Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model

Pao, Hsiao-Tien; Fu, Hsin-Chia; Tseng, Cheng-Lung

doi:10.1016/j.energy.2012.01.037

Cited by 320 publications

(130 citation statements)

References 42 publications

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“…The major emitters need to reduce emissions while maintaining economic development. As a result, the future emissions of these major emitters have become a research hotspot [5,6]. To calculate the CO2 emissions of each province in the future, the emissions from previous years must be determined first.…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variations in Provincial CO2 Emissions in China from 2005 to 2015 and Assessment of a Reduction Plan

Deng

Liu

2015

Energies

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This study calculated the provincial carbon dioxide (CO2) emissions in China, analyzed the temporal and spatial variations in emissions, and determined the emission intensity from 2005 to 2015. The total emissions control was forecasted in 2015, and the reduction pressure of the 30 provinces in China was assessed based on historical emissions and the 12th five-year (2011-2015) reduction plan. Results indicate that CO2 emissions eventually increased and gradually decreased from east to west, whereas the emission intensity ultimately decreased and gradually increased from south to north. By the end of 2015, the total control of provincial emissions will increase significantly compared to the 2010 level, whereas the emission intensity will decrease. The provinces in the North, East, and South Coast regions will maintain the highest emission levels. The provinces in the Southwest and Northwest regions will experience a rapid growth rate of emissions. However, the national emission reduction target will nearly be achieved if all provinces can implement reduction targets as planned. Pressure indices show that the South Coast and Northwest regions are confronted with a greater reduction pressure of emission intensity. Finally, policy implications are provided for CO2 reductions in China.

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Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variations in Provincial CO2 Emissions in China from 2005 to 2015 and Assessment of a Reduction Plan

Deng

Liu

2015

Energies

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“…However, forecasting results depended on statistical data, which change rapidly over time. The grey prediction model is an alternative forecasting tool for systems with complex, uncertain and chaotic structures because of their low data requirements to build forecasting models [10]. Initially proposed by Deng [11], the grey model (GM) was used to quantify uncertainty and information insufficiency.…”

Section: Introductionmentioning

confidence: 99%

Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020

Tang

et al. 2016

Sustainability

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According to the economic and energy consumption statistics in Jiangsu Province, we combined the GM (1, 1) grey model and polynomial regression to forecast carbon emissions. Historical and projected emissions were decomposed using the Logarithmic Mean Divisia Index (LMDI) approach to assess the relative contribution of different factors to emission variability. The results showed that carbon emissions will continue to increase in Jiangsu province during 2015-2020 period and cumulative carbon emissions will increase by 39.5487 million tons within the forecast period. The growth of gross domestic product (GDP) per capita plays the greatest positive role in driving carbon emission growth. Furthermore, the improvement of energy usage efficiency is the primary factor responsible for reducing carbon emissions. Factors of population, industry structure adjustment and the optimization of fuel mix also help to reduce carbon emissions. Based on the LMDI analysis, we provide some advice for policy-makers in Jiangsu and other provinces in China.

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“…The model is designed to work with systems where available information is insufficient to characterize the system, and where a complex relationship between response variables and the main influencing factors exists. The benefits of the MGM is that it has higher demonstrative accuracy prediction than traditional multivariate forecasting models (Jun et al, 1997;Pao, Fu, & Tseng, 2012).…”

Section: Forecasting Models and Data Analysismentioning

confidence: 99%

Immigration trends’ impacts on state vocational rehabilitation agency minority application rates: An empirical forecast model demonstration study

Moore

Wang²,

Eugene-Cross³

et al. 2016

JVR

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Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model

Cited by 320 publications

References 42 publications

Temporal and Spatial Variations in Provincial CO2 Emissions in China from 2005 to 2015 and Assessment of a Reduction Plan

Temporal and Spatial Variations in Provincial CO2 Emissions in China from 2005 to 2015 and Assessment of a Reduction Plan

Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020

Immigration trends’ impacts on state vocational rehabilitation agency minority application rates: An empirical forecast model demonstration study

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