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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]

Power split configurations exhibit potential on decreasing fuel consumption. However, how to select the best power split configuration among all the possible configurations still need to be explored. In this paper, a one-dimensional searching ECMS (Equivalent Consumption Minimization Strategy) is developed to evaluate fuel consumption for input power split configuration. Additionally, a battery degradation model based on experimental data is applied to evaluate the battery degradation for input power split configuration. According to one-dimensional searching ECMS and battery degradation model, the impact of different input power split configurations on fuel consumption and battery degradation is investigated. Simulations show that configuration T1 has fuel consumption and battery degradation advantages for PHEV city bus application in China. These advantages are observed because the system efficiency operation points of configuration T1 are closer to the high efficiency area than the system efficiency operation points of other input power split configurations. Furthermore, according to the lever model and one-dimensional searching ECMS method, a power split configuration selection method is provided in this paper. The simulation results based on configuration selection method still indicate that configuration T1 appears to have the best fuel economy for PHEV city bus application. Moreover, configuration T5, with a lever length close to 0, exhibits the worst fuel consumption and battery degradation. [ABSTRACT FROM AUTHOR]

• We develop a microeconomic model of VMT choice under time and budget constraints. • Using NHTS data, we estimate VMT elasticities with respect to fuel and time costs. • We use these elasticities to forecast CAV-induced travel and energy rebound. • A 38% drop in time cost offsets energy savings from a 20% fuel efficiency rise. Connected and automated vehicles (CAVs) are expected to yield significant improvements in safety, energy efficiency, and time utilization. However, their net effect on energy and environmental outcomes is unclear. Higher fuel economy reduces the energy required per mile of travel, but it also reduces the fuel cost of travel, incentivizing more travel and causing an energy " rebound effect." Moreover, CAVs are predicted to vastly reduce the time cost of travel, inducing further increases in travel and energy use. In this paper, we forecast the induced travel and rebound from CAVs using data on existing travel behavior. We develop a microeconomic model of vehicle miles traveled (VMT) choice under income and time constraints; then we use it to estimate elasticities of VMT demand with respect to fuel and time costs, with fuel cost data from the 2017 United States National Household Travel Survey (NHTS) and wage-derived predictions of travel time cost. Our central estimate of the combined price elasticity of VMT demand is −0.4, which differs substantially from previous estimates. We also find evidence that wealthier households have more elastic demand, and that households at all income levels are more sensitive to time costs than to fuel costs. We use our estimated elasticities to simulate VMT and energy use impacts of full, private CAV adoption under a range of possible changes to the fuel and time costs of travel. We forecast a 2–47% increase in travel demand for an average household. Our results indicate that backfire – i.e., a net rise in energy use – is a possibility, especially in higher income groups. This presents a stiff challenge to policy goals for reductions in not only energy use but also traffic congestion and local and global air pollution, as CAV use increases. [ABSTRACT FROM AUTHOR]

Abstract: Optimization of combined cooling, heating, and power (CCHP) systems operation commonly focuses only on energy cost. Different algorithms have been developed to attain optimal utilization of CCHP units by minimizing the energy cost in CCHP systems operation. However, other outcomes resulting from CCHP operation such as primary energy consumption and emission of pollutants should also be considered during CCHP systems evaluation as one would expect these outcomes can be subject to regulation. This paper presents an optimization of the operation of CCHP systems for different climate conditions based on operational cost, primary energy consumption (PEC), and carbon dioxide emissions (CDE) using an optimal energy dispatch algorithm. The results for the selected cities demonstrate that in general there is not a common trend among the three optimization modes presented in this paper since optimizing one parameter may reduce or increase the other two parameters. The only cities that show reduction of PEC while also reducing the CDE are Columbus, MS; Minneapolis, MN; and Miami, FL. For these cities the operational cost always increases when compared to the reference case consisting of using a vapor/compression cycle for cooling and natural gas for heating. On the other hand, for San Francisco and Boston, CCHP systems increase the CDE. In general, if CCHP systems increase the cost of operation, as long as energy savings and reduction of emissions are guaranteed, the implementation of these systems should be considered. [Copyright &y& Elsevier]

Occupants are integral elements of a building ecosystem and their behavior can have a substantial impact on energy consumption in buildings. A wide range of energy feedback programs have been developed to make energy consumption more visible and interpretable to occupants and help them learn how to control and save energy. In this paper, we conduct a critical review of the literature related to energy feedback and identify four key methodological approaches to designing and studying energy feedback programs: experiments, analytics, surveys and simulation. Our meta-analysis reveals five research gaps and opportunities for future methodological fusion at the intersection between such approaches, including the analytics-survey, experiments-analytics, experiments-analytics-surveys, simulation-experiments and analytics-simulation interfaces. Future research at these crucial interfaces could provide the deeper understanding necessary to develop energy feedback programs that yield substantial and persistent energy savings. [ABSTRACT FROM AUTHOR]

Building energy consumption makes up 40% of the total energy consumption in the United States. Given that energy consumption in buildings is influenced by aspects of urban form such as density and floor-area-ratios (FAR), understanding the distribution of energy intensities is critical for city planners. This paper presents a novel technique for estimating commercial building energy consumption from a small number of building features by training machine learning models on national data from the Commercial Buildings Energy Consumption Survey (CBECS). Our results show that gradient boosting regression models perform the best at predicting commercial building energy consumption, and can make predictions that are on average within a factor of 2 from the true energy consumption values (with an r 2 score of 0.82). We validate our models using the New York City Local Law 84 energy consumption dataset, then apply them to the city of Atlanta to create aggregate energy consumption estimates. In general, the models developed only depend on five commonly accessible building and climate features, and can therefore be applied to diverse metropolitan areas in the United States and to other countries through replication of our methodology. [ABSTRACT FROM AUTHOR]

Market penetration of energy-efficient technologies can be estimated using energy optimization models that minimize cost; however, such models typically estimate the minimum cost of optimal pathways under a certain set of non-dynamic assumptions, so technology penetrations determined for the long-term do not fully respond to changing circumstances or costs. In this study, investment costs of energy-efficient technologies are modeled dynamically in the Industrial Sector Energy-Efficiency Model (ISEEM) using a technological learning formula. Results from 24 energy-efficient technologies – 14 existing, 10 emerging – selected from the United States (U.S.) iron and steel sector show that when technological learning is incorporated into the model, total energy consumption of this sector is expected to decrease by 13% (180 PJ) in 2050 compared to energy consumption in a non-learning scenario. Average energy intensity of the steel production improves from 12.3 GJ/t in the non-learning scenario to 10.7 GJ/t in the learning scenario in 2050. This decrease represents a cost savings of US$1.6 billion and a carbon dioxide emissions reduction potential of 14.9 billion tonnes. Results discussed in this paper focus on the U.S. iron and steel sector, but the proposed framework can be applied to study new technology development in any other industrial processes and regions. [ABSTRACT FROM AUTHOR]

Long-term Load Forecasting (LTLF) plays a vital role in the planning of electric utilities. In the long run, utilities face various uncertainties caused by economic and environmental factors. These uncertainties have made LTLF more complex and inaccurate, thus, amplifying financial risks of utilities. A potent contributor to such losses in LTLF accuracy is economic shocks. This study proposes two probabilistic Time-Varying (TV) approaches to capture such shocks in LTLF and minimise accuracy loss, namely TV-XGB-X and TV-PR, and their combinations considering economic policy uncertainty. Both eXtreme Gradient Boosting (XGB) and Polynomial Regression (PR) are extended to include the long-run TV effects of economic shocks as eXogeneous predictors in this paper. These models and their combinations are compared with various non-TV approaches through experiments on the monthly electricity consumption of eight energy-intensive states in the United States. The results reveal that the proposed combined approaches outperform stand-alone models on all datasets. The findings of this study can help utilities in hedging financial risks under shocks. • Proposed Time-Varying framework to detect and capture economic shocks for LTLF. • Framework includes probabilistic TV-PR, TV-XGB-X, their variants and combinations. • Compared the point and probabilistic accuracies of proposed and benchmark models. • Proposed models outperform existing benchmark models. [ABSTRACT FROM AUTHOR]

We model scenarios of the U.S. electric sector in which wind generation reaches 10% of end-use electricity demand in 2020, 20% in 2030, and 35% in 2050. As shown in a companion paper, achieving these penetration levels would have significant implications for the wind industry and the broader electric sector. Compared to a baseline that assumes no new wind deployment, under the primary scenario modeled, achieving these penetrations imposes an incremental cost to electricity consumers of less than 1% through 2030. These cost implications, however, should be balanced against the variety of environmental and social implications of such a scenario. Relative to a baseline that assumes no new wind deployment, our analysis shows that the high-penetration wind scenario yields potential greenhouse-gas benefits of $85–$1,230 billion in present-value terms, with a central estimate of $400 billion. Air-pollution-related health benefits are estimated at $52–$272 billion, while annual electric-sector water withdrawals and consumption are lower by 15% and 23% in 2050, respectively. We also find that a high-wind-energy future would have implications for the diversity and risk of energy supply, local economic development, and land use and related local impacts on communities and ecosystems; however, these additional impacts may not greatly affect aggregate social welfare owing to their nature, in part, as resource transfers. [ABSTRACT FROM AUTHOR]

In the United States, buildings account for 35% of total energy-related carbon dioxide emissions, making them important contributors to decarbonization. Carbon intensities in the power grid are time-varying and can fluctuate significantly within hours, so shifting building loads in response to the carbon intensities can reduce a building's operational carbon emissions. This paper presents a rule-based carbon responsive control framework that controls the setpoints of thermostatically controlled loads responding to the grid's carbon emission signals in real time. Based on this framework, four controllers are proposed with different combinations of carbon accounting methods and control rules. To evaluate their performance, we performed simulation studies using models of a 27-home, all-electric, net zero energy residential community located in Basalt, Colorado, United States. The carbon intensity data of four future years from the Cambium data set are adopted to account for the evolving resource mix in the power grid. Various performance metrics, including energy consumption, carbon emission, energy cost, and thermal discomfort, were used to evaluate the performance of the controllers. Sensitivity analysis was also conducted to determine how the control thresholds and intervals affect the controllers' performance. Simulation results indicate that the carbon responsive controllers can reduce the homes' annual carbon emissions by 6.0% to 20.5%. However, the energy consumption increased by 0.9% to 6.7%, except in one scenario where it decreased by 2.2%. Compared to the baseline, the change in energy cost was between −2.9% and 3.4%, and thermal discomfort was also maintained within an acceptable range. The little impact on energy cost and thermal discomfort indicates there are no potential roadblocks for customer acceptance when rolling out the controllers in utility programs. [ABSTRACT FROM AUTHOR]

Abstract: To meet rising energy demands in a sustainable manner, the United States Renewable Fuel Standard 2 (RFS2) mandates the production of 3.78billion L (1billion gallons) of biomass-based biodiesel per year by 2020. Urban marginal lands, herein defined as lots with poor agricultural potential and unfit for residential purposes, can be used to cultivate biodiesel feedstock, which can contribute toward the RFS2. GIS is particularly well suited to identify the amount and location of these urban marginal lands. Biofuel crop production is one way for urban neighborhoods to reclaim vacant, blighted properties and also generate green energy for consumption. Combining soil classification data with urban development data, a GIS framework was developed to determine the amount of urban marginal land available in Pittsburgh, Pennsylvania. This location was chosen because small-scale sunflower crops are already being grown on urban marginal lands for biodiesel production. This paper evaluates two methods for identifying marginal lands for biofuel cultivation. The first is based on NRCS soil classifications derived from previous literature approaches. When compared with the locations of a nonprofit that is actually cultivating marginal lands, the NRCS soil classification method captures less than 35% of the marginal lands being cultivated in Pittsburgh, and 2400ha (6000acres) of marginal land in total that could be used to grow sunflowers for biodiesel production. We modify the NRCS marginal soil classification method to include certain types of urban commercial lands and lands not currently classified as marginal, and create a framework that identifies almost 90% of the marginal lands currently under cultivation in Pittsburgh, with approximately 3500ha (8700 acres) of available marginal lands. Pittsburgh’s marginal lands could be used to produce up to 129,000L (34,100gallons) of sunflower-based biodiesel. This could contribute a maximum of 0.1% to Pennsylvania’s biofuel mandate and 0.003% to the RFS2. While a city’s individual contribution may be incremental on the national scale, multiple cities and regions may be able to contribute to the RFS2. In addition, there is an unquantified social benefit that results from community engagement and revitalization, which is part of the mission of the Pittsburgh nonprofit. [Copyright &y& Elsevier]

Abstract: In this paper using historical monthly data on the US oil stocks (Crude Oil and Petroleum Products Ending Stock-coppes), industrial production, energy use for transportation, oil production, and oil imports, we examine whether supply and demand shocks explain the apparent decline in the volatility of the growth of COPPES since about the mid-1980s. We find that nearly 70% of the variation in the US COPPES growth is explained by its supply and demand factors, each sharing about half of this variation. This is on account of sharp decline in the contribution of persistence to the US COPPES growth variation from about 47% in the pre-break period to about 17% in the post-break period. This reduction is taken up by increased contribution of demand and supply factors since mid 1980s, of which growth variances have declined on net since then. This in turn contributes to the stability of the US COPPES growth fluctuations. [Copyright &y& Elsevier]

Abstract: Off-farm work is a growing reality in the US agricultural sector as a whole. Another staple program in the US agriculture is the use of crop insurance. This paper assesses hitherto unaddressed issues of fuel consumption and hence pollution generated by farm households associated with off-farm work and crop insurance. We applied a quantile regression method on a unique national farm-level survey data to address the fuel consumption issues. Results indicate that off-farm work by operators tends to decrease fuel expenses. In contrast, households with crop insurance had higher fuel consumption thereby increasing fuel usage. Finally, our study shows that the net effect of these two activities resulted in an increase in the pollution level. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to summarize the findings reported recently in the literature on non-energy benefits attributable to the weatherizing of low-income homes. Non-energy benefits are divided into three major categories: (1) ratepayer benefits; (2) household benefits; and (3) societal benefits. The ratepayer benefits can be divided into two main subcategories: payment-related benefits and service-provision benefits. Similarly, there are two key types of household benefits: those associated with affordable housing and those related to safety, health and comfort. Societal benefits can be classified as either environmental, social, or economic. Our study found the total lifetime value for all non-energy benefit-categories to be $3346 (in 2001 dollars) per household, which is slightly greater than the average value of energy savings for houses heated by natural gas, and substantially higher than the total cost per low-income weatherization. Societal benefits were much larger than either ratepayer or household benefits. [Copyright &y& Elsevier]