Study on Driving Factors of Carbon Emissions in Inner Mongolia of China Based on Geographically Weighted Regression Model

2014 ◽  
Vol 962-965 ◽  
pp. 2355-2359
Author(s):  
Ri Na Wu ◽  
Ming Xiang Huang ◽  
Yu Hai Bao ◽  
Gang Bao
Keyword(s):  
Spatial Heterogeneity ◽  
Carbon Emissions ◽  
Geographically Weighted Regression ◽  
Inner Mongolia ◽  
Industrial Structure ◽  
Social Economic ◽  
Driving Factors ◽  
Weighted Regression ◽  
Road Density ◽  
Geographically Weighted Regression Model

In this paper, based on the data of carbon emissions of county-level in Inner Mongolia autonomous region of China, using the Geographically Weighted Regression (GWR) model, we quantitatively analyze the effects of six social-economic driving factors, including Gross Domestic Product (GDP), population (Popu), economic growth rate (EconGR), urbanization (Urba), industrial structure (InduS) and road density (RoadD) on regional carbon emissions. The results were achieved as follow:(1) The spatial heterogeneity of carbon emissions of Inner Mongolia and the social-economic factors of affecting carbon emissions are obviously; (2) the correlation among the six factors is low. (3) GDP, InduS and Popu have significant effect on carbon emissions, and effects of EconGR, Urba and RoadD are smaller. The impacts of different factors on carbon emissions at different spatial region show spatial heterogeneity.

scholarly journals Examining the driving factors of industrial CO2 emissions in Chinese cities using geographically weighted regression model

2021 ◽  
Author(s):  
Huiping Wang ◽  
Xueying Zhang
Keyword(s):  
Spatial Heterogeneity ◽  
Geographically Weighted Regression ◽  
Service Industry ◽  
Industrial Sector ◽  
Weighted Regression ◽  
Impact Factors ◽  
Geographically Weighted Regression Model ◽  
Level Of Economic Development ◽  
Investment In Fixed Assets ◽  
The Impact

Abstract The industrial sector is the sector with the largest CO2 emissions, and to reduce overall CO2 emissions, analysis of the impact factors holds significance. Based on the 2015 industrial CO2 emissions of 282 cities in China combined with economic and social data, and a geographically weighted regression (GWR) model, we analysed the characteristics of the spatial distribution of CO2 emissions and the influencing factors of spatial heterogeneity. The results show that China's urban industrial CO2 emissions present a significant spatial agglomeration state that includes Shandong, Beijing, Tianjin, Shanghai, Zhejiang, and Jiangsu, and the core of the coastal areas form a high-high (H-H) concentration; a low-low aggregation (L-L) is formed in less developed areas such as Guizhou, Yunnan, Sichuan and Guangxi. The influence of various factors on industrial CO2 emissions has significant spatial heterogeneity. The Industrial scale, industry share of GDP, and share of the service industry in GDP are factors that promote industrial CO2 emissions. The technological innovation, population density, and social investment in fixed assets are important factors that inhibit industrial CO2 emissions, but their impact on industrial CO2 emissions shows spatial differences. In contrast, the level of economic development, foreign direct investment, financial development and government intervention have a two-way impact on industrial CO2 emissions.

scholarly journals Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model

2020 ◽  
Vol 12 (18) ◽  
pp. 7235
Author(s):  
Quan Shao ◽  
Yan Zhou ◽  
Pei Zhu ◽  
Yan Ma ◽  
Mengxue Shao
Keyword(s):  
Spatial Heterogeneity ◽  
Density Distribution ◽  
Geographically Weighted Regression ◽  
Spatial Variations ◽  
Least Square ◽  
Civil Aviation ◽  
Weighted Regression ◽  
Bird Strike ◽  
Key Factors ◽  
Geographically Weighted Regression Model

Although the factors influencing bird strikes have been studied extensively, few works focused on the spatial variations in bird strikes affected by factors due to the difference in the geographical environment around the airport. In this paper, the bird strike density distribution of different seasons affected by factors in a rectangular region of 800 square kilometers centered on the Xi’an Airport runway was investigated based on collected bird strike data. The ordinary least square (OLS) model was used to analyze the global effects of different factors, and the Geographically Weighted Regression (GWR) model was used to analyze the spatial variations in the factors of bird strike density. The results showed that key factors on the kernel density of bird strikes showed evident spatial heterogeneity and the seasonal difference in the different habitats. Based on the results of the study, airport managers are provided with some specific defense measures to reduce the number of bird strikes from the two aspects of expelling birds on the airfield area and reducing the attractiveness of habitats outside the airport to birds, so that achieve the sustainable and safe development of civil aviation and the ecological environment.

scholarly journals Analysis of Land Surface Temperature Driving Factors and Spatial Heterogeneity Research Based on Geographically Weighted Regression Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yin Zhi ◽  
Liang Shan ◽  
Lina Ke ◽  
Ruxin Yang
Keyword(s):  
Spatial Heterogeneity ◽  
Urban Heat Island ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Driving Factors ◽  
Weighted Regression ◽  
Heat Island ◽  
Moran's I ◽  
Urban Heat ◽  
Gwr Model

Acceleration of urbanization has brought about a series of problems, which include irreversible changes to urban surfaces and continuous increases in land surface temperatures (LSTs). In this context, analysis of the driving factors and spatial heterogeneity of urban LST is of considerable importance for mitigating urban heat island effects and promoting healthy and comfortable urban living environments. This study explored the relationship between the spatial characteristics and driving factors of the LST by using a geographically weighted regression (GWR) model to analyze multisource data from the Xigang District of Dalian City. The results showed that the urban heat island effect in Xigang District is significant, with LSTs generally above 28°C at the end of August, mostly concentrated in the range of 38–40°C. The highest LST values were detected in northern port and harbor areas; the lowest LST values occurred in mountainous forest areas. The global Moran’s I value was 0.994, which was indicative of a very high positive correlation, and local Moran’s I values formed H-H and L-L type clusters concentrated in the northern harbor area and southern mountainous area, respectively. Finally, the GWR model could reflect the spatial heterogeneity of the relationships between LST and its driving factors well. Among these, in terms of natural physical factors, digital elevation model, normalized difference vegetation index, and modified normalized difference water index data were found to be negatively correlated with LSTs in most cases; in the social dimension, the point-of-interest number and building-coverage ratio were generally positively correlated with LSTs.

scholarly journals Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 673
Author(s):  
Chen Yang ◽  
Meichen Fu ◽  
Dingrao Feng ◽  
Yiyu Sun ◽  
Guohui Zhai
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Vegetation Cover ◽  
Weighted Regression ◽  
Vegetation Coverage ◽  
The Loess Plateau ◽  
Geographically Weighted Regression Model ◽  
Spatiotemporal Changes

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.

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