The Impact on Carbon Emissions of Xinjiang's Population Structure

2013 ◽  
Vol 734-737 ◽  
pp. 1956-1959
Author(s):  
Ying Ying Guo ◽  
Xiao Yu Ma ◽  
A Di Li Tu Er Gong
Keyword(s):  
Population Structure ◽  
Energy Saving ◽  
Population Size ◽  
Empirical Analysis ◽  
Carbon Emissions ◽  
Positive Relationship ◽  
Industrial Structure ◽  
Population Urbanization ◽  
The Impact

In this paper, through 1995-2010 data, it studies population size of Xinjiang, population urbanization and population industrial structure, which have some degree impact on carbon emissions. Through empirical analysis suggests that the size of the population curbs carbon emissions, urbanization and industrial structure have a positive relationship with carbon emissions. Finally, the resource-rich Xinjiang, the paper puts forward corresponding energy saving policy.

scholarly journals Research on the spatial effect and threshold effect of industrial structure upgrading on carbon emissions in China

2021 ◽  
Author(s):  
Zhenqiang Li ◽  
Qiuyang Zhou
Keyword(s):  
Energy Consumption ◽  
Population Size ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Threshold Effect ◽  
Local Areas ◽  
The Impact ◽  
Consumption Intensity ◽  
Industrial Structure Upgrading

Abstract Based on panel data from 2000 to 2017 in 29 Chinese provinces, this paper analyzes the impact of industrial structure upgrading on carbon emissions by constructing a spatial panel model and a panel threshold model. The results show that (1) there is a significant spatial correlation between carbon emissions in Chinese provinces, and the carbon emissions of a province are affected by the carbon emissions of surrounding provinces; (2) in China, carbon emissions have a significant time lag feature, and current carbon emissions are largely affected by previous carbon emissions; (3) industrial structure upgrading can effectively promote carbon emission reductions in local areas, and the impact of industrial structure upgrading on carbon emissions has a significant threshold effect. With continued economic development, the promotion effect of industrial structure upgrading on carbon emission reductions will decrease slightly, but this carbon emission reduction effect is still significant. (4) In addition, there is a clear difference between the impact of energy consumption intensity and population size on carbon emissions in short and long terms. In the short term, the increase in energy consumption intensity and the expansion of population size not only increase the carbon emissions of a local area but also increase the carbon emissions of neighboring areas. In the long term, the impact of energy consumption intensity and population size on carbon emissions of neighboring areas will be weakened, but the promotion impact on carbon emissions in local areas will be strengthened.

scholarly journals Employee Behaviors toward Using and Saving Energy at Work. The Impact of Personality Traits

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3404
Author(s):  
Dawid Szostek
Keyword(s):  
Personality Traits ◽  
Energy Saving ◽  
Organizational Citizenship ◽  
Positive Relationship ◽  
Negative Relationship ◽  
Organizational Citizenship Behaviors ◽  
Equation Modeling ◽  
Future Research ◽  
Citizenship Behaviors ◽  
The Impact

The purpose of the article is to determine how personality traits (extraversion, neuroticism, conscientiousness, agreeableness and openness to experience) affect organizational citizenship behaviors for the environment (OCBE), especially in the context of energy saving. The purpose is also to verify the hypothesis that this impact is significantly moderated by individuals’ demographic characteristic (sex, age, length of service, work type and economic sector of employment). To achieve the purposes, a survey was conducted in 2020 on 454 working people from Poland. The analysis was based on structural equation modeling (SEM). The research model assumed that particular types of personality affect direct and indirect OCBEs, including energy-saving patterns. The model also included the aforementioned demographic characteristics of respondents. I proved that personality traits have a significant impact on direct and indirect organizational citizenship behaviors for the environment. In the case of direct OCBEs, the energy-saving items that were most significantly affected by employee personality were: I am a person who turns off my lights when leaving my office for any reason; I am a person who turns off the lights in a vacant room; I am a person who makes sure all of the lights are turned off if I am the last to leave. The strongest predicators were Neuroticism (negative relationship) and Agreeableness (positive relationship) for direct OCBE, but Extraversion (positive relationship) and Agreeableness (negative relationship) for indirect OCBE. The impact of an individual’s personality on OCBE was significantly moderated mainly for indirect behaviors. This applied to all the analyzed demographic variables, but it was stronger for women, employees aged up to 40 years, those with 10 years or more experience, office/clerical workers, and public sector employees. The article discusses the theoretical framework, research limitations, future research directions and practical implications.

scholarly journals An Examination of Green Credit Promoting Carbon Dioxide Emissions Reduction: A Provincial Panel Analysis of China

2021 ◽  
Vol 13 (13) ◽  
pp. 7148
Author(s):  
Wenjie Zhang ◽  
Mingyong Hong ◽  
Juan Li ◽  
Fuhong Li
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Emissions Reduction ◽  
Green Finance ◽  
Carbon Emissions Reduction ◽  
Negative Effect ◽  
Emissions Intensity ◽  
The Impact

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.

scholarly journals How Do the Population Structure Changes of China Affect Carbon Emissions? An Empirical Study Based on Ridge Regression Analysis

2021 ◽  
Vol 13 (6) ◽  
pp. 3319
Author(s):  
Chulin Pan ◽  
Huayi Wang ◽  
Hongpeng Guo ◽  
Hong Pan
Keyword(s):  
Population Structure ◽  
Regression Analysis ◽  
Population Size ◽  
Carbon Emissions ◽  
Ridge Regression ◽  
Employment Structure ◽  
Consumption Structure ◽  
Structure Changes ◽  
Size Population ◽  
Ridge Regression Analysis

This study focuses on the impact of population structure changes on carbon emissions in China from 1995 to 2018. This paper constructs the multiple regression model and uses the ridge regression to analyze the relationship between population structure changes and carbon emissions from four aspects: population size, population age structure, population consumption structure, and population employment structure. The results showed that these four variables all had a significant impact on carbon emissions in China. The ridge regression analysis confirmed that the population size, population age structure, and population employment structure promoted the increase in carbon emissions, and their contribution ratios were 3.316%, 2.468%, 1.280%, respectively. However, the influence of population consumption structure (−0.667%) on carbon emissions was negative. The results showed that the population size had the greatest impact on carbon emissions, which was the main driving factor of carbon emissions in China. Chinese population will bring huge pressure on the environment and resources in the future. Therefore, based on the comprehensive analysis, implementing the one-child policy will help slow down China’s population growth, control the number of populations, optimize the population structure, so as to reduce carbon emissions. In terms of employment structure and consumption structure, we should strengthen policy guidance and market incentives, raising people’s low-carbon awareness, optimizing energy-consumption structure, improving energy efficiency, so as to effectively control China’s carbon emissions.

scholarly journals Factors Affecting Energy-Related Carbon Emissions in Beijing-Tianjin-Hebei Region

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Jing-min Wang ◽  
Yu-fang Shi ◽  
Xue Zhao ◽  
Xue-ting Zhang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Factors Affecting ◽  
Economic Output ◽  
Level Model ◽  
The Impact ◽  
The Relationship

Beijing-Tianjin-Hebei is a typical developed region in China. The development of economy has brought lots of carbon emissions. To explore an effective way to reduce carbon emissions, we applied the Logarithmic Mean Divisia Index (LMDI) model to find drivers behind carbon emission from 2003 to 2013. Results showed that, in Beijing, Tianjin, and Hebei, economic output was main contributor to carbon emissions. Then we utilized the decoupling model to comprehensively analyze the relationship between economic output and carbon emission. Based on the two-level model, results indicated the following: (1) Industry sector accounted for almost 80% of energy consumption in whole region. The reduced proportion of industrial GDP will directly reduce the carbon emissions. (2) The carbon factor for CO2/energy in whole region was higher than that of Beijing and Tianjin but lower than that of Hebei. The impact of energy structure on carbon emission depends largely on the proportion of coal in industry. (3) The energy intensity in whole region decreased from 0.79 in 2003 to 0.40 in 2013 (unit: tons of standard coal/ten thousand yuan), which was lower than national average. (4) The cumulative effects of industrial structure, energy structure, and energy intensity were negative, positive, and negative, respectively.

ENTREPRENEURSHIP AND ECONOMIC GROWTH: AN EMPIRICAL ANALYSIS

2007 ◽  
Vol 12 (01) ◽  
pp. 3-29 ◽  
Author(s):  
HÉCTOR SALGADO-BANDA
Keyword(s):  
Economic Growth ◽  
Empirical Analysis ◽  
Positive Relationship ◽  
Oecd Countries ◽  
Alternative Measure ◽  
Patent Data ◽  
Self Employment ◽  
The Impact

This study examines the impact of entrepreneurship on economic growth by using a new variable based on patent data to proxy for productive entrepreneurship. Data on self-employment is used as an alternative proxy. The study considers 22 OECD countries and finds a positive relationship between the proposed measure of productive entrepreneurship — degree of innovativeness of different nations — and economic growth, while the alternative measure, based on self-employment, appears to be negatively correlated with economic growth. A battery of econometric specifications and techniques backs the findings.

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