In the carbon emissions trading market, forest resources as carbon sinks can obtain economic value through carbon trading. By participating in carbon sequestration projects or ecological conservation projects, rural residents can obtain benefits related to carbon emission trading [45]. This market-oriented mechanism enables the forest resources of these provinces to be transformed into sustainable economic assets, creating a long-term source of income for rural residents. Final, policy support from local governments in non-carbon emission trading pilot provinces also plays an important role. Pilot provinces are often able to develop more flexible and differentiated policies based on actual situations, helping rural residents better adapt to the economic changes brought about by the cessation of natural forest logging. These policies may include financial support for green industries, incentives for ecological protection, and guidance for transformational development projects, thereby promoting income growth for rural residents.

4.6.2. Impact on State-Owned Forest Areas

Considering that the distribution of state-owned forest areas may influence the effect of the NFLBP on rural income, this section employs a triple-difference (DDD) model to examine this heterogeneity. A dummy variable is introduced to capture regional differences in state-owned forest area distribution. It takes a value of 1 for nine provinces with a predominant share—Inner Mongolia, Jilin, Heilongjiang, Shanxi, Gansu, Xinjiang, Qinghai, Sichuan, and Yunnan—and 0 for all other provinces.

Regression results, reported in

Table 10. Heterogeneity analysis of state-owned forest areas.

Variable	(1)	(2)
	Rural Residents’ Income	Rural Residents’ Income
Triple difference term	0.0345 *** (0.0074)	0.0553 *** (0.0076)
Economic development level		0.0009 ** (0.0004)
Transportation infrastructure		0.0927 *** (0.0168)
Forest coverage		0.0038 *** (0.0009)
Forest damage		0.0008 (0.0005)
Fiscal expenditure		0.0019 *** (0.0006)
Constant term	8.1221 *** (0.0071)	7.9195 *** (0.0325)
Time fixed effects	control	control
Province fixed effects	control	control
N	540	540
R2	0.996	0.996

Note: ** and *** indicate significance at the 5% and 1% levels, respectively. The data in brackets are robust standard errors, and the same applies below.

, show that the triple-difference coefficient is positive and statistically significant at the 1% level, irrespective of the inclusion of control variables. This indicates that the NFLBP’s impact on rural income is significantly stronger in regions with a high concentration of state-owned forests.

A plausible explanation is that, in the short term, the policy disrupted livelihoods in state-owned forest areas by reducing employment in timber harvesting and processing, increasing surplus labor, and creating challenges for local economic development. Over time, however, both the government and forest rural residents implemented adaptive measures [46]. As a result, the NFLBP gradually facilitated the reallocation of labor away from single-sector forestry, promoted diversified employment, and substantially increased household income.

5. Discussion and Policy Implications

The findings of this study indicate that the NFLBP exerts a significant positive effect on rural residents’ income. This result aligns with prior research demonstrating the role of natural forest logging bans and restrictions in enhancing rural income and well-being [47]. Beyond its ecological functions, such as maintaining biodiversity and regulating regional carbon cycles, the NFLBP also provides an important material foundation and has ecological benefits that support economic and social development.

Further analysis suggests that the policy increases rural residents’ income through two main channels. First, the NFLBP offers rural residents additional income via ecological compensation and investment. Many local governments implement financial support mechanisms to assist rural residents affected by the logging ban. Similar practices are observed in several developed countries, including Germany and Canada, where forest protection is often accompanied by ecological compensation programs. These policies allow rural residents to receive government support despite the loss of logging income, thereby mitigating economic pressures.

Second, the NFLBP enhances rural residents’ income by optimizing employment structures. Abundant forest resources facilitate the development of forestry-related ecological industries, such as forest health services and ecotourism. The growth of these industries can stimulate related sectors—such as catering, lodging, transportation, retail, and entertainment—providing more job opportunities and diversifying income sources for rural households. For example, in the United States, forest conservation policies that promote ecotourism have allowed rural residents to generate supplementary income through active participation in ecotourism operations.

In summary, the NFLBP is not only a critical environmental protection measure but also an effective approach to increasing rural income, offering valuable lessons for developing countries seeking to enhance rural residents’ livelihoods through forest protection policies. The policy promotes income growth through multiple pathways, with forest ecological investment and non-agricultural employment playing particularly important roles in supporting sustainable development in rural areas. These findings provide practical insights for policymakers and relevant authorities.

However, some limitations remain. First, due to data constraints, this study relies on provincial-level data from 2005 to 2022, which may overlook intra-provincial variation. Second, the analysis focuses solely on China’s NFLBP, and the generalizability of these findings to other developing countries or forest-rich regions requires further investigation. Future research could compare and analyze similar policies across countries and regions to inform the management and evaluation of global natural forest conservation initiatives.

Based on the above analysis, the following policy recommendations are proposed:

(1)

Strengthen the natural forest protection policy system. Considering the positive effect of the NFLBP on rural income, the policy framework should be continuously improved in three key areas:

Enhance the forest protection and management system. Establish and optimize natural forest conservation institutions. At the national level, clearly define the responsibilities of the National Forestry and Grassland Administration. At the local level, build or improve institutions for forest protection and management at all administrative tiers to form a coordinated and unified governance structure.

Promote fundamental research on natural forest conservation. Support scientific institutions in studying the structure, function, and succession of natural forest ecosystems and developing key technologies for ecological restoration. Improve mechanisms for translating research outcomes into practical applications, and apply advanced tools such as remote sensing and GIS to enhance monitoring and management.

Expand public participation channels. Strengthen information disclosure to safeguard public access to information, and establish effective procedures for citizen involvement in decision-making. Conduct multi-platform education and awareness campaigns to raise ecological protection consciousness across society.

(2)

Improve mechanisms to increase rural income.

Diversify compensation mechanisms. In addition to existing fiscal compensation, develop market-based ecological compensation channels and create mechanisms for realizing the economic value of ecological products, such as carbon trading. Encourage social capital participation in forest conservation to broaden funding sources. Adjust compensation standards and methods according to regional differences and ecological values, and develop innovative, differentiated approaches.

Establish multi-stakeholder training networks. Involve government, enterprises, and individuals in planning and delivering training programs. Facilitate the transition of forest-area rural residents from forestry to non-agricultural sectors, linking employment opportunities with income growth. Promote a virtuous cycle of “skill improvement → employment → income growth” to create endogenous drivers for rural income enhancement.

Leverage forest resources to develop emerging industries. Utilize ecological advantages to promote forest ecotourism, forest health services, and diversified forest-based industries. Expand local employment opportunities and alleviate the pressure of rural labor outflow.

(3)

Implement region-specific forestry development strategies.

Adopt differentiated development paths. Set targeted forestry development objectives, resource cultivation plans, and industrial structural optimization strategies based on regional resource endowments and development stages. Dynamically adjust factor allocation within the forestry system to improve resource utilization efficiency.

Transform ecological advantages into development momentum. In regions rich in natural forests, deepen technical exchanges and management experience with developed areas, innovate forestry management models, and unlock resource potential through institutional reform.

Promote low-carbon forestry in carbon trading pilot areas. Build platforms for the circulation of production factors, including technology, talent, and capital, across regions. Rely on carbon sink market mechanisms to enhance resource efficiency and establish replicable low-carbon development models.

6. Conclusions

This study empirically examines the effect of China’s natural forest logging ban policy (NFLBP) on rural residents’ income. Robustness is assessed through five approaches: parallel trend tests, placebo tests, exclusion of concurrent policies, Winsorization, and PSM-DID estimation. The transmission mechanisms of the NFLBP are also explored, focusing on non-agricultural employment and ecological protection investment. Finally, the heterogeneity of policy effects is analyzed with respect to the scale of state-owned forest areas and the characteristics of the carbon trading market.

The main findings are summarized as follows. First, baseline regressions indicate that the NFLBP has a significant positive effect on rural residents’ income, and these results are confirmed by multiple robustness checks. Notably, within the first four years of implementation, the policy coefficient is positive but not statistically significant, suggesting a four-year lag in its effect.

Second, an analysis of structural effects shows that the NFLBP significantly increases rural residents’ wage and property income, while its impact on operational and transfer income is not statistically significant.

Third, the mechanism analysis reveals that the NFLBP promotes non-agricultural employment. Mediation tests indicate that both non-agricultural employment and ecological protection investment serve as key channels through which the policy enhances rural residents’ income.

Finally, heterogeneity analysis demonstrates that the impact of the NFLBP varies across regions. In provinces with a larger share of state-owned forests, the policy effect on income is more pronounced. Similarly, regarding the carbon trading market, the income effect of the NFLBP is stronger in provinces not participating in the carbon trading pilot program.