Modeling CO2 emissions from fossil fuel combustion using the logistic equation

Meng, Ming; Niu, Dongxiao

doi:10.1016/j.energy.2011.03.032

Cited by 88 publications

(51 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CO 2 emissions data were calculated according to the formula provided by the Intergovernmental Panel on Climate Change (IPCC) (2006) (Provided by website: http:// www.ipcc-nggip.iges.or.jp/). The primary energy carbon emission coefficient for coal, petroleum, natural gas, and non-fossil energy are 0.7476, 0.5825, 0.4435 and 0, respectively (ton C/ton stand coal), which are sourced from the Energy Research Institute of Chinese National Development and Reform Commission [52]. To eliminate the influence of price index, the GDP is calculated at a constant price (1990 prices) and the proportion of industry output in GDP is calculated accordingly.…”

Section: Data Sourcesmentioning

confidence: 99%

Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China

2016

View full text Add to dashboard Cite

h i g h l i g h t sWe reassessed the impact of demographic and GDP changes on CO 2 emissions in China. Income rather than population growth was a major contributor to growing emissions. Urbanisation increases energy use and CO 2 emissions, except in western China. Shrinking household size did not reduce energy use and emissions. The impact of human activities on the environment varies across regions. a r t i c l e i n f o a b s t r a c tAs global warming intensifies, the accumulation of carbon dioxide (CO 2 ) and other greenhouse gases have attracted great global attention. However, questions regarding whether, how and to what extent demographic factors and processes affect carbon emissions have not yet been fully explained -particularly in China. This study used an improved STIRPAT model to reassess the impact of demographic and income changes on China's energy-related CO 2 emissions at the national and regional levels using balanced provincial panel data from the 1990-2012 period. Whereas most previous studies of emission-popula tion/income elasticity in China have yielded wide-ranging estimates, this study showed that income rather than demographic change has been the dominant driving force behind China's growing CO 2 emissions. Urbanisation has increased energy consumption and emissions, except in western China. Changes in the age structure have had a statistically insignificant effect on energy use, but resulted in increased national emissions -particularly in eastern China. Shrinking household size did not reduce energy use and emissions, indicating that improved residential energy efficiency might reduce emissions. Changing the traditional mode of economic growth, reasonable controlling the pace of urbanisation, improving energy efficiency and upgrading industrial structures may yet be necessary to mitigate the environmental impact of human activities in China.

show abstract

Section: Data Sourcesmentioning

confidence: 99%

Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China

2016

View full text Add to dashboard Cite

show abstract

“…Previous studies on the literature have attempted to model and forecast univariate CO 2 emission time series via a variety of techniques. See, for example, Meng and Niu (2011) [3] for the use of logistic equation for CO 2 emission modelling or Silva (2013) [4], which employs different forecasting techniques (i.e., ARIMA, Holt-Winters, and exponential smoothing) with the aim of providing a reliable alternative to the EIA forecast for energy-related CO 2 emissions in the US. Silva (2013) [4] also introduces singular spectrum analysis (SSA) as an alternative technique for forecasting CO 2 emissions around the world and also proposes a new combination (EIA-SSA) by merging the SSA and EIA forecast, which is found to outperform all other models.…”

Section: The Kyoto Protocolmentioning

confidence: 99%

Predicting the Evolution of CO2 Emissions in Bahrain with Automated Forecasting Methods

Tudor¹

2016

Sustainability

View full text Add to dashboard Cite

Abstract:The 2012 Doha meeting established the continuation of the Kyoto protocol, the legally-binding global agreement under which signatory countries had agreed to reduce their carbon emissions. Contrary to this assumed obligation, all G20 countries with the exception of France and the UK saw significant increases in their CO 2 emissions over the last 25 years, surpassing 300% in the case of China. This paper attempts to forecast the evolution of carbon dioxide emissions in Bahrain over the 2012-2021 decade by employing seven Automated Forecasting Methods, including the exponential smoothing state space model (ETS), the Holt-Winters Model, the BATS/TBATS model, ARIMA, the structural time series model (STS), the naive model, and the neural network time series forecasting method (NNAR). Results indicate a reversal of the current decreasing trend of pollution in the country, with a point estimate of 2309 metric tons per capita at the end of 2020 and 2317 at the end of 2021, as compared to the 1934 level achieved in 2010. The country's baseline level corresponding to year 1990 (as specified by the Doha amendment of the Kyoto protocol) is approximately 25.54 metric tons per capita, which implies a maximum level of 20.96 metric tons per capita for the year 2020 (corresponding to a decrease of 18% relative to the baseline level) in order for Bahrain to comply with the protocol. Our results therefore suggest that Bahrain cannot meet its assumed target.

show abstract

“…According to trend analysis, the concentration of GHGs will increase from the present 430 ppm to over 550 ppm in 2050, which will continue to cause a temperature increase of over 2 • C, with a likelihood of 99% [2]. Current research shows that energy-related CO 2 emissions have caused over two-thirds of greenhouse effects and will continue to increase in the future [3,4]. The electric power industry is a significant energy-related CO 2 emitter.…”

Section: Introductionmentioning

confidence: 99%

“…regression parameters using few observations with multicollinearity [4,27]. The PLS algorithm can also be used to estimate the parameters of the linear log equation form of the extended STIRPAT model.…”

Section: Introductionmentioning

confidence: 99%

CO2 Emissions from China’s Power Industry: Scenarios and Policies for 13th Five-Year Plan

Sun

Meng

et al. 2016

Energies

Self Cite

View full text Add to dashboard Cite

Abstract:The extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model has been applied to analyzing the relationship between CO 2 emissions from power industry and the influential factors for the period from 1997 to 2020. The two groups found through partial least square (PLS) regularity test show two important areas for CO 2 emissions reduction from the power industry: economic activity and low-carbon electric technology. Moreover, considering seven influential factors (economic activity, population, urbanization level, industrial structure, electricity intensity, generation structure, and energy intensity) that affect the power CO 2 emissions and the practical situation in the power sector, possible development scenarios for the 13th Five-Year Plan period were designed, and the corresponding CO 2 emissions from the power sector for different scenarios were estimated. Through scenario analysis, the potential mitigation of emissions from power industry can be determined. Moreover, the CO 2 emissions reduction rates in the different scenarios indicate the possible low-carbon development directions and policies for the power industry during the period of the 13th Five Year Plan.

show abstract

Modeling CO2 emissions from fossil fuel combustion using the logistic equation

Cited by 88 publications

References 22 publications

Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China

Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China

Predicting the Evolution of CO2 Emissions in Bahrain with Automated Forecasting Methods

CO2 Emissions from China’s Power Industry: Scenarios and Policies for 13th Five-Year Plan

Contact Info

Product

Resources

About