Study On The Spatialization Of Industrial Production Under Different Climate Change Scenario

Due to the unreasonable exploitation and utilization of resources by humans for a long time,the global climate is showing a clear warming trend,and the frequency of extreme events is increasing.Climate change has a tremendous impact and threat on the natural ecological and socio-economic systems.China,as a responsible and developing country,has actively undertaken and formulated the goals and tasks of energy conservation and emission reduction.It will undoubtedly have a major impact on the pillar industries of the national economy,the industrial industry.To cope with the impact of climate change on the industrial economic system,it is necessary to clarify the spatial distribution trends and characteristics of the industrial economic system(output value)under different climate change scenarios.The difficulty lies in the mismatch between the temporal and spatial resolution of current climate and socio-economic data.It is difficult to achieve high accuracy exposure and risk assessment requirements.This paper takes the mainland of China as the research area,analyzes the characteristics of temperature and precipitation changes in different scenarios of China,and uses the methods of remote sensing inversion and multi-source data fusion to construct the current industrial output of 1km grid data.At the same time,it constructs future differe nt climate based on the random forest model.Two models of industrial output spatialization to analyze the spatial distribution characteristics of industrial output under different climate change scenarios in different periods.The results showed that,(1)Based on the ANSPLIN model's 839 meteorological station data from 2000 to 2010 and the CMIP 5 multi-model coupling model's downscaling analysis of meteorological data from 2010 to 2050,we can see that from 2000 to 2050,the temperature and precipitation in the mainland of China changed significantly,and the overall trend showed an increase.Temperatures increase from the northwest to the south,with high annual average temperature areas concentrated in the southern coastal areas,low-value areas concentrated in the Qinghai-Tibet Plateau.Annual average precipitation showing significant differences between the north and south in space.The highest values are found in the southeastern part of the Tibet Autonomous Region and the borders of Sichuan Province and Yunnan Province,as well as in the lower reaches of the Yangtze River.The low value areas are mainly distributed in Xinjiang Autonomous Region,Inner Mongolia Autonomous Region,Ningxia Province,and Gansu Province.Under different scenarios,the temperature and precipitation are quite different.Temperatures increased by 1.47°C and 2.19°C in 2010-2050 under RCP4.5 and 8.5 scenarios respectively,and the precipitation respectively increased by 24.58 mm and 17.53 mm.(2)Developed a method for inverting current industrial output values.Based on remote sensing inversion and multi-source data fusion models,a set of methods for the spatialization of current industrial production values was developed using DMSP/OLS nighttime lighting data,industrial land data,vegetation indices,and urbanization rates.The n random selection of 105 cities for accuracy verification,with a relative average accuracy of 79.78% in 2000 and 80.32% in 2010.At the same time,using the urbanization rate data to correct the areas with lower accuracy in the western region,the precision has increased by 12.1%.It can be used for the research on the spatial distribution law of industrial production value,the intensive degree of industrial land,the exposure degree,and the risk assessment of industrial economic system.(3)Based on Kernel Density Estimation(KDE)and Exploratory Spatial Data(ESDA),the current industrial output values were analyzed.From 2000 to 2010,the changes in the nuclear density of industrial output values were mainly increase,especially the Yangtze River Delta and the Pearl River Delta and Bohai Rim increased obviously.At the same time,central and northeastern regions also had significant increases in nuclear density in some regions.However,although the industrial output in the western region has increased,the overall output value is relatively low.At the same time,there are still some areas in which the industria l output value has decreased in nuclear density.According to the analysis of exploratory spatial data,it can be known that in 2000,the industrial output value of 1 km grid has a spatially significant positive correlation with space,and it shows a spatial clustering of highly similar production values.Compared with 2000,the industrial output value in 2010 is more obvious.At the same time,the clustering characteristics are even more pronounce d.China's industrial industry has gradually shifted from a concentrated and dense distribution to a more extensive and distributed distribution around high-density areas.(4)Based on the random forest model,a spatial spatialization simulation method for industrial output under different climate change scenarios was constructed and validated using the 1 km grid industrial output data obtained from remote sensing in 2010.The simula tio n accuracy reached 93.77%.Therefore,this method is used to simulate the distribution data of industrial output grids under the RCP4.5 and RCP8.5 scenarios in the 2020-2050.Industrial output values under the RCP8.5 scenario has increased overall compared to the RCP4.5 scenario.Due to the impact of climate change,the average annual growth rate of industria l output in 2010-2020 is 11.57% and 10.15% under RCP4.5 and RCP8.5 scenarios respectively,the growth rate of industrial output in 2030-2050 has slowed down significantly,and the average annual growth rate of industrial output in 2030-2050 has dropped to 0.724% and 0.772%.Although the industrial output value shows an increasing trend,the proportio n of industrial output(the ratio of each grid industrial output value to the total industrial output value)has been decreasing year by year.The industrial production value under 2050 in the RCP4.5 and RCP8.5 scenarios has decreased compared to the proportion of industria l output in 2010,especially in the eastern and southern regions of the central and coastal regions showing a significant decrease.The Proportion of industrial output under the RCP4.5 scenario has decreased from 0-15.148?in 2010 to 0-3.305 ?,and from 0-14.984? to 0-3.236? in the RCP 8.5 scenario,with an average ratio of 0.352?.0.352?.