System and method for transnational enterprise habitat selection based on ESG visual angle

Abstract

The invention relates to the technical field of enterprise risk prediction, and discloses a transnational enterprise habitat selection system based on an ESG perspective, and the system comprises a habitat factor collection module which obtains and collects habitat factor data of three dimensions of climate, soil and biology through the Internet; the enterprise habitat prediction module collects habitat factors of three dimensions to perform index weight calculation, predicts to obtain habitat suitability values and sorts the habitat suitability values; the database is used for storing various data and enterprise habitat selection results. The invention also provides a habitat selection method, and habitat factors and secondary indexes are constructed from three dimensions of climate, soil and organisms; constructing a multi-attribute decision model for transnational enterprise habitat selection; and based on an OPA-F algorithm, determining the weight of the second-level index so as to obtain a habitat evaluation result of the transnational enterprise. Decision basis and reference are provided for an enterprise to select a target country, and a quantitative evaluation model is provided for market layout and competitive situation of transnational enterprises from a macroscopic perspective.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise risk prediction, and specifically relates to a system and method for habitat selection of multinational enterprises based on an ESG perspective. Background Art

[0002] The enterprise habitat selection system regards the international markets of different countries or regions as different enterprise habitats, and helps enterprises to scientifically judge and grasp the international market environment from a new perspective, select the habitat type suitable for enterprise survival from multiple enterprise habitats, and establish an effective habitat strategy system. The enterprise habitat selection system analyzes and studies the market competition situation of multinational enterprises through specific evaluation indicators and provides options for them to enter the market. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a system and method for multinational corporations' habitat selection based on the ESG perspective, which is used to provide a quantitative evaluation model for the market layout and competitive situation of multinational corporations based on the multinational corporation habitat selection system, and to objectively predict the habitat of multinational corporations.

[0004] In order to solve the above technical problems, the present invention provides a system for habitat selection of multinational enterprises based on the ESG perspective, including a central processing module, a habitat factor collection module, an enterprise habitat prediction module, a data storage module, a display module, a network communication module and a database are all connected to the central processing module by signal;

[0005] The habitat factor collection module obtains data on habitat factors in three dimensions: climate, soil and biology from the Internet through the network communication module; the enterprise habitat prediction module is used to calculate the indicator weights of the habitat factors in three dimensions: climate, soil and biology obtained by the habitat factor collection module, and then predict the "habitat" suitability value of each target country and sort them; the display module is used to display the habitat selection results of multinational enterprises output by the enterprise habitat prediction module; the database is used to store various types of data and enterprise habitat selection results, including real-time database, enterprise database and expert database.

[0006] The present invention also provides a method for habitat selection using the multinational enterprise habitat selection system:

[0007] The specific process is:

[0008] Step 1: Construct habitat factors and secondary indicators from three dimensions: climate, soil, and biology;

[0009] Step 2: Using ESG performance as the habitat selection criterion, a multi-attribute decision-making model for habitat selection of multinational enterprises is constructed;

[0010] Step 3: Determine the weights of the secondary indicators based on the OPA-F algorithm;

[0011] Step 4: Obtain data from multinational enterprises and collect and normalize habitat assessment indicator data;

[0012] Step 5: Obtain the habitat assessment results of multinational enterprises based on the weights of each indicator and the pre-processed data.

[0013] As an improvement of the habitat selection method of the present invention:

[0014] The habitat factors and secondary indicators include: climate habitat factors include national macro-environmental factors, which are composed of three secondary indicators: political law, economy, and cultural security; soil habitat factors include the development of various industries, which are composed of three secondary indicators: multinational corporate market, resource supply, and development of related industries; biological habitat factors include the situation of market participants, which are composed of one secondary indicator: competitors;

[0015] The political and legal indicators in the secondary indicators include diplomatic relations, political stability, government credit, and regulatory environment; the economy includes foreign exchange reserves, inflation, government debt level, and bilateral economic and trade level; cultural security includes security level and cultural distance; multinational corporate market includes market size and market growth rate; resource supply includes labor supply and capital supply; the development of related industries includes the information industry; competitors include the number of competitors and their competitiveness.

[0016] As a further improvement of the habitat selection method of the present invention:

[0017] The multi-attribute decision-making model includes an attribute layer and an alternative solution layer. The attribute layer includes environmental performance, social performance and economic performance, and the alternative solution layer includes the secondary indicators. Then, the triangular fuzzy number method is used to convert the fuzzy language variables into specific fuzzy numbers for conversion calculation.

[0018] As a further improvement of the habitat selection method of the present invention:

[0019] The process of determining the weight of the secondary index is as follows:

[0020] Convert fuzzy language into fuzzy numbers according to the fuzzy language variable level; solve the attribute model of OPA-F and obtain the weight of each attribute;

[0021] Solve the alternative model of OPA-F, and calculate the weights of each habitat factor by equating the 17 habitat factors to alternatives; after obtaining the scores of each alternative, calculate the total fuzzy score of each habitat factor, defuzzify it, and get the total weight value of each habitat factor. Use Lingo to get the weight of each habitat factor, and finally get the weight of each indicator.

[0022] As a further improvement of the habitat selection method of the present invention:

[0023] The process of obtaining multinational enterprise data is as follows:

[0024] Relevant data of multinational enterprises are obtained through the official websites of various databases and the national financial network, and various academic literatures and authoritative algorithm documents are used to score various factors of multinational enterprises in the ENR files; each habitat factor is first scored or ranked or data is collected, and then linear function normalization is performed according to the formula based on whether each habitat factor is beneficial to the entry of the enterprise; the higher the value, the more competitive the factor of the multinational enterprise is.

[0025] As a further improvement of the habitat selection method of the present invention:

[0026] The habitat evaluation results are calculated based on the collected data and the weights of the evaluation indicators to obtain the total score and ranking of each entered country to obtain the habitat selection results of the enterprise.

[0027] The beneficial effects of the present invention are mainly reflected in:

[0028] The present invention designs a habitat selection system and evaluation method for multinational enterprises based on the ESG perspective, providing decision-making basis and reference for enterprises to select target countries. The design of the overall evaluation method has high applicability, and the evaluation process and data collection are easy to implement. At the same time, the present invention establishes an evaluation index for the habitat selection of multinational enterprises based on the ESG perspective. In view of the lack of guidance and cognition of enterprises in the international market competition, a quantitative evaluation model for the market layout and competitive situation of multinational enterprises is provided from a macro perspective. Scientifically and objectively evaluate the habitat of multinational enterprises, and clarify the key habitat risks faced by multinational enterprises from the ESG perspective through the evaluation results, provide effective guidance for multinational enterprises to select overseas markets and prevent habitat risks, and solve the technical difficulties that existing enterprises have difficulty in habitat selection due to imperfect evaluation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0030] Figure 1 The membership function graph associated with triangular fuzzy numbers (TFN);

[0031] Figure 2 It is a graphical relationship diagram between fuzzy linguistic variables and fuzzy numbers;

[0032] Figure 3 It is a schematic diagram of the MADM model of the present invention;

[0033] Figure 4 The data source for each factor;

[0034] Figure 5 It is a module diagram of the multinational enterprise habitat selection system of the present invention. DETAILED DESCRIPTION

[0035] The present invention is further described below in conjunction with specific embodiments, but the protection scope of the present invention is not limited thereto:

[0036] Example 1: Multinational enterprise habitat selection system, such as Figure 5 As shown, it includes a habitat factor collection module, an enterprise habitat prediction module, a central processing module, a data storage module, a display module, a network communication module and a database. Among them, the habitat factor collection module, the enterprise habitat prediction module, the data storage module, the display module, the network communication module and the database are all connected to the central processing module by signal and are scheduled to run by the central processing module.

[0037] The habitat factor collection module is used to obtain data on habitat factors in three dimensions, namely, climate, soil and biology, from various database official websites and national financial networks through the network communication module, such as industry turnover in various countries, regional turnover entered, habitat factor data, etc.;

[0038] The enterprise habitat prediction module is used to calculate the corresponding indicator weights of the habitat factors in the three dimensions of climate, soil and biology obtained by the habitat factor collection module, and then predict the "habitat" suitability value of each target country and rank them.

[0039] The display module is used to display the habitat selection results of multinational enterprises output by the enterprise habitat prediction module, including displaying the sorted "habitat" suitability value. Finally, a habitat report is generated for each country to provide decision-making basis and reference for enterprises to select target countries.

[0040] The database is used to store various data and the results of enterprise habitat selection, including real-time database, enterprise database and expert database. The real-time database mainly obtains a large amount of data related to multinational enterprises through the habitat factor collection module, relying on web crawlers and big data technology, such as industry turnover in various countries, regional turnover entered, habitat factor data, etc. The enterprise database mainly comes from two aspects. One is to collect enterprise data and information entered by enterprise managers during use, including relevant data on their multinational business methods, financial management, marketing, organizational structure, personnel management, and corporate financial reports. The second is to capture corporate business data, corporate public opinion information, industry-related data, macro and micro data in the international engineering industry through web crawlers. The expert database mainly provides expert experience and subjective judgment when evaluating indicators and factors that are difficult to quantitatively analyze using other methods by statistics, processing, analysis and induction of expert opinions.

[0041] The method for multinational enterprises to select habitats based on the ESG perspective draws on ESG theory and ecological theory, introduces three ecological factors of "climate", "soil" and "biology" to construct a "habitat selection model", and evaluates the "habitat" suitability of 11 target countries on the Engineering News Record (ENR) list according to the evaluation system. The Ordinal Priority Approach for Fuzzy Linguistic Information (OPA-F) method is used to obtain the weight of each evaluation factor, and then the "habitat" suitability value of each target country is calculated to obtain the ranking of markets suitable for Chinese companies to enter. The specific process is as follows:

[0042] S1. Construct habitat factors from three dimensions: climate, soil, and biology, and then set up secondary indicators.

[0043] The "climate" factor refers to factors such as the national macro-environment, and is composed of three secondary indicators: politics and law, economy, and cultural security; the "soil" factor refers to the development of various industries, and is composed of three secondary indicators: multinational enterprise market, resource supply, and development of related industries; the "biology" factor refers to the situation of market participants, and is composed of one secondary indicator: competitors. The above secondary indicators are further divided into several third-level indicators, including: the political law in the secondary indicators includes diplomatic relations, political stability, government credit, and regulatory environment; the economy in the secondary indicators includes foreign exchange reserves, inflation, government debt level, and bilateral economic and trade level; the cultural security in the secondary indicators includes security level and cultural distance; the multinational enterprise market in the secondary indicators includes market size and market growth rate; the resource supply in the secondary indicators includes labor supply and capital supply; the development of related industries in the secondary indicators includes the information industry; the competitors in the secondary indicators include the number of competitors and the competitive ability of competitors. The three types of ecological factors influence and act on each other, causing adjustments and changes in the various stakeholders in the habitat of multinational enterprises. The habitat factors constructed in the three dimensions of climate, soil, and biology are shown in Table 1.1.

[0044] Table 1.1 Habitat factor indicators

[0045]

[0046] S2. Using ESG performance as the habitat selection criterion, a multi-attribute decision-making model for habitat selection of multinational corporations is constructed.

[0047] Combining ESG theory with the habitat factor model, a multiple attribute decision making model (MADM) can be constructed for the evaluation of multinational companies, in which environmental performance, social performance and economic performance are the attribute layer, and 17 habitat factor indicators (secondary indicators) are the alternative option layer. This constructs an evaluation index system for habitat selection of multinational companies. The MADM model is as follows: Figure 2 In order to solve the MADM model with fuzzy attributes, the triangular fuzzy number method is used to convert the fuzzy language into specific fuzzy numbers and perform conversion calculations on them.

[0048] Definition 1: The triangular fuzzy number (TFN) can be expressed as q = (l, m, u), and satisfies l ≤ m ≤ u. Its membership function can be expressed as formula (1):

[0049]

[0050] Among them, x is the element to be evaluated in the domain, l is the lower limit of the fuzzy number, m is the most likely value (also called the median or peak), and u is the upper limit of the fuzzy number.

[0051] The membership function associated with TFN is Figure 1 shown.

[0052] Definition 2: Assume there are two fuzzy numbers, A = (l, m, u), B = (l′, m′, u′), and the operation between them can be seen from formula (2) to formula (5):

[0053] A+B=(l+l′,m+m′,u+u′) (2)

[0054] AB=(ll′,mm′,uu′) (3)

[0055] A×B=(l×l′,m×m′,u×u′) (4)

[0056] A÷B=(l÷l′,m÷m′,u÷u′) (5)

[0057] Definition 3: The calculation formula of the step mean comprehensive representation (GMIR) score of the fuzzy number q = (l, m, u) is formula (6):

[0058]

[0059] Definition 4: Fuzzy linguistic variables can be converted into fuzzy numbers. Table 1.2 shows the fuzzy linguistic variables used in this study:

[0060] Table 1.2

[0061]

[0062] During calculation, to avoid multiplication with zero which would invalidate the constraint, the 0 in TFN is taken as 0.01.

[0063] Among them, the graphical relationship between fuzzy linguistic variables and fuzzy numbers is as follows: Figure 3 shown.

[0064] Definition 5: The fuzzy linear programming model of TFN can be transformed into model (7):

[0065]

[0066] Should meet:

[0067] (x j ,y j ,z j ),(p j ,q j ,r j ),(a ij ,b ij ,c ij ) is a non-negative confusion number.

[0068] Among them, x j ,y j ,z j Represent different decision variables, p j ,q j ,r j It is the coefficient in the objective function, indicating the influence of each decision variable on the objective function. ij ,b ij ,c ij is the coefficient in the constraint condition, indicating the influence of each decision variable on the constraint condition. i ,g i ,h i It is the right-hand side value of the constraint, indicating the restriction conditions of each constraint.

[0069] To solve model (7), it should be converted into a traditional linear programming model and then solved using the simplex algorithm. The conversion method is shown in model (8):

[0070]

[0071] Should meet:

[0072]

[0073] (x j ,y j ,zj ),(p j ,q j ,r j ),(a ij ,b ij ,c ij ) is a non-negative fuzzy number, x j ≤y j ≤z j ,s i ≤t i ≤f i .

[0074] s i , t i , f i They respectively represent the slack variables introduced in the process of converting to the traditional linear programming model.

[0075] S3. Determine the weights of the secondary indicators based on the Ordinal Priority Approach for Fuzzy Linguistic Information (OPA-F).

[0076] By converting fuzzy language into fuzzy numbers according to Definition 4, we can get:

[0077] Table 1.3

[0078] property TFN grade serial number Economic performance (0.01,0.1,0,3) 2 <![CDATA[W1]]> Social Performance (0.01,0.01,0.1) 1 <![CDATA[W2]]> Environmental Performance (0.1，0.3，0.5) 3 <![CDATA[W3]]>

[0079] In order to resolve the uncertainty of expert opinions, the OPA-F algorithm is used to convert fuzzy language into specific fuzzy numbers. The variables, parameters and set information involved in the OPA-F algorithm are shown in Table 1.4.

[0080] Table 1.4 Information about each letter in OPA-F

[0081]

[0082] Where p is the number of experts, n is the number of attributes, and m is the number of alternatives.

[0083] S3.1: Solve the attribute model of OPA-F and obtain the weight of each attribute:

[0084]

[0085]

[0086] S3.2: Solve the alternative model of OPA-F and obtain the scores of each alternative:

[0087]

[0088] S3.3: Calculate the total fuzzy score for each alternative using the following formula:

[0089]

[0090] S3.4: Substitute the 17 habitat factors (secondary indicators) into model (12) as alternatives to calculate the weight of each habitat factor. Then use formula (11) to calculate the total fuzzy score of each habitat factor. Use formula (6) to defuzzify and obtain the total weight value of each habitat factor.

[0091] From model (9), we get:

[0092]

[0093] Use model (8) to transform the attribute fuzzy model (12) of OPA-F into a traditional linear programming model (11):

[0094]

[0095]

[0096] Based on the above formula, Lingo can be used to obtain the weights of each habitat factor. Finally, the weights of each indicator are obtained.

[0097] S4. Obtain relevant data of multinational companies through various database official websites and national financial networks, and collect and normalize habitat evaluation indicator data.

[0098] The present invention obtains relevant data of multinational enterprises through various database official websites and national finance websites. In the "climate" factor, diplomatic relations are mainly obtained through the international situation, the cultural distance factor comes from the Hofstadter official website, the government debt level comes from the TRADING ECONOMIC database, the bilateral economic and trade level comes from the government finance official website, the public security level comes from the IEP database, the political stability comes from the WCI database, the government credit comes from Dagong International Credit Rating, the regulatory environment comes from the WCI database, and the foreign exchange reserves and inflation come from the TRADING ECONOMIC database; in the "soil" factor, the market size and market growth rate come from the national finance network, the labor supply, capital supply and information industry come from the TRADING ECONOMIC database; in the "biological" factor, the number of competitors comes from ENR, and the competitor's competitiveness comes from the TRADING ECONOMIC database. The specific sources of the data of each factor are as follows: Figure 4 shown.

[0099] Relying on various academic documents, algorithms and other authoritative documents, we score the various habitat factors of multinational companies in the ENR file. In order to achieve uniformity in scoring, we first grade or rank each habitat factor or collect data, and then select and normalize the linear function according to the formula based on whether each habitat factor is beneficial to the company's entry. The higher the value, the more competitive the multinational company's factor is.

[0100]

[0101] Y——Normalized data

[0102] a——quasi-normalized data

[0103] min——the minimum value of this type of data

[0104] max——the maximum value of this type of data

[0105] S5. Based on the weights of each indicator and the preprocessed data, the habitat evaluation results of multinational enterprises are obtained.

[0106] The evaluation index is based on environmental benefits, social benefits, and economic benefits as attribute layers, and there are a total of 17 evaluation indicators in the three types of habitat factors. The weights of the attribute layers and habitat factors in the index system are calculated, and the collected data are normalized and weighted to obtain the total score and ranking of each country. In this way, the best market entry for enterprises is determined, and the technical difficulties of existing enterprises in habitat selection due to imperfect evaluation systems are solved, providing enterprises with an evaluation method for habitat selection.

[0107] experiment:

[0108] The three ecological factors of "climate", "soil" and "biology" were introduced to construct a "habitat selection model" and the "habitat" suitability of the 11 target countries on the ENR list was evaluated according to the evaluation system. The OPA-F method was used to obtain the weight of each evaluation factor, and then the "habitat" suitability value of each target country was calculated to rank the markets suitable for Chinese companies to enter and analyze the "habitat status" of the target countries. The original data of various habitat factors in various countries were collected as shown in Table 2.1.

[0109] Table 2.1 Raw data of various habitat factors in various countries

[0110]

[0111] According to the collected data, the normalized data is weighted and the results are shown in the following table:

[0112] Table 2.2 Normalized data

[0113]

[0114]

[0115] The results of OPA-F calculation show that multinational companies should focus on economic performance while taking into account social and environmental performance. Companies are profit-making organizations engaged in economic activities such as production, distribution and services, so the first priority of companies is to make profits. However, in recent years, all sectors of society have begun to pay attention to the impact of companies on society and the environment, so companies also need to make achievements in social and environmental performance in order to shape a good corporate image.

[0116] Among the weight values ​​of various habitat factors, government credit and political stability have the highest weight, and labor supply has the lowest weight. The government credit and political stability of the host country are important indicators for whether international engineering consulting companies enter. It can be seen that the better the government credit and the more stable the political situation, the more beneficial it is for enterprises. International engineering consulting companies provide engineering consulting services throughout the entire process of engineering project construction, from preliminary consultation and bidding agency to survey, design, supervision, cost, project management, etc. The engineering consulting industry belongs to the high-tech service industry, which has the characteristics of high technical content and added value, strong innovation and radiation. Therefore, enterprises do not need the host country to provide a large number of workers who can engage in the industry, but need professional talents in the industry, so the weight of labor supply is relatively small.

[0117] Use lingo to solve model (13), and the results are shown in the following table:

[0118] Table 2.3

[0119]

[0120]

[0121] This case collected data from various database websites and national financial websites. The normalized data of various habitat factors in various countries are shown in Table 2.4.

[0122] Table 2.4 Normalized data of habitat factors in various countries

[0123]

[0124] After weighted calculation based on the collected data, we get the ranking and total score chart.

[0125] The "soil" and "biological" indicators are analyzed. Finally, all indicators are combined to determine the country that is suitable for the enterprise to enter.

[0126] Table 2.5 Scores of each country

[0127]

[0128]

[0129] According to the comprehensive analysis of all habitat indicators, the most suitable countries for my country to enter are J (0.6764), B (0.6345), and K (0.6082), and the comprehensive scores of these countries are all greater than 0.6. The macro-environment of J and B is outstanding, and the market development of international engineering consulting companies in K is good, with more engineering opportunities. Entering these countries, you can set up subsidiaries or branches in the local area to carry out business activities. The countries with comprehensive scores of 0.4-0.6 are: D (0.5358), I (0.5308), F (0.4987), C (0.4580), H (0.4093), and E (0.4083). These countries are more suitable for Chinese companies to enter, and all indicators are relatively good, but you also need to pay attention to market changes at any time. When entering such countries, it is recommended to form a joint venture by cooperating with local companies to learn about local conditions and avoid certain risks. Finally, the countries that are relatively unsuitable to enter are Country A (0.3166) and Country G (0.3067). Country A's macroeconomic environment is relatively complex, and there are many strong local competitors. It is not recommended to enter and compete with them. The engineering industry in Country G has been affected by the epidemic and is in poor development. According to the latest data from the Statistics Bureau of Country G, the construction industry production index has declined for four consecutive months after seasonal adjustment, including a month-on-month decline of 3.0% in July. Entering the country requires waiting for the market to recover.

[0130] Finally, it should be noted that the above examples are only some specific embodiments of the present invention. Obviously, the present invention is not limited to the above embodiments, and there are many variations. All variations that can be directly derived or associated with the content disclosed by a person skilled in the art should be considered as the protection scope of the present invention.

Claims

1. A system for selecting habitats for multinational companies based on an ESG perspective, characterized by: It includes a central processing module, a habitat factor collection module, an enterprise habitat prediction module, a data storage module, a display module, a network communication module and a database, all of which are connected to the central processing module by signal; The habitat factor collection module obtains data on habitat factors in three dimensions: climate, soil and biology from the Internet through the network communication module; the enterprise habitat prediction module is used to calculate the index weights of the habitat factors in three dimensions: climate, soil and biology obtained by the habitat factor collection module, and then predict the "habitat" suitability values ​​of each target country and rank them; The display module is used to display the transnational enterprise habitat selection results output by the enterprise habitat prediction module; the database is used to store various data and enterprise habitat selection results, including a real-time database, an enterprise database and an expert database.

2. A method for habitat selection using the multinational enterprise habitat selection system as claimed in claim 1, characterized in that: The specific process is: Step 1: Construct habitat factors and secondary indicators from three dimensions: climate, soil, and biology; Step 2: Using ESG performance as the habitat selection criterion, a multi-attribute decision-making model for habitat selection of multinational enterprises is constructed; Step 3: Determine the weights of the secondary indicators based on the OPA-F algorithm; Step 4: Obtain data from multinational enterprises and collect and normalize habitat assessment indicator data; Step 5: Obtain the habitat assessment results of multinational enterprises based on the weights of each indicator and the pre-processed data.

3. The method for habitat selection according to claim 2, characterized in that: The habitat factors and secondary indicators include: climate habitat factors include national macro-environmental factors, which are composed of three secondary indicators: political law, economy, and cultural security; soil habitat factors include the development of various industries, which are composed of three secondary indicators: multinational corporate market, resource supply, and development of related industries; biological habitat factors include the situation of market participants, which are composed of one secondary indicator: competitors; The political and legal indicators in the secondary indicators include diplomatic relations, political stability, government credit, and regulatory environment; the economy includes foreign exchange reserves, inflation, government debt level, and bilateral economic and trade level; cultural security includes security level and cultural distance; multinational corporate market includes market size and market growth rate; resource supply includes labor supply and capital supply; the development of related industries includes the information industry; competitors include the number of competitors and their competitiveness.

4. The method for habitat selection according to claim 3, characterized in that: The multi-attribute decision-making model includes an attribute layer and an alternative solution layer. The attribute layer includes environmental performance, social performance and economic performance, and the alternative solution layer includes the secondary indicators. Then, the triangular fuzzy number method is used to convert the fuzzy language variables into specific fuzzy numbers for conversion calculation.

5. The method for habitat selection according to claim 4, characterized in that: The process of determining the weight of the secondary index is as follows: Convert fuzzy language into fuzzy numbers according to the fuzzy language variable level; solve the attribute model of OPA-F and obtain the weight of each attribute; Solve the alternative model of OPA-F, and calculate the weight of each habitat factor by equating 17 habitat factors to alternatives. After obtaining the scores of each alternative plan, the total fuzzy score of each habitat factor is calculated, and defuzzification is performed to obtain the total weight value of each habitat factor. Lingo can be used to obtain the weight of each habitat factor, and finally the weight of each indicator is obtained.

6. The method for habitat selection according to claim 5, characterized in that: The process of obtaining multinational enterprise data is as follows: Relevant data of multinational enterprises are obtained through the official websites of various databases and the national financial network, and various academic literatures and authoritative algorithm documents are used to score various factors of multinational enterprises in the ENR files; each habitat factor is first scored or ranked or data is collected, and then linear function normalization is performed according to the formula based on whether each habitat factor is beneficial to the entry of the enterprise; the higher the value, the more competitive the factor of the multinational enterprise is.

7. The method for habitat selection according to claim 6, characterized in that: The habitat evaluation results are calculated based on the collected data and the weights of the evaluation indicators to obtain the total score and ranking of each entered country to obtain the habitat selection results of the enterprise.

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