Research on Coordination Level and Influencing Factors Spatial Heterogeneity of China's Urban CO2 Emissions

The amount of CO2 emissions from steelworks accounts for a great share of the total CO2 emissions from industry in China. Thus, reducing CO2 emissions from steelworks is urgent for China’s environmental protection and sustainable development. This study aims at identifying factors that influence CO2 emissions from steelworks and proposing measures to reduce CO2 emissions. The life cycle inventory (LCI) of iron and steel products implies the relationship between the CO2 emissions of the steelworks and the input variables of the LCI. The Tornado Chart Tool is utilized to calculate the variation of CO2 emissions caused by the change of each input variables of LCI. Then, mean sensitivity of each input variable is calculated and the ranking criterion developed is used to identify the main factors influencing the integrated steelworks. Subsequently, measures for reducing CO2 emissions are proposed. The results indicate that the very important influencing factors of CO2 emissions in steelworks are the CO2 emission factor of Blast Furnace Gas (BFG), liquid steel unit consumption of continuous casting, continuous casting slab unit consumption of hot rolling and hot metal ratio of steelmaking. Consequently, many efficient measures for reducing CO2 emissions have been proposed, such as removing CO2 contained in BFG, decreasing the hot metal ratio of Basic Oxygen Furnace (BOF), recycling BFG, optimizing the products’ structure, etc.

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Study on the spatial heterogeneity of urban heat islands and influencing factors

Building and Environment ◽

10.1016/j.buildenv.2021.108604 ◽

2021 ◽

pp. 108604

Author(s):

Yanqiu Chen ◽

Baoyan Shan ◽

Xinwei Yu

Keyword(s):

Spatial Heterogeneity ◽

Influencing Factors ◽

Urban Heat Islands ◽

Heat Islands ◽

Urban Heat

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Which Influencing Factors Cause CO2 Emissions Differences in China’s Provincial Construction Industry: Empirical Analysis from a Quantile Regression Model

Polish Journal of Environmental Studies ◽

10.15244/pjoes/105239 ◽

2019 ◽

Vol 29 (1) ◽

pp. 331-347 ◽

Cited By ~ 2

Author(s):

Jingmin Wang ◽

Xiaojing Song ◽

Keke Chen

Keyword(s):

Quantile Regression ◽

Regression Model ◽

Construction Industry ◽

Co2 Emissions ◽

Empirical Analysis ◽

Influencing Factors ◽

Quantile Regression Model

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Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi11010067 ◽

2022 ◽

Vol 11 (1) ◽

pp. 67

Author(s):

Meijie Chen ◽

Yumin Chen ◽

John P. Wilson ◽

Huangyuan Tan ◽

Tianyou Chu

Keyword(s):

Risk Factors ◽

Health Risk ◽

Spatial Heterogeneity ◽

Influencing Factors ◽

Mainland China ◽

Spatial Filtering ◽

Varying Coefficient Model ◽

Health Risk Factors ◽

Eigenvector Spatial Filtering ◽

Spatially Varying

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

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Exploring spatial characteristics of city-level CO2 emissions in China and their influencing factors from global and local perspectives

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.142206 ◽

2021 ◽

Vol 754 ◽

pp. 142206 ◽

Cited By ~ 1

Author(s):

Quanwen Liu ◽

Sanmang Wu ◽

Yalin Lei ◽

Shantong Li ◽

Li Li

Keyword(s):

Co2 Emissions ◽

Influencing Factors ◽

Spatial Characteristics ◽

Global And Local ◽

Local Perspectives

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Driving Factor Analysis and Forecasting of CO2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method

Energies ◽

10.3390/en11102709 ◽

2018 ◽

Vol 11 (10) ◽

pp. 2709 ◽

Cited By ~ 2

Author(s):

Weijun Wang ◽

Weisong Peng ◽

Jiaming Xu ◽

Ran Zhang ◽

Yaxuan Zhao

Keyword(s):

Co2 Emissions ◽

Influencing Factors ◽

Power Output ◽

Policy Implications ◽

Emissions Reduction ◽

Development Scenarios ◽

Learning Machine ◽

Set Up ◽

Grey Relational ◽

Average Contribution

With power consumption increasing in China, the CO2 emissions from electricity pose a serious threat to the environment. Therefore, it is of great significance to explore the influencing factors of power CO2 emissions, which is conducive to sustainable economic development. Taking the characteristics of power generation, transmission and consumption into consideration, the grey relational analysis method (GRA) is adopted to select 11 influencing factors, which are further converted into 5 main factors by hierarchical clustering analysis (HCA). According to the possible variation tendency of each factor, 48 development scenarios are set up from 2018–2025, and then an extreme learning machine optimized by whale algorithm based on chaotic sine cosine operator (CSCWOA-ELM) is established to predict the power CO2 emissions respectively. The results show that gross domestic product (GDP) has the greatest impact on the CO2 emissions from power output, of which the average contribution rate is 1.28%. Similarly, power structure and living consumption level also have an enormous influence, with average contribution rates over 0.6%. Eventually, the analysis made in this study can provide valuable policy implications for power CO2 emissions reduction, which can be regarded as a reference for China’s 14th Five-Year development plan in the future.

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