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    Study on technical efficiency of traditional Chinese medicine industry of the Belt and Road Initiative based on environmental complexity

    2022-02-26 03:15:48TongShaoYanZhou
    Traditional Medicine Research 2022年2期

    Tong Shao,Yan Zhou*

    1School of Pharmaceutical Science and Technology,Tianjin University,Tianjin 300072,China.

    Abstract Background: Based on the principle of “joint consultation, co-building, and sharing”, with the aim of creating an energetic new model of economic growth, the Belt and Road Initiative achieves connectivity and mutual benefit.However, the degree of coordinated development of innovation subjects directly affects the improvement and optimization of the Belt and Road Initiative’s strategy implementation environment, and technology efficiency needs improvement.Methods: In this study, a data envelopment analysis model based on the environmental noise elimination algorithm (traditional Chinese medicine - Belt and Road Initiative) was used to identify the technical efficiency of the traditional Chinese medicine industry of Belt and Road Initiative by eliminating the impact of environmental factors.Results: Pure technical ineffectiveness, regional impact, and environmental factors represented by policy responses have a non-linear impact on technical efficiency.The overall technical efficiency of the traditional Chinese medicine industry in the countries along the route shows a trend of annual increases, technical efficiency and scale efficiency are relatively high, and operational efficiency is influenced by the degree of response of the countries along the route and its supporting policies.Conclusion: Pure technical inefficiency is the main factor affecting total technical loss.Geographical advantage is the key factor in operational efficiency.External environmental factors have a significant influence on the operational efficiency of the traditional Chinese medicine industry.

    Keywords: traditional Chinese medicine; Belt and Road Initiative; environmental complexity;data envelopment analysis;wavelet model

    Background

    The ancient Silk Road originated in the Western Han Dynasty and was opened by Emperor Wu of the Han Dynasty when Zhang Qian was sent to the Western regions (around 164 B.C.E.-114 B.C.E.) [1].It took the capital Chang’an as the starting point, passed through Central Asia, West Asia, connected the Mediterranean countries, and ended at Rome [2].In 1877, the German geographer Ferdinand von Richthofen first defined the road, which was the medium of silk trade between China and Central Asia and between China and India in the period from 114 B.C.E.to 127 C.E., as the “Silk Road” in his bookChina:The Results of My Travels and the Studies Based Thereon[3].In the book,The Ancient Silk Road between China and Syriapublished at the beginning of the 20thcentury, German historian Holman further extended the Silk Road to the west coast of the Mediterranean Sea and Asia Minor.It defines the basic connotation of the Silk Road as the passage of land trade exchanges between China and South Asia,West Asia, Europe, and North Africa through Central Asia.However,the Silk Road has now become the general name for all the political,economic, and cultural exchanges between ancient China and the West.

    During his visit to Kazakhstan in September 2013, Chinese President Xi Jinping advocated the establishment of the “Silk Road Economic Belt”, a new regional cooperation model.One month later,President Xi again advocated for the construction of the “21stCentury Maritime Silk Road” in Indonesia.These two proposals are officially combined and termed as “Belt and Road Initiative (BRI)” [4].As soon as the BRI initiative was announced, it attracted wide attention from the international community, which gave new meaning to the ancient Silk Road and opened a new chapter in jointly building a“community with a shared future for mankind” [5].

    BRI is strongly supported by the countries along the route.By 2020,nearly 170 countries and international organizations have responded positively to the initiative and signed more than 200 cooperation agreements with the Chinese government.The countries along the BRI have a large population base and market volume.The development of the BRI strategy spurred increasingly vigorous cooperation between China and relevant countries in Asia and Europe in the field of health.This shows the great importance attached by the Chinese government to strengthening cooperation in the fields of medicine and health in the countries along the route.The BRI strategy not only promotes the diversification of pharmaceutical trade in route countries, but also plays an important role in promoting the balanced, inclusive, and sustainable development of the global pharmaceutical industry.Diversified economic and trade cooperation in the pharmaceutical field also promotes the internal and external interaction between China and the countries along the route, further deepening international production capacity cooperation.The number of China-Europe trains has reached thousands each month.China-Europe trains have operated more than 1,000 trains for 19 consecutive months, with a total of 13,817 trains in the first 11 months of 2021, reaching more than 170 cities in 23 European countries.Traditional Chinese medicine (TCM) has spread to 183 countries and regions worldwide, involving 65 countries and regions along the BRI route.In the context of the global epidemic,China should speed up the integration of Chinese medicine into the development of the BRI and the world can share the hard power of TCM and the soft power of excellent Chinese culture.

    In recent years, Chinese TCM enterprises have attempted to go abroad to actively expand overseas markets.According to the statistics released by the China Chamber of Commerce for Import and Export of Medicines and Health Products, Chinese pharmaceutical enterprises exported 22.3 billion US dollars of drugs and related products to the markets of countries along the BRI in 2019.However,TCM products account for only 4.85% of this, which does not match China’s status as a country with a large TCM market.There are some problems in the development of China’s TCM industry, such as individual farmers’ planting transactions, short and loose industrial chains, lack of brand value and influence, lack of core competitiveness, and lack of original research drugs.Limited by industrial structure, innovation capacity, and other conditions, the coordinated development of the pharmaceutical industry in the countries along the route still faces severe challenges.

    Driven by the BRI strategy, countries along the route have gradually formed an open innovation ecosystem, and new technologies have brought new forms to the TCM industry’s development [6].Big data is deeply integrated with the biomedical industry.Artificial intelligence is constantly exploring precision medicine, and non-traditional medical enterprises are also attempting to break medical industry boundaries.With the continuous progress of new ideas and technologies, the market of the TCM industry has broad prospects in the future.

    The coordinated development of the TCM industry in countries along the route is affected by complex environmental factors.

    (a) Complexity of external environment structures.

    The external environmental structure of the BRI’s innovation system includes natural resources, regional environment and economy, policy, science and technology, and so on.The BRI innovation system is a large, complex system that constantly exchanges information and energy with its innovation subject.Environmental diversity and dynamic changes make the interaction between countries along the route and the BRI’s external environment extremely complex.

    (b) Complexity of the policy and market environments.

    The influence of the policy environment is mainly reflected in administrative management and government policies.Government support plays a decisive role in the formation of the TCM industry ecology.Optimization of the external environment requires guidance,support, and protection [7].The development and evolution of TCM enterprises rely on the latest science and technology, the efficient exchange of a large amount of information, and the agglomeration ecosystem.However, in some countries and regions with unstable policy environments, the innovation and development of TCM enterprises is relatively slow due to the influence of long-term violent political factors, bad ecological environment, and low worker quality.

    (c) Complexity of scientific and technological environments.

    The change in the scientific and technological environment is one of the most influential factors in the TCM industrial ecosystem [8].The emergence of technology breaks the original technological ecological order and improves the function/cost ratio of products as well as the relevant market and consumption structures.Change in technological environment will also have a direct impact on changes in labor force structure; simultaneously, the difference in education level will have an impact on the demand for important products and the scale of users.

    Environmental complexity creates noise interference factors in the BRI’s innovative data.This study focuses on an algorithm that effectively eliminates environmental noise on technological efficiency,and attempts to identify the fundamental factors restricting the growth of technical efficiency in the TCM-BRI industry.The study provides suggestions for the TCM industry’s rapid integration and development and for Chinese traditional medicine enterprises to accelerate the process of internationalization.

    Material and methods

    Data envelopment analysis was developed by Charnes and Cooper in 1978 [9].It is a quantitative analysis method to evaluate the relative effectiveness of comparable units of the same type by using the method of linear programming according to multiple input and output indicators; it is widely used in different industries [10].To reduce the impact of data noise, scholars use a three-stage data envelopment analysis model for analysis, but the stochastic frontier analysis model relies on prior assumptions and does not pay attention to the model’s limitations [11].To more comprehensively eliminate the impact of environmental factors on the data, this study introduces the wavelet model based on the characteristics of environmental complexity and effectively eliminating the interference factors of data environmental noise, further clarifying the technological efficiency of the TCM-BRI industry.

    The basic idea of the environmental noise elimination model (TCMBRI) is that after the data is transformed by wavelet, the wavelet coefficients generated by the data contain important information.After decomposing the data by wavelet, the wavelet coefficients of the real data are larger, and the wavelet coefficients of the noise are smaller than those of the real data [12].By selecting an appropriate threshold, the wavelet coefficients of the real data are smaller than those of the real data.Wavelet coefficients are greater than the threshold generated by real data and should be retained, while the threshold is considered to be caused by noise and set to zero to achieve denoising[13].

    TCM-BRI model construction

    Assuming the BRI’s real datas(n) is polluted by noisef(n), the general denoising model is

    Assume f(t)、ψ(t) are all square integrable functions, and∫Rψ(t)dt =0 , then the following integral transform is called the continuous wavelet transform.

    ψ is called basic wavelet or mother wavelet.

    is called the wavelet function, which is a family of functions ψ(t)obtained by the mother wavelet after expansion and translation.Continuous wavelet transform is a binary function that transforms a univariate function f(t) into a binary function Tψf (a,b) in the time and frequency domains.At the same time, the frequency domain representation of the wavelet transform can be easily obtained from the Parseval identity.

    Assume ψ(t) is a square integrable function and satisfy

    Then for any square integrable function f(t)

    This is an inverse transform of the continuous wavelet transform.Formula 6 is called the admissibility condition, which indicates that it should have rapid decay.We know that in the continuous wavelet transform, the scale factor a, time t, and offset b are all continuous transformations.However, computer characteristics require discretizing them when using them.Therefore, the transformation obtained by discretizing the scale factors and offset b is called the discrete wavelet transform.

    In formula 6,a=aj0,b=kaj0b0, then

    Assume ψ(t)∈L2(R),a0>0,ψj,k(t), then

    is the discrete wavelet transform off(t).Compared with the continuous wavelet transform, it has fewer values on the points.When the frame condition of ψj,k(t) is satisfied, the reconstruction formula is:

    Discrete wavelet transform is used tof(n),obtain:

    Tf(j,k) is a wavelet coefficient, but formula 10 has a large amount of complex calculations, and ψ(t) generally does not show the expression, so we often use the fast algorithm of wavelet decomposition and reconstruction of the Mallat algorithm, that is,

    If the low-pass and high-pass filters of φ and ψ correspond topandg, the wavelet reconstruction formula is as follows:

    The basic idea of wavelet threshold denoising proposed by Donoho is that after the signal is transformed by wavelet, the wavelet coefficient produced by the signal contains the important information,the wavelet coefficient of the signal is larger, the wavelet coefficient of noise is smaller, and the wavelet coefficient of noise is smaller than that of the signal.By selecting an appropriate threshold, the wavelet coefficient that is greater than the threshold is generated by the signal and should be retained.Others less than the threshold are caused by noise and set to zero to achieve denoising.The basic steps are:

    (a) Decomposition: a wavelet with N layers is selected to decompose the signal.

    (b) Threshold processing process: after decomposition, a suitable threshold is selected, and the threshold function is used to calculate the coefficients of each layer.

    (c) Reconstruction: the signal is reconstructed using the processed coefficients.

    The two most commonly used threshold functions are the hard threshold function and the soft threshold function.Hard threshold function

    Soft threshold function

    When using the hard threshold function, the local characteristics of the data can be well preserved, but it is worth noting that the hard threshold function at λ is discontinuous, and the denoising result will often have a large variance when using the hard threshold function;the final data may exhibit a pseudo-Gibbs phenomenon (Figure 1A).Although the soft threshold function overcomes the defect of discontinuity and the result after denoising is relatively smooth, the soft threshold function retains and reduces all the wavelet coefficients larger than the threshold, which reduces the wavelet coefficients with larger absolute values, resulting in the loss of some high-frequency data information (Figure 1B).Thus, there is a large deviation in the denoising results, which may also cause signal edge blurring[14].

    Figure 1 Hard and soft threshold function.

    Based on the defects of the above threshold function, Gao Hongye proposed a method to realize a new threshold function with the Garrote function, and its analytical formula is as follows:

    Based on the soft and hard threshold function and the Garrote threshold function, we find that they have in common that the wavelet coefficients smaller than the threshold are all set to zero,which may remove the data signals with smaller coefficients as noise.

    The hard and soft threshold functions are combined with the weighted average method, and the weighting factor is set toμto construct the environmental noise elimination model TCM-BRI.

    The formula of weighting factorμis as follows:

    Experimental study

    Select a set of original signals:

    Signal original =sin(2 ×pi× 10 ×t) +sin(2 ×pi× 20 ×t)+sin(2 ×pi× 30 ×t).The sampling frequency was 1,000 Hz, and the sampling points were 1,000.Gaussian white noise was added to the first 500 points of the signal, and uniformly distributed white noise was added to the last 500 points.

    As shown in Figure 2, the comparison of similar stochastic frontier analysis model, Kalman filter, and environmental noise elimination model (TCM-BRI), shows that the environmental noise elimination model (TCM-BRI) can eliminate environmental noise more accurately,and the signal-to-noise separation performance is optimal.

    Figure 2 Comparison of different models.

    Results

    Indicator selection

    This study selects a country’s population density and life expectancy,proportion of national medical expenditure, the number of hospital beds per thousand people, per capita gross domestic product, and foreign direct investment, and also select outputs from three aspects:economic output, technological output, and knowledge output [15].See Table 1.

    Table 1 Indicators and resources

    The correlation analysis of the indicators is shown in Table 2,which shows that the correlation of most indicators is not high,which shows that the choice of this index system is more reasonable.

    Table 2 Pearson correlation matrix

    Calculation result

    The environmental noise elimination model (TCM-BRI) was used to calculate the new input variables after excluding random factors and environmental factors to ensure that the input variables of each decision-making unit are in the same environment.According to the adjusted input variables and original output variables, using DEAP2.1 analysis software, the calculation results of TCM trade productivity index of China and 10 Association of Southeast Asian Nations(ASEAN) countries from 2009 to 2018 are shown in Table 3.

    Because the BRI was put forward in 2013, in order to obtain a comparative analysis of TCM trade efficiency between China and 10 ASEAN countries, the evaluation interval is divided into three stages:2009-2012 is before the implementation of the initiative, 2013-2015 is the initial stage of the implementation, and 2016-2018 is the second phase of the implementation; the data on the productivity of TCM trade in the three stages are shown in Table 4.The trade productivity index of TCM between China and ASEAN in 2009 and 2018 is shown in Table 5.

    Discussion

    According to our analysis, there is a noticeable increase between 2010-2011 and 2011-2012 in Table 3.In 2009, the National Food and Drug Administration issued guiding principles for the variety protection of TCM, and invested 24.5 million RMB in raising TCM standards and 21 million to improve the variety standards of TCM injections.The aim is to complete the overall improvement of all listed drug standards and form a relatively sound system of drug standard in 3-5 years.In 2010, the new edition of theChinese Pharmacopoeiawas completed, and the TCM standards, proprietary of TCM extract were greatly improved.In particular, this solved the longstanding problem of lack of standards for prepared slices of TCM,greatly increased the number of standards applicable to prepared slices of TCM, and initially solved problems such as the lack of national standards and the lack of unity in local processing standards that have long perplexed the development of the industry of prepared slices of TCM.This will play a positive role in solving the safety problems of TCM injections and provide strong technical support for drug supervision.

    Table3 TCM trade productivity index in China and ASEAN

    After removing the influence of external factors and random errors,the overall technical efficiency of the TCM industry in the countries along the route shows an annual increasing trend, with an average annual growth rate of 58.23%; the growth rates of technological progress and scale efficiency were 15.73% and 21.5%, respectively.This is much higher than the 11.45% increase in pure technical efficiency, which shows that pure technical inefficiency is the main factor affecting the total technical loss of the TCM industry in countries along the BRI (Table 4).Specific performance is as follows:under BRI policy incentives, the TCM industry in the countries along the route shows a good development trend, restricted by the low level of pharmaceutical practitioners and the lack of R&D and innovation abilities.The technical efficiency growth rate is much lower than scale efficiency.This also reflects that under the guidance of the BRI strategy, the integration of medical resources in the route countries has accelerated, and the concentration and economies of scale of the pharmaceutical industry have been promoted.

    Table 4 Three stages of TCM trade productivity index in China and ASEAN

    As shown in Table 5, the Tfpch of TCM trade in ASEAN countries is low; specifically, Thailand and Singapore have relatively high levels of Effch, Sech, and Tfpch.In recent years, the Thai government has actively implemented theAct on the Protection and Promotion of Traditional Thai Medicine intellectualsystem for the protection of traditional medicine [16].Enacting theAct on the Protection and Promotion of Traditional Thai Medicine intellectualhas curbed plagiarism in usage of herbs and prescriptions through legislation;effectively promoted the spread of traditional herbal knowledge in Thailand; and achieved a balance of interests among the subjects in the process of traditional herbal medicine protection, forming a good situation for the benign development of Thai medicine [17].

    Table 5 Trade productivity index of TCM between China and ASEAN in 2009 and 2018

    The biomedical development strategy has always been a key R&D strategy established by the Science and Technology Research Council of Singapore.By launching the biomedical science incentive program,Singapore focuses on strengthening human, patent, and industrial capital (3C) investment.The Singapore government attaches importance to protecting intellectual property rights, and has a sound law enforcement mechanism, as well as good R&D and venture capital environments.This has become a key factor in its success in the biomedical field.It is worth noting that compared with ASEAN countries, China’s TCM trade efficiency does not show an advantage,which is not commensurate with China’s status as a major TCM country.

    From a regional perspective, TCM industry technical efficiency and scale efficiency for Asian countries along the route are relatively high,which is partly due to geographical advantages [18]( Table 5).Most Asian countries are deeply influenced by Confucian culture, and it is much easier to accept BRI policies.From a dynamic point of view,the total factor productivity of Israel, Russia, and Singapore is higher than that of other countries, which indicates that the overall operational efficiency of the biomedical industry in countries with a higher per capita gross national product.This is mainly because the biomedical industry is highly dependent on employee education level and national medical level.Indicators such as the average life expectancy and the number of hospital beds per capita in these three countries are among the best, and these factors have a positive effect on the improvement of total factor productivity.

    The degree of response of the countries along the BRI strategic route and its supporting policies have a significant impact on the TCM industry’s operational efficiency [19].For example, the tax policies of countries along the route play a positive role in promoting efficiency, and intellectual property protection policies also promote technical efficiency.Singapore has established the goal of building an Asian medicine and medical technology manufacturing center by launching the Biomedical Science Incentive Program, which has effectively promoted the healthy development of the new Singapore biomedical industry.

    Conclusion

    In the present study, the technical efficiency of the TCM industry in countries along the route is affected by complex environmental factors.This study shows that pure technical inefficiency is the key factor affecting the improvement of the TCM industry’s efficiency in the countries along the route.Regional factors have an obvious influence on TCM industry operational efficiency in the countries along the route.External environmental factors have a great influence on the TCM industry’s operational efficiency in these countries.Thus, this study focuses on the evolution of TCM industry innovation efficiency in countries along the route for future research.

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