Study On The Land Use Structure Optimization On Chengdu Plain

Chengdu Plain was taken as an example, directed by the most superior of land use structure, studied the regional characteristic of economical developed degree in the various counties of Chengdu Plain, through data collection, reorganization and analysis, and divided them into the developed area, the comparatively developed area and the underdeveloped area; Then analyzed the correlation between the kinds of land and the economic benefits by the multiple regression analysis. Finally utilized the multi-objective programming by the multiple regression quation which comes from the multiple regression analysis. At the same time, the decision-making model of land use structure optimization was established in the different economical developed regions, the restricted terms was hypothesized according to the actual situation. With an eye to the economic benefits maximization, social and ecology benefits was considered, we found the most superior land use structure of the various regions. The findings indicated that:(1) The regional characteristic of the economy developed degree of Chengdu Plain: The developed areas: Jin jiang, Qingyang, Jiangjiang, Wuhou, Chenghua;The comparatively developed areas: Shuangliu, Pi, Xindu, Dujiangyan, Longquan, wenjiang, Jingyang, Chongzhou, Shifang, Qionglai, Qingbaijiang, Mianzhu;The underdeveloped areas: Jintang, Dayi ,Guanghan, Pengzhou, Xinjin, The center area of Leshan, Dongpo, Pujiang, Emeishan, Pengshan, Jiajiang, Luojiang.(2) The correlation between the economic benefits and the kinds of land: â‘  The correlation between the GDP and the kinds of landThe construction land, agricultural land and non-utilized land in the developed area basically does not have any correlation with GDP. In the comparatively developed area and the underdeveloped area, construction land has obvious positive correlation with GDP, and the correlation elevated along with the economical developed degree. The agricultural land and GDP has the negative correlation, and the correlation elevated along with the economical developed degree. The urban land and the transportation land have the positive correlation with GDP; factory land and GDP has negative correlation in the developed area, but in the underdeveloped area and the comparatively developed area has the positivecorrelation, and correlation strengthens gradually.â‘¡ The correlation between the primary industry output value and the agricultural landThe cultivated land and the garden land both have the positive correlation with the primary industry output value, specially the cultivated land, correlation strengthens gradually along with the reduction of economical developed degree; But the forest land and the primary industry output value has negative correlation; The grass land of Chengdu Plain are few, which has not any inevitable correlation with the primary industry output value from the regression analysis .â‘¢ The correlation between the industrial output value and the kinds of landIn the developed areas, factory land and the industrial output value has negative correlation, but in the comparatively developed area and the underdeveloped area has positive correlation. That indicated in the developed area the industry has already developed comprehensively to the high and new technology, the factory land was already moved towards to the intensified path, but in the comparatively developed area and the underdeveloped area the industry is still the main power which make the economy progressed, therefore the more factory land, the higher benefits. The transportation land and the industrial output value has positive correlation, specially in the underdeveloped area, correlation is the strongest.â‘£ The correlation between the tertiary industry output value and the kinds of landThe urban land and tertiary industry output value has the positive correlation; transportation land has positive correlation with tertiary industry output value in the comparatively developed area and the underdeveloped area, especially in the underdeveloped area.(3) The optimization of land use structure:Through the multiple regression analysis and the multi-objective programming, the decision-making model of land use structure optimization was established, restricted terms were hypothesized, and C++ language programming was used. With an eye to the economic benefits maximization, social and ecology benefits was considered, we found the most superior land use structure of the various regions.â‘  the results of the land use structure optimization of the developed area in Chengdu PlainCultivated land 0.00%, garden land 1.90%, forest land 0.29%, grass land 0.00%, urban land 63.00%, village land 0.00%, factory land 15.60%, particular area 0.45%, transportation land 11.00%, water area 6.13%, non-utilized land 1.63%.The GDP index of Chengdu Plain developed area enhanced 197.0% trough the land use structure optimization. The primary industry output value index dropped 67.0%; the industrial output index increased 44.8%; the tertiary industry output value index enhanced 80.6%.â‘¡ The results of the land use structure optimization of the comparatively developed area in the Chengdu PlainCultivated land 40.50%, garden land 4.10%, forest land 24.80%, gross land 0.65%, urban land 5.30%, village land 3.10%, factory land 5.20%, particular area 0.38%, transportation land 4.90%, water area 5.97%, non-utilized land 5.10%.the GDP index of Chengdu Plain comparatively developed area enhanced 89.0% through the land use structure optimization; The primary industry output value index dropped 7.0%; the industrial output index increased 124.0%; the tertiary industry output value index increased 65.0%.â‘¢ The results of land use structure optimization of the underdeveloped area in Chengdu PlainCultivated land 41.90%, garden land 4.70%, forest land 30.00%, gross land 0.14%, urban land 2.80%, village land 2.90%, factory land 2.36%, particular area 0.32%, transportation land 3.70%, water area 6.88%, non-utilized land 3.30%.The GDP index of the plain of Chengdu underdeveloped area enhanced 103.0% through the land use structure optimization; The primary industry output value index increased 22.1%; the industrial output index increased 200.0%; the tertiary industry output value Index enhanced 162.2%.