scholarly journals Spatial Heterogeneity of Regional Carbon Emissions and Its Driving Factors in China

2021 ◽  
Vol 859 (1) ◽  
pp. 012091
Author(s):  
Wei Liu
Keyword(s):  
Spatial Heterogeneity ◽  
Carbon Emissions ◽  
Driving Factors

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 the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1339
Author(s):  
Ziyuan Chai ◽  
Zibibula Simayi ◽  
Zhihan Yang ◽  
Shengtian Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Driving Factors ◽  
Series Data ◽  
Lake Basin ◽  
Stirpat Model ◽  
Consumption Structure ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

scholarly journals Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1056
Author(s):  
Yu Chen ◽  
Mengke Zhu ◽  
Qian Zhou ◽  
Yurong Qiao
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Central Area ◽  
Urban System ◽  
Entropy Method ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Eastern Region ◽  
Urban Resilience

Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

scholarly journals Spatial Differentiation of Non-Grain Production on Cultivated Land and Its Driving Factors in Coastal China

2021 ◽  
Vol 13 (23) ◽  
pp. 13064
Author(s):  
Yan Sun ◽  
Yuanyuan Chang ◽  
Junna Liu ◽  
Xiaoping Ge ◽  
Gang-Jun Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Heterogeneity ◽  
Spatial Differentiation ◽  
Driving Factors ◽  
Cultivated Land ◽  
Grain Production ◽  
Land Area ◽  
Bottom Line ◽  
Land Transfer ◽  
Coastal China

The rapid development of urbanization and industrialization in coastal China in the past 20 years has exerted a huge squeezing effect on agricultural land use. The phenomenon of non-grain production on cultivated land (NGP) is very common, seriously threatening the protection of high-quality arable land and national food security. In order to find out the overall situation regarding NGP on cultivated land in coastal China, this study revealed the spatial differentiation of NGP and its main driving factors by spatial autocorrelation analysis, multiple linear regression models and geographically weighted regression analysis (GWR). The results show that: (1) in 2018, the non-grain cultivated land area of 11 provinces along the coast of China was about 15.82 × 106 hm2, accounting for 33.65% of the total cultivated land area. (2) The NGP rate in 11 provinces gradually decreased from south to north, but the NGP area showed two peak centers in Guangxi province and Shandong province, then decreased gradually outwards. (3) The low economic benefit of the planting industry (per capita GDP and urban-to-rural disposable income ratio) was the most important driving force, leading to the spatial differentiation of NGP, while the number of rural laborers and land transfer areas also acted as the main driving factors for the spatial differentiation of NGP. However, the influence of each driving factor has obvious spatial heterogeneity. The non-grained area and the non-grain production rate at the municipal level were completely different from those at the provincial level, and the spatial heterogeneity was more prominent. In the future, the local government should control the disorganized spread of NGP, scientifically set the bottom line of NGP, reduce the external pressure of NGP, regulate multi-party land transfer behavior, and strengthen land-use responsibilities. This study can provide a scientific foundation for adjusting land-use planning and cultivated land protection policies in China and other developing countries.

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