Economic Effects Assessment of Forest City Construction: Empirical Evidence from the County-Level Areas in China

Abstract

Forests are both an irreplaceable natural resource and a vital economic asset for all humankind. Based on the data of counties in mainland China from 2007 to 2020, the article explores the direct impact and spatial spillover effects of the policy implementation on the economic growth of counties with the help of the forest city pilot policy and the policy evaluation model. The results reveal that policy implementation can have a positive economic growth effect on the pilot counties, which, in turn, can significantly increase the size of the county’s GDP, the level of GDP per capita, and the total amount of nighttime lighting brightness. The implementation of forest city construction can bring about 2.74% of total GDP size, about 2.63% of per capita GDP development level, and about 7.25% of nighttime light brightness to the county on average. Cost–benefit analysis also indicates that forest city construction can bring about a comprehensive economic benefit of approximately CNY 686.453 million (approximately USD 96.82 million) to the counties. The rapid improvement in labor productivity, significant influx of high-end factors, and continuous expansion of market potential are important mechanisms through which policy implementation promotes economic growth in pilot counties. While promoting economic growth in the pilot counties, forest city construction can also have positive spatial spillover effects on neighboring areas in the pilot counties. Furthermore, when the deficits in atmospheric vapor pressure and annual evapotranspiration are used as instrumental variables for forest city construction, the empirical estimates are not significantly altered. In the process of building forest cities, county governments should be wary of issues such as the high cost of forest maintenance. This study provides a Chinese model and policy reference for other countries and regions in the world to deal with the relationship between forest city construction and county economic growth.

1. Introduction

A good ecosystem itself has infinite economic value and can continuously create comprehensive benefits, thereby realizing sustainable regional economic and social development [1]. In January 2023, the white paper “China’s Green Development in a New Era” similarly noted that the Chinese government should want both gold mountains and green mountains, accelerate the transformation of green mountains into gold mountains, and then allow natural and ecological wealth to bring economic wealth.

The contradiction between goals and specific plans for the conservation of forest values has triggered concerns about sustainable economic development in European countries [2]. As the world’s largest developing country, China is in a period of transition to an urban ecology under an ecological civilization whose phase focuses on green growth, afforestation, environmental protection, and ecological restoration, and even more so, focuses on the special role of urban ecological functions and environmental quality [3]. In the new stage of development, the marginal benefits of the county’s continued additional factor inputs are diminishing, and the traditional impetus for development is weakening. County urban and rural construction is still dominated by epitaxial expansion, and the risks and challenges of simply expanding the economic scale are increasingly visible, while cities and forests have not yet fully formed a harmonious symbiosis as a whole [4]. The bottleneck constraints faced by a county’s economic development are ultimately because the county has failed to carry out systematic planning and the integrated management of mountains, waters, forests, fields, lakes, and grasses within the region [5].

The pilot project for the construction of forest cities is a practical innovation that adapts to China’s current national conditions and stage of development, promotes urban and rural ecological construction, and enhances the ecological welfare of residents. Based on the protection, construction, and management of natural infrastructures such as forests, trees, and wetlands, the construction of forest cities can continuously expand the new space for green ecological development in the region, thus forming an economic and social development mode in which human beings and nature coexist harmoniously [6]. Figure 1 shows that the forest city is an ecosystem model with forests and trees as the main body and mountains, waters, forests, fields, lakes, and grasses in harmony and symbiosis [7].

With the increase in urban spatial scale and population density, the construction of forest cities becomes increasingly important for regional sustainability and livability [8]. Therefore, forest city construction, as a nature-based solution, is gradually becoming the focus of global urban governance with the goal of improving the well-being of urban residents and maintaining ecological resilience [9]. The government urgently needs to use the new concept of forest cities to guide the construction, which is driven by green and intelligent industries, and use ecological ways to crack the big city’s disease and other problems. The county must curb the tendency of blindly following the trend of overly transforming the natural form, repeated randomization, and disorderly urbanization, and effectively enhance the sustainable development capacity of the county’s economy [10].

The possible marginal contributions of the article are as follows:

First, to construct a county-level analytical framework based on the use of data from 1988 counties in mainland China.

Second, to assess the direct and spatial spillover effects of forest city construction on county economic growth.

Third, to explore the mechanism and heterogeneous impact of forest city construction to promote county economic growth.

Fourth, the costs and benefits of forest city construction are analyzed at the county level.

The flowchart of the research framework of the article is shown in Figure 2.

2. Literature Review and Theoretical Mechanisms

2.1. Policy Background

From an international perspective, the construction of forest cities originated in Western developed countries such as Europe and the United States, with a history of more than 50 years. In 1972, the United States promulgated the Urban Forestry Act in accordance with the laws of economics and nature. This law specifically organizes research on the economic and social benefits of urban forests, which are used to improve the urban living environment, and then builds a complete ecological network of urban forests and a synergistic system of industries. The Freiburg Forest Convention, adopted in 2001, was the first forest convention at the local level in Germany. In 2004, the Greater London Spatial Development Strategy, issued by the Greater London Authority (GLA), incorporated green rings, green chains, and public spaces into overall environmental planning, thereby promoting the symbiosis between green space and urban development. In 2007, the government of Tokyo, Japan, through the formulation of the Green Ten-Year Plan, vigorously promoted the “gap space” greening construction pattern, thereby realizing the scientific advancement of the regional integration of ecology, production, and life. In 2013, the European Union comprehensively implemented a green infrastructure strategy, including forests, to further enhance the quality and capacity of sustainable urban development. In 2016, Malaysia implemented the Forest City Program. In the construction program, the government established a “people-centered” infrastructure system, emphasizing complex ecological functions and thus achieving the goal of rational coexistence among industries, cities, and people.

Domestically, the construction of forest cities in China started relatively late, with the concept not being introduced until the 1990s. However, as the world’s most powerful developing country, China has made it clear that, in exploring the path of modernization toward harmonious coexistence between human beings and nature, it will never repeat the development model of Western industrial civilization, which pits human beings against nature, and it will never again follow the old path of pollution followed by governance, which is the path of Western modernization. At present and in the coming period, green development remains a major strategy for China’s development. In January 2016, Chinese President Xi Jinping made a clear request at a meeting of the Central Finance and Economic Leadership Group to focus on forest city construction, marking the rise of forest city construction as a national strategy. By 2035, the construction of forest city clusters and forest cities will be fully advanced, and the value of the ecological assets and services in forest cities will have significantly increased, enabling the realization of the goal of sharing ecological welfare among all people.

2.2. Literature Review

According to foreign studies, as an important part of the urban landscape, forest city construction can increase the total green coverage space by approximately 7.5%, increase the green area per capita by approximately 10.9%, and increase the urban forest coverage rate by approximately 1% [11]. Increasing urban and peri-urban habitats will not only increase overall vegetation cover (natural, seminatural, and man-made), but also optimize the quality and quantity of ecological services, thereby helping to conserve biodiversity. Compared with that of public parks, the accessibility of green space (convenience and opportunity for urban residents to access and use green space) in urban residential areas has increased by an average of 27.97% [12]. Effective national forest management policies can be an effective means of addressing food insecurity among rural households with low levels of social capital. This policy can significantly improve market linkages for non-timber forest products, which, in turn, leads to regional residents having greater income growth [13]. Ecological reserves require restrictions on natural resource extraction and agricultural activities, which, in turn, may seriously threaten the production and livelihoods of the local population [14]. For example, the establishment of nature reserves can increase the household development index and lower the likelihood of poverty by approximately 0.0105 standard deviations per county per year (16.1%), but can significantly reduce total employment by approximately 2.6% [15]. On the other hand, nature reserves can provide alternative development pathways for local socio-economies through measures such as the creation of non-extractive employment opportunities (e.g., ecotourism), the enhancement of ecosystem service functions, and the improvement of landscape-type infrastructure [16].

Due to the close relationship between the natural environment and the economic system, governments should encourage voluntary social action and ecological conservation groups aimed at developing a green economy and protecting ecosystems in order to minimize the possible contradictions between social, economic, and environmental goals [17]. Eco-entrepreneurship can solve prominent local ecological problems, ensure public health, improve the quality of life, and thus, realize sustainable regional economic development [18]. In the case of the Polissia region of Ukraine, insufficient investment in the development of the technical and technological base of local forestry enterprises has weakened the market competitiveness of the urban economy [19]. Therefore, the construction of forest cities not only directly increases the amount of green space and vegetation carbon sequestration, but also gradually achieves low-carbon sustainable development by optimizing the green development model in cities [20].

Under the balanced analysis framework of modern economic growth, China has traveled the path of economic development of most developed countries over hundreds of years in only forty years, which is unprecedented in the world’s economic development [21]. China’s green modernization should be green modernization with the harmonious development of humans and nature and with Chinese characteristics [22]. China’s forest growth has created the world’s green miracle, benefiting both China and humankind, and creating a civilized development path of production development, affluent living, and good ecology for the whole world [23]. In the context of the rapid development of global urbanization, the disorderly development of land at the expense of ecological resources has seriously damaged the livability and business quality of cities and is constantly reducing their ability to supply high-quality ecological products [24]. A practical investigation revealed that China’s forest recreation tourism industry still faces a series of problems such that the shortcomings of the industry are more prominent, and the matrix of digital technology integration has not yet been formed [25]. Therefore, in the overall development process, the government should actively advocate for ecological economics and ecosystem engineering decision-making, advocate for ecological diversity, create a zero-waste system, and implement environmentally sound policies, and thus, transform green advantages into developmental, economic, and competitive advantages [26].

Policymakers are more inclined to view green space as a luxury rather than a necessity. Most of the domestic and international literature also focuses on the impact of forest city construction on green development based on data at the national level, provincial level, or prefecture level. The literature suggests that forest city construction can, to a certain extent, reduce regional ecological pollution and improve human health and well-being. The county level is an important foundation for high-quality economic development and ecological green and low-carbon development. Few studies have explored the effect of forest city construction on economic growth, and thus, there is a lack of theoretical mechanism framework for the impact of forest city construction on economic growth at the county level.

2.3. Mechanism Pathways

2.3.1. Enhance Labor Efficiency

First, the construction of forest cities can improve psychological conditions. The expansion of green living space in the county can alleviate the negative mental health emotions of workers and enhance stress resilience, which, in turn, indirectly generates economic scale benefits [27]. There is evidence that mere exposure to nature’s scenery can improve people’s health and well-being by relieving stress and mental fatigue [28]. Within the confines of the green space, positive emotions can increase employees’ work engagement and well-being and enhance creativity and self-confidence, which, in turn, helps to increase labor productivity, while negative emotions can lead to an increased psychological burden and less energy devoted to work, which, in turn, reduces labor productivity [29]. Enhanced psychological empowerment reduces the burnout effect of caregivers and can motivate them to provide better services to local and foreign patients, which, in turn, increases the work efficiency of the healthcare workers [30]. Continuous improvement in the psychological status of intimate partners can significantly reduce the probability of violence against women, both increasing women’s work attendance and boosting their labor productivity [31]. The direct effect of construction workers’ physical and mental health on work efficiency and productivity is more significant than the effect of social network and capital; therefore, the government should propose strategies to improve the work efficiency and productivity of construction workers from the perspective of physical and mental health [32].

Second, the construction of forest cities can improve the quality of work. Lower ecological quality creates multiple negative incentives for human health, education, and social well-being, ultimately working against sustainable urban habitats [33]. By introducing and laying out green enterprises and low-carbon industrial parks, the county can effectively build a new production model and a new management structure, while the productivity and survivability of enterprises will be significantly improved [34]. Work quality involves the effectiveness of labor in each department (position); the work quality of the top managers (decision-makers) plays a leading role in the enterprise’s labor productivity, and the work quality of the general management and the executive level plays a role in the implementation of the enterprise’s labor productivity [35]. In firms with enabling environments, work quality and cognitive behaviors not only affect employees’ work attitudes and affective skills, but also have a significant positive impact on the firm’s economic performance and social well-being [36]. It has been confirmed by studies that a stable social environment provides workers with job security and health, making them more productive by about 0.314 indexes [37].

2.3.2. Increased Factor Agglomeration

First, the industry has improved in quality and efficiency. Through the introduction of elements such as highly sophisticated technology, high-quality talent, and high-end equipment, industries in county-level regions can obtain positive externality effects, thus enhancing the competitiveness of the regional economic structure. The government actively promotes the reduction, resource utilization, and harmless treatment of industrial solid waste and vigorously promotes clean production and the development of a circular economy. The county actively centers on the requirements of building a modern industrial economic system and focuses on forming an industrial regional agglomeration pattern with a reasonable division of labor, prominent main industries, and full play of comparative advantages [38].

Second, high-end element agglomeration occurs. The introduction of high-end elements promotes the specialized division of labor and industry, strengthens the close integration of ecologically advantageous industries with emerging green industries, and subsequently improves the utilization efficiency of public infrastructure [39]. Utilizing the advantages and characteristics of forest city construction, county-level areas can further develop and strengthen the agglomeration and relocation of similar industries to promote the expansion and upgrading of modern, low-carbon emerging industries. Increasingly frequent exchanges and cooperation between high-end enterprises in the county can help to increase the greening capacity of key industries and fields. Such cooperation can form positive forward and backward linkage effects, thus generating favorable extrinsic scale benefits.

2.3.3. Broaden Market Potential

First, the construction of forest cities can increase total consumption. Economy-driven urban forest construction stems from people’s pursuit of direct or indirect economic benefits from urban forests. The construction of forest cities can promote the accelerated development of “green industry (recreation industry, forest tourism)”. The direct economic benefits include income from timber, forest fruits, and byproducts, as well as income from direct economic consumption by tourists in forest parks and recreational activities [40]. The county government has improved the path of realizing the value of ecological products to encourage enterprises to develop toward the higher end of the value chain, thereby enhancing the value and supply capacity of ecological products. The county government enhances the employment probability of underdeveloped areas and drives farmers to realize multiple paths to increase income [41].

Second, the construction of forest cities can improve brand reputation. County branding can realize a large increase in county public branding, enhance the efficiency of forest ecological product value transformation, and boost the high-quality development of the county economy. County government departments hope to enhance the image of cities through urban forest construction to attract more tourists and economic or commercial investors, thus promoting the benign development of the county economy. After the construction of the forest city, the government constantly encourages enterprises to form cooperative platforms and high-end intermediary service systems, which, in turn, better serve the development of industrial economic resilience [42].

3. Research Design

3.1. Estimation Method

In this section, the article conducts baseline empirical estimation with the help of a panel regression model to explore the direct impact effect of forest city policy on economic growth in pilot counties. In addition, the article conducts spatial spillover estimation with the help of a spatial econometric model to explore the spatial impact effect of forest city policy on economic growth in pilot counties. See Figure S1 in the Supplementary Materials.

Panel regression model. The construction of China’s forest cities is oriented to the whole spatial scale of the whole administrative region’s scope. It also has to follow the idea of the systematic governance of mountains, water, forests, fields, lakes, grasses, and sands in the municipal area as a whole. The construction of forest cities not only requires that all the standards are met at the whole-area scale, but also requires that the subordinate administrative areas meet the standards separately, thus making up for the short boards of the weak ecological construction areas. Based on the list of counties in the forest city pilot, a benchmark regression model is constructed as follows:

$$Y_{it}=\zeta +\gamma Di{d}_{it}+\upsilon X_{it}+{\vartheta }_i+{\psi }_t+{\mu }_{it}$$

(1)

In Equation (1), the $i$ denotes the individual county, $t$ denotes the year, and $Y$ denotes the dependent variables. $Did$ is the core explanatory variable indicating the interaction term between the forest city pilot counties and the year of policy implementation. The key core explanatory variable, $\gamma$, indicates the impact of forest city construction on county economic growth. The model adds a series of covariates ($X$), controlling for time ($\psi$) and county fixed effects ($\vartheta$). The model adds a random perturbation term ($\mu$) and is estimated using county-level clustered robust standard errors.

Spatial Durbin model. The spatial Durbin model is a combined extended form of the spatial lag model and spatial error term model, which simultaneously considers the autocorrelation of the dependent and independent variables containing a spatial weight matrix.

When studying forest city construction and county economic growth, ignoring spatial factors can easily lead to bias in the empirical results. The spatial Durbin model simultaneously considers the spatial correlation of the dependent variables and control variables, which can not only explain the spatial spillover effect and the impact of spatially lagged variables, but also effectively avoid the regression bias of ignoring spatial factors. Therefore, the article firstly considers the spatial Durbin model. The spatial Durbin model is set up as follows:

$$Y_{it}=\rho W{Y}_{it}+{\beta }_1{X}_{it}+{\beta }_{2}W{X}_{it}+{\delta }_{it}$$

(2)

$${\delta }_{it}={\gamma }_{1}W{\delta }_{it}+{\mu }_1,\hspace{1em}\delta ~N[0,{\psi }^{2}I]$$

(3)

In Equations (2) and (3), $Y_{it}$ denotes the dependent variables, $X_{it}$ is the control variable, $W$ is the $n\times n$ dimensional spatial weight matrix (spatial adjacency matrix, 0–1 matrix; spatial geographic distance matrix, the inverse of the latitude/longitude distance; and spatial economic distance matrix, the inverse of the absolute number of the difference in the mean value of GDP per capita), ${\beta }_1$ represents the correlation coefficients of the core explanatory variables, $\rho$ and ${\beta }_2$ represent the spatial correlation coefficients, ${\gamma }_1$ represents the spatial error coefficients, and ${\mu }_1$ and ${\delta }_{it}$ represent the random error terms.

3.2. Sample Scope

The pilot areas for forest city construction are shown in

Table 1. Statistical table on the distribution of pilot counties for forest city construction.

Serial Number	Regional Location	Number of Counties with Forest Cities
1	All regions	1091 (units) or 38.37%
2	Eastern region	414 (units) or 47.75%
3	Central region	357 (units) or 40.38%.
4	Western region	320 (units) or 29.30%.

. In Table 1, administrative divisions are categorized into eastern, central, and western regions. In the eastern region, there are 414 counties implementing the forest city pilot policy, accounting for 47.75% of the counties in the entire eastern region. In the central region, there are 357 counties implementing the forest city pilot policy, accounting for 40.38% of the counties in the entire central region. In the western region, there are 320 counties implementing the forest city pilot policy, accounting for 29.30% of the counties in the entire western region. The article also calculates the percentage of all pilot counties in the nation’s counties. The calculation shows that 1091 counties (about 38.37% of all counties in the country) have a pilot forest city policy. The article uses 1988 counties (970 counties in the policy implementation treatment group and 1018 counties in the policy non-implementation control group). Please see the Supplementary Materials for a detailed explanation of the chapter.

According to the National Bureau of Statistics (NBS), the eastern region of mainland China includes 11 provinces and cities (Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan); the central region includes 8 provinces (Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan); the western region includes 12 provinces, cities, and districts (Inner Mongolia, Guangxi, Sichuan, Chongqing, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang). Hong Kong, China; Macao, China; and the Taiwan Province of China are seriously missing data and do not belong to the scope of the article.

3.3. Variable Selection

Dependent variables are as follows. Economic growth is proxied using the total county GDP ($Lngdp$) and the level of development of county GDP per capita ($Lnpergdp$), taking the logarithm. In the robustness test section, county nighttime light brightness ($Lnlight$) is used for analysis. The dependent variables are continuous and use panel data for the county from 2007 to 2020.

The core explanatory variable is the interaction term ($Did$) between policy pilot counties and year of policy implementation. The core explanatory variables are binary variables. For example, County A is a pilot county for the forest city policy and is assigned a value of 1. Counties that do not have a forest city policy are assigned a value of 0. County A implemented the forest city pilot policy in 2016, which means assigning a value of 1 to County A for 2016 and subsequent years and a value of 0 for years prior to 2016. In other words, the interaction term is the forest city pilot counties multiplied by the year of policy implementation.

Control variables: Drawing on Yang [43] and Xie [44], the article selects the county-level control variables in terms of industrial structure, investment scale, fiscal expenditure, educational status, credit level, and infrastructure. The control variables selected for the article are calculated as follows. Industrial structure ($Industru$) is proxied by the ratio of county secondary industry value added and county total GDP; investment scale ($Invest$) is measured by the share of total county social fixed-asset investment in county GDP; the size of financial autonomy ($Finauto$) can be obtained from the ratio of the county’s local financial general budget revenue to the county’s local financial general budget expenditure; the level of credit ($Ocredit$), on the other hand, can be characterized by the ratio of the year-end balance of loans in local and foreign currencies of financial institutions in the county to the real GDP of the county; the quality of education ($Oedu$) is obtained by using the ratio of the number of students enrolled in general elementary school and the number of students enrolled in general secondary schools in the county to the total population of the county; infrastructure ($Infrast$) is characterized by the ratio of the number of hospital and health center beds in the county to the total population of the county. The control variables selected above are all continuous and use panel data for the county from 2007 to 2020. See Table S1 in the Supplementary Materials for reasons for the selection of the control variables.

The instrumental variables are the county atmospheric water vapor pressure difference ($Watersp$) indicator and county average annual evapotranspiration ($Evapot$) indicator. The instrumental variables are continuous and use panel data for the county from 2007 to 2020.

3.4. Data Source

The county deficit in atmospheric water vapor pressure and annual evapotranspiration were calculated by the authors based on the following formulas.

The variables were obtained from the GSOD weather station website. The county atmospheric water vapor pressure is the difference between the saturated atmospheric water vapor pressure and the actual water vapor pressure. The formula for saturated atmospheric water vapor pressure is derived from the Goff–Gratch saturated water vapor pressure formula. This formula was recommended by the World Meteorological Organization (WMO) in 1966 and has been praised as “the most accurate formula”.

The formula for calculating the saturated atmospheric water vapor pressure is as follows:

$$\begin{array}{l}Ln(E)=10.79574\times (1-T_1/T)-5.02800\times Ln(T/T_1)\\ +1.50475\times {10}^{-4}\times [1-{10}^{-8.2969\times (T/T_1-1)}]\\ +0.42873\times {10}^{-3}\times [{10}^{4.76955\times (1-T_1/T)}-1]+0.78614\end{array}$$

(4)

In Equation (4), $E$ represents the value of saturated atmospheric water vapor pressure; $T_1$ represents the triple-phase point temperature of water (273.16 K), $T=273.15+t$; and $t$ represents the county average temperature.

The actual atmospheric water vapor pressure is calculated as follows:

$$F=F_s\times F_m/100=[(T_{max}+T_{min})/2]\times F_m/100$$

(5)

In Equation (5), $F$ represents the actual atmospheric water vapor pressure value, $F_m$ represents the average annual relative humidity of the county, $F_s$ represents the average saturated water vapor pressure, and $T_{max}$ and $T_{min}$ represent the average annual maximum and minimum temperature values of the county, respectively. The actual water vapor pressure is approximated by the average humidity and average air temperature.

The county’s atmospheric water vapor pressure difference is the difference between the atmospheric saturated water vapor pressure value ($E$) and the actual water vapor pressure value ($F$).

The county annual evapotranspiration is calculated as follows:

$$ET=kp(0.46\times T_s+8.13)$$

(6)

In Equation (6), $ET$ represents county evapotranspiration, $k$ represents an empirical coefficient, $p$ represents the proportion of daylight hours to full daylight hours, and $T_s$ represents the average county temperature. Drawing on the article by Zhang [45], the annual evapotranspiration for the county was approximated with the help of Equation (6) and by taking the value of $k$ as 0.85.

Because variables such as social fixed asset investment in counties are updated only to 2020, the article uses data from counties in mainland China from 2007 to 2020 for empirical analysis. The number and type of new businesses registered in the county in the calendar year are obtained from the official website of Tianyan and aggregated to the county level. Data for all other variables are obtained from the China Regional Economic Statistics Yearbook, China Urban Statistics Yearbook, China Energy Statistics Yearbook, China Environmental Statistics Yearbook, China County Statistics Yearbook, and the statistical yearbooks of each province (city, municipality, and autonomous region) in previous years. In the case of missing data, the CSMAR database (https://data.csmar.com/, accessed on 12 December 2023), the CEEC database (https://db.cei.cn/jsps/Home, accessed on 23 December 2023), and the EPS database (https://www.epsnet.com.cn/, accessed on 12 January 2024) were used to fill in the gaps. See Table S2 in the Supplementary Materials for descriptive statistics of the data.

4. Empirical Results Analysis

4.1. Direct Effect Estimation

In this section, the article analyzes the direct impact effect of the forest city policy on the economic growth of the pilot counties with the help of Equation (1).

According to the baseline regression estimation results in

Table 2. Estimates of the baseline results.

	$\left(1\right) \mathit{L}\mathit{n}\mathit{g}\mathit{d}\mathit{p}$	$\left(2\right) \mathit{L}\mathit{n}\mathit{g}\mathit{d}\mathit{p}$	$\left(3\right) \mathit{L}\mathit{n}\mathit{p}\mathit{e}\mathit{r}\mathit{g}\mathit{d}\mathit{p}$	$\left(4\right) \mathit{L}\mathit{n}\mathit{p}\mathit{e}\mathit{r}\mathit{g}\mathit{d}\mathit{p}$	$\left(5\right) \mathit{L}\mathit{n}\mathit{l}\mathit{i}\mathit{g}\mathit{h}\mathit{t}$	$\left(6\right) \mathit{L}\mathit{n}\mathit{l}\mathit{i}\mathit{g}\mathit{h}\mathit{t}$
$Did$	0.018 ** (0.007)	0.027 *** (0.005)	0.014 * (0.008)	0.026 *** (0.006)	0.226 *** (0.008)	0.070 *** (4.885)
$Industru$		1.062 *** (0.044)		1.048 *** (0.046)		0.214 *** (0.052)
$Invest$		−0.095 *** (0.010)		−0.094 *** (0.010)		0.035 *** (0.011)
$Finauto$		0.118 *** (0.042)		0.126 *** (0.044)		0.088 *** (0.028)
$Ocredit$		−0.193 *** (0.021)		−0.191 *** (0.021)		0.136 *** (0.019)
$Oedu$		0.040 ** (0.016)		0.149 ** (0.051)		−0.299 *** (0.051)
$Infrast$		0.037 ** (0.016)		0.172 *** (0.035)		0.673 *** (0.043)
$\_Cons$	13.734 *** (0.001)	13.415 *** (0.033)	10.100 *** (0.001)	9.653 *** (0.068)	5.451 *** (0.001)	5.463 *** (0.018)
Year fixed	Yes	Yes	Yes	Yes	Yes	Yes
County fixed	Yes	Yes	Yes	Yes	Yes	Yes
County clustering	Yes	No	Yes	Yes	Yes	Yes
$N$	27 832	27 832	27 832	27 832	27 832	27 832
$R^2$	0.989	0.993	0.971	0.981	0.998	0.998

Notes: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. Models (1), (3), and (5) are empirically estimated without adding control variables, whereas models (2), (4), and (6) are empirically estimated by adding all control variables. The values inside parentheses represent robust standard errors.

, the estimated coefficients of models (1)–(6) are all positive and basically pass the 5% confidence level test. The synthesis shows that the construction of forest cities can lead to a continuous increase in the total size of the county economy and improve the quality and stability of economic growth.

In Table 2, models (1) and (2) are estimated with $Lngdp$ (county GDP taken as logarithm) as the dependent variable, models (3) and (4) are estimated with $Lnpergdp$ (county GDP per capita taken as the logarithm) as the dependent variable, and models (5) and (6) are estimated with $Lnlight$ (the total value of nighttime light luminance in the county) as the dependent variable. The positive significance of the core parameters still holds whether or not control variables are added and whether or not they are clustered at the county level. In terms of statistical significance, after the implementation of the forest city construction, the GDP of the counties in the treatment group increased by approximately 0.027 units on average, the GDP per capita increased by approximately 0.026 units on average, and the nighttime light brightness increased by approximately 0.070 units on average. In terms of increase, the implementation of forest city construction can result in approximately 2.736% of the total GDP size, approximately 2.633% of the per capita GDP development level, and approximately 7.250% of the nighttime light brightness to the county on average.

Since the implementation of the forest city construction policy, the county governments have been promoting the transformation of the economic development mode from roughness to intensification, thus promoting the value-add of ecological products. The counties continue to utilize the high-value ecological environment to attract high-level industrial investment, thereby promoting the accelerated development of strategic emerging industries, high-tech industries, and modern service industries. The county governments have deepened the relationship between urban subjects and nature from all spatial dimensions, thereby enhancing the safety, ecological resilience, and risk resistance of the city.

According to the analysis of the results in Table 2, it is obtained that the control variables have diversified influence effects on the counties’ economic growth. Industrial structure, financial autonomy, infrastructure, and education quality have positive effects on county economic growth, whereas credit levels and investment size significantly and negatively affect the quality of county economic growth. More land resources are needed for industrial and commercial activities and infrastructure development in the counties. The increase in the total amount of credit does not enhance the rise of efficient investment projects, but rather, allows the continuous realization of inefficient credit investment. For some areas, investment efficiency is not high, the construction of new towns and new areas spread too large, and part of the industry and local investment has produced excessive problems. The county government must highlight the quality of investment and credit. For a more detailed analysis of the results in Table 2, please see the Supplementary Materials.

4.2. Cost–Benefit Analysis

Forests are reservoirs of water, money, and food as well as carbon. Therefore, it is necessary to analyze in detail the cost and benefit situation of forest city construction [46].

The article’s analysis of the benefits of forest city construction mainly includes the following aspects: firstly, economic scale benefits; secondly, sterilization and noise reduction benefits; thirdly, carbon sinks and oxygen production benefits. The article’s analysis of the costs of constructing a forested city mainly includes the costs of restoration and maintenance of the forest greenery by the regional government.

First, the economic scale benefit of forests is important. It is calculated as follows: [(Total GDP before pilot batch 1 × 2.74% × 14 years × number of pilot counties in batch 1) + (Total GDP before pilot batch 2 × 2.74% × 13 years × number of pilot counties in batch 2) + (Total GDP before pilot batch 14 × 2.74% × 1 year × number of pilot counties in batch 14)]/Total number of pilot counties.

According to the results of the empirical analysis in Table 2, the article calculates that the implementation of forest city construction can result in approximately 2.74% of the total GDP size. [(Total GDP before pilot batch 1 × 2.74% × 14 years × number of first batch of pilot counties) + (Total GDP before pilot batch 2 × 2.74% × 13 years × number of second batch of pilot counties) + … + (Total GDP before pilot batch 14 × 2.74% × 1 year × number of fourteenth batch of pilot counties)] = CNY 165,340.5225 million + CNY 253,189.8483 million + CNY 133,925.2063 million + CNY 161,853.0029 million + CNY 16,776.7356 million + CNY 210,438.1792 million + CNY 260,116.6551 million + CNY 384,521.7179 million + CNY 426,380.9147 million + CNY 399,661.2969 million + CNY 252,381.0479 million + CNY 142,758.384 million + CNY 99,807.9982 million = CNY 2,907,151.51 million (approximately USD 409,699.049 million). Then, dividing CNY 2,907,151.51 million (approximately USD 409,699.049 million) by the total number of pilot counties (32 + 32 + 29 + 31 + 7 + 56 + 82 + 100 + 115 + 143 + 112 + 78 + 155) mentioned in the article’s empirical evidence would calculate that the construction of forest cities can bring approximately CNY 2990.897 million (approximately USD 421.847 million) of economic scale benefits to the pilot counties, on average.

Second, the sterilization and noise reduction benefits of forests are important. According to the relevant formula, $V=(a+b)TS(1/r-1)$, $a$ and $b$ denote the proportion coefficients of forest sterilization value and forest noise reduction value in the total ecological function value of the whole forest, and here take the value of 0.2 and 0.15, respectively; $T$ denotes the real average price of the forest (CNY/hm²); $S$ denotes the area of forest cover in pilot counties; and $r$ denotes the proportion coefficient of the direct physical use value of the forest in the total tangible and intangible value of the forest. The value of $r$ is generally taken as 0.1.

In more detail, the sterilization and noise reduction benefits of forests = 0.35 × (10 − 1) × forestry price × area of additional forest cover in pilot counties. An empirical analysis using county forest area as the dependent variable found that forest city construction was able to increase the pilot counties by about 473.92 square kilometers on average. Forestry prices are from the research data in the Deng article [47]. Based on field research data on 900 transactions of standing trees in Longquan City, Zhejiang Province, Deng’s article found that the transaction price per unit area of forest trees was about 3976 CNY/hm². Longquan City is also a pilot county for the forest city policy mentioned in the article. In more detail, the sterilization and noise reduction benefits of forest cities = 3.15 × 3976 CNY/hm² × 473.92 km² × 100 (unit conversion factor) = CNY 593.556 million (approximately USD 83.717 million) The formula shows that the construction of forest cities can bring approximately CNY 593.556 million (approximately USD 83.717 million) of sterilizing noise reduction benefits to the pilot counties on average.

Third, the carbon sink and oxygen production benefits of forests are important. An empirical analysis using county forest area as the dependent variable found that forest city construction was able to increase the pilot counties by about 473.92 square kilometers on average. According to the Rong-bo article [48], the construction of forest cities can absorb (fix) approximately 172,981.822 tons of total carbon dioxide and release approximately 345.964 tons of oxygen. In more detail, carbon sink benefits of forested cities = 473.92 km² × 365 (days) × 1 (t/km²) = 172,981.822 (t). Oxygen production benefits of forest cities = 473.92 km² × 365 (days) × 0.73 (t/km²) = 126,275.984 (t).

Calculated from the average price of 66.49 CNY/t in the national carbon market in August 2023 and the price of 400 CNY/t for industrial oxygen production, the construction of forest cities can bring an average of approximately CNY 62.012 million (approximately USD 8.743 million) of carbon sink and oxygen production revenue to the pilot counties. In more detail, carbon sink and oxygen production benefits for forest cities = 172,981.822 (t) × 66.49 CNY/t + 126,275.984 (t) × 400 CNY/t = CNY 62.012 million (approximately USD 8.746 million).

Fourth, the costs of forest maintenance and restoration are important. By analyzing the winning contracts (information from the Chinese government procurement network) and the winning amount of the county related to forest restoration, maintenance, and greening, it was found that after the construction of the forest city, the amount of expenditure on forest conservation and maintenance in the pilot counties was approximately CNY 2960.012 million (approximately USD 417.491 million) [49]. In more detail, with the help of python tools, the authors read the data of the amount of money in the awarded contracts related to forest restoration, maintenance, and greening in all the pilot counties on the China Government Procurement Network (http://www.ccgp.gov.cn/, accessed on 24 September 2023). Based on the data obtained, the authors added up the expenditure data generated by all pilot counties after the implementation of the policy. In this section, the authors did not use formulas for extrapolation. Dachang Hui Autonomous County is part of Baoding City, Hebei Province, in eastern China. Since the implementation of the forest city pilot policy in 2019, the local government has spent a cumulative total of CNY 37.98 million (approximately USD 5.357 million) on forest protection, greening of green areas, and biological control. Shexian County is part of Huangshan City, Anhui Province, in central China. Since the implementation of the forest city pilot policy in 2015, the local government has spent a cumulative total of CNY 223.4303 million (approximately USD 31.513 million) on ecological protection within the scope of the nature reserve, forest firefighting, and forest pest control. Xishui County is part of Zunyi City, Guizhou Province, in western China. Since the implementation of the pilot forest city policy in 2010, the local government has spent a cumulative total of CNY 1181.769 million (approximately USD 166.681 million) on forest protection, ecological restoration, seedling purchases, returning farmland to forests, greening of green areas, and biological control.

After the costs and benefits are analyzed, the implementation of the policy can bring approximately CNY 3646.465 million (approximately USD 514.311 million) to the pilot counties. At the same time, the maintenance and conservation costs of the forest are also as high as CNY 2960.012 million (approximately USD 417.491 million), so much so that it generates a surplus profit of CNY 686.453 million (approximately USD 96.82 million). The construction of forest cities is a real livelihood project [50]. For more detail on this section, see the Supplementary Materials.

4.3. Spatial Spillover Effect Estimation

Against the background of deepening regional integration, accurately understanding the impact of forest city policy implementation on the economic growth of adjacent areas in pilot counties is crucial to the construction of a new development pattern of large and superior cities. Because of the spatial correlation, continuity of pollution, and indivisibility of the ecological environment, a single model of ecological environment management is particularly difficult. Therefore, the construction and development of China’s forest cities should pursue the goal of establishing an environmentally friendly society and achieving harmonious development between human beings and nature so as to promote the cities to achieve coordinated economic–social–environmental development on a wider scale, in a wider field, and at a wider level [51].

In this section, the article analyzes the spatial spillover effects of forest city policies on economic growth in the pilot counties with the help of Equations (2) and (3). The empirical estimation results show that, from the full sample, the autocorrelation coefficients of the dependent variables are all positive at the 1% significance level after controlling for the control variables and two-way fixed effects. The spatial weighted term coefficients (W × Did) of the core explanatory variables all remain statistically significant at the 1% confidence level. The values are 0.978 and 0.971, respectively, indicating that forest city construction has a significant spatial spillover effect on economic growth. In other words, forest city construction has a positive spillover effect on economic growth. The construction of forest cities will improve the level of economic development in the adjacent areas of the pilot counties, thus achieving a positive interaction of synergistic regional economic development. See Tables S3 and S4 and Figures S2 and S3 in the Supplementary Materials for detailed test results.

On a broader regional scale, county-level governments should establish the principle of ecological shared governance and implement comprehensive governance implementation plans, thereby enhancing the capacity for cross-regional green governance cooperation. The improvement of the ecological governance mechanism and legal system can achieve synergistic and integrated development of county economies. Based on the interests of the counties under their jurisdiction, prefecture-level city governments should work together to solve common problems faced in the course of economic development, and thus achieve integrated regional economic development. Adjacent county governments should improve the operational efficiency of collaborative ecological governance to avoid the negative spillover effects of ecological damage, thus contributing to the sustainable economic and social development of urban areas.

4.4. Heterogeneity Analysis

4.4.1. Heterogeneity Analysis of Direct Effects

The construction of forest cities has a diverse effect on the size of the economy of the region in terms of different geographic locations, administrative levels, levels of accessibility, social attributes, and endowments. The government should pay attention to the extent of forest city construction in areas such as the western region sample, the northern region sample, the municipal district sample, and the sample to the right of the Hu Huanyong line, and thus prevent a negative effect on the scale of economic growth. The empirical results of the heterogeneity analysis are detailed in Tables S5–S8 in the Supplementary Materials.

4.4.2. Heterogeneity Analysis of Spatial Spillover Effects

Specific analyses reveal that the spatial spillover effects of economic growth are greatest in the adjacent counties of the pilot counties in the western region. While the estimated coefficients of direct, indirect, and total effects for the overall sample and the eastern region sample are mostly negative, most of the values are not statistically significant. Owing to interregional barriers, localism, administrative fragmentation, and the reinforcement of border effects, the implementation of forest city construction in the pilot counties may inspire adjacent counties to be “free-riders” in ecological governance. Adjacent governments have a weak sense of sharing ecological resources, which leads to unhealthy competition. The vicious competition between adjacent governments undermines the motivation to promote regular synergies on a regional scale, which, in turn, undermines the efforts of adjacent counties toward economic growth [52]. See Table S9 in the Supplementary Materials for the empirical results.

5. Robustness Test and Mechanism Test

5.1. Instrumental Variables Test

On the basis of existing studies, the county atmospheric water vapor pressure difference data and the county annual average evapotranspiration data are selected as the instrumental variables of forest city construction.

The specific reasons are as follows: First, the atmospheric water vapor pressure difference and annual average evapotranspiration are purely natural factor variables, which are not related to economic or social development factors. Second, temperature, air pressure, duration of sunshine time, and economic change factors are still correlated, and their ability to meet the correlation requirements of instrumental variables is biased. Climate change factors such as temperature can negatively affect movie attendance. In the long term, the value is almost twice as high as the medium-term effect, resulting in an economic cost of approximately USD 3.532 billion [53]. River density, watershed area, and total precipitation may also have some impact on economic development, and their independence assumptions are difficult to completely strip away [54]. However, as far as this paper is concerned, none of these instrumental variables may be applied. Third, an increase in the atmospheric water vapor pressure difference means an increase in the amount of water vapor lost to the atmosphere through plant transpiration and soil evaporation. This phenomenon largely increases the exposure of vegetation to drought stress, resulting in reduced vegetation productivity. Under increased atmospheric drought stress, ecosystems close their stomata to minimize water loss, which, in turn, reduces photosynthesis and limits vegetation growth. Fourth, the average annual evapotranspiration indicator can have a great influence on the ecological vegetation of the watershed and remains independent of economic growth factors. Fifth, the selection of instrumental variables is not correlated with any other control variables, thus eliminating the way to influence the dependent variables from other channels.

The empirical test revealed that the statistical significance of the coefficients of the instrumental variables is significantly nonzero, the $Kleibergen\text{-}Paap rk Wald F statistic$ is 203.061, and the p-value of $Score chi2$ is 0.201. Overall, the model does not have a significant weak instrumental variable problem. The results of the first stage of the regression show that the county’s atmospheric water vapor pressure difference and average annual evapotranspiration are significantly negatively correlated with the construction of a forested city. The relationship basically has a significant negative impact result at the 10% level. This finding also indirectly confirms the analysis of the reasons for selection, above. In the second stage of the instrumental variable approach, the estimated coefficients obtained through statistical significance indicate that the correctness of the baseline regression results is verified again. With $Lngdp$ as the dependent variable, the estimated coefficient of the forest city pilot policy on county economic growth is 1.15. With $Lnpergdp$ as the dependent variable, the estimated coefficient of the forest cities pilot policy on county economic growth is 1.12. The implementation of the forest city construction policy can contribute to the rapid growth of the county’s economy. See Table S10 in the Supplementary Materials for the results of this section.

5.2. Robustness Test

To ensure the accuracy of the results, the empirical estimation is re-estimated in terms of the estimation model, variable transformation, and empirical method. The parallel trend test shows that the conclusions satisfy the relevant premise assumption. The replacement variable test reveals that the conclusions drawn from the baseline estimates can still be clearly established. The baseline variable test also yields no significant change in the results. The omitted variable test, on the other hand, indicates that the article does not suffer from omitted variable bias. See Tables S11 and S12 and Figures S4–S7 in the Supplementary Materials for the results of the robustness tests.

5.3. Mechanism Test

Labor efficiency has increased significantly. The construction of forest cities can significantly increase the productivity of county laborers. Green spaces such as public parks can provide activities suitable for a wide range of leisure and sports activities, all of which encourage the public to participate in social life and socializing to a certain extent. The construction of forest cities can further improve the well-being and social health of residents, effectively regulate the functions of the organism, and enhance people’s sense of acquisition and well-being, which, in turn, will continuously improve work efficiency and quality of life. The construction of forest cities can promote the input–output level and full labor productivity of county enterprises, thereby creating increasingly high-quality and more stable jobs. The continuous improvement of jobs can ease the pressure of human resource employment, which, in turn, will open up the cycle between the various production links in the city and promote the continuous rise of the economic scale. Please see Table S13 Model (1) and Model (2) in the Supplementary Materials for the results of the empirical tests.

Rapid agglomeration of high-end elements: The construction of the forest city significantly improves the spatial agglomeration degree of high-end modern industries in the county, greatly increases the inflow of high-quality labor, and effectively enhances the county government’s support for science and technology industries. Through the introduction of new technologies, new forms of business, and new models, the county government accelerates the transformation of scientific and technological achievements to encourage industrial enterprises to realize the goals of reducing quantity, improving quality, and increasing efficiency [55]. After the implementation of the policy, the county government will continue to cultivate new high-end industrial subjects and accelerate the realization of green low-carbon industrial project clusters and recycling levels. See Table S13, Models (3)–(5) in the Supplementary Materials for test results.

Expanding market potential: The test results reveal that the construction of forest cities can significantly increase the market potential of counties, broaden the sales range of county products, and increase the total sales volume of county product markets. Following the implementation of the policy, county governments have promoted ecological greening and restoration projects to ensure the contribution of the ecological space, thereby continuously improving the ecological quality and resilience of the county. County governments have continued to enhance the ecological functions of industrial space, creating new highlands of ecological and green value, which, in turn, drive more people to increase their income and become rich. See Table S13, Model (6) in the Supplementary Materials for test results.

6. Conclusions and Policy Suggestions

6.1. Conclusions

“Letting the forest come into the city, let the city embrace the forest” is the common wish of people worldwide. With the help of county data from 2007 to 2020, the article evaluates the effects and mechanisms of forest city construction on county economic growth.

(1).

Compared with the control group, forest city construction can lead to an average in-crease of approximately 0.027 units in county-level GDP growth, an average increase of approximately 0.026 units in per capita GDP, and an average increase of approximately 0.070 units in nighttime light intensity in county-level areas. Compared with the sample mean, policy implementation can lead to an average increase of approximately 2.736% in county-level GDP, an average increase of approximately 2.633% in per capita GDP, and an average increase of approximately 7.250% in nighttime light intensity in county-level areas.

(2).

Forest city construction has diverse effects on the economic scale of the domain in different geographical locations, administrative levels, conveniences, social attributes, and external environments. The empirical test reveals that the significant improvement of county labor productivity, the accelerated entry of high-end factors, and the continuous improvement of market potential are the important mechanism paths for the economic growth effect of the policy implementation.

(3).

The construction of forest cities can produce obvious positive spatial spillover effects on the economic scale of neighboring areas in pilot counties. The indirect and total spatial spillover effects of the pilot counties in the eastern region on the neighboring counties are statistically significant in the negative direction.

(4).

The cost–benefit analysis revealed that forest city construction can bring approximately CNY 686.453 million (approximately USD 96.82 million) of comprehensive economic benefits to the pilot county, although there is also the problem of the high cost of forest maintenance.

6.2. Policy Suggestions

On the basis of the above conclusions, the following policy recommendations are proposed:

The “direct effect estimation” indicates that county governments should shift from “prioritizing efficiency” to “promoting comprehensive human development”, thus reserving space for the sustainable development of the county economy in the future. According to the results of the literature review, the county government should take a good ecological environment as the most popular form of people’s welfare and livelihoods, and then contribute wise programs for the construction of a healthy community for all human beings. According to the optimization strategy of “three types (production, living and ecology) of space”, the government should build a spatial layout in which production (town construction), life (agricultural activities), and ecology (natural vegetation) are coupled with each other.

According to the mechanism path analysis, the county government should actively carry out the assessment of spatial suitability for afforestation and greening, promote the development of eco-industry agglomeration, enhance the production environment and output efficiency of workers, and then realize the green and low-carbon development of the county economy.

From the cost–benefit analysis, the county government must fully recognize the importance and urgency of carrying out forest city construction from the perspective of overall national strategy, regional coordinated development, and the well-being of the people. The county government should also be vigilant about issues such as excessive luxury and carbonization, excessive pursuit of foreign influence, and excessive emphasis on landscaping that may occur during the forest city construction process.

According to the results of the spatial spillover effect estimation and heterogeneity analysis, neighboring county governments should cultivate ecological community awareness, enhance the synergy of ecological governance, and then jointly promote the integration of regional economic development. Prefectural city governments should deepen the division of labor and interaction among their counties to help promote coordinated regional economic development and solve the problem of unbalanced development.