Determinates of the competitiveness of provincial forest health care industry in China

LIU Fang,ZOU Zai-jin

School of Economics and Management,Southwest Forestry University,Kunming 650224,CHINA

Abstract: Due to the late start of China’s forest health industry,related research is lagging behind,which results in the inability to propose a corresponding path to enhance competitiveness.Based on this,the evaluation index system of provincial forest health industry competitiveness was constructed by using the Porter diamond model,and the Entropy-TOPSIS model is used to evaluate the competitiveness of China’s provincial forest health industry,and the key influencing factors of the forest health industry competitiveness are identified by using the grey correlation analysis method,and the specific promotion path was designed.The empirical results show that the grey correlation degree between related and supporting industries and the competitiveness score of the forest health industry is the highest,which confirms that it is the key influencing factor of the competitiveness of the forest health industry.Therefore,government departments should vigorously promote the integration of cultural tourism,medical care,pension and other related industries with the forest health care industry;enterprises should strengthen the construction of forest health infrastructure and supporting facilities to improve the level of forest health services.

Key words: industrial competitiveness;forest health;influencing factors;promotion path

1 Introduction

At the beginning of this century,due to the frequent occurrence of ecological and environmental problems,the construction of ecological civilization was put on the agenda,and the physical and mental health of the people was highlighted.China gradually discovered and began to build the relationship between forest resources and the economy and society.In 2012,Beijing took the lead in introducing the concept of forest health care,and then this concept began to be promoted in other regions of China.For example,Zhejiang,Fujian,Guizhou,Jiangxi,Henan and other provincial administrative regions have successively issued documents such as forest health industry development planning and implementation opinions to promote forest health industry development and facility construction.Nowadays,forest health is getting more and more attention from local governments and the public.As a blue ocean for the development of rural tourism and leisure agriculture in China,forest health is showing a booming trend and has a broad market prospect.And the key to the development of the forest health industry is to explore the influencing factors of forest health industry competitiveness and design the path to enhance competitiveness.The completion of this study will fill the gap in the research on the influencing factors of the competitiveness of the forest health care industry,supplement and improve the research system of the competitiveness of the forest health care industry,put forward the path suggestions to promote the competitiveness of China’s forest health care industry and provide references for the improvement of the competitiveness of China’s forest health care industry.

As an emerging industry in China,the development of the forest health industry is in its infancy.At present,academic research on forest health mainly focuses on forest health base planning,development model(Yao et al.,2021)environmental conditions(Cui et al.,2022),and the impact of forest health activities on the human body(Morita et al.,2011;Lee et al.,2014;Bang et al.,2018;Kim et al.,2019),while the quantitative analysis of forest health industry competitiveness is mostly carried out at the provincial level(Zou et al.,2022).At the same time,the research on the evolution of the spatial and temporal pattern of forest health industry competitiveness distribution is almost blank.However,many domestic scholars have analyzed the influencing factors of industrial competitiveness in other industries according to the characteristics of different industries.Some scholars have drawn on Porter’s “diamond model” to sort out the factors influencing the development of China’s new energy automobile industry and to identify the focus points for improving the international competitiveness of China’s new energy automobile industry(Gong et al.,2022).CMS models have also been used to analyze the heterogeneous factors affecting the export of edible mushrooms(Liu et al.,2022).More scholars are based on the diamond model to construct the index system of influencing factors,and then evaluate the influence degree of each factor by grey correlation analysis (Zhu et al.,2022).Li et al.(2022) took the marine chemical industry of Shandong province as the research object and constructed the evaluation index system of the competitiveness of the marine chemical industry from four aspects: industrial foundation,output,structure,and scientific research.The entropy method was used to calculate the competitiveness of the marine chemical industry,and the grey correlation analysis method was used to analyze the influence of various factors on the competitiveness of the marine chemical industry in Shandong province.Huang (2018) constructed the tourism industry competitiveness index system of 31 provincial administrative regions in China and used quantile regression to study the influencing factors of spatial differences in the competitiveness of the forest park tourism industry.The study confirmed that the economic foundation,capital investment,policy support,and resource advantages all have an impact on the competitiveness of the forest park tourism industry.Based on Porter's diamond model,Lan et al.(2019)analyzed the influencing factors of the international competitiveness of China’s digital culture industry from six aspects: production factors,demand conditions,related and supporting industries,enterprise organizations,strategies and competition,and government and opportunities.Seven indicators were selected for quantitative analysis through stepwise regression,and it was confirmed that the openness of the digital culture industry had the greatest impact on the international competitiveness of China's digital culture industry.Liu (2018) takes the Yangtze River Delta region as a sample and uses Eviews6 cross-sectional model to analyze the influencing factors of the competitiveness of the commercial circulation industry in the Yangtze River region.The results show that the per capita disposable income has the closest relationship with the competitiveness of the commercial circulation industry in the Yangtze River region.Yao (2007) divided the influencing factors of industrial competitiveness into basic factors,core factors,and environmental factors,using system analysis to determine the key factor affecting the competitiveness of China's construction industry.Gan et al.(2017) systematically analyzed the influencing factors of the competitiveness of the Sichuan-Xizang tourism industry from the three dimensions of regional tourism products,tourism support conditions,and regional tourism management,and applied the DEMATEL method to quantitatively analyze the interaction between the factors.According to the quantitative results,the causal classification and importance analysis were carried out,and the factors such as policy support,market demand,tourism service quality and market order,and infrastructure capacity played a key role in the competitiveness of the Sichuan-Xizang tourism industry.Wang et al.(2014)evaluated the competitiveness of the tourism industry by establishing an evaluation index system for the competitiveness of the tourism industry,analyzed the rankings of the competitiveness of the tourism industry in Heilongjiang province,and proposed targeted strategies to enhance the competitiveness of the tourism industry in Heilongjiang province.Xue (2014) obtained the main influencing factors of Shandong sports industry competitiveness by constructing the index system of sports industry competitiveness and using principal component analysis.According to the theory of industrial competitiveness and the characteristics of the industry,Hu et al.(2014)divided the influencing factors affecting the competitiveness of high-tech industries into four categories:technological innovation competitiveness,economic development competitiveness,financial benefit competitiveness and industrial cluster competitiveness,and then used the grey correlation analysis method for empirical analysis to identify the key influencing factor.Based on understanding the development status of the wine industry in Gansu province,Li et al.(2014)used Porter's diamond model and eliminated opportunity factors to analyze the factors affecting the competitiveness of the wine industry in Gansu province,and put forward corresponding countermeasures and suggestions.

Based on the previous results,this study will use Porter’s diamond model as the theoretical basis to construct an evaluation index system from the dimensions of production factors,demand conditions,related and supporting industries,government factors,and ecological factors.Taking 31 provincial administrative regions in China as the research object,based on the cross-sectional data of relevant indicators of forest health industry competitiveness in each provincial administrative regions in 2020,the Entropy-TOPSIS model is used to analyze the competitiveness level of forest health industry and its regional differences,and the key factors affecting the competitiveness of forest health industry are analyzed by grey correlation analysis.Based on the results,we further explore how to scientifically guide the development of China’s forest health industry and clarify the key work of improving the competitiveness of the forest health industry.

2 Research method

Forest health care is a new form of forestry industry developed based on the attributes of forest ecological protection.It explores the transformation channel of “lucid waters and lush mountains into golden mountains and silver mountains” (Sun et al.,2021).Therefore,the competitiveness evaluation of the forest health care industry should also combine and implement the “two mountains” theory.Based on the “two mountains” theory and the “diamond” theory,the evaluation index system of forest health care industry competitiveness should be constructed.Then,the Entropy-TOPSIS method is used to evaluate,and the grey correlation model is used to identify the key influencing factors of the competitiveness of China's provincial forest health industry,so as to provide a basis for the analysis of the development status of the forest health industry in each provincial administrative regions and the design of promotion path.

2.1 The evaluation index system

By comprehensively considering the representativeness of the indicators and the collectability of the data,the index system is designed from the five dimensions of production factors,demand conditions,related and supporting industries,government factors and ecological factors(Table 1).The observation indicators of different elements are as follows:

The main selection of production factors is the material resources that play a key role in the development of the forest health industry,which should include forest resources status:including forest coverage,forest volume,and forest area;status of forest health resources: number of pilot construction units of national forest parks,national nature reserves and forest health bases.

The analysis of demand conditions is usually carried out around the number of consumers and the level of consumption,and the groups participating in forest rehabilitation activities are mostly urban elderly people.Therefore,the three indicators of urbanization rate,aging rate,and per capita disposable income of urban residents are selected first.Due to the strong correlation between the demand for forest health services and the development of tourism,two indicators are added here:the number of visitors per 10000 people receive and the per capita consumption of forest and grass tourism.In addition,the academic attention of forest health care can also reflect the market situation of the industry to a certain extent,so the academic attention index of forest health care is added.

The related and supporting industries mainly selected the infrastructure and human resources that support the development of the forest health industry.Here,the relative indicators were selected to reflect the basic conditions and reception capacity of each provincial administrative regions,including the density of grade highways,the density of railways,the number of airports per unit area,the proportion of health technicians per thousand population,the proportion of employees in catering and accommodation industries,the number of employees per unit area of forest parks and the density of trails.

The ecological environment competitiveness needs to reflect the ecological environment condition of each provincial administrative regions,so we mainly select the excellent and good rate of air,the COD of main pollutant discharge per unit area of wastewater,the SO2discharge per unit area of waste gas,the environmental emergencies per unit area as the ecological environment index.

Government behavior plays a vital role in the development of the forest health care industry.In order to quantify the impact of the government in the development of the forest health care industry,the number of forest health care special policies issued by each province and the business environment index are selected to reflect the government support.

2.2 Data sources

The data in this paper mainly come from the 2021ChinaStatisticalYearbookand the 2020ChinaForestryandGrasslandStatisticalYearbookissued by the National Bureau of Statistics.The number of pilot construction units of the national forest rehabilitation base was counted through the list of pilot construction units of the national forest rehabilitation base issued by the China Forestry Industry Federation from 2015 to 2020.The number of China’s civil aviation airports is derived from the 2021NationalCivilTransportAirportProductionStatisticsBulletinissued by the Development Planning Department of China Civil Aviation Administration;the number of provincial special policies is derived from the collation of public information on the official government website;the business environment index is derived from the 2020ChinaProvincial BusinessEnvironmentResearchReport;the data of academic attention of forest health care comes from China National Knowledge Infrastructure (CNKI),which is quantified as the number of academic journals retrieved with the theme of “forest health care” and various provinces in advanced retrieval.Due to the long publication period of the paper,the publication time is pushed forward by one year as the academic attention index data of the year,that is,the number of papers published in 2021 is used as the academic attention data of forest health care in 2020.Due to the lack of data in Taiwan Province,Hong Kong SAR,and Macao SAR,the sample data of 31 provincial administrative regions were obtained.

2.3 Analysis of competitiveness and influencing factors

Based on the principles of objectivity and feasibility,the Entropy-TOPSIS model is selected to evaluate the competitiveness of China’s provincial forest health industry,and the comprehensive competitiveness level of the provincial forest health industry is evaluated and ranked.Finally,the calculated competitiveness score is used as a reference sequence,and the grey correlation analysis method is used to analyze the influencing factors,so as to put forward targeted suggestions.

2.3.1 Entropy-TOPSIS model

Entropy-TOPSIS method refers to the use of the entropy weight method to determine the index weight and then calculate the relative proximity between each evaluation object and the optimal solution by TOPSIS method.The greater the relative proximity,the higher the level of competitiveness.The normalized index value is represented by,first,calculate the proportion of index value:

Then calculate the information entropy of each index:

wherekis a positive constant,usually=wherem=31.

The difference coefficient of each index:

Substitute the index weight obtained by the entropy weight method into the formula,the weighting matrix is obtained:

Then determine the ideal solutionand the negative ideal solution

And calculate the Euclidean distance between each evaluated object and ideal solution,negative ideal solution

Finally,the relative proximitybetween each evaluation object and the optimal solution is calculated:

2.3.2 Grey relational analysis

In order to further determine the contribution of various factors to the competitiveness of the forest health industry,the grey correlation analysis method was used to determine the key influencing factors of the competitiveness of the forest health industry.Grey correlation analysis is a method of set comparison of data sequences that reflect the changing characteristics of various factors.The basic steps are as follows.

First,we need to determine the reference sequence.Because it is necessary to examine the relationship between the competitiveness of the forest health industry and the indicators,the comprehensive score of the competitiveness of the forest health industry obtained above is selected as the reference sequence,and the selected index data after dimensionless processing is the comparison sequence.The reference sequence isand the comparison sequence is

Next,we can calculate the correlation coefficient

Next,we can calculate the correlation of the indicators

3 Analysis of empirical results

According to the evaluation,the comprehensive score of the competitiveness of the forest health industry in each provincial administrative region is obtained,and the grey correlation degree between each index and the competitiveness of the forest health industry is calculated based on the comprehensive score,and then the influencing factors of the competitiveness of forest health industry are analyzed concretely.

3.1 Analysis of comprehensive evaluation results

The comprehensive evaluation results are shown in Table 2.It can be seen from the evaluation results that the comprehensive competitiveness of China’s provincial forest health industry is the strongest in Guizhou (comprehensive score of 0.508),which is quite different from the relative proximity of Qinghai (comprehensive score of 0.115) with the weakest comprehensive competitiveness.

Table 2 Evaluation results of TOPSIS method

The average score of the comprehensive competitiveness of 31 provincial administrative regions is 0.255,of which the comprehensive score of 15 provincial administrative regions is higher than the average score,that is,48% of the provincial administrative regions have a higher level of competitiveness than the average level,and 16 provincial administrative regions have a lower score than the average score,accounting for 52%,indicating that the competitiveness of forest health industry in most provincial administrative regions is at a low level.

The average level of comprehensive competitiveness in southwest China(Guizhou,Chongqing,Sichuan,Yunnan,Xizang) (based on the arithmetic average of relative proximity) is the strongest.The average level of comprehensive competitiveness in East China (Shandong,Jiangsu,Zhejiang,Shanghai,Anhui,Jiangxi,Fujian) is relatively stronger.The average level of comprehensive competitiveness of Central China (Henan,Hubei,Hunan) and North China(Inner Mongolia,Hebei,Beijing,Tianjin,Shanxi) is medium and almost the same.The average level of comprehensive competitiveness of Northeast (Heilongjiang,Jilin,Liaoning),South China (Guangdong,Guangxi,Hainan)is relatively weaker.The average level of comprehensive competitiveness in the northwest(Shanxi,Gansu,Ningxia,Qinghai,and Xinjiang)is the weakest,and the gap with South China is wide(Table 3).

Table 3 Average score and ranking of comprehensive competitiveness of forest health care industry in various regions

It can be seen from Figure 1 that the comprehensive competitiveness of the forest health care industry in Guizhou,Shanghai,and Sichuan is far ahead and located in the first echelon.Followed by Beijing,Yunnan,Fujian,Hunan,Shanxi,Heilongjiang,Zhejiang,and Guangxi,its comprehensive competitiveness is relatively stronger,in the second echelon;the third echelon includes Jiangxi,Xizang,Inner Mongolia,Jiangsu,Jilin,Hubei,Guangdong,and Chongqing,whose comprehensive competitiveness is at a medium level.Shanxi,Henan,Anhui,Hainan,Shandong,and Tianjin are located in the fourth echelon,and the comprehensive competitiveness of the forest health industry is weaker.Liaoning,Hebei,Xinjiang,Gansu,Ningxia,and Qinghai are in the fifth echelon,and the comprehensive competitiveness of the forest health industry is the weakest.Overall,the comprehensive competitiveness of the forest health industry in southwest China is the strongest,with the extension to the central region,the comprehensive competitiveness of the forest health industry gradually weakened,and the northwest region is the weakest.

Figure 1 Schematic diagram of comprehensive score of forest health care industry competitiveness in each provincial administrative region

3.2 Analysis of influencing factors

Based on the grey correlation analysis method,the correlation degree between the competitiveness of China's provincial forest health industry and various influencing factors is obtained(Table 4).

Table 4 Grey correlation degree between each index and the competitiveness of the forest health care industry

Among the 25 selected indexes,the correlation degree between 16 indicators and the competitiveness of the provincial forest health industry are above 0.7,indicating that more than 64% of the independent variables have a greater impact on the competitiveness of China's provincial forest health industry.Among them,the number of health technicians per 1000 population,the number of airports per unit area,the per capita disposable income of urban residents,the per capita consumption of forest and grass tourism,the density of railways,the academic attention of forest health care,the number of forest and grass tourists per 10000 people receive,the number of national nature reserves,and the number of employees in the catering and lodging industry have a grey correlation degree of more than 0.75 with the competitiveness of the forest health care industry,which plays a key role in the development of the forest health care industry.

Because of the different roles of each index in the comprehensive evaluation,the mean value of the correlation coefficient of each criterion layer can be figured out by Eq.(13).The calculation results are shown in Table 5.

Table 5 Grey correlation between the criterion level indicators of the competitiveness of China’s provincial forest health care industry and the competitiveness of the forest health care industry

The grey correlation coefficient of each criterion layer index from large to small is related and supporting industries,factors of production,government factors,demand conditions,and ecological factors.The grey correlation degree between the related and supporting industries and the competitiveness of the forest health industry is above 0.74,which indicates that the related and supporting industries are the key influencing factors of the competitiveness of the forest health industry and play an important role in promoting the competitiveness of forest health industry.The grey correlation degree between factors of production,government factors,demand conditions and the competitiveness of the forest health industry is above 0.7,and the gap is not large,indicating that these three factors also have a significant impact on the competitiveness of forest health industry;The correlation degree of ecological factors is about 0.5,and its impact on the competitiveness of forest health industry is far lower than other factors.This shows that although ecological factors are crucial to the environment of the forest health care base,it’s not the decisive factors to enhance the competitiveness of the forest health care industry.Enhancing the competitiveness of the forest health care industry is more dependent on related and supporting industries,factors of production,government factors,and demand conditions.

4 Conclusions,discussions and policy implications

Based on the revised Porter’s “diamond model”,the evaluation index system of the competitiveness of China’s provincial forest health industry is constructed.The Entropy-TOPSIS method is used to calculate the comprehensive evaluation results of each provincial administrative region,and the grey correlation analysis method is used to identify the key influencing factors of the competitiveness of the forest health industry.It can clearly show the current development status of the forest health industry in each provincial administrative region,make the relevant departments clear about the direction of their efforts,and also provide some reference and inspiration for the follow-up research on the competitiveness of the provincial forest health industry and the precise implementation of the government.

4.1 Conclusions

Through the evaluation of the competitiveness of China’s provincial forest health industry,it is found that the level of competitiveness of China’s provincial forest health industry varies greatly,and generally presents the spatial characteristics of“higher in the southwest and southern regions and weakest in the northwest region”;among them,Guizhou,Shanghai,Sichuan and other regions among the best comprehensive competitiveness,shows a good momentum of development;Xinjiang,Gansu,Ningxia,Qinghai and other regions ranked bottom in the comprehensive competitiveness,who have the weakest comprehensive competitiveness.

Guizhou,Shanghai,Sichuan,Beijing and Yunnan are the top five provincial administrative region in the national forest health competitiveness ranking.Among them,Guizhou,Sichuan and Yunnan are all in the southwest region and rich in forest resources,indicating that the development of the forest health industry is in good agreement with the development of regional forest resources.As the capital and international metropolis of China,Beijing and Shanghai are far superior to other provinces(cities and districts)in terms of basic conditions,and the development strength of the forest health industry is beyond doubt.

By identifying the key influencing factors of forest health industry competitiveness,it is found that the key factors affecting the competitiveness of the forest health industry are mainly related and supporting industries,followed by factors of production,government factors and demand conditions.Specifically,the number of health technicians per thousand population,the number of airports per unit area,per capita disposable income of urban residents,per capita consumption of forest and grass tourism,railway density,academic attention to forest health and other factors have a higher contribution.

Through the evaluation results,it can be seen that the competitiveness level of the forest health industry in China's provinces is very different.The first region with strong competitiveness of the forest health industry is rich in forest resources,and the second is the highly economically developed region.The related and supporting industries are the key influencing factors of the competitiveness of the forest health industry,and the related and supporting industries in the economically developed areas are also strong,indicating that the development of the forest health industry will also be subject to the local economic development to a certain extent.

4.2 Practical implications

The development of the forest health industry is a powerful driving force to promote the construction of ecological civilization and the goal of “carbon peak carbon neutralization”,and enhancing the competitiveness of the forest health industry is the top priority for the development of the forest health industry.According to the evaluation results,the following policy implications are proposed for the promotion strategy of forest health industry competitiveness.

Government departments should vigorously promote the integration of cultural tourism,medical care,pension and other related industries with the forest health care industry,organically integrate forest health care into various policy plans,fully integrate the resources of development such as reform,finance,health and health,civil affairs,natural resources,forestry,agriculture,cultural tourism and sports,and comprehensively promote the deep integration of forest health care and related industries.At the same time,forest health can be included in the scope of medical insurance,effectively promoting the integration and development of the forest health industry and health care industry.

Enterprises should strengthen the construction of forest health infrastructure and supporting facilities to improve the level of forest health services.The construction of forest health base infrastructure and supporting facilities is the basis for holidaymakers to participate in forest health resorts,and also an important factor affecting tourists ' holiday experience and satisfaction and whether to revisit.Therefore,in the process of forest health base construction,we should improve the service facilities such as walkways,signboards,and parking lots,and strengthen the construction of safety facilities such as fire safety and emergency rescue to ensure the safety of tourists.To further improve the construction of catering,accommodation,transportation,sightseeing,shopping,entertainment and other supporting facilities,so as to ensure the basic tourism needs of tourists and improve the satisfaction of tourists.

4.3 Limitation and future research

Although this study is valuable in constructing competitiveness evaluation index system of forest health industry,it also has some potential bias that need to be improved by future studies.First of all,this study only evaluated the competitiveness of forest health industry in 31 provincial administrative regions of China,without considering the impact of industry competition on the competitiveness of forest health industry.Secondly,due to the difficulty of data acquisition,this study only statically measures the competitiveness of China 's forest health industry in 2020.Therefore,the dynamic evolution characteristics of China's forest health industry competitiveness can be analyzed in the future.

Acknowledgments

This research is supported by the Humanities and Social Sciences Fund for Yunnan Provincial Academy and School Education Cooperation (Grant No.SYSX202107),Scientific Research Fund of Yunnan Provincial Department of Education (Grant No.2022Y630),and Science and Technology Innovation Fund of Southwest Forestry University(Grant No.KZ21006).