scholarly journals Reconstructed and Projected U.S. Residential Natural Gas Consumption during 1896–2043

2018 ◽  
Vol 57 (3) ◽  
pp. 607-625 ◽  
Author(s):  
Steven A. Mauget
Keyword(s):  
Natural Gas ◽  
Climate Variation ◽  
Temperature Cycle ◽  
Total Consumption ◽  
Climate Conditions ◽  
Positive Effects ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Per Capita

AbstractUsing state-level monthly heating degree-day data, reconstructed per capita natural gas (NGr) consumption records for each state of the continental United States were calculated for 1895–2014 using linear regressions. The regressed monthly NGr values estimate the effects of twentieth- and early twenty-first-century climate variation on per capita natural gas usage, assuming a modern (1990–2013) consumption environment. Using these extended consumption records, the hypothetical effects of climate on past, current, and future natural gas (NG) use are estimated. By controlling for nonclimatic consumption effects, these extended reconstructions provide estimates of the sensitivity of NG consumption to historical climate variation, particularly long-term warming trends, occurring before the period of available consumption records. After detrending, the reconstructions are used to form improved estimates of interannual NG variation under current climate conditions. Given estimates of each state’s current consumption climatology and long-term trends in per capita consumption and current population trends, the net effect of warming and increasing population on future consumption is estimated. Significant long-term negative trends in per capita NG consumption are found in western and northeastern states and in Florida, while southeastern consumption effects reflect a multidecadal temperature cycle. Climate-related consumption effects found here are generally consistent with previous studies, with long-term trend effects limited to less than 12% and multidecadal regime effects limited to less than 9%. Given the stronger positive effects of increasing population on total state natural gas consumption, reduced per capita use associated with warming trends has a weak moderating effect on estimates of projected total consumption in 2043.

scholarly journals Comparison of CO2 emissions reduction efficiency of household fuel consumption in China

2019 ◽  
Vol 11 (4) ◽  
pp. 979 ◽  
Author(s):  
Bingjie Xu ◽  
Ruoyu Zhong ◽  
Yifeng Liu
Keyword(s):  
Natural Gas ◽  
Co2 Emissions ◽  
Negative Impact ◽  
National Level ◽  
Energy Structure ◽  
Kuznets Curve ◽  
Liquefied Petroleum Gas ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Per Capita

The purpose of this paper is to analyze the correlations among per capita gross domestic product (GDP), household fuel (natural gas and liquefied petroleum gas) consumption, and carbon dioxide (CO2) emissions through the environmental Kuznets curve (EKC) at the regional and national level in China using data from 2003 to 2015. The results validate the EKC assumption and show that per capita GDP is positively related to CO2 emissions; per capita natural gas consumption has a negative impact on CO2 emissions; however, per capita liquefied petroleum gas (LPG) consumption has a positive effect on CO2 emissions. Therefore, increasing natural gas consumption can effectively slow down the environmental degradation of China. Given rapid economic growth, changing the energy structure can improve the environment.

scholarly journals Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4905
Author(s):  
Bartłomiej Gaweł ◽  
Andrzej Paliński
Keyword(s):  
Natural Gas ◽  
Decision Trees ◽  
Energy Intensity ◽  
Fuzzy Decision ◽  
Energy Mix ◽  
Starting Point ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Fuzzy Decision Trees

Classic forecasting methods of natural gas consumption extrapolate trends from the past to subsequent periods of time. The paper presents a different approach that uses analogues to create long-term forecasts of the annual natural gas consumption. The energy intensity (energy consumption per dollar of Gross Domestic Product—GDP) and gas share in energy mix in some countries, usually more developed, are the starting point for forecasts of other countries in the later period. The novelty of the approach arises in the use of cluster analysis to create similar groups of countries and periods based on two indicators: energy intensity of GDP and share of natural gas consumption in the energy mix, and then the use of fuzzy decision trees for classifying countries in different years into clusters based on several other economic indicators. The final long-term forecasts are obtained with the use of fuzzy decision trees by combining the forecasts for different fuzzy sets made by the method of relative chain increments. The forecast accuracy of our method is higher than that of other benchmark methods. The proposed method may be an excellent tool for forecasting long-term territorial natural gas consumption for any administrative unit.

The application of trigonometric grey prediction model to average per capita natural gas consumption of households in China

2019 ◽  
Vol 9 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Qiuping Wang ◽  
Subing Liu ◽  
Haixia Yan
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Forecasting Model ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Grey Forecasting ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Per Capita

Purpose Due to high efficiency and low carbon of natural gas, the consumption of natural gas is increasing rapidly, and the prediction of natural gas consumption has become the focus. The purpose of this paper is to employ a prediction technique by combining grey prediction model and trigonometric residual modification for predicting average per capita natural gas consumption of households in China. Design/methodology/approach The GM(1,1) model is utilised to obtain the tendency term, then the generalised trigonometric model is used to catch the periodic phenomenon from the residual data of GM(1,1) model for improving predicting accuracy. Findings The case verified the view of Xie and Liu: “When the value of a is less, DGM model and GM(1,1) model can substitute each other.” The combination of the GM(1,1) and the trigonometric residual modification technique can observably improve the predicting accuracy of average per capita natural gas consumption of households in China. The mean absolute percentage errors of GM(1,1) model, DGM(1,1), unbiased grey forecasting model, and TGM model in ex post testing stage (from 2013 to 2015) are 32.5510, 33.5985, 36.9980, and 5.2996 per cent, respectively. The TGM model is suitable for the prediction of average per capita natural gas consumption of households in China. Practical implications According to the historical data of average per capita natural gas consumption of households in China, the authors construct GM(1,1) model, DGM(1,1) model, unbiased grey forecasting model, and GM(1,1) model with trigonometric residual modification. The accuracy of TGM is the best. TGM helps to improve the accuracy of GM(1,1). Originality/value This paper gives a successful practical application of grey model GM(1,1) with the trigonometric residual modification, where the cyclic variations exist in the residual series. The case demonstrates the effectiveness of trigonometric grey prediction model, which is helpful to understand the modeling mechanism of trigonometric grey prediction model.

Energy Demand Projections in the 21st Century for Coastal California

2012 ◽  
Author(s):  
Pedro Sequera ◽  
Yanelly Molina ◽  
Jorge E. Gonzalez ◽  
Robert Bornstein
Keyword(s):  
Natural Gas ◽  
21St Century ◽  
Electricity Consumption ◽  
Sea Breeze ◽  
Electricity Demand ◽  
Summer Temperature ◽  
Temperature Trends ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Per Capita

Previous studies conducted by Lebassi et al. (2010) and Sequera et al. (2011) have showed a strong correlation between summer temperature and electricity demand per capita trends for the past four decades for California. Decreasing summer temperature trends in low elevation coastal California sites between 1970–2010 resulted in decreasing electricity demand for the same locations. On the other hand, increasing temperature trends in high-elevation and inland California sites for the same period showed increasing electricity demand during summers. As a consequence of an increased gradient of the concurrent sea breeze potential for the same period, the authors suggested that this increased in sea breeze was responsible for the observed coastal cooling, attributing the seabreeze increase to a counter effect of global warming. The authors also reported increasing temperatures during winter throughout California for the same period, resulting in decreasing natural gas consumption. This work extends this analysis by determining spatial and temporal trends in residential electricity and natural gas consumption using 1990 to 2009 data from the California Energy Commission. Results show yearly electricity consumption per person is lower for coastal counties than inland counties. In contrast, yearly natural gas consumption per capita is decreasing for both coastal and inland counties. Additional work includes the examination of future summer axysymmetric warming and winter homogenous warming as well as their implications on energy demands into the 21st century. Results from 16 downscaled Global Circulation Models for 2 green-house gas emissions scenarios are used to forecast future average temperatures. These projections are correlated with electricity consumption during the summer and natural gas consumption during the winter. Statistical analysis of these results is provided in order to quantify uncertainty on these forecasts.

Short-term natural gas consumption forecasting from long-term data collection

Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 119430
Author(s):  
Radek Svoboda ◽  
Vojtech Kotik ◽  
Jan Platos
Keyword(s):  
Natural Gas ◽  
Data Collection ◽  
Short Term ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Term Data
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