본 연구에서는 정태적 패널모형 중 고정효과모형을 활용하여 지역 내 총생산액 대비 국유기업 생산 비중이 지역경제 성장에 주는 영향을 분석하였다. 기존 연구에 의하면 중국의 국유기업은 지속적인 개혁에도 불구하고 비효율적인 운영과 생산수준을 보이고 있으나, 지역별 개발정책에 따라 지역경제에 주는 영향은 상이할 것으로 생각된다. 이에 지역을 4대와 8대 권역으로 구분하여 분석하였다. 분석결과, 4대 권역에서는 동부지역만 국유기업의 생산비중과 지역경제 성장 간, 음(-)의 관계를 보였고, 동북부 및 중부, 서부에서는 양(+)의 관계로 나타났다. 8대 권역별 분석에서는 동부연해, 북부연해, 서남지역에서 국유기업 생산비중이 커질수록 지역경제 성장에 음(-)의 영향을 주고, 동북, 황하중류, 장강중류, 서북, 남부연해에서는 양(+)의 영향을 주는 것으로 분석되었다. 다만, 남부연해지역은 통계적으로 유의하지 않았다.

Regional climate simulations for the CORDEX East Asia domain were conducted between 1981 and 2100 using five models to project future climate change based on RCP2.6, 4.5, 6.0, and 8.5 scenarios. By using the ensemble mean of five model results, future changes in climate zones and four extreme temperature events of South Korea were investigated according to Köppen-Trewartha’s classification criteria. The four temporal periods of historical (1981-2005), early future (2021-2040), middle future (2041-2070), and late future (2071-2100) were defined to examine future changes. The analysis domain was divided into 230 administrative districts of South Korea. In historical (1981-2005) period, the subtropical zones are only dominant in the southern coastal regions and Jeju island, while those tend to expand in the future periods. Depending on the RCP scenarios, the more radiative forcing results in the larger subtropical zone over South Korea in the future. The expansion of the subtropical zone in metropolitan areas is more evident than that in rural areas. In addition, the enlargement of the subtropical zone in coastal regions is more prominent than that of in inland regions. Particularly, the subtropical climate zone for the late future period of RCP8.5 scenario is significantly dominant in most South Korea. All scenarios show that cold related extreme temperature events are expected to decrease and hot related extreme temperature events to increase in late future. This study can be utilized by administrative districts for the strategic plan of responses to future climate change.

In this study, the intra-seasonal fluctuation (ISF) of wintertime temperature change in East Asia was classified by a cluster analysis of complete linkage. A ISF of temperature change was defined as a difference of synthesized harmonics (1 to 36 harmonic) of daily temperature averaged for 30 years (1951~1980, 1981~2010). Eight clusters were gained from the ISF curves of 96 stations in East Asia. Regions of the cluster C, G and A1 seem to be affected by the Siberian High (SH) center, whereas the cluster A1, A2, D, B and F by the SH main pathways. Regions of the cluster E are apart from the SH main pathways and appear to be in the area of influence of other factors. Wintertime temperatures in Northwest China (clusters C, G) and Northeast China (cluster A1) were increased very largely. In most clusters, around late January there were less warming periods than the winter mean of the mean ISF of the clusters, before and after this time there were more warming periods than the winter mean.

The selection of land for fishing village development project, and the standard used to classify fishing villages has been determined based on the guidelines developed by fishing village cooperatives. The approach fishing village cooperatives follows is likely to classify fishing villages without first reflecting on the overall development environment of the region, such as other industries and workers in the area. It also acts as a barrier for business promotion or evaluation, because the cooperatives do not match the administrative districts, which are the units of administration, and the main policy enforcement agent in regional development. Against this background, this study aimed to identify categories to situate the development direction, as well as the size and distribution of fishing villages based on eup, myeon, and dong administrative units as defined by the Fishing Villages and Fishery Harbors Act. This study was based on the Census of Agriculture, Forestry, and Fisheries of 2010, and analyzed 826 eups, myeon, and dongs with fishery households using the principal component analysis, and 2-Step cluster analysis methods. Therefore, 95% of the variance was explained using the covariance matrix for types of fishing villages, but it was analyzed as one component focusing on the number and ratio of fishery households, and used the cluster-type analysis, which focused on the sizes of fishing villages. The clusters were categorized into three types: (1) the development type based on the number of fishermen in the eups, myeons and dongs was analyzed as village size (682); (2) administrative district size (121); and (3) total eups, myeons and dongs (23), which revealed that the size of most fishing villages was small. We could explain 73% of the variance using the correlation coefficient matrix, which was divided into three types according to the three principal component scores, namely fishery household power, fishery industry power, and fishing village tourism power. Most fishing villages did not have a clear development direction because all business areas within the region were diversified, and 552 regions could be categorized under the harmonious development type, which is in need of balanced development. The fishery industry type typified by industrial strength included 159 regions in need of an approach based on industrialization of fishery product processing. Specialized production areas, which specialized in producing fishery products, were 115 regions with a high percentage of fishermen. The analysis results indicated that various situations in terms of size and development of fishing villages existed. However, because several regions exist in the form of small village units, it was necessary to approach the project in a manner that directed the diversification of regional development projects, such as places for local residents to relax or enjoy tourism experiences within the region, while considering the overall conditions of the relevant eups, myeons, and dongs. Reinforcement of individual support for fishermen based on the Fisheries Act must take precedence over providing support for fishermen through regional development. In addition, it is necessary to approach the development of fishing villages by focusing on industrializing the processing techniques of fishery products. Areas specialized in the production of fishery products are required to consider the facilities for fisheries production, and must make efforts to increase fishery resources, such as releasing fry.

Because of full liquidity of financial resources, financial industry cluster has emerged and developed. In China, Shanghai1, Beijing, Shenzhen and other cities assemble plenty of financial resources and financial institutions, promoting economy development. This paper takes 40 cities as sample, uses factor analysis to measure the degree of financial industry cluster in big and medium cities. The result is that: Shanghai and Beijing are national financial centers, Shenzhen, Guangzhou, Tianjin, and other cities are regional financial centers, Ningbo, Xian and other cities have not formed financial cluster.

It is necessary that the basic concept of rural planning update from economics based on the production and sale into experience of natural resources and traditional culture. For the purpose of set up development direction for rural district, it is requisite to the multivariate analysis. In this study, the methods of the classification of rural village with existing data are studied, the results looking for applying to the making of principal viewpoint of the development. The analysis methods of classification are used the PCA, CA and combination of these, and making the revised method for localization of the rural district. In this study, we implement classification of regional pattern analysis for the planning of rural district in Chungbuk province.