The Role of the Forest Recreation Industry in China’s National Economy: An Input–Output Analysis

Abstract

Forest recreation not only enables directly creates impacts on the entire tourism industry but also indirectly creates effects on other industries composed in an economic system via inter-industrial interdependence, such as backward and forward linkages or interactions. However, due to the lack of data available, few studies have been made exclusively for evaluating the impacts of forest recreation on the economy in a national dimension. Therefore, this paper attempts to analyze the economic impacts of the forest recreation industry. Using an input–output modeling approach, the industrial linkage and a cluster of economic multipliers are estimated based on new input–output tables for 2012, 2017, and 2018 that includes the forest recreation industry. The results indicate that, first, both the forward and backward linkages caused by forest recreation show rising trends over the analysis periods and the backward linkages are stronger than forward linkages. Second, the economic contribution of forest recreation has been strengthened, although the multipliers have shown a downward trend. The total output impact in 2018 is calculated to be $838.187 billion, with a total added impact of $352.713 billion. Furthermore, forest recreation could create over 18 million jobs with an average annual growth rate of 15.73%, which are mainly in the catering, accommodation, and retail industries with low skill requirements. Finally, policy applications are proposed to provide reliable and useful information for future planning and investment decision associated with the forestry and ecotourism industry.

1. Introduction

Forest recreation is defined as numerous types of activities engaged by tourists in forest and woodland areas, including sightseeing, leisure vacation, health care, and various relevant business activities in either direct or indirect manners, such as food and transportation services [1]. The world’s terrestrial protected areas receive approximately 8 billion visits, and global gross direct expenditure associated with protected area visits are US $600 billion annually, many of which are forest covered [2]. Similar to other regions in the world, the market demand for forest recreation in China is also huge. In the past five years, the number of national forest recreation tourists has reached an average of 1.5 billion per year, accounting for 28% of the total number of domestic tourists, and the average annual social comprehensive output value is $3.34 billion [3]. Under the policy background of global forest resource harvesting restrictions, forest recreation uses intangible services to increase environmental income, lessen the reliance on unsustainable use and optimize the structure of traditional forestry industries, which can alleviate the contradiction between forest resource protection and forest economic development [4]. Non-logging use of forest resources is conducive to protecting species diversity, alleviating climate change, and improving air quality [5,6]. Moreover, global forest recreation areas are highly spatially coincident with poor areas [7]. In China, over 60 percent of the poor people are located in mountainous and forest areas, while nearly 50 percent of national forest parks are located in poor areas [8]. Forest resources and the environment in poor areas meet the needs of public leisure and entertainment. Studies have found that tourists are more attracted by natural beauty attributes, such as forest coverage, biodiversity, and environmental quality, and tend to have a fair amount of willingness to pay to enjoy those natural features [9,10,11,12]. Forest recreation has a strong industrial poverty alleviation function. Due to the various advantages, since the 1950s, forest recreation has been valued around the world and has gradually developed into an important goal of forest operations in various countries.

Forest recreation is neither a single industry nor a single market; rather, it includes a cluster of products and services, such as transportation, logistics, catering, accommodation, etc. [13]. Michael E Porter [14] defines clusters as geographic concentrations of interconnected companies and institutions in a particular field, encompassing an array of linked industries and other entities important to competition. As members of the cluster are complementary and mutually dependent, a host of linkages among members results in a whole greater than the sum of its parts. In the process of forest recreation consumption, in addition to direct revenue, it can also trigger the need for tourism companies to purchase products and services from other companies, then cause an indirect impact to create new employment opportunities and increase residential income [15]. Forest recreation demand also leads to an increase in infrastructure, while it has also become a significant factor not only in forest planning but also in regional planning and county development plans [16]. The communities around forests bear the brunt of adverse impacts brought about by ecological protection, causing losses to regional economic, social, and industrial development. If economic compensation cannot be obtained through other means, residents will lose interest in the conservation work. Forest recreation takes into account both resource protection and economic development, which can have a positive impact on the regional economic structure, and contribute to strengthening confidence in sustainable development [17,18]. However, an unreasonable tourism economic system and supply chain structure would limit its socio-economic contribution [19,20]. Therefore, in the era of mass tourism and leisure, How to organically connect and integrate forest tourism with other industries, driving and promoting coordinated economic and social development? How can forestry regions successfully transform based on forest recreation? What forms of employment should agricultural and forestry community residents participate in and benefit from forest recreation industry? This study explores the industrial integration path and socio-economic effects of forest recreation from the perspective of industrial linkage, which is conducive to establishing a sound industrial chain and fully leveraging the potential synergistic effects of forest recreation resources, providing reference information for forestry and tourism development planning and investment decision-making.

Klijs et al. [21] compared five economic impact models of tourism (export base, Keynes, ad hoc, input–output and CGE), pointed out that the input–output model has advantages in calculating indirect impacts, model trust, standardization, and comparability, and offer good potential for application in a tourism context. Based on the above series of multiple desirable features, the input–output (I-O) model has been widely used in assessing the impacts of the whole tourism [22,23,24] and the specific such as culture heritage [25], special event [26,27], health tourism [28,29] and academic tourism [30]. Additionally, several authors have conducted applications of I-O model on the nature based tourism. For example, Guo et al. [31] analyzed the local and regional economic impacts of coastal tourism using a computer software so-called the IMPLAN (Impact Analysis for Planning), an I-O modeling system for economic impact analysis and social accounting, to capture the economic connections between the regional economies of Mississippi and Alabama Gulf Coast. Artal-Tur et al. [32] made comparisons on the economic benefits of international tourism along the Mediterranean coast of Spain based on national and regional input–output models. Hjerpe and Kim [33] assessed the regional economic impacts of rafting in Grand Canyon National Park based on the regional multipliers and types and quantity of employment created. Ke et al. [34] measured the employment effect of the National Forest Parks.

Furthermore, a large number of economic impact studies on tourism has been well documented. However, few attempts are made in dealing with the economic impact analysis with exclusively focused on the forest recreation industry in a national scale. In part, it is due to limited data availability and the degree of complexity of data collection. According to the China’s I-O model, there is no separate forest recreation industry. Naturally, all this industry-related production and service activities are scattered among other industries in the I-O table, making it difficult to accurately assess economic impacts of the forest recreation industry. Therefore, the primary and foremost task is to construct new I-O tables containing forest recreation as a separate sector. First, define or identify those forest recreation related industries based on depicted throughput and service flows among the relevant industries. Second, by calculating the disaggregating weights, the forest recreation industry is separated from related industries and new input–output tables based on the forest recreation industry are constructed. After that, use the adjusted input–output models to analyze the industrial linkage and socio-economic effects of forest recreation, including output multiplier, value-added multiplier, and employment multiplier. Finally, based on the research results, potential policy implications are proposed from aspects such as forest recreation development planning, industrial integration, and farmer employment, providing reference for the government and management personnel to understand the economic structure of forest recreation and formulate reasonable management policies and investment decisions.

2. Materials and Methods

2.1. Input–Output Model

Input–output model is also known as the Leontief model, is an analytical framework for exploring the interdependence between industries in an economy developed by Professor Wassily Leontief in the late 1930s, in recognition of which he received the Nobel Prize in Economic Science in 1973. Currently, the basic concepts proposed by Leontief have become key components of various economic analysis and, indeed, I-O analysis is one of the most widely applied methods in economics [35].

Leontief input–output model divides an economic system into multiple industries or sectors according to production activities and the interdependence of industries can be described by a set of linear equations. Suppose the economy is divided into N industrial sectors, standard representation of Leontief input–output model can be expressed as [36]:

$$X_i=z_{i1} +\dots + z_{ij} +\dots + z_{in}+F_i={\sum }_{j=1}^n{a}_{ij}{X}_j + F_i$$

(1)

where X_i is the total output of sector i; z_ij is the intermediate sales by industry i to all sectors j (including itself, when j = i); F_i represents the total final demand for products of sector i. a_ij is the technical coefficient or direct input coefficient defined as $A=a_{ij}=z_{ij}/X_j$. Additionally, Equation (1) can be rewritten as $X=(1-A{)}^{-1}F$, where $(1-A{)}^{-1}$ is known as the Leontief inverse or the total requirements matrix.

The above-mentioned standard Leontief model cannot accurately estimate the influence of new production activity in forest recreation on remaining industries because the changes in final demand are determined by forces outside the model (such as changes in consumer tastes and government purchases). To this end, we use the mixed model [35], and the individual forest recreation industry is treated as exogenous and included into the final demand group [37,38]. Suppose f is the forest recreation industry, and e represents remaining industries, the variant of Equation (1) can be:

$$\left[\begin{array}{c}{X}_f\\ X_e\end{array}\right]=\left[\begin{array}{cc}{A}^{ff}& A^{fe}\\ A^{ef}& A^{ee}\end{array}\right]\left[\begin{array}{c}{X}_f\\ X_e\end{array}\right]+\left[\begin{array}{c}{F}_f\\ F_e\end{array}\right]$$

(2)

Then, yields:

$$X_e=(I-A^{ee}{)}^{-1}(F_e+A^{ef}{X}_f)$$

(3)

Assuming that the final demand of the remaining industries remains unchanged (ΔF_e = 0), then the contribution made by the forest recreation investment to these industries can be expressed as:

$$\mathsf{\Delta }{X}_e=(I-A^{ee}{)}^{-1}{A}^{ef}{\mathsf{\Delta }X}_f$$

(4)

where ΔX_e is the intermediate demand for other industries when the production activities of the forest recreation industry increase by one unit. Summing up these elements and the initial unit output change in the forest recreation industry, the output multiplier can be calculated as:

$$M_O=1+\sum \mathsf{\Delta }{X}_e$$

(5)

Different from the standard multiplier driven by final demand, M_O is an “output-to-output” multiplier (output-drive) [35], representing the direct and indirect output effects of one unit of forest recreation output change on the entire national economy. Since the data on forest recreation in the China Forestry Statistical Yearbook is based on output value rather than final demand, we believe that this multiplier is more suitable for measuring the total effect, so the study uses this variant instead of the standard multiplier.

By multiplying Equation (5) by labor-input matrix $\widehat{l}$ and added value matrix $\widehat{v}$, the multipliers of employment and added value are given by:

$$M_y=\widehat{y}(1+\sum \mathsf{\Delta }{X}_e), \mathrm{y}=l, v$$

(6)

where these multipliers can estimate the direct and indirect effects of investment in the forest recreation on the generated employment and added value.

2.2. Linkage Analysis

Backward linkage considers the forest recreation as demanders, measuring the inter-industrial linkage of forest recreation industry with upstream industries from which it purchases inputs. Forward linkage considers the forest recreation as suppliers, measuring the inter-industrial linkage of forest recreation industry with downstream industries to which it sells its output.

In terms of measuring the backward linkage, a column vector analysis of the Leontief inverse matrix is applied [35]:

$$B{L}_j=\frac{{\sum }_{i=1}^n{b}_{ij}}{\frac{1}{n}{\sum }_{j=1}^n{\sum }_{i=1}^n{b}_{ij}}$$

(7)

where the backward linkage effect BL_j is the “Index of the Power of Dispersion” suggested by [39], indicating the impact on national economic production when the final demand of the forest recreation industry increases by one unit. $\sum _{i=1}^n{b}_{ij}$ denotes the sum of column vector of Leontief inverse matrix, $\frac{1}{n}\sum _{j=1}^n\sum _{i=1}^n{b}_{ij}$ denotes the average of the Leontief inverse matrix. The average value of BL_j is unity, so that industry with index greater than one is considered to have a stronger impact on national economy that exceeds the social average.

In terms of measuring the forward linkage, a row vector analysis of the Leontief inverse matrix is applied [35]:

$$F{L}_i=\frac{{\sum }_{j=1}^n{b}_{ij}}{\frac{1}{n}{\sum }_{j=1}^n{\sum }_{i=1}^n{b}_{ij}}$$

(8)

where the forward linkage effect FL_i is the “Index of Sensitivity of Dispersion” [39], representing the change of forest recreation industry when the final use in the remaining industries increases by one unit. $\sum _{i=1}^n{b}_{ij}$ denotes the sum of row vector of Leontief inverse matrix.

2.3. Data Source

The data used in this study are the national I-O tables made by the National Bureau of Statistics of China [40,41,42]. In accordance with the requirements of The State Council, a national input–output survey has been conducted every five years (for years ending with 2 and 7) since 1987, and an input–output table has been compiled for the current year. At present, China has conducted seven national input–output surveys in 1987, 1992, 1997, 2002, 2007, 2012, and 2017, respectively, and the national I-O table for 2022 has not been released yet. Specially, the National Bureau of Statistics released an additional “2018 National Input Table” based on the data from the fourth economic census in 2018 and the input–output survey in 2017 for the first time. We believe that adding the 2018 input–output table can contribute to the temporal continuity of our research data and provide more useful information. Therefore, we used the national input–output tables for 2012, 2017, and 2018 as analysis data.

Since the forest recreation is not regarded as an independent industry in the input–output tables, it is essential to disaggregate the economic activities related to the forest recreation industry part from the national I-O tables in order to achieve the goal of the economic impact analysis of the focal industry.

First, according to the Tourism Satellite Account and the Industrial Classification for National Tourism and Related Activities (ICNTRA) 2018, the forest recreation is classified into six sub-sectors, including transportation_(F), accommodation_(F), catering_(F), retail_(F), sightseeing_(F), entertainment, and other service_(F) (In order to distinguish the forest recreation sub-industries from those of national economy without forest recreation (such as forest recreation transportation and the transportation industry excluded forest recreation), the forest recreation sub-industries are marked by _(F), where _(F) represents the forest recreation industry). Matching the Standard of Industrial Classification for National Economic Activities (ICNEA) [43,44], the sectors which have connections with the forest recreation industry are readily identified. Taking 2018 as an example, in ICNTRA, Sightseeing_(F) is divided into Park Sightseeing (including Urban Park Management, Scenic Area Management and Ecological tourism) and Other forms of sightseeing (including Tourism exhibition services and Agricultural sightseeing), which can correspond to the sectors of Ecological protection and environmental management, Water management, Business services and Forest products and services in I-O table (see

Table 1. Breakdown of the forest recreation industry related sectors.

Forest Recreation Sub-Sectors	Sectors in ICNTRA	Sectors in I-O Table (2018)
Transportation(F)	Tourism railway transportation; Tourism road transportation; Tourism water transportation; Tourism air transportation; Passenger ticket agent	Railway passenger transport; Urban public transport and road passenger transport; Water passenger transport; Air passenger transport; Transport agency
Accommodation(F)	General tourist accommodation services; Health tourism accommodation services	Accommodation
Catering(F)	Tourism dinner services; Tourism fast food services; Tourism beverage services Tourism snack services; Tourism catering delivery services	Catering
Retail(F)	Travel tools and fuel shopping; Tourism product shopping	Retail trade
Sightseeing(F)	Park Sightseeing; Other forms of sightseeing;	Ecological protection and environmental management; Water management; Forest products and services; Business services
Entertainment and other services(F)	Tourism culture and entertainment; Tourism fitness entertainment; Leisure entertainment; Other tourism services (Travel agencies and related services, Tourism financial services, Other comprehensive tourism services)	Culture and art; Sports; Entertainment; Households Services; Health, Postal, Telecommunications, Internet and related services, Monetary and other financial services, Insurance, Leasing; Public management and social organizations

). However, as the changes in the ICNEA, the industry classifications in the I-O tables vary from time to time, causing some differences in the sector cluster involved in forest recreation. For example, in 2012, the sector of railway transportation is categorized into the sector of forest recreation transportation. However, in 2017 and 2018, the industry of railway transportation has been subdivided into two parts: “railway passenger transportation” and “railway cargo transportation”. To save space, only the sectors related to forest recreation in the 2018 I-O table are listed in Table 1.

Second, in order to separate the forest recreation industry and non-forest recreation industries presented in 21 sectors in Table 1, we calculate the disaggregating weights suggested by [45], which is the proportions of forest recreation in total economic activities in these specific sectors. To obtain the data of the output value of sub-industries of forest recreation, we use the consumption composition data from the Tourism Sample Survey Data [46,47,48] (The consumption structure data is composed of weighted average expenses for tourism intentions as sightseeing, leisure, entertainment and health, excluding business trips and visiting relatives that are not related to forest recreation motivation). The total output values of forest recreation are obtained from the China Forestry Statistical Yearbook [49,50,51]. The output values of forest recreation sub-industries and their weights are shown in

Table 2. Output value of forest recreation sub-industries and their weights.

Forest Recreation Sub-Industries	Output ($Billion) 1			Weight
	2012	2017	2018	2012	2017	2018
Transportation(F)	39.45	101.17	117.76	0.0456	0.2502	0.2274
Accommodation(F)	20.17	56.18	68.96	0.2613	0.4469	0.4364
Catering(F)	36.55	75.94	79.37	0.1250	0.1734	0.1508
Retail(F)	29.66	42.83	52.28	0.0259	0.0483	0.0598
Sightseeing(F)	12.10	25.07	25.37	0.0168	0.0196	0.0172
Entertainment and other services(F)	6.26	20.59	15.07	0.0030	0.0058	0.0034
Total	144.18	321.78	358.81

¹ In 2012, 2017 and 2018, the conversion from USD to CNY were about 6.3125 ($1 = ¥6.3125), 6.7518 and 6.6174, respectively.

. Finally, in order to ensure the comparability of the I-O tables in different years, we use ICNEA2017 to adjust the I-O tables to 43 industries including the forest recreation, which can be seen in the Appendix A.

3. Direct Effects of Forest Recreation Industry in China

China has abundant forest landscape resources. According to Global Forest Resources Assessment Report 2020, China’s forest area accounts for 5% of the world’s forest area, ranking fifth after Russia, Brazil, Canada, and the United States [52]. Relying on its resource advantages, after more than 40 years of development, China’s forest recreation economy has developed rapidly. As shown in Figure 1, in 2018, the number of visits reached 3.6 billion, creating a total output value of $358.81 billion, with an average annual growth rate of 19.1% and 24.25%, respectively. In addition, the development of forest recreation has also promoted the optimization of the forestry industry structure. The proportion of the forestry primary, secondary, and tertiary industries has been adjusted from 67:27:6 in 1994 to 33:48:19 in 2017 [53]. The tertiary industry represented by forest recreation has the fastest growth rate. Forest recreation have become the third forestry pillar industry with an annual output value of more than ¥1 trillion after the economic forest planting and collection industry and wood processing and wood products manufacturing industry in China [54], which is an important composition and new economic growth point of China’s forestry and tourism industry.

To clarify the direct contribution of forest recreation to GDP and employment, the direct value added, and direct employment of forest recreation are calculated using the value-added rate and labor input rate of industries related to forest recreation in Table 1. As shown in Figure 2, the added value surges from $66.715 billion to $160.824 billion over the period from 2012 to 2018, accounting for 0.81% and 1.16% of GDP, respectively. In 2018, the sub-industries with the largest added value gained are transportation_(F) ($54 billion) and retail_(F) ($34 billion), accounting for 33.77% and 21.31% of the total added value of the forest recreation industry. In terms of employment, the forest recreation industry produces a large number of employment opportunities. For example, in 2018, the direct employment made by the forest recreation exceeds 12.76 million, a 144% increase over 2012. The sub-industries with the most direct employment are retail_(F) (3.95 million), catering_(F) (3.72 million), and accommodation_(F) (3.24 million), which account for more than 85% of the forest recreation direct employment, despite producing just half (that is, 55.91%) of forest recreation gross output. This occurs because their average labor-input rate of 54.46 persons employed per $1 million output is over twice the national average.

4. Indirect and Total Effects of Forest Recreation Industry in China

4.1. Inter-Industry Linkage Effect

Table 3. Backward and forward linkage effects of China’s forest recreation industry.

	Backward Linkage			Forward Linkage
Forest Recreation Sub-Industries	2012	2017	2018	2012	2017	2018
Transportation(F)	0.963	0.907	0.921	0.402	0.491	0.507
Accommodation(F)	0.898	0.919	0.947	0.379	0.456	0.468
Catering(F)	0.881	0.988	1.009	0.377	0.428	0.435
Retail(F)	0.624	0.670	0.698	0.378	0.411	0.428
Sightseeing(F)	0.996	1.010	1.038	0.366	0.406	0.416
Entertainment and other services(F)	0.765	0.817	0.841	0.352	0.386	0.394
Average	0.855	0.885	0.909	0.376	0.430	0.441

shows the backward and forward linkages of forest recreation industry in years of 2012, 2017, and 2018, respectively. The average backward linkages of the forest recreation are in a range of 0.855 to 0.909, showing a rising trend of closing to 1, which implies the forest recreation having a certain power of dispersion but is still lower than the level of social average. The average forward linkages of the forest recreation are between 0.372–0.403, which are much weaker than the social average level, indicating a relatively weak sensitivity of dispersion. Furthermore, in terms of the sub-industries, the linkage effect of catering_(F) increases fastest, and similar to sightseeing_(F), presents strong backward linkage and weak forward linkage. Thus, it can be deduced that sightseeing_(F) and catering_(F) belong to the intermediate primary production industries. Although the linkage effects of accommodation_(F), transportation_(F), entertainment and other service_(F) and retail_(F) are up-trending over the analytical periods, their forward and backward linkages are both weak, suggesting they belong to the final primary production industries.

4.2. Output Effect

The direct and indirect output impacts of the forest recreation investment for the period of analysis are presented in

Table 4. Output multipliers of the forest recreation industry.

Forest Recreation Sub-Industries	2012	2017	2018
Transportation(F)	0.753	0.749	0.770
Accommodation(F)	0.360	0.422	0.464
Catering(F)	0.642	0.614	0.570
Retail(F)	0.367	0.234	0.259
Sightseeing(F)	0.238	0.202	0.184
Entertainment and other services(F)	0.094	0.136	0.089
Total	2.454	2.357	2.336

. It can be seen that, when the investment of forest recreation increases by $1 in 2012, 2017, and 2018, the total output impacts are $2.454, $2.357, and $2.336, respectively, which includes indirect output impacts on other sectors of $1.454, $1.357 and $1.336, respectively. The output multiplier of the forest recreation appears a decreasing trend. This is mainly due to the weak performance of the retail_(F), sightseeing_(F), and catering_(F), which exhibit negative average annual growth rate of 5.64%, 4.20%, and 1.96%, respectively. In contrast, accommodation_(F) surges from 0.360 to 0.464 with an average of 4.32% in annual growth rate, and transportation_(F) surges to 0.770 with an average annual growth rate of 0.37%.

Nevertheless, the total output contributed by the forest recreation increases. Based on the output value of the forest recreation in the corresponding years, the total output effects on the entire national economy are $353.811 billion, $758.437 billion, and $838.187 billion, accounting for 1.39%, 2.27%, and 2.22% of the gross national output. Among them, the total indirect outputs of other industries are $209.634 billion, $436.657 billion, and $479.374 billion, respectively. Furthermore, as shown in

Table 5. Industries mostly affected by output of forest recreation in 2018.

Forest Recreation Industries	National Economy Industries
Forest recreation	No. 6 *	No. 1	No. 30	No. 35	No. 33
	(0.158)	(0.120)	(0.114)	(0.090)	(0.084)
Transportation(F)	No. 30	No. 18	No. 11	No. 33	No. 3
	(0.059)	(0.037)	(0.035)	(0.034)	(0.026)
Accommodation(F)	No. 6	No. 34	No. 12	No. 35	No. 1
	(0.033)	(0.027)	(0.022)	(0.020)	(0.019)
Catering(F)	No. 6	No. 1	No. 29	No. 12	No. 30
	(0.104)	(0.082)	(0.026)	(0.020)	(0.019)
Retail(F)	No. 35	No. 34	No. 30	No. 33	No. 12
	(0.019)	(0.015)	(0.013)	(0.011)	(0.004)
Sightseeing(F)	No. 35	No. 33	No. 12	No. 10	No. 30
	(0.010)	(0.008)	(0.008)	(0.008)	(0.007)
Entertainment and other services(F)	No. 12	No. 35	No. 33	No. 32	No. 34
	(0.006)	(0.004)	(0.004)	(0.003)	(0.003)

* Note: The industry numbers are consistent with those in the Appendix A.

, the most benefited five industries resulting from output effect of the forest recreation in 2018 are food and tobacco (No. 6, 0.158), agriculture, forestry, animal husbandry, fishery (No. 1, 0.120), transportation, storage and post (No. 30, 0.114), leasing and business services (No. 35, 0.090), and finance (No. 33, 0.084), which means that the activities of forest recreation production generate a lot of intermediate demand from these industries.

In terms of the forest recreation sub-industries, the three key industries with the largest output multipliers are transportation_(F), catering_(F), and accommodation_(F), following by retail_(F), sightseeing_(F) and entertainment and other service_(F). Moreover, there exist differences of those six sub-industries regarding each of them intermediate consumption, respectively. For instance, the consumption of food and tobacco (No. 6, 0.158) by forest recreation is mainly attributed to accommodation_(F) (0.033) and catering_(F) (0.104); whereas transportation_(F) demand is primarily connected with traffic, storage and post (No. 30, 0.059), and transportation equipment (No. 18, 0.037).

4.3. Added Value and Employment Effects

The total (direct and indirect) effects of the added value and employment over the three periods are explored (see

Table 6. Multipliers of the added value and employment of forest recreation.

	Added Value Multiplier			Employment Multiplier (Unit: Person/$ Million)
Forest Recreation Sub-Industries	2012	2017	2018	2012	2017	2018
Transportation(F)	0.293	0.308	0.319	9.664	9.230	8.854
Accommodation(F)	0.144	0.177	0.196	8.168	12.383	12.295
Catering(F)	0.266	0.256	0.242	14.159	17.055	14.452
Retail(F)	0.157	0.102	0.114	17.612	12.417	12.566
Sightseeing(F)	0.090	0.082	0.074	2.695	2.829	2.435
Entertainment and other services(F)	0.039	0.057	0.037	1.281	1.904	1.125
Total	0.989	0.981	0.983	53.581	55.824	51.722

). Although the results of added value multiplier show a downward trend, the total contribution of the forest recreation industry is significantly surged from $142.592 billion in 2012 to $352.713 billion in 2018, with an average annual growth rate of 17.21%. Its share of GDP contribution also surges from 1.68% to 2.55%, indicating that the development of the forest recreation has a marked impact on the GDP growth.

With respect to the total employment effect, as the forest recreation output increases by $1 million in each of the three years, 53.581, 55.824, and 51.722 direct and indirect work opportunities are created, respectively. Multiplying by the forest recreation output value, the total employments created are 7.725 million, 17.963 million, and 18.559 million, which are equivalent to 1.01%, 2.31%, and 2.39% of the total national employment, respectively. Catering_(F), retail_(F), and accommodation_(F) appear to generate the greatest impact on employment promotion, following by transportation_(F), sightseeing_(F) and entertainment, and other services_(F). For example, in 2018, when the output in the forest recreation industry changes by $1 million, it created 51.722 job opportunities, which includes catering_(F) (14.452), retail_(F) (12.566), accommodation_(F) (12.295), transportation_(F) (8.854), sightseeing_(F) (2.435) and entertainment, and other service_(F)(1.125), respectively. In addition, through the interdependence between industries, forest recreation can indirectly create 5.88 million job opportunities in 2018. The five industries with the highest indirect employment are: wholesale and retail (No. 29, 5.678), leasing and business services (No. 35, 2.548), transportation, storage and post (No. 30, 1.191), food and tobacco (No. 6, 0.973), and hotel and catering services (No. 31, 0.973), respectively (as shown in

Table 7. Most impacted industries in terms of employment by forest recreation in 2018.

Forest Recreation Sub-Industries	Remaining Industries
Forest recreation	No. 29	No. 35	No. 30	No. 6	No. 31
	(5.678)	(2.548)	(1.191)	(0.973)	(0.973)
Transportation(F)	No. 29	No. 35	No. 30	No. 38	No. 31
	(1.158)	(0.682)	(0.615)	(0.443)	(0.390)
Accommodation(F)	No. 29	No. 35	No. 6	No. 38	No. 30
	(1.191)	(0.562)	(0.205)	(0.172)	(0.132)
Catering(F)	No. 29	No. 6	No. 35	No. 30	No. 12
	(1.979)	(0.642)	(0.351)	(0.199)	(0.126)
Retail(F)	No. 35	No. 29	No. 30	No. 31	No. 38
	(0.549)	(0.291)	(0.132)	(0.099)	(0.066)
Sightseeing(F)	No. 29	No. 35	No. 31	No. 30	No. 38
	(0.457)	(0.285)	(0.172)	(0.079)	(0.066)
Entertainment and other services(F)	No. 29	No. 35	No. 31	No. 38	No. 12
	(0.172)	(0.119)	(0.066)	(0.033)	(0.033)

).

5. Discussion

The forgoing analysis on the inter-industry linkage, output production, value added, and employment effects clearly evidences there exist positive economic impacts imposed by the forest recreation industry on the China’s economy, which implies the fact that the investments initiated from the forest recreation industry can perform a powerful part in stimulating the national economy and employment.

5.1. Economic Implication

In terms of inter-industry chain effects, both forward and backward linkages of the forest recreation exhibit a rising trend in the analytical periods of time from 2012 to 2018, where it appears the backward linkages are greater than forward linkages, suggesting that the forest recreation industry draws the significant consumption and service demand from the upstream industries and at the same time pushing its downstream industries’ move forward.

The analysis of output effect and added value effect can be applied to guide forest recreation investment planning and to evaluate the benefits of potential projects. First, the results of surged output and added value prove that even if the multiplier declines, the forest recreation demand can still boost the total economic expansion. This is mainly due to the close linkages between the connected industries and the focal sub-industries of transportation_(F) and accommodation_(F), as both displayed increased multipliers and tourism revenues. Additionally, as a result of the substantial increase in the tourist spending, it outweighs for the negative impact attributable to the multiplier declining. For example, the added value multiplier of catering_(F) falls by 5.47% from 2017 to 2018, but the total added value surges by 3.61% owing to the revenue rising from the forest recreation catering industry by more than $3 billion. Therefore, it can be deduced that the pulling impact of forest recreation on the national economy has been strengthened over time [55]. Within the industry, the transportation_(F) industry has the greatest economic contribution, which is different from the mass tourism industry (where tourism shopping accounts for the largest proportion of added value) (The data on the added value of mass tourism industry is sourced from the website of the National Bureau of Statistics), indicating that tourists have a greater demand for transportation when engaging in forest recreation activities. Second, the indirect economic effect of forest recreation is greater than the direct economic effect, which is similar to the results of Mazumder et al. [56]. Increased investment in this industry can not only enhance its own development but would also favorably stimulate the growth in other industries. Specifically, the industries that benefit the most from the forest recreation are “food and tobacco”, “agriculture, forestry, animal husbandry and fishery”, “transportation, storage and post”, “leasing and business services”, and “finance”. In addition, the output multipliers for forest recreation (2.344–2.336) are larger than the multiplier of logging and hauling (1.876) in China [57], which suggests that forest recreation can be a feasible means to alleviate the pressure of logging, the investment of forest recreation can effectively reduce the negative impact of logging bans, and contribute to sustainable resource utilization.

5.2. Employment Implication

In terms of employment, the forest recreation employment multipliers are between 7.816 and 8.488, higher than that of coastal tourism in China reported by Wang and Wang [58]. Apart from possible methodological differences, this manifests that forest recreation is more powerful in generating employment than the coastal tourism as far as per unit output produced. In addition, the results of employment multiplier show a downward trend. One reason for this decline may be related to the improvement of technology, which increases work efficiency and reduces the marginal use of labor, reflecting the industrial trend from labor-intensiveness to technology-intensiveness. Nevertheless, the total employment contribution of the forest recreation remains increasing, indicating that rising forest recreation demand is still able to generate a large number of employments, even if the employment multiplier drops. Different from the output and added value effects, the employment created by forest recreation is mainly ascribed to the direct effect, that is, attributable to its own industry, rather than through indirect effects. Therefore, the government could develop high employment-inducing sectors, such as catering_(F), accommodation_(F), and retail_(F), which is consistent with the findings made by previous studies [31,59]. Due to the characteristics of more labor usage, low employment thresholds, diverse ways of participation, and less skill requirements, the forest recreation and related industries benefit poor people and employees who work for small-scale businesses with low level of education and capital investment [60]. Local communities can easily participate in the forest recreation economic activities through the businesses of accommodation, restaurants and the sale of agricultural products. Thus, the significance of developing forest recreation is not only able to enhance welfare for tourists, but also to drive farmers out of poverty through improving their income and job opportunities.

5.3. Standard Multipliers

To better compare with other studies with respect to the magnitudes of the estimated multipliers, in this research, we also calculated the standard type I multipliers to reflect the effects (direct and indirect) commonly measured by a typical input–output study [35]. To put this in perspective, taking 2018 as an example, the standard multipliers of the output, added value and employment are 2.377, 0.999, and 1.464 employment. The latter stands that for every 1 direct job created, 0.464 additional jobs will be generated. These multipliers are close to the ones developed by the study of American wildlife watching (output multipliers of 2.53–2.04) [61] and Indian tourism (output multiplier of 2.141) [62]. Indeed, those multipliers are much higher than those estimated in South Korea (output multiplier of 1.873 and added value multiplier of 0.788) [63] and Ireland (output multiplier of 1.33 and employment multiplier of 1.37) [64]. One possible reason is that China, US, and Indian are considered as the three large economies in the world in terms both size of the economy and comprehensiveness of industries inclusiveness. As the I-O analysis conducted for a larger and more industries inclusiveness automatically results in larger multipliers due to more and closely connected economic activities built up within the economic system, i.e., more linking effects and less leaking effects [31,65]. Along the line, the contribution made by the Irish tourism industry in terms of the percentage of the GDP (5.3%) was lower than that of national employment (8%) [64]. However, in this study, the forest recreation shares 2.55% of GDP, which is higher than its employment share (2.39%). As such, the relative significance of the impacts imposed by the forest recreation industry on the GDP’s and employment’s stimulation depends on the structure of the economy and its length and complexity of supply chains.

6. Conclusions

This study applies an input–output analysis to quantify the direct and indirect economic impacts potentially imposed by the national forest recreation industry on China’s economy in 2012, 2017, and 2018, respectively. In the process, we extract out the forest recreation industry from the national I-O tables to reformulate the new I-O tables which contain the forest recreation as the focal industry. The synthesis is based on the guidelines proposed by the WTO Tourism Satellite Account and the protocol adopted by the National Statistical Bureau in China. The modified I-O tables with the forest recreation centered are then utilized in analyzing industry linkages, output effect, added value effect, employment effect, and their dynamic changes over time through respective estimated multipliers.

Overall, the research results indicate that the forest recreation industry has performed a positive role in boosting China’s economy. Taking the strategy of developing national forest recreation industry not only serves to forcefully facilitating national economic growth and development, but also safeguards the ecological civilization in China, which is a truly win-win solution. Last but not the least, featuring with low skills and more intensive labor, the forest recreation is of significance to alleviating poverty for those underprivileged rural and remote areas, thus benefiting more equal income distribution.

This study examines the feasibility of using I-O analysis to analyze the economic contribution of China’s forest recreation industry. The results have the merits of providing some benchmark references for investment strategy makings, especially so with regard to concerning over the synergetic effects potentially resulted from the forest recreation investment, so as to be able to achieve maximum capital returns and overall benefits. However, we have to admit the fact that the data we used for the I-O model is not ideal situation since they were all second-hand data being sorted out from the pre-existent national I-O tables. It is reasonable to imagine that the estimated multiplier outcomes could be improved in terms of their accuracy and reliability as more accurate first-hand data of forest recreation industry is available. Thus, as far as policy-makings, some caveat should be exercised. All the quantitative impact results developed in this study only partially reflect economic value accrued to the forest recreation industry, so-called use value or marketable value. Therefore, by their alone, justifiable policy decisions could not be made and implemented. In the process, all the non-market value associated with the forest recreation industry, such as ecological value, must be taken into consideration. For the policy consideration, the future economic impact analysis must be combined with the information of non-market valuation, such as the willingness to pay and willingness to accept for compensation, so that more viable strategies can be made.