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Carbon emissions from China’s electricity sector account for about one-seventh of the global carbon dioxide emissions, or half of China’s carbon dioxide emissions. A better understanding of the relationship between CO2 emissions and electric output would help develop and adjust carbon emission mitigation strategies for China’s electricity sector. Thus, we applied the electricity elasticity of carbon emissions to a decoupling index that we combined with advanced multilevel Logarithmic Mean Divisia Index tools in order to test the carbon emission response to the electric output and the main drivers. Then, we proposed a comparative decoupling stability analysis method. The results show that the electric output effect played the most significant role in increasing CO2 emissions from China’s electric sector. Also, “relative decoupling” was the main state during the study period (1991–2012). Moreover, the electricity elasticity of CO2 emissions had a better performance regarding stability in the analysis of China’s electricity output.
Xue-Ting Jiang; Min Su; Rongrong Li. Decomposition Analysis in Electricity Sector Output from Carbon Emissions in China. Sustainability 2018, 10, 3251 .
AMA StyleXue-Ting Jiang, Min Su, Rongrong Li. Decomposition Analysis in Electricity Sector Output from Carbon Emissions in China. Sustainability. 2018; 10 (9):3251.
Chicago/Turabian StyleXue-Ting Jiang; Min Su; Rongrong Li. 2018. "Decomposition Analysis in Electricity Sector Output from Carbon Emissions in China." Sustainability 10, no. 9: 3251.
With the boom of vehicles, especially the dramatic rise of private car ownership, in China, transport CO2 emission in China has surged. However, China has been taking the responsibility to cut down carbon emissions and to make positive efforts towards technology innovations in the transport sector. Breaking the link between transport carbon emissions and transport turnover capacity for the past decades should be analyzed. The paper tested the decoupling degree and ranked its potential determinants for every transport mode in consideration of specific transport mode characteristics. We extended the original Kaya identity to make the factor analysis more pertinent to the analysis of transport-related CO2 emissions. Besides, we combined the decomposition technique with decoupling analysis, decomposing the transport decoupling index into five distinct aspects to detect the key drivers of the decoupling of transport-related CO2 emissions from transport turnover volume. Moreover, we analyzed the relationship between transport-related CO2 emission and transport output, which also offers a novel perspective on transport and corresponding environmental research. The results uncovered that a weak decoupling state appeared between 1990–1995 and 2000–2010 in China’s transport sector. Transport energy efficiency exerted the most significant impact in accelerating the decoupling of transport-related CO2 emissions from turnover volume for all transport modes while the energy mix effect impeded the decoupling evolution in most observed periods. Railway transport turnover and rail locomotives shared rises boosted by decoupling evolution, while vehicular transport showed adverse effects. The rise of the transport facilities’ shares of railways, waterways, and airways also advanced the decoupling evolution. Hence, policies of switching travel modes and establishing a “smart growth” pattern for private vehicles should be considered.
Xue-Ting Jiang; Min Su; Rongrong Li. Investigating the Factors Influencing the Decoupling of Transport-Related Carbon Emissions from Turnover Volume in China. Sustainability 2018, 10, 3034 .
AMA StyleXue-Ting Jiang, Min Su, Rongrong Li. Investigating the Factors Influencing the Decoupling of Transport-Related Carbon Emissions from Turnover Volume in China. Sustainability. 2018; 10 (9):3034.
Chicago/Turabian StyleXue-Ting Jiang; Min Su; Rongrong Li. 2018. "Investigating the Factors Influencing the Decoupling of Transport-Related Carbon Emissions from Turnover Volume in China." Sustainability 10, no. 9: 3034.
South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey Model), nonlinear (Non-linear Grey Model), and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models have been applied to forecast South Africa’s coal consumption for the period of 2017–2030, based on the coal consumption in 2000–2016. The mean absolute percentage errors of the three models are respectively 4.9%, 3.8%, and 3.4%. The forecasting results indicate that the future coal consumption of South Africa appears a downward trend in 2017–2030, dropping by 1.9% per year. Analysis results can provide the data support for the formulation of carbon emission and energy policy.
Minglu Ma; Min Su; Shuyu Li; Feng Jiang; Rongrong Li. Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models. Sustainability 2018, 10, 2552 .
AMA StyleMinglu Ma, Min Su, Shuyu Li, Feng Jiang, Rongrong Li. Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models. Sustainability. 2018; 10 (7):2552.
Chicago/Turabian StyleMinglu Ma; Min Su; Shuyu Li; Feng Jiang; Rongrong Li. 2018. "Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models." Sustainability 10, no. 7: 2552.
Developing low-carbon agriculture requires investigating the trajectory, decoupling statuses, and driving forces of agricultural carbon emissions. This study explored the evolution of agricultural carbon emissions based on 18 kinds of major carbon emission sources in Henan Province of China, which produces approximately one-tenth of China’s total grain output. We then analyzed the relationship between carbon emissions and economic growth using the decoupling elasticity model, and identified the factors driving the decoupling status. This analysis was done with a decoupling elasticity model, using the Logarithmic Mean Divisia Index technique. There were three key results: (1) Agricultural carbon emissions totaled 16.61 million tons in 1999, and increased by 7.99% to 17.93 million tons in 2014, with an average growth rate of approximately 0.65%; (2) The decoupling relationship between agricultural carbon emissions and economic output was dominated by weak decoupling during the study period; (3) Agricultural labor productivity was the leading contributor to changes in agricultural carbon emissions, followed by farming-animal husbandry carbon intensity, labor, and agricultural structure.
Min Su; Rui Jiang; Rongrong Li. Investigating Low-Carbon Agriculture: Case Study of China’s Henan Province. Sustainability 2017, 9, 2295 .
AMA StyleMin Su, Rui Jiang, Rongrong Li. Investigating Low-Carbon Agriculture: Case Study of China’s Henan Province. Sustainability. 2017; 9 (12):2295.
Chicago/Turabian StyleMin Su; Rui Jiang; Rongrong Li. 2017. "Investigating Low-Carbon Agriculture: Case Study of China’s Henan Province." Sustainability 9, no. 12: 2295.
This paper adopts the vector auto-regression model (VAR) to study the dynamic effect of renewable energy consumption on carbon dioxide emissions. Our model is based on a given level of primary energy consumption, economic growth and natural gas consumption in the US, from 1990 to 2015. Our results indicate that a long-running equilibrium relationship exists between carbon emissions and four other variables. According to the variance decomposition of carbon dioxide emissions, the use of primary energy has a positive and notable influence on CO2 emissions, compared to other variables. From the Impulse Response Function (IRF) results, we find that the use of renewable energy would remarkably reduce carbon emissions, despite leading to an increase in emissions in the early stages. Natural gas consumption will have a negative impact on CO2 emissions in the beginning, but will have only a modest impact on carbon emission reductions in the long run. Finally, our study indicates that the use of renewable forms of energy is an effective solution to help reduce carbon dioxide emissions. The findings of our study will help policy makers develop energy-saving and emission-reduction policies.
Rongrong Li; Min Su. The Role of Natural Gas and Renewable Energy in Curbing Carbon Emission: Case Study of the United States. Sustainability 2017, 9, 600 .
AMA StyleRongrong Li, Min Su. The Role of Natural Gas and Renewable Energy in Curbing Carbon Emission: Case Study of the United States. Sustainability. 2017; 9 (4):600.
Chicago/Turabian StyleRongrong Li; Min Su. 2017. "The Role of Natural Gas and Renewable Energy in Curbing Carbon Emission: Case Study of the United States." Sustainability 9, no. 4: 600.