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• Decoupling of GDP from all energy sources under multiple principles are examined. • World economy achieved weak energy decoupling in most years during 2000–2011. • World economy coupled with coal use and tended to couple with cleaner energy use. • None of studied economies achieved long-term GDP-embodied energy decoupling. • Decoupling delusions occurred when only considering direct and total energy use. Decoupling indicators are widely used to understand links between economic growth and energy use. However, traditional decoupling analyses mostly focus on domestic energy consumption (i.e., the production-based principle) and neglect off-site energy use across global supply chains to satisfy an economy's final consumption (i.e., the consumption-based principle). Moreover, analyses for total primary energy conceal an economy's preference for different energy sources. Therefore, this paper evaluates decoupling states of GDP from all types of primary energy use under consumption-based principle, for world economy and eight typical economies during 2000–2011. Regarding total primary energy, world economy witnessed weak decoupling in most years, and most economies studied (e.g., USA, Japan and China) achieved decoupling initially but performed negative decoupling finally. For EU, USA, Japan, Russia and India, production-based decoupling performances were generally better than consumption-based ones. Decoupling phenomena detected under production-based principle even became coupling or negative decoupling under consumption-based principle in some cases. As for each energy source, world economy decoupled from oil use, but still coupled with coal use, and gradually showed a trend to couple with natural gas and renewables use. Different energy sources showed distinct decoupling degrees from GDP, affected by individual embodied energy requirement structure. This paper uncovers potential energy decoupling delusions to deepen the understanding of relationships between energy use and economic growth. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between energy consumption and economic growth for the United States both at a country and at a sectoral level (Industry, Residential, Electric Power and Transportation) using an asymmetric threshold cointegration approach and monthly data from January 1991 to May 2016. Granger causality tests support a neutrality hypothesis for all sectors, except for the case of total consumption at the country level where a unidirectional causality is running from energy consumption to economic growth. [ABSTRACT FROM AUTHOR]

This paper offers a new perspective on the energy efficiency literature by bringing evidence of political contextual factors as the predictors of energy efficiency. Specifically, we posit that the Democrat administration is more energy-efficient considering the reduction of environmental impact, in contrast, the Republican administration is more efficient considering only financial expenditures leading to the production of economic growth. In addition, we predict that political administration tenure is negatively correlated with green energy efficiency and that political distancing moderates the relationship between political party administration and energy efficiency. This study sheds light on these matters by performing an efficiency analysis of fifty North American states through a bootstrap DEA non-parametric model, followed by Tobit regressions to evaluate our hypotheses concerning the effect of the contextual factors on the calculated efficiency scores. • We investigate the impact of political factors on energy efficiency in the United States. • We perform a two-stage DEA-Tobit bootstrapped regression analysis. • The Democrats consistently demonstrated higher energy efficiency scores. • The political tenure as a feature had mild or mixed impact on energy efficiency. • We confirmed the positive time-passage effect on energy efficiency. [ABSTRACT FROM AUTHOR]

In the United States, the industrial sector is the driving engine of economic development, and energy consumption in this sector may be considered as the fuel for this engine. In order to keep this sector sustainable (diverse and productive over the time), energy planning should be carried out comprehensively and precisely. This paper describes the development of two types of numerical energy models which are able to predict the United States' future industrial energy-demand. One model uses an ANN (artificial neural network) technique, and the other model uses a MLR (multiple linear regression) technique. Various independent variables (GDP, price of energy carriers) are tested. The future industrial energy demand can then be forecasted based on a defined scenario. The ANN model anticipates a 16% increase in energy demand from 2012 by 2030. In this forecast, the model assumes that the effective independent parameters remain constant during this period and only GDP grows with a second-order polynomial trend. The forecast result, which shows consistency with published predictions, may be considered as an indication of the need for development of new and low-cost energy sources. This study suggests that the ANN technique is a reliable and powerful technique which can effectively perform input/output mapping. In order to validate the performance of the models, the results of the ANN model is compared to the projections from the Energy Information Administration of the U.S. Department of Energy. [ABSTRACT FROM AUTHOR]

Abstract: The rapid growth of energy consumption in conjunction with economic development during past decades in East Asia, especially China, caused severe air pollution problems at local and regional scales. Understanding of the meteorological conditions for air pollution is essential to the understanding of the formation mechanism of air pollutants and the development of effective emission control strategies to reduce air pollution. In this paper, the Fifth Generation National Center for Atmospheric Research (NCAR)/Pennsylvania State University (PSU) Mesoscale Model (MM5) modeling system is applied to simulate meteorological fields during selected six 1-month periods in 2007/2008 over a triple-nested modeling domain covering East Asia, the eastern China, and Shandong Province at horizontal grid resolutions of 36-, 12-, and 4-km, respectively. MM5 generally reproduces well the observations in the eastern China but performs worse in the western China and northeastern China. Largest biases occur in 2-m temperatures (T2) and wind speed and wind direction at 10-m in haze months (i.e., winter) and daily mean precipitation (Precip) in non-haze months (i.e., summer), due to limitations of the model in simulating snow cover and convective precipitation. Meteorological predictions agree more closely with observations at urban sites than those at the coastal and mountain sites where the model performance deteriorates because of complex terrains, influences of urban heat island effect and land/sea breezes, and higher elevations. Model results at 12-km in Shandong Province show an overall better performance than those at 4- or 36-km while the results at 4-km show worst performance due to inaccurate land use and the model’s incapability in simulating meteorological processes at a fine scale. [Copyright &y& Elsevier]