The Analysis of Automobile Industry and Enviorment课件

1、The Analysis of Automobile Industry and Environmental ImplicationAbstract This essay studies the automobile industry,focusing on the core competitiveness of Chinas provinces and municipalities in terms of automobile industry comprehensively and the implication of traditional automobile industry and

2、new energy automotive industry from an environmental perspective.For question a,comprehensive evaluation of competitiveness. We first established the evaluation institution, including eight indicators that reflect the scale of the total assets, GDP(Gross Domestic Production), industrial output, pate

3、nts that indicate the innovative capability,the total profits that demonstrate the profitability,the total sales volume, market share,fiscal policy denoting state support.After the processing of data standardization, we go for the principal component analysis. According to the contribution rate obta

4、ined, the original eight indicators are compressed to five main components, reducing the number of indicators to evaluate while avoiding overlapping information. By utilizing the five main components expression,we set up the model for comprehensive principal assessment: to give an overall score and

5、ranking. Subsequently, we have this ranking method modified,integrating both the weighted TOPSIS and the contribution rate of each component from principal component analysis as weights, such sequence results in greater accuracy.Nine provinces and municipalities ranked as follow(attached in Annex on

6、e):ProvincesShangHaiJilinGuangdongBeijingHubeiChongqingTianjinZhejiangJiangsuTopsis Rank123456789As for question two, the analysis of environmental implication,we select life cycle assessment model, where data mainly comes from the built-in database of GREET model. First of all,we select the traditi

7、onal fuel vehicles and new electromotive car as research object, considering production of raw materials, production and processing, usage, decommissioning phases,energy consumption and pollutant emissions are analyzed separately at all stages. Then the various stages of energy consumption and pollu

8、tant emissions are integrated into an implication assessment.Taking global warming, acidification, photo-chemical smog, dust into account,we solve out the potential implication value of these four types in terms of environmental implication based on equivalency factors. Subsequently,potential implic

9、ation value of the four environmental implication types are substituted into environmental implication index, obtained by weighting integrated environmental implication index. Eventually, the index for traditional fuel vehicles integrated environmental implication is 55.54, indicating the life cycle

10、 implication of conventional fuel vehicle life cycle is equivalent to environmental implication of a standard living in 1990 as a Chinese for 55.54 years. The integrated environmental implication index for new type of electromotive cars is 20.97 , indicating that the environmental implication of a n

11、ew electromotive car with a full life-cycle is equivalent to that of a standard Chinese living in 1990 for 20.97 years.We can draw the conclusion that the new electromotive cars are much more fuel-efficient than conventional cars and also indicates that the promotion of electromotive vehicles can,to

12、 some extent,alleviate the environmental issues, especially problems concerned with air pollution.Keywords: Competitiveness Evaluation Principal Component Analysis TOPSIS GREET Model Life Cycling Assessment Rephrasing the ProblemAs the rapid development of Chinas automobile industry furthers,the aut

13、omotive industry has already become one of the essential industries of the state economy and its leading role on the development of other corresponding industry chains are becoming increasingly evident,acting an crucial part in the rapid growth of the national economy,. Ever since joining the WTO,Ch

14、inese automobile industry has experienced explosive growth and it has become a principal producer of cars in terms of size.Despite the great success,the Chinese automobile industry still demands further specification.It is,therefore,beneficial for us to study self-development and innovation of China

15、s automobile industry innovation research and the circumstance for brand management. Proposing effective automobile industry development strategy so as to be able to respond more effectively to the increasingly severe international competition in the market environment is increasingly imperative.On

16、the other hand,with the rapid growth of vehicle population, the total vehicle emissions continue to rise,while the implication on the environment is growing as well.Developing electromotive vehicles is not only a good solution for our energy and environmental issues but also a decent way of cultivat

17、ing their own innovation ability, improving the international competitiveness.We are required to use the materials that can be found in the appendix to set up a mathematical model for quantitative analysis for the following two questions:A.Comprehensive evaluation of the core competitiveness of the

18、automotive industry in terms of provinces and municipalities. B.The environmental implication analysis model for traditional auto industry and new energy automotive industry.General Assumptions for the Model1. Due to the availability of data,We assume that the data presented in Schedule I of the aut

19、hentic;2.We assume Automotive Competitiveness is affected exclusively by the relevant indicators we select;3. Joint and normal development of the region and local enterprises are assumed;4. We assume that all steel products are ordinary steel, copper and other metals.Rubber,plastics,glass,etc.are co

20、unted in regardless in mean value,regardless of the type and variety;5. Suppose the life cycle mileage of car is160000mile( 257495km.)Table 1 The Definitions and Symbol Descriptions SymbolDescriptionSymbolDescriptiontotal assetsthe fiscal policytotal outputThe correlation coefficient of the i-index

21、and index of the jPatentcontribution rateIndustrial productioncontribution of the j kindof potential to environmental implicationsGross profitthe first kind of environmental implication index jTotal salesweighted environmental implicationShareModel for the Problem A4.1, An Analysis of the Problem AP

22、roblem A requires us to evaluate core competence of Chinas auto industry in terms of provinces and municipalities comprehensively.Annex one gives some data associated with the automotive industry in terms of provinces and municipalities.After a perusal of the relevant literature, we found that the q

23、uantitative evaluation for industrial competitiveness tend to be:Ports diamond model,entropy method and the modified entropy method,AHP(Analytic Hierarchy Process), factor analysis, principal component analysis,fuzzy clustering method.Various methods have their pros and cons, such as the Analytic Hi

24、erarchy Process advantage lies in its simplicity, but objectivity is impaired.When using statistical analysis method to study multivariate topics,too many variables increase the complexity of the issue 1.After qualitative inspection,it can be seen that there may be a strong correlation between some

25、of the indicators. For example, the total sales volume and market share may exist a strong correlation, there may be a strong correlation between total production and GDP.Direct usage of these indicators for comprehensive evaluation will inevitably lead to overlapping of information,affecting the ob

26、jectivity of the evaluation findings.The principal component analysis can take several indicators into a small number of integrated and unrelated indicators.In addition determining the weight by the principal component analysis can effectively avoid problem of excessive subjectivity of AHP. Therefor

27、e,we decide to use the method of principal component analysis to attain a comprehensive assessment of the issue.TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution) is suitable for evaluation of multi-index, multi-program decision analysis systems.This basic principle is

28、 to figure out the distance between assessed subject,desired solutions and the negative ideal solution so as to select the right sequence.This method has been widely used in areas such as business, health policy and management.In recent years,fields like tourism competitiveness, the countys economic

29、 competitiveness, and competitiveness in the aviation industry has also begun to use the comprehensive evaluation.We,therefore,decide to improve the model by the integrating the principal component analysis into the TOPSIS Method.4.2,the Setting Up and Solution of Problem AIn this essay,we evaluate

30、the core competitiveness of automobile industry in terms of provinces with the principal component and weighted TOPSIS method.The model has the following steps: firstly, the establishment of the index system;secondly,data preprocessing; and thirdly,the principal component analysis of the index to at

31、tain the contribution rate for each major component, namely the weight and compute the total score.Eventually, the main component method is modified,integrating a comprehensive evaluation into the weighted TOPSIS. As follows (Figure)4.2.1, The Establishment of Index SystemAs for the evaluation of th

32、e core competitiveness of the automotive industry,the index system is established on the following three points: 1) synthesizing the background,practical problems and related literature; 2) following the rules of comprehensiveness, feasibility, accuracy, particularity; 3) based on existing,available

33、 data.According to the relevant data quoted from Schedule 1 and China Automotive Industry Yearbook 2014, we collect data for eight indicators that reflect the core competence from four levels including the scale of strength, innovation, economic efficiency, the government investement,to set up the e

34、valuation system (Table 1). Let the total assets, total output value, patents, industrial output,the total profit, total sales, market share, fiscal policy be respectively.4.2.2 Data standardizationPrincipal component analysis evaluation has m evaluation subjects,n evaluation indexes.According to th

35、e data evaluation,establish an evaluation matrix with m rows and n columns, wherein is the i-the evaluation objects value of the j-the index.Due to the dimension between indicators, to exclude the interference of dimension and magnitude of each index on the findings,we standardized the X-matrix. In

36、this essay, the standard transformation of sample method to convert various index value into a standardized index, calculated as follows:Where , Is the mean sample and standard sample of j-th indicator.Matrix normalized is denoted as.4.2.3 Calculate the Complex ScoreAccording to a weighted feature v

37、ector corresponding to the main component,we figure out the expression of the relationship between the original index and the main components.Complex score is calculated by the following formula:Among them, the contribution rate for the informationof all the main components. 4.3, The Solution of Mod

38、el: Principal Component AnalysisSince the data from China Automotive Industry Yearbook 2014 and other information is lack of a full eight indicators,we test our model with the statistics in appendix A as a sample.Using MATLAB software,we impose principal component analysis on eight evaluation indica

39、tors in nine provinces in.(Procedure is attached in appendix A)4.3.1, The Correlation Coefficient MatrixThe finding for correlation coefficient matrix is attached in appendix II.It is clear that correlation coefficient between the total sales volume and market share is one,which indicates totally po

40、sitive correlation,agreeing with the observation in the analysis of our problems.Other indicators such asandalso has a strong correlation, reaching a correlation coefficient of approximately 0.85.It explains that conduction of principal component analysis to reduce dimension is necessary.4.3.2, The

41、Contribution Rate of The Index Correlation matrix of each characteristic roots and its contribution rate are dissipated in the following table: Table 2 Principal component analysis-Contribution rateSequenceValue acc.14.819560.243360.243321.534819.185679.428930.72229.027388.456240.45435.678194.134350

42、.39514.938799.07360.060.7599.82370.01420.177100800100It is clear that,the cumulative contribution rate of the first four characteristic roots piled up more than 90%.Principal component analysis works well.We select the following five main components of the former (cumulative contribution rate reachi

43、ng 99 percent, it has reflected most of the information provided in the original index) comprehensive evaluation.4.3.3 Calculate The Composite Score of Competitiveness Feature vector corresponding the first five characteristic roots can be seen in the following table: Table 3 Feature vector correspo

44、nding to the characteristic rootPrincipal component indexFirstFeature vectorSecondFeature vectorThirdFeature vectorFourthFeature vectorFifthFeature vector0.3754 0.1894 -0.1844 -0.6807 -0.2424 0.4236 0.0326 -0.1166 0.4740 -0.0817 -0.1124 0.6616 0.4602 -0.0901 0.5265 0.3238 0.2251 0.6060 0.0806 -0.610

45、6 0.4043 -0.1889 0.1973 0.3738 0.2942 0.4385 0.0801 -0.1897 -0.1012 0.2695 0.4385 0.0801 -0.1897 -0.1012 0.2695 -0.1185 0.6528 -0.5123 0.3704 -0.2297 Five main components are obtained as follow:It is clear that the first principal component reflects the total assets, total profit, total sales volume

46、, market share information with four indicators, redefined as economies of scale.The second principal component reflects the message of fiscal policy indicators, reflecting the evaluation criteria - governmental support;The third principal componentreflects the information industry output indicators

47、, redefined as production capacity; Fourth principal componentreflects the information indicators, redefined as output efficiency;Fifth principal componentreflects the information patent indicators, demonstrating the evaluation criteria - innovation.Substitute the standardized data of the various re

48、gions of the original eight indicators into five main components of the expression, the modified model is:You can get five principal components provinces.Respectively, the contribution rate of the five main components of the right to re-build the main components of comprehensive evaluation model:Sub

49、stitute the comprehensive evaluation value of the four main components of the provinces of values into the formula,we get the provinces as well as the automotive industry competitive ranking results are shown in Table 4: Table 4 Comprehensive evaluation results of principal component analysisProvinc

50、esScoresRankingShanghai2.82961 Jilin0.73482Guangdong0.45773Hubei0.45644Beijing0.07455Chongqing-0.62636Jiangsu-1.23127 Tianjin-1.33128Zhejiang-1.362694.4, Modified Model: Weighted TOPSIS Comprehensive Evaluation Method 2Principal component analysis and evaluation method is modified,by introducing wei

51、ghted TOPSIS into the calculation of total scores and the contribution rate of each component from principal component analysis is treated as weights.4.4.1, The Principle of Weighted TOPSISStep 1: Harmonization the Monotony of Evaluated IndicatorsIn this decent indicator, the higher the number the b

52、etter.Through analysis,the core competitiveness evaluation index we use are in line with the conditions,needless of reconversion.Step 2: Construction of a Comprehensive Evaluation MatrixThe contribution rate to eight indexes as weights,In comprehensive evaluation of the main component score matrix s

53、tructure,we create the comprehensive matrixwhich is attached in appendix.Step 3: The establishment of a standardized multi-objective decision matrixNormalized Matrix denoted as, which is calculated as follows:Step 4: Obtain the Optimum Value and The Worst Value Vector According to The Matrix Tamong

54、them,Step 5: Calculate ach Evaluation Object With the Optimal Value and The Worst Value of The DistanceAmong them, the weighting of each index is obtained by principal component analysis of the contribution rate.Step Six: Calculate the relative proximity of the individual to be evaluated and the bes

55、t value and the worst valueA higher number indicates stronger competitive evaluation object,vice versa. If the evaluation object indexes are in optimal state, then;If the index were to be evaluated in the worst state, then.4.4.2, TOPSIS ResultsUsing MATLAB program in Appendix IV. TOPSIS results is o

56、btained as follows:Table 5 TOPSIS Competitiveness Ranking ResultsProvincesd+d-Topsis RankShanghai0.41340.92661Jilin0.59140.56822Guangdong0.68070.58323Beijing0.71820.57454Hubei0.7670.48385Chongqing0.83350.36886Tianjing0.95560.3617Zhejiang0.98450.25898Jiangsu0.94420.21999From the results above,it can

57、be seen that competitive areas like Shanghai, Jilin, Guangdong. Due to the vehicle driven by large foreign companies,Shanghai (SAIC), Jilin (FAW), Guangdong (Honda, Nissan, Toyota) , strong momentum of development is ensured 2. In particular, Shanghai, with its enormous size of automotive industry total assets and total output value, creates huge total profit, with the highest market share, ranking in the first place.Ranked fourth,Beijing (BA