scholarly journals Forecasting China’s CO2 Emissions for Energy Consumption Based on Cointegration Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xiangmei Li ◽  
Yan Song ◽  
Zhishuang Yao ◽  
Renbin Xiao
Keyword(s):  
Energy Consumption ◽  
Policy Decision ◽  
Influential Factors ◽  
Carbon Intensity ◽  
Chinese Government ◽  
Level Energy ◽  
Total Energy Consumption ◽  
Continuous Increase ◽  
Carbon Reduction ◽  
Great Pressure

Forecasting CO2 emissions is important for climate policy decision making. The paper attempts to implement empirically the long-term forecast of CO2 emissions based on cointegration theory under the business-as-usual scenario, by using statistical data from China over the period 1953 to 2016. We focus on the relationships between CO2 emissions for energy consumption and influential factors: per capita GDP, urbanization level, energy intensity, and total energy consumption. The empirical results are presented as follows: (1) continuous increase of carbon pollution resulting from energy consumption (1953-2016) indicates that China has beard great pressure of carbon reduction. (2) Though reduction of carbon intensity in 2020 would account for 50.14% that of 2005, which meets the requirements announced by Chinese government in 2009, China would bear carbon emissions for energy consumption of 14.4853 billion tCO2 by 2030, which is nearly 1.59 times that of 2016 and nearly 105 times that of 1953. The results suggest that the policymakers in China may take more effective measures such as reducing energy intensities and formulating stricter environmental regulations in order to mitigate the CO2 emissions and realize the win-win of economic and ecological benefits.

scholarly journals Is There Any Difference in the Effect of Different R and D Sources on Carbon Intensity in China?

2019 ◽  
Vol 11 (6) ◽  
pp. 1701 ◽  
Author(s):  
Feng Feng ◽  
Linlin Peng
Keyword(s):  
Critical Issue ◽  
Government Support ◽  
Carbon Intensity ◽  
Threshold Regression ◽  
Carbon Reduction ◽  
R And D ◽  
Reduction Strategies ◽  
Great Pressure ◽  
The Government ◽  
Threshold Regression Model

In recent decades, climate change, mostly caused by CO2 emissions, has become a critical issue of concern to people worldwide. It is necessary for countries all around the world to reduce carbon emissions. China, as the world’s largest carbon emitter, is under great pressure to implement carbon-reduction strategies. Technological progress plays a crucial role in balancing environmental and economic development. The main objective of this work is to empirically compare the effects of government and enterprise research and development (R and D) on carbon-emission reduction using the panel data of 30 Chinese provinces from 2009 to 2016. The effects of both government and enterprise R and D investment on carbon intensity are compared in detail through a linear model and a threshold-regression model. Linear-regression results shows that both government and enterprise R and D decrease carbon intensity, while enterprise investment tends to be more instant. Further threshold-regression results indicate that the effects of government and enterprise R and D on carbon intensity are different in different urbanization stages. Guiding enterprises to invest in R and D in medium-developing areas, and increasing government support and subsidies for R and D activities in underdeveloped areas should be an important goal of the government policies.

Grey Forecasting Model for CO2 Emissions

2012 ◽  
Vol 518-523 ◽  
pp. 1664-1668 ◽  
Author(s):  
Guo Lin Bao ◽  
Hong Qi Hui
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Energy Consumption ◽  
Greenhouse Gases ◽  
Carbon Intensity ◽  
Forecasting Model ◽  
Chinese Government ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Average Residual

CO2 is the most frequently implicated in global warming among the various greenhouse gases associated with climate change. Chinese government has been taking serious measures to control energy consumption to reduce CO2 emissions. This study applies the grey forecasting model to estimate future CO2 emissions and carbon intensity in Shijiazhuang from 2010 until 2020. Forecasts of CO2 emissions in this study show that the average residual error of the GM(1, 1) is below 1.5%. The average increasing rate of CO2 emissions will be about 6.71%; and the carbon intensity will be 2.10 tons/104GDP until year 2020. If the GDP of Shijiazhuang city can be quadruple, the carbon intensity will be half to the 2005 levels until 2020. The findings of this study provide a valuable reference with which the Shijiazhuang government can formulate measures to reduce CO2 emissions by curbing the unnecessary the consumption of energy.

Plant Level Energy Supply Analysis and Optimization in Energy, Economy and Environment

2014 ◽  
Author(s):  
Lujia Feng ◽  
Laine Mears ◽  
Qilun Zhu ◽  
Cleveland Beaufort ◽  
Joerg Schulte
Keyword(s):  
Energy Consumption ◽  
Manufacturing Process ◽  
Energy Supply ◽  
Raw Material ◽  
Level Energy ◽  
Optimization Strategy ◽  
Total Energy Consumption ◽  
Energy Supply System ◽  
Automotive Assembly ◽  
Plant Level

Increasing attention has recently been drawn to the energy consumption of the manufacturing process. Facing the challenges from reducing emission, rising raw material prices and energy costs, manufacturers are trying to balance the energy usage strategy among the total energy consumption, economy and environment, which can be self-conflicting at times. This paper focuses on the objective optimizations of a plant level energy supply system, and describes how a multi-objective optimization strategy can be effectively formulated for making the best use of energy delivered to the manufacturing process. An example from an automotive assembly manufacturer is described.

Forecasting the total energy consumption in China using a new-structure grey system model

2017 ◽  
Vol 7 (2) ◽  
pp. 194-217 ◽  
Author(s):  
Bo Zeng ◽  
Chengming Luo
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Extreme Values ◽  
System Model ◽  
Chinese Government ◽  
Grey System ◽  
Total Energy Consumption ◽  
Content Type ◽  
Grey Models ◽  
Grey System Model

Purpose China is by far the world’s largest energy consumer and importer. Reasonably forecasting the trend of China’s total energy consumption (CTEC) is of great significance. The purpose of this paper is to propose a new-structure grey system model (NSGM (1, 1)) to forecast CTEC. Design/methodology/approach Two matrices for computing the parameters of NSGM (1, 1) were defined and the specific calculation formula was derived. Since the NSGM (1, 1) model increases the number of its background values, which improves the smoothness effect of the background value and weakens the effects of extreme values in the raw sequence on the model’s performance; hence it has better simulation and prediction performances than traditional grey models. Finally, NSGM (1, 1) was used to forecast China’s total energy consumption during 2016-2025. The forecast showed CTEC will grow rapidly in the next ten years. Findings Therefore, in order to meet the target of keeping CTEC under control at 4.8 billion tons of standard coal in 2020, Chinese government needs to take necessary measures such as transforming the economic development pattern and enhancing the energy utilization efficiency. Originality/value A new-structure grey forecasting model, NSGM (1, 1), is proposed in this paper, which improves the smoothness and weakens the effects of extreme values and has a better structure and performance than those of other grey models. The authors successfully employ the new model to simulate and forecast CTEC. The research findings could aid Chinese government in formulating energy policies and help energy exporters make rational energy yield plans.

The Research on the Influential Factors of Civil Building Energy Consumption in Shaanxi Province

2014 ◽  
Vol 1065-1069 ◽  
pp. 2213-2218
Author(s):  
Wang Zhen
Keyword(s):  
Energy Consumption ◽  
Influence Factors ◽  
Building Energy ◽  
Influential Factors ◽  
Shaanxi Province ◽  
Building Energy Efficiency ◽  
Building Energy Consumption ◽  
Total Energy Consumption ◽  
The Sustainable Development ◽  
Hypothesis Model

Civil building energy consumption is an important part of the social total energy consumption, building energy efficiency has great significance to the sustainable development. This paper through the construction of civil building energy consumption influence factors hypothesis model of civil building energy consumption influence factors are analyzed. The results show that the total population, urban and rural population structure, economic development, the consumption level of residents and the third industry development will promote the civil building energy consumption in Shaanxi Province, while the technological progress is not obvious to reduce the energy consumption of civil building in Shaanxi. On this basis, put forward the energy saving proposal.

scholarly journals Examining the Dynamics and Determinants of Energy Consumption in China’s Megacity Based on Industrial and Residential Perspectives

2021 ◽  
Vol 13 (2) ◽  
pp. 764
Author(s):  
Changjian Wang ◽  
Fei Wang ◽  
Gengzhi Huang ◽  
Yang Wang ◽  
Xinlin Zhang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Population Size ◽  
Urban Areas ◽  
Growth Stage ◽  
Energy Intensity ◽  
Level Energy ◽  
Total Energy Consumption ◽  
Economic Output ◽  
Moderate Growth

Cities are regarded as the main areas for conducting strategies for energy sustainability and climate adaptation, specifically in the world’s top energy consumer—China. To uncover dynamic features and main drivers for the city-level energy consumption, a comprehensive and systematic city-level total energy consumption accounting approach was established and applied in China’s megacity, which has the highest industrial electricity consumption. Compared with previous studies, this study systematically analyzes drivers for energy consumption based on industrial and residential perspectives. Additionally, this study analyzes not only the mechanisms by which population size, economic growth, and energy intensity affect energy consumption but also the effects of population and industry structural factors. According to the extended Logarithmic mean Divisia index (LMDI) method, the main conclusions drawn from this research are as follows: (1) The total energy consumption of Suzhou presented an overall increasing trend, with 2006–2012 as a rapid growth stage and 2013–2016 as a moderate growth stage. (2) The energy consumption structure was mainly dominated by coal, which was followed by outsourced electricity and natural gas. (3) Scale-related factors have dominated changes in energy consumption, and structural and technological factors have had profound effects on energy consumption in different development periods. (4) Population size and economic output were the main drivers for increments in industrial energy consumption, whereas energy intensity and economic structure performed the important curbing effects. The income effect of urban residents was the biggest driver behind the increase in residential energy consumption, whereas energy intensity was the main limiter. These findings provide a scientific basis for an in-depth understanding of the determinants of the evolution of urban energy consumption in China’s megacity, including similar cities or urban areas in the developing world.

scholarly journals Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2721 ◽  
Author(s):  
Ying Wang ◽  
Peipei Shang ◽  
Lichun He ◽  
Yingchun Zhang ◽  
Dandan Liu
Keyword(s):  
Energy Consumption ◽  
Programming Model ◽  
Structure Optimization ◽  
Primary Energy ◽  
Carbon Intensity ◽  
Energy Structure ◽  
Chinese Government ◽  
Primary Energy Consumption ◽  
Main Research ◽  
Carbon Intensity Target

To mitigate global warming, the Chinese government has successively set carbon intensity targets for 2020 and 2030. Energy restructuring is critical for achieving these targets. In this paper, a combined forecasting model is utilized to predict primary energy consumption in China. Subsequently, the Markov model and non-linear programming model are used to forecast China’s energy structure in 2020 and 2030 in three scenarios. Carbon intensities were forecasted by combining primary energy consumption, energy structure and economic forecasting. Finally, this paper analyzes the contribution potential of energy structure optimization in each scenario. Our main research conclusions are that in 2020, the optimal energy structure will enable China to achieve its carbon intensity target under the conditions of the unconstrained scenario, policy-constrained scenario and minimum external costs of carbon emissions scenario. Under the three scenarios, the carbon intensity will decrease by 42.39%, 43.74%, and 42.67%, respectively, relative to 2005 levels. However, in 2030, energy structure optimization cannot fully achieve China’s carbon intensity target under any of the three scenarios. It is necessary to undertake other types of energy-saving emission reduction measures. Thus, our paper concludes with some policy suggestions to further mitigate China’s carbon intensities.

scholarly journals A Critical Analysis on Influential Factors on Power Energy Resources in China

2018 ◽  
Vol 12 (2) ◽  
pp. 1
Author(s):  
Lubing Xie ◽  
Xiaoming Rui ◽  
Shuai Li ◽  
Xiaozhao Fan ◽  
Ruijing Shi ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Swot Analysis ◽  
Primary Energy ◽  
Influential Factors ◽  
Biomass Energy ◽  
Energy Industry ◽  
Foreign Firms ◽  
Total Energy Consumption ◽  
Wide Range

China is facing a number of challenges, such as environmental pollution, energy security, and slowing down of economic growth. China's total energy consumption has been leading the worldwide consumption for several years. China's annual primary energy consumption accounts for more than 90% of total energy consumption, and the country's utilization of wind energy, solar energy, biomass energy, and other new form of energy remains very low. This research has adopted a strength, weakness, opportunity, and threat (SWOT) analysis approach to examine the internal and external factors that affect the competitiveness of the energy industry in China. An extensive and critical review of a wide range of literature was conducted, including academic papers, industry reports, statistical data, relevant regulations, and policy documents. Eighteen factors were identified from the literature review. These factors form part of an integrated framework that provides a useful tool for policy makers and the industry to gain a better understanding of the factors that affect the sustainable development of the Chinese energy industry. The results also provide a useful reference for foreign firms that intend to explore the Chinese energy industry market.

Energy savings potential of new aeration system: Full scale trials

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
A. Lazić ◽  
V. Larsson ◽  
Å. Nordenborg
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Total Energy ◽  
Energy Savings ◽  
Treatment Plant ◽  
Full Scale ◽  
Total Energy Consumption ◽  
Aeration System ◽  
Fine Bubble ◽  
Aeration Control

The objective of this work is to decrease energy consumption of the aeration system at a mid-size conventional wastewater treatment plant in the south of Sweden where aeration consumes 44% of the total energy consumption of the plant. By designing an energy optimised aeration system (with aeration grids, blowers, controlling valves) and then operating it with a new aeration control system (dissolved oxygen cascade control and most open valve logic) one can save energy. The concept has been tested in full scale by comparing two treatment lines: a reference line (consisting of old fine bubble tube diffusers, old lobe blowers, simple DO control) with a test line (consisting of new Sanitaire Silver Series Low Pressure fine bubble diffusers, a new screw blower and the Flygt aeration control system). Energy savings with the new aeration system measured as Aeration Efficiency was 65%. Furthermore, 13% of the total energy consumption of the whole plant, or 21 000 €/year, could be saved when the tested line was operated with the new aeration system.

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