Study on the Decomposition of Factors Affecting Energy-Related Carbon Emissions in Guangdong Province, China

Wang, Wenxiu; Kuang, Yaoqiu; Huang, Ningsheng

doi:10.3390/en4122249

Cited by 57 publications

(45 citation statements)

References 18 publications

(9 reference statements)

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“…Researchers have quantified the impact of different factors on the change of CO2 emissions from the regional [3][4][5][6] to the sectoral [7][8][9] perspective, and generally divided the factors into energy mix, energy intensity, industrial structure, economic activity, and population scale. Pani and Mukhopadhyay [3] undertook a decomposition study of CO2 emission of the top ten emitting countries and indicated that although rising income and population are the main driving forces, they are neither necessary nor sufficient for increasing emission, rather, energy structure and emission intensities are the crucial determinants.…”

Section: Introductionmentioning

confidence: 99%

Influencing Factors of Energy-Related CO2 Emissions in China: A Decomposition Analysis

et al. 2015

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Abstract:China is the largest CO2 emitter in the world and is still reliant on energy consumption for economic growth. Research has focused on effective approaches of reducing and mitigating CO2 emissions. This paper undertakes a decomposition study of energy-related CO2 emissions from the industrial and household sectors during the period 1996-2012, with the objectives of investigating trends of the changes in energy-related CO2 emissions, the driving forces of these changes, and approaches of mitigating CO2 emission. Results show the following: (1) the expansion of economic activity is the dominant stimulatory factor of the increase of CO2 emissions in China and that a sustained increase in CO2 emissions can be expected; (2) the decline in energy intensity and the adjustment of energy mix and industrial structure effectively mitigate CO2 emissions; and (3) the government should give more attention to enhancing the energy utility efficiency and reducing CO2 emissions in rural households.

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Section: Introductionmentioning

confidence: 99%

Influencing Factors of Energy-Related CO2 Emissions in China: A Decomposition Analysis

et al. 2015

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“… is the optimal β matrix for China, 2  for US, 3  for Russia, 4  for India and 5  for Japan. It could be found that the optimal β it values vary quite a lot from each other even for the same county.…”

Section: Experimental Simulationmentioning

confidence: 99%

“…Fossil fuel consumption, attributed to economic growth in a large part, comprises 80% of the World's energy use [2]. It is scientifically understood that the detrimental impacts of GHG emissions, especially carbon dioxide (CO 2 ) emissions, on the living environment such as global warming, greenhouse effect, and climate change are mainly the result of fossil fuel combustion for heat supply, electricity generation and transportation purposes [3]. About three quarters of the human-caused carbon emissions of the past 20 years derived from fossil fuel burning.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

Hong

Sun

2013

Energies

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Abstract:The accurate forecasting of carbon dioxide (CO 2 ) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β) arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn't consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE) objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO 2 emission of the World top-5 CO 2 emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE) and mean absolute error (MAE) are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree.

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“…In the case of ANN, a large number of applications have shown that ANN can be a quite suitable tool within the stationary forecasting domain, predicting factors such as carbon emissions [21,22], construction costs [23], and stock market behavior [24]. ANN has been adopted to predict building cooling loads [25,26] and annual building energy consumption levels [19], given that energy consumption datasets are highly non-stationary as regards the relationship between input variables and the outputs of a complex system [26].…”

Section: Energy Consumption Modeling Researchesmentioning

confidence: 99%

An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea

Suh

Chang

2012

Energies

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This paper proposes and develops a residential energy and resource consumption estimation model in the context of multi-family residential housing in Korea using a multi-layer perceptron (MLP) neural network. Eight indicators are introduced which affect the energy and water resource usage characteristics of Korean residential complexes. The proposed model precisely estimated the electricity, gas energy and water consumption for each examined residential complex. In terms of validation, the results showed the highest level of agreement with actually collected datasets. The model shows promising prospects in providing necessary estimations, not only for optimally scaling and sizing energy-and water-related infrastructures, but also to promote reliable energy and resource savings through greenhouse gas (GHG) reduction planning in multi-family housing complexes. The model could also be of use in framing guidelines for the better planning of national or regional energy and resource policies and for forming a foundation of decision-making with definite references regarding the facility management of each apartment complex to enhance the energy and resource use efficiency at these locations.

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Study on the Decomposition of Factors Affecting Energy-Related Carbon Emissions in Guangdong Province, China

Cited by 57 publications

References 18 publications

Influencing Factors of Energy-Related CO2 Emissions in China: A Decomposition Analysis

Influencing Factors of Energy-Related CO2 Emissions in China: A Decomposition Analysis

Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea

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