Grey Relational Analysis of the Land- Sea Economy in China 2016 International Conference on Grey Systems and Uncertainty Analysis Reporter ： Xue Jin Supervisor ： Professor Kedong Yin

Catalogua Backgroud 1 Causality test of land-sea economy 2 Grey correlation degree analysis 3 Relational schema analysis4 Conclusions and Future Work 5 I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 1 Backgroud

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work Blue economy Energy consumption power Fully effective use of all kinds of Marine resources and energy Practice and develope the strategy of "sea power"

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 2 Causality test of land-sea economy

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work Data: select the value of ocean gross domestic product and regional GDP from Table 1 Related indexs of land-sea economy

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 2.1 Time series data processing Table 2 Processed data of land-sea economy

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 2.2 ADF test of time series After the Logarithmic yield processing, all series have good stability. Thus Granger causality test can be done. With the time sequence in table 1 and table 2, do a unit roots test and analysis using Eviews. The results are shown as follows.

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 2.3 Granger causality test of land-sea economy

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 3 Grey correlation degree analysis

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 3.1 Indicators selection

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 3.2 Dimensionless processing

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 3.3 Difference sequence, maximum and minimum difference

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 3.4 Calculation of grey correlation

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work measure the grey correlation degree The relevance between marine economy and the tertiary industry is strongest followed is the second industry finally is the first industry

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 4 Relational schema analysis

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work There are certain differences between the development of national economy and marine economy in China’s 11 coastal provinces and cities. Table 10 GDP and ocean gross domestic product of China’s 11 coastal provinces and cities in 2013

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work Three kinds of patterns a a Land-sea weak type Guangxi Hebei b b Land-sea strong type Tianjin Shandong Zhejiang Liaoning c c land-sea asymmetrical type Hainan Jiangsu

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work 5 Conclusions and Future Work

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work The article uses methods such as ADF test, Granger causality test and grey correlation degree analysis to preliminary demonstrate the relationship of land-sea economy. ② Three kinds of patterns sum up the relationship between the sea and land of 11coastal provinces and cities in our country,. ① There is a certain correlation between marine economy and land area economy. Planning economic layout of the land and sea, better promote the integration development of sea and land

I Backgroud II Causality test of land- sea economy III Grey correlation degree analysis IV Relational schema analysis V Conclusions and Future Work