scholarly journals Research on Energy-Related CO2 Emissions Characteristics, Decoupling Relationship and LMDI Factor Decomposition in Qinghai

2021 ◽  
Vol 9 ◽  
Author(s):  
Can Huang ◽  
Yin-Jun Zhou ◽  
Jin-Hua Cheng
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Carbon Emissions ◽  
Average Annual Growth Rate ◽  
Utilization Efficiency ◽  
Carbon Accounting ◽  
Annual Growth ◽  
Energy Structure ◽  
Annual Growth Rate ◽  
Intensity Effect

Based on the statistical data from 1997 to 2017, with the utilization of the IPCC carbon accounting method, Tapio decoupling model, and Logarithmic Mean Divisia Index (LMDI), the temporal evolution characteristics of Qinghai’s energy-related carbon emissions, the decoupling relationship, and its driving factors were analyzed. The results indicated that 1) The carbon emissions of Qinghai showed a trend of first slowly increasing, then rapidly increasing, and finally fluctuating and decreasing. It increased from 3.85 million tons in 1997 to 14.33 million tons in 2017, with an average annual growth rate of 6.79%. The carbon emission intensity revealed a steady downward trend, from 189.82 tons/million CNY in 1997 to 54.6 tons/million CNY in 2017, with an average annual growth rate of –6.04%. 2) The relationship between carbon emissions and economic growth was represented by four types: weak decoupling, strong decoupling, expansion negative decoupling, and expansion coupling. Among them, a strong decoupling was achieved only in the five periods of 1997–1998, 1999–2000, 2001–2002, 2013–2015, and 2016–2017. 3) The structural effect of energy consumption was the paramount factor in restraining carbon emissions, followed by the energy intensity effect, while economic growth, and population size were important factors facilitating the increase in carbon emissions. To this end, Qinghai should continuously optimize its energy structure and improve energy utilization efficiency, thus achieving economic green and high-quality development.

Overseas Chinese Investments in China – Patterns of Growth, Diversification and Finance: The Case of Charoen Pokphand

1998 ◽  
Vol 155 ◽  
pp. 610-636 ◽  
Author(s):  
Rajeswary Ampalavanar-Brown
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
European Countries ◽  
Average Annual Growth Rate ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Overseas Chinese ◽  
Remarkable Increase ◽  
Industrial Employment ◽  
Average Rise

The accelerated economic growth of Asia over the last three decades is well documented. While Britain and many other European countries experienced an average rise of real productivity by 2–3 per cent every year from 1973–1992, Asian growth frequently soared over 8 per cent, particularly after 1978. China in particular saw a remarkable increase in the average annual growth rate of GDP from 7 per cent in 1976 to a constant 9 per cent in the 1978 to 1988 period. In 1992 it rose again to 13 per cent, subsequently fluctuating between 8 per cent and 9 per cent. The contribution of agriculture to GDP increased from 28 per cent 1978 to 34 per cent in 1982. Thereafter a contraction in agriculture's share – from 34 per cent back to 24 per cent – reflected a major expansion in industry and services. There was an increase in industrial employment from 18 per cent to 21 per cent, and in that of services from 14 per cent to 18 per cent.

scholarly journals Spatial and temporal characteristics of carbon emissions from livestock farming in Yunnan province by state and city

2021 ◽  
Vol 275 ◽  
pp. 02052
Author(s):  
Rui Chen ◽  
Ji Chen
Keyword(s):  
Growth Rate ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Yunnan Province ◽  
Average Annual Growth Rate ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Livestock Industry ◽  
Temporal Characteristics ◽  
Spatial And Temporal Characteristics

The livestock industry is a pillar industry of the rural economy and an important industry of the national economy in Yunnan Province, so it is important to study the spatial and temporal characteristics of carbon emissions from the livestock industry in Yunnan Province for the development of a modern, lowcarbon and recycling livestock industry. This study draws on provincial greenhouse gas emission factors to calculate the carbon dioxide equivalents generated by enteric fermentation and manure management of cattle, sheep, pigs and poultry in each state and city of Yunnan Province. The results show that: (1) the total carbon emissions from the livestock sector in Yunnan Province decreased from 25, 643, 900 t in 2008 to 24, 758, 200 t in 2018, with an average annual carbon emission of 30, 534, 500 t and an average annual growth rate of The average annual growth rate was 0.35%, showing a characteristic of “rising first then falling”. (2) In terms of spatial and temporal evolution, the layout of the low and high livestock carbon emission areas in Yunnan is stable, while the medium and high livestock carbon emission areas fluctuate frequently and the spatial and temporal differences in carbon emissions are obvious. Finally, based on the conclusions, targeted countermeasure suggestions are put forward.

The Impact of COVID-19 on Energy Consumption in the United States: An Overview

2021 ◽  
Author(s):  
Lindsey Kahn ◽  
Hamidreza Najafi
Keyword(s):  
Growth Rate ◽  
Energy Consumption ◽  
Energy Use ◽  
The United States ◽  
Average Annual Growth Rate ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
End User ◽  
End Use ◽  
The Impact

Abstract Lockdown measures and mobility restrictions implemented to combat the spread of the novel COVID-19 virus have impacted energy consumption patterns, particularly in the United States. A review of available data and literature on the impact of the pandemic on energy consumption is performed to understand the current knowledge on this topic. The overall decline of energy use during lockdown restrictions can best be identified through the analysis of energy consumption by source and end-user breakdown. Using monthly energy consumption data, the total 9-months use between January and September for the years 2015–2020 are calculated for each end-use. The cumulative consumption within these 9 months of the petroleum, natural gas, biomass, and electricity energy by the various end-use sectors are compared to identify a shift in use throughout time with the calculation of the percent change from 2019 to 2020. The analysis shows that the transportation sector experienced the most dramatic decline, having a subsequent impact on the primary energy it uses. A steep decline in the use of petroleum and natural gas by the transportation sector has had an inevitable impact on the emission of carbon dioxide and other air pollutants during the pandemic. Additionally, the most current data for the consumption of electricity by each state and each end-user in the times before and during the pandemic highlights the impact of specific lockdown procedures on energy use. The average total consumption for each state was found for the years 2015–2019. This result is used calculation of yearly growth rate and average annual growth rate in 2020 for each state and end-user. The total average annual growth rate for 2020 was used to find a correlation coefficient between COVID-19 case and death rates as well as population density and lockdown duration. To further examine the relationship a correlation coefficient was calculated between the 2020 average annual growth rate for all sectors and average annual growth rate for each individual end-user.

scholarly journals Analysis of Factors Influencing Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China

2020 ◽  
Vol 12 (3) ◽  
pp. 1089
Author(s):  
Jiancheng Qin ◽  
Hui Tao ◽  
Chinhsien Cheng ◽  
Karthikeyan Brindha ◽  
Minjin Zhan ◽  
...  
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Economic Structure ◽  
Energy Structure ◽  
Intensity Effect ◽  
Entire Energy ◽  
Key Factor ◽  
Main Factors

Analyzing the driving factors of regional carbon emissions is important for achieving emissions reduction. Based on the Kaya identity and Logarithmic Mean Divisia Index method, we analyzed the effect of population, economic development, energy intensity, renewable energy penetration, and coefficient on carbon emissions during 1990–2016. Afterwards, we analyzed the contribution rate of sectors’ energy intensity effect and sectors’ economic structure effect to the entire energy intensity. The results showed that the influencing factors have different effects on carbon emissions under different stages. During 1990–2000, economic development and population were the main factors contributing to the increase in carbon emissions, and energy intensity was an important factor to curb the carbon emissions increase. The energy intensity of industry and the economic structure of agriculture were the main factors to promote the decline of entire energy intensity. During 2001–2010, economic growth and emission coefficient were the main drivers to escalate the carbon emissions, and energy intensity was the key factor to offset the carbon emissions growth. The economic structure of transportation, and the energy intensity of industry and service were the main factors contributing to the decline of the entire energy intensity. During 2011–2016, economic growth and energy intensity were the main drivers of enhancing carbon emissions, while the coefficient was the key factor in curbing the growth of carbon emissions. The industry’s economic structure and transportation’s energy intensity were the main factors to promote the decline of the entire energy intensity. Finally, the suggestions of emissions reductions are put forward from the aspects of improving energy efficiency, optimizing energy structure and adjusting industrial structure etc.

The Effect of Urban Aging on Human Capital

2014 ◽  
Vol 651-653 ◽  
pp. 2430-2434
Author(s):  
Chun Yi Zhou ◽  
Yang Liu ◽  
Ying Zi Li
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Human Capital ◽  
Population Structure ◽  
Average Annual Growth Rate ◽  
Annual Growth Rate ◽  
Logarithmic Mean ◽  
Age And Gender ◽  
Index Decomposition ◽  
And Gender

This paper uses the Chinese data of 1990-2012 and logarithmic mean Divisia index decomposition model to test the contribution of each factor on the human capital. This paper found the average annual growth rate of education and area are higher than age and gender; after analyzing the contributions, the contribution of education and area are still far higher than that of age and gender. Especially with the aging of population, the contribution of human capital was negative which inhibited the growth of human capital in our country. Therefore, to optimize the population structure is one of the most important measures to promote human capital and economic growth.

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