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Abstract: Adoption of energy efficient technologies is considered a key sustainability strategy for reducing greenhouse gas emissions in global industries. This article aims to assess costs of energy savings and carbon reduction from applying energy efficiency technologies in the US pulp and paper sector. Applicable efficiency measures were identified and their cost effectiveness was evaluated in two historical years – 1994 and 2006. Potential savings of final energy use resulting from applicable sustainability options were estimated as 707 PJ in 1994 and 1064 PJ in 2006, respectively, corresponding to approximately 32% of the sector''s annual final energy use in 1994 and 62% in 2006. The associated carbon-emission reduction was 8.1 million ton of carbon (MtC) in 1994 and 11.8MtC in 2006, corresponding to 26% and 45% of sector''s total energy-related carbon emissions in 1994 and 2006, respectively. Using the concepts of cost of conserved energy and cost of carbon reduction, we estimated that cost effective sustainability options contributed to final energy savings in the range of 15–25% of the annual energy use, and carbon-emission reduction from 14% to 20% of annual carbon emissions from the sector. [Copyright &y& Elsevier]

The climate change policy debate and ensuing discussions about industrial energy use and carbon emissions have highlighted the need to: (a) aggregate engineering information to a level relevant for economic policy analysis while maintaining sufficient detail so that results are meaningful for industry decision makers, (b) properly represent an industry’s capital vintage structure to better understand inertia associated with changes in aggregate industrial emissions profiles, and (c) identify policy instruments that leverage an industry’s potential for technological change such that carbon emissions can be noticeably reduced. This paper presents an econometric analysis of energy use and emissions profiles of the US Pulp and Paper Industry and uses the resulting set of equations to specify a dynamic model for the analysis of select climate change policies. Scenarios of cost of carbon, energy tax, and investment-led policies indicate that a combination of cost of carbon and investment-led policies can achieve the desired result of rapidly improving overall efficiency of the industry and promoting changes in fuel mix, which together can result in drastic reductions of carbon emissions. [Copyright &y& Elsevier]

Paper as a consumer product offers a undisputed benefits to human society, and it has been proven to be critical in driving most sensitive needs of mankind—principally in areas of security, education, sanitation, and communication—and thus has been produced and consumed worldwide. Its sustainable production and usage is one topic that has featured prominently in many discussion fronts and more often than not ends with recommendations for an alternative source of raw material, improvement in production procedures, and ecological concerns. Paper use is an ecological concern that has triggered many paper intervention actions around the world such as the Paper Task Force in USA, Sustainable Paper Alliance in China, and Paper and Beyond in Europe. In Nigeria, however, challenges associated with paper production, consumption, and economic prospects have not been adequately tackled. This is conspicuously evident in the huge volume of paper products imported annually into the country as a result of unsustainable local production. The cumulative impact of these issues reflect negatively on the Nigeria's paper industry and spread indirectly to the entire country's economy in terms of high capital freight spent on importation, volatile product price, and loss of employment opportunities. This work sought to review the challenges and opportunities associated with raw material sources, energy and water consumption, environmental pollution, paper consumption, and disposal of used paper products in Nigeria. The current review also advocated for wastepaper as a variant of raw material with a sustainable potential. [ABSTRACT FROM AUTHOR]

The industrial sector is a large source of carbon dioxide (CO2) and other greenhouse gas (GHG) emissions. Much of these industrial emissions result from the energy used to produce heat for the manufacturing production process. In industrial manufacturing, thermal energy delivers process and nonprocess heating and cooling, as well as building space heating and cooling. Across the globe, industrial heat makes up two‐thirds of industrial energy demand and almost one‐fifth of total energy consumption. In the United States, GHG emissions associated with industrial heat account for 32 percent of all industrial‐sector emissions and 7 percent of total US emissions. These emissions are concentrated in six energy‐intensive basic material manufacturing sectors—steel, chemicals, cement, pulp and paper, aluminum, and oil refining—that produce more than 77 percent of global industrial emissions. Climate solutions must include approaches to reduce emissions associated with heat production. [ABSTRACT FROM AUTHOR]

Disruptions to key lifelines, especially electrical power, can cause outsized impacts on human functioning. The state of the art on developed countries has focused on enhancing resilience to electrical grid infrastructure but has neglected to track changes regarding how the private market has developed electricity continuity measures over time. Backup generators are among the most accessible tools to maintain electricity continuity in case of power failure, but their role as a buffer remains understudied outside the technical domain, along with the humanitarian and emergency response sectors. This paper analyzes generator sales across the U.S. to understand some underlying trends that may have influenced changes in consumer preference for electricity resilience. Reports from major backup generator sellers and import data of backup generators reveal an increase in backup generators across the U.S. and find that private demand for energy resilience is likely increasing due to consumers' perceived risk and rising levels of intolerance to power disruptions. The discussion finds that an increase in private demand and use of backup generators may be impacting electricity resilience at a communal and societal level, which seems to be underexamined by studies focusing on private generator usage in the U.S.. [ABSTRACT FROM AUTHOR]

This paper uses data from the 2015 Residential Energy Consumption Survey to explore the extent to which renters' electricity use in the United States exceeds that of otherwise similar non-renters. Renting households are found to use approximately 9% more electricity than non-renters when controlling for location, socioeconomic, and many appliance-quantity controls. There are multiple factors that explain this extra electricity use, including inferior energy efficiency of appliances, behavioral factors, differences in bill payment responsibilities, and additional reliance by renters on electric space and water heaters. The paper finds that none of these factors are dominant. The phenomenon of renters' (conditionally) higher electricity use is thus best understood as one that emerges from multiple sources. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of economic (in)stability on carbon emissions and the role of renewable energy in these effects at the crossroads of accelerated climate change and rising global economic instability. The paper hypothesizes that these implications should be examined not by direct impacts across variables, but rather via indirect effects serially mediated by renewable energy consumption and economic growth. This paper analyzes the United States from January 1980 and April 2022 using manifest structural equation modeling. The paper reaches two major conclusions. First, there are clear distinctions between (i) the direct and indirect effects of economic stability, surrogated by inflation and policy interest rate, on carbon emissions, (ii) the symmetric and asymmetric indirect effects, and (iii) the roles of aggregate renewable energy and its components, such as biomass and hydropower energy use, in mediating these symmetric and asymmetric indirect effects. Second, renewable energy significantly reduces the carbon-increasing effects of inflation and interest rates. The paper contains proper policy suggestions based on the findings. [ABSTRACT FROM AUTHOR]

Policymakers at the federal and state level have begun to incorporate energy burden into equity goals and program evaluations, aiming to reduce energy burden below a high level of 6% for lower income households in the United States. Pairing an empirical household-level dataset spanning United States geographies together with modeled hourly energy demand curves, we show that rooftop solar reduces energy burden across a majority of adopters during our study period from a median of 3.3% to 2.6%. For low- and moderate-income adopters (at or below 80% and 120% of area median income, respectively), solar reduces median 2021 energy burden from 7.7% to 6.2%, and 4.1% to 3.3%, respectively. Importantly, solar reduces the rate of high or severe energy burden from 67% of all low-income households before adoption to 52% of households following adoption, and correspondingly from 21% to 13% for moderate-income households. Here, we show rooftop solar can support policy goals to reduce energy burden along with strategies such as weatherization and bill assistance. Modeled results show that rooftop solar reduced energy burden for most adopters in 2021 from a median of 3.3% to 2.6% with the average adopter seeing a 0.6 point ($691 annual) reduction in burden, accounting for costs, incentives, and savings. [ABSTRACT FROM AUTHOR]

This paper provides an example of several modeling and econometric advances used in the panel estimation of energy demand elasticities. The paper models the demand of total, industrial, and transport energy consumption and residential and commercial electricity consumption by analyzing US state-based panel data. The paper employs recently developed dynamic panel methods that address heterogeneity, nonstationarity, and cross-sectional dependence. In addition, the paper (i) considers possible nonlinear relationships between energy consumption and income without employing polynomial transformations of integrated income; and (ii) allows for and calculates possible asymmetric relationships between energy consumption and price. Finally, the paper models energy efficiency improvements by a nonlinear time trend. To our knowledge no other paper has combined all of the econometric and modeling advances that are applied here. Most of the results conformed to expectations; however, limited to no evidence of nonlinearities and asymmetries were uncovered. [ABSTRACT FROM AUTHOR]

Electricity usage of buildings (including offices, malls, and residential apartments) represents a significant portion of a nation's energy expenditure and carbon footprint. In the United States, the buildings' appliances consume 72% of the total produced electricity approximately. In this regard, cyber‐physical system (CPS) researchers have put forth associated research questions to reduce cyber‐physical building environment energy consumption by minimizing the energy dissipation while securing occupants' comfort. Some of the questions in CPS building include finding the optimal HVAC control, monitoring appliances' energy usage, detecting insulation problems, estimating the occupants' number and activities, managing thermal comfort, intelligently interacting with the smart grid. Various machine learning (ML) applications have been studied in recent CPS researches to improve building energy efficiency by addressing these questions. In this paper, we comprehensively review and report on the contemporary applications of ML algorithms such as deep learning, transfer learning, active learning, reinforcement learning, and other emerging techniques that propose and envision to address the above challenges in the CPS building environment. Finally, we conclude this article by discussing diverse existing open questions and prospective future directions in the CPS building environment research. This article is categorized under:Technologies > Machine LearningTechnologies > Reinforcement Learning [ABSTRACT FROM AUTHOR]

This paper collects monthly data for the United States from January 2005 to April 2021 and develops a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model to investigate the time-varying relationship between climate policy uncertainty, oil prices, and renewable energy consumption. In addition, this paper discusses the dynamic impact of climate policy uncertainty on five types of renewable energy consumption. Our results show that the relationship between them is time-varying. In most periods, climate policy uncertainty positively affects oil prices in the short and medium term, and positively affects total renewable energy consumption in the short and long term. In addition, the impact of climate policy uncertainty on the consumption of the five types of renewable energy is heterogeneous. The results of the robustness tests indicate that our results are robust and our study has important implications for the development of relevant policies. [ABSTRACT FROM AUTHOR]

Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States. [ABSTRACT FROM AUTHOR]

Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non- White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities. [ABSTRACT FROM AUTHOR]

The purpose of this paper is twofold: analyzing stationarity of energy consumption by source in the United States and studying their cycles and pairwise synchronization. We study a panel of nine time series of monthly energy consumption for the period 1973–2022. Four of the series (namely coal, natural gas, petroleum, and nuclear electric power consumption) are non-renewables, whereas the remaining ones (hydroelectric power, geothermal, biomass, solar, and wind energy consumption) are renewable energy sources. We employ a nonparametric, panel stationarity testing approach. The results indicate that most of the series may be trend-stationarity, with nuclear and geothermal energy consumption being the only exceptions. Additionally, a study on potential cycles in the series of energy consumption by source is carried out, and subsequently we analyze pairwise concordance between states of different energy sources and between states of energy sources and the business cycle. Significant correlations are detected in the latter analysis, which are positive in the case of fossil fuel sources and negative for two renewable sources, namely geothermal and biomass energy consumption. [ABSTRACT FROM AUTHOR]

Green Hydrogen (GH) is increasingly recognized as a viable solution in the United States (US) for meeting energy demands, achieving net zero emissions by 2050, and mitigating the intermittent nature of renewable sources such as wind and solar. This review paper identifies and examines three crucial research areas concerning GH. Despite significant federal, state, and business investment in the development of hydrogen technology, numerous questions have remained unanswered including economic viability, social acceptance, and policies and regulations. In comparison to the US, Europe, and Asia have taken a lead role in studying the social and regulatory aspects of GH. The study shows limited focus on social acceptance, and the absence of dedicated policies and regulations for hydrogen. The US lacks coordinated policies for GH and is blended with natural gas. The literature shows that the relationship between economics, social sciences, and policies and regulations are intrinsic. The success of GH in the US will require massive investments, public and private collaboration, research on social sciences, and regulatory support. Hence, the GH rollout will bridge the energy transition, and climate and development targets which guide future industrial, government and public decisions. • Knowledge and experience play a major role in the acceptance of green hydrogen. • Green hydrogen technology lacks a value chain. • Lack of green hydrogen-specific regulatory agencies and standards in the US. • Economic, social, and regulatory challenges interlinked for green hydrogen. [ABSTRACT FROM AUTHOR]

Energy efficiency improvement is often hindered by the energy efficiency gap. This paper examines the effect of short-run temperature fluctuations on the Energy Star air conditioner purchases in the United States from 2006 to 2019 using transaction-level data. Results show that the probability of purchasing an Energy Star air conditioner increases as the weekly temperature before the transaction deviates from 20–22 °C. A larger response is related to fewer cooling degree days in the previous years, higher electricity prices/income/educational levels/age/rate of owners, more common use of electricity, and stronger concern about climate change. 1 °C increase and decrease from 21 °C would lead to a reduction of total energy expenditure by 35.46 and 17.73 million dollars nationwide (0.13% and 0.06% of the annual total energy expenditure on air conditioning), respectively. Our findings have important policy implications for demand-end interventions to incorporate the potential impact of the ambient physical environment. The probability of purchasing an energy-efficient air conditioner increases as the temperature deviates from 20–22 °C in the United States, with the response varying by electricity price, background climate, and demographic characteristics. [ABSTRACT FROM AUTHOR]

Energy prosumer participates in the balance of the energy system actively through their production and consumption of energy and will become a major participant in the open energy market increasingly in the future. This paper used the bibliometric method to sort out 1251 literature data related to energy prosumer in the "Web of Science Core Collection" and used the software named CiteSpace to perform statistics and cooccurrence analysis. The research found the following: (a) journals in the field of prosumer have their emphasis on theory, application, policy, and modeling; (b) the United States and European institutions have published the articles most, and the academic influence of Chinese scholars needs to be further improved; (c) the focus of the prosumers' research in the past decade has turned the energy system designing to the market transaction research; (d) P2P, P2G, and prosumer community are three market models that have attracted attention for scholars; besides, self-consumption, market matching, district heating, aggregators, and household energy management systems are also the current research hotspots; (e) digital technologies such as big data, the Internet of Things, and blockchain have a significant impact on the prosumer participating in energy market transactions, which will change the existing energy supply chain model profoundly. In the context of the energy Internet, energy trading platforms, digital ecosystems, cross-border innovation, and other fields will become the hotspots in future research on prosumer. [ABSTRACT FROM AUTHOR]

This paper evaluates the impact of climate policy uncertainty on renewable and non-renewable energy consumption in the United States over the quarterly data from 2000Q1 to 2021Q3. Economic growth and crude oil prices are added to the energy consumption functions as control variables. The paper considers several approaches to model both renewable and non-renewable energy demand. It is found that crude oil prices promote non-renewable energy demand and climate policy uncertainty reduces it. Surprisingly, the impact of economic growth on non-renewable energy consumption is positive but insignificant. It is also observed that economic growth promotes renewable energy demand, and crude oil prices reduce it. Furthermore, climate policy uncertainty positively affects renewable energy demand in the long run. Some policy implications are provided for reducing non-renewable energy consumption and promoting renewable energy use in the United States through climate policy implementation. • We examine the impact of the Climate Policy Uncertainty (CPU) on renewable and non-renewable energy consumption. • We use the quarterly data from 2000Q1 to 2021Q3 in the United States. • The CPU reduces non-renewable energy demand. • The CPU positively affects renewable energy demand in the long run. • Promoting renewable energy use through climate policy implementation is discussed. [ABSTRACT FROM AUTHOR]

In the United States, freight transportation consumes 9% of all energy consumed and emits 12% of all CO2 emissions in the entire economy. Freight energy consumption and CO2 emissions have also been growing faster than those of other economic sectors since 1980. In response, this paper surveys freight energy consumption trends from both a modal-disaggregate (e.g. truck, rail, ship, etc.) and commodity-disaggregate (e.g. food products, wood products, consumer electronics, etc.) perspective. A systems approach is used to delineate the boundary between the freight systems and other economic systems with which it interacts, and to provide a framework for understanding the structure of freight. From a modal perspective, it is found that the amount of freight energy consumed by trucks has remained at about two-thirds of the total for the past two decades. However, there are also some recent examples of growing use of rail, which can be expanded to make U.S. freight less energy intensive. On the commodity side, freight energy use is found to be concentrated in a limited number of products, such as food products, farm products, and nonmetallic minerals, so that collaboration with these industries can provide a focal point for reducing freight energy. The subsequent discussion considers other factors such as changes to supply chains, impact of geographic patterns of freight, dematerialization of products, and transitioning to low-carbon fuels. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of energy consumption's Green Quality Index (GQI) on carbon dioxide (CO 2) emissions in the United States from 1970 to 2021. At this point, the estimated models control for the effects of gross domestic product (GDP) per capita, urban population, and globalization on CO 2 emissions. The Autoregressive Distributed Lag estimations demonstrate that GQI and globalization reduce CO 2 emissions in the short- and long run. However, GDP per capita and urban population are positively related to CO 2 emissions in the short- and long-run. Different estimation techniques confirm the long-run baseline findings. Potential policy implications for reducing CO 2 emissions in the United States are also provided. [ABSTRACT FROM AUTHOR]

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building's heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively. • A novel cloud-based MPC scheme is proposed for controlling the educational building HVAC system. • Real-world case installation details are provided with a focus on cost and sensor information. • A comprehensive discussion on thermal modeling, occupants' comfort, and control performance. [ABSTRACT FROM AUTHOR]

The reduction in oil prices might make crude oil a cheaper alternative to renewable energy (RE). Given this, the present paper examines the effect of fluctuation of oil prices on the use of RE in the United States (US) during the period 1970 to 2018. We constructed two nonlinear autoregressive distributed lag (NARDL) models to examine the effect of the positive and negative oil price shocks on the use of RE in the US. The RE consumption is taken as the dependent variable and the gross domestic product (GDP), Brent crude prices, population density, trade openness, and price index as independent variables. The result revealed that the rise in crude oil price, GDP, and population density will increase RE use in the short run and in the long run as well. Moreover, the study finds that any decrease in oil prices will decrease RE use in the short run and its effect will eventually diminish in the long run. On the policy front, it is suggested that US should raise its energy security by reducing its dependency on imported crude oil and increase the role of RE through the imposition of taxes on oil and increase the base of production and consumption through a series of measures. [ABSTRACT FROM AUTHOR]

China is now the world's second largest economy, and it is expected to overtake the United States to become the largest by 2020. What are the implications for the global environment and climate change if China surpasses the United States? There are major concerns with China's rapid rise because its economic and industrial structure is increasingly dependent on the consumption of energy, raw materials, and electricity. In 2010, China's GDP was approximately 40 percent of the United States' GDP, yet it was the world's largest polluter and the biggest consumer of energy and electricity. This implies that the energy efficiency of the Chinese economy measured by energy consumption per unit of GDP is about one-third of that of the United States and one-fourth that of the EU and Japan. If the Chinese economy continues to grow as fast as it has in the past, without changing its structure and improving energy efficiency, China's growth will cause severe damage to the global environment. This paper analyzes the evolution of energy efficiency in the Chinese economy and stresses the importance of transforming China's economic structure. [ABSTRACT FROM AUTHOR]

Researchers have been quantifying energy savings from lighting controls in commercial buildings for more than 30 years. This study provides a meta-analysis of lighting energy savings identified in the literature -- 240 savings estimates from 88 papers and case studies, categorized into daylighting strategies, occupancy strategies, personal tuning, and institutional tuning. Beginning with an overall average of savings estimates by control strategy, successive analytical filters are added to identify potential biases introduced to the estimates by different analytical approaches. Based on this meta-analysis, the best estimates of average lighting energy savings potential are 24 percent for occupancy, 28 percent for daylighting, 31 percent for personal tuning, 36 percent for institutional tuning, and 38 percent for multiple approaches. The results also suggest that simulations significantly overestimate (by at least 10 percent) the average savings obtainable from daylighting in actual buildings. [ABSTRACT FROM AUTHOR]

Energy efficiency policies require the implementation of strategies to reduce energy use during the operation of integrated urban water systems. In Italy, the overall amount of electrical energy consumption for water systems is close to 2.3% of the total national consumption, thus efforts to minimize costs are crucial. This paper analyzes a case study of urban water systems in a specific area in the Piedmont Region, Italy; the case study includes the data collection methodology and the analysis of the energy consumption of seven large water providers. The discussion includes the quantification of the energy consumption reduction, achievable with the replacement of existing transformers with high-efficiency transformers, power factor correction, and the use of variable-frequency drives for the centrifugal pumps. [ABSTRACT FROM PUBLISHER]

Load participation is vital for the smart grid development. As an effective tool to improve reliability, stability, and financial efficiency of the power grids, demand response (DR) has brought significant financial and technical benefits to power systems. As one of the price-based DR programs with less control costs, the time-of-use (TOU) program has been applied as the default rate structure by many utility companies. To avoid financial risks and make the most profit from the market, utility companies treat TOU as an effective strategy to change customers’ electricity consumption patterns. As reported in many literature, existing TOU programs are not as effective as expected in many developed countries due to the complexity of human behaviors and disparities of residential customers. To examine whether to obtain different outcomes of TOU on the residential customers in developing countries, actual utility usage data from residential consumers in Shanghai, China, are analyzed in this paper. The result shows current TOU in Shanghai, China, has similar trends as TOU in developed countries. With high penetration level of renewable energy, an effective TOU program is urgently needed in the utility industry. In recent years, a creative TOU pricing structure has been introduced at the Electrical Reliability Council of Texas deregulated market, and it shows that the introduced “zero pricing” strategy has a significant impact on customers’ consumption patterns. The purpose of this research is to examine the key reasons that underlie ineffectiveness/effectiveness of TOU programs at residential level. [ABSTRACT FROM PUBLISHER]

The article provides energy saving tips from energy industries in the U.S. AC variable speed pumps replacing constant speed pumps with throttling valves make good economic sense in many applications. Examine auxiliary systems on mill site owned and operated by others who may be getting a free or discounted ride in energy if they were developed in times of relatively inexpensive energy. It is important to assure that all gauges, meters and monitoring devices related to your energy consumption are working and are accurate.

Obtaining reliable and high-quality wind speed probability forecast results is of great significance to wind energy utilization and power system management. In this paper, multi-network deep ensemble method, which combines the intelligent optimization algorithm and deep ensemble method, is proposed to deal with the probabilistic prediction problems. This method can effectively integrate a variety of different deep learning neural networks and provide reliable uncertainty estimates for prediction. Furthermore, spatiotemporal multi-network deep ensemble model, which employs multi-network deep ensemble method, is proposed to deal with the probabilistic spatiotemporal wind speed forecasting problems. In the model, three advanced convolutional recurrent neural networks are integrated to capture spatiotemporal information from the underlying meteorological variables. Intelligent optimization algorithm is used to assign weights to each network in the ensemble. In addition, an uncertainty quantification method, which quantify the uncertainty by adjusting the network structure and optimize the uncertainty by utilizing the truncated negative log-likelihood scoring rule, is introduced to provide reliable probability forecasts. The proposed model is applied to a real-world case in the United States. The test results demonstrate that spatiotemporal multi-network deep ensemble model can not only provide high-precision point prediction results, but also provide suitable interval predictions and reliable probability prediction results. Moreover, the impact of input features on model prediction results is also evaluated. [ABSTRACT FROM AUTHOR]

In this paper, a management strategy is developed to realize the real-time optimal torque distribution between the internal combustion (IC) engine and the electric motor of parallel hybrid electric vehicles (HEVs). Without depending on future information, a set of instantaneous cost functions is defined as the objective of a multiobjective problem, which includes vehicle energy consumption, selected emission species, and an evaluation criterion for the battery state of charge (SOC). The varying-domain method is then utilized to introduce a flexible priority among objectives and to transform the multiobjective problem into a nonlinear programming problem, the optimal solution of which is subsequently found by a genetic algorithm, i.e., GENOCOPIII. A comparison of the simulation results demonstrates the flexibility of the proposed varying-domain optimal management strategy (VOMS) under different driving conditions. Compared with the rule-based management strategy (RBMS) and the weighted sum management strategy (WSMS), the VOMS potentially improves the fuel economy, emission reduction, and stability of the SOC. [ABSTRACT FROM PUBLISHER]

This paper explores the spatial and temporal patterns of green building in the commercial and institutional sectors in the US. While these buildings are becoming more commonplace, they have yet to reach a critical mass to affect the entire construction industry. Given the potential for green building practices to reduce energy consumption and carbon emissions, the paper seeks to understand the geography of green building. Using multiple metrics, it explains the patterning of geography of LEED and Energy Star certified buildings in the US. Strong evidence is found of clustering at the metropolitan and sub-metropolitan scales. This exploratory research serves as a foundation for future research aimed at specifying the nature of agglomerative processes in green buildings. [ABSTRACT FROM PUBLISHER]

Improvements to building energy efficiency are critical to a more sustainable future. In this paper, an analytic hierarchy process method was applied to systematically highlight how experts prioritize various factors and contexts in their decisions to implement energy efficiency retrofit. This study surveyed 236 facility management experts in the United States to obtain a consensus about how they prioritized five decision criteria, including 15 factors to retrofit. Additionally, a cross-context comparison was conducted to examine differing perceptions of retrofit as a function of physical, functional, and social contexts. The three most important factors across all US respondents were payback period, life-cycle cost, and funding mechanism. The least considered factors were CO2 emissions, educational programs, and demand pressure. This study revealed no significant disagreement between public and private institutions or between executives and nonexecutives. Larger magnitudes of weight differences were observed mainly among diverse climate zones and industry sectors. Findings are relevant to regulators proposing initiatives to increase energy efficiency adoption. [ABSTRACT FROM AUTHOR]

Several states and transit agencies have recently introduced zero-emission buses (ZEBs) to their fleets, including battery electric buses, fuel cell battery electric buses, and fuel cell plug-in hybrid electric buses in an effort to mitigate transportation-related impacts on air quality. The objective of this paper was to summarise information and insights from ZEB implementations across the United States (U.S.) to advance the state of practice and facilitate transit agencies' transition to ZEBs. Information was obtained through a critical review of relevant literature from peer-reviewed journals and agency reports released by transit agencies and other relevant stakeholders, an online survey of several transit agencies that implemented or are planning to implement ZEBs, and interviews with transit agency representatives and other relevant stakeholders. This review focuses on ZEB in-service performance, cost, fuelling, and implementation strategies. In addition, challenges and lessons learned as reported by U.S. transit agencies are described. A comparison among the three technologies and conventional fuel buses (i.e. diesel and compressed natural gas) suggests that ZEBs outperform conventional buses in terms of fuel efficiency, but their procurement cost is higher. Battery electric buses present the highest fuel efficiency and lowest procurement, operation, and maintenance costs, and have been chosen by most transit agencies followed by fuel cell battery electric buses. While fuel cell hybrid plug-in buses have been implemented by several agencies over the past ten years, they have not gained popularity due to increased purchase and maintenance costs. Important factors for agencies to consider when implementing any of these ZEB technologies include: (1) fleet size: starting with a small fleet and gradually expanding; (2) technology type: understanding the technology and properly choosing the one that matches the needs and limitations of a service area (e.g. available space, existing schedule) as well as available resources and agency goals; (3) staff training: proper training for a suitable amount of time of drivers and maintenance personnel; and (4) stakeholder collaboration: having an effective level of collaboration, cooperation, and support between stakeholders. Overall, ZEBs are a viable and promising approach for reducing emissions from transit fleets. This review can assist in transit agencies' transition to ZEB fleets by providing useful information and insights to ensure optimal technology choice and efficient implementations. It can also provide insights on ZEB implementation issues that warrant further research. [ABSTRACT FROM AUTHOR]

Smart buildings are complex systems, yet architecture, engineering, and construction (AEC) professionals often perform their work without considering the human factors of building occupants. Traditionally, the AEC industry has employed a linear design and delivery approach. As buildings become smarter, the AEC industry must adapt. To maximize human well-being and the operational performance of smart buildings, an iterative, human-centred approach must be employed. The omission of human factors in the design and delivery of smart building systems risks misalignment between occupant-user needs and the AEC industry's perception of occupant-user needs. This research proposes a human-centred approach to smart housing. The study employed a multi-phase, mixed-methods research design. Data were collected from 309 high performance housing units in the United States. Longitudinal energy use data, occupant surveys, and semi-structured interviews are the primary data inputs. Affinity diagramming was leveraged to categorize the qualitative data. The output of the affinity diagramming analysis and energy analysis led to the development of data-driven Personas that communicate smart housing user needs. While these data were gathered in the United States, researchers, practitioners, and policy-makers can leverage the human-centred approach presented in this paper toward the design of other human-centred buildings and infrastructure. [ABSTRACT FROM AUTHOR]

How can programme design and strategy drive the decision actually to undertake retrofit upgrades? The US Better Buildings Neighborhood Program (BBNP) and the UK Green Deal both represent ambitious efforts to drive domestic retrofit markets. These programmes are compared and the differences in their conversion rates explored in the context of marketing, outreach and workforce engagement strategies. The impact of financial incentives is also explored. This paper supports the consensus that the US BBNP was successful against most of its stated objectives. The UK Green Deal, while slow to start, was gaining momentum before it was effectively ended. Semi-structured interviews with programme organizers give evidence that the Green Deal would have benefited from a more active approach to marketing and outreach, specifically emphasizing the distinction between the two. Marketing generates interest, but local outreach through word of mouth and personal referrals is needed to drive upgrades. The Green Deal did not sufficiently engage the workforce through an ongoing communication strategy and should have better facilitated both technical and non-technical skills development. The interest rate of the Green Deal is shown to be but one factor influencing participation, and can be effectively balanced through programme design in other areas. [ABSTRACT FROM AUTHOR]

This paper examines the long-run and the causal relationship among CO emissions, militarization, economic growth, and energy consumption for USA for the period 1960-2013. Using the bound test approach to cointegration, a short-run as well as a long-run relationship among the variables with a positive and a statistically significant relationship between CO emissions and militarization was found. To determine the causal link, MWALD and Rao's F tests were applied. According to Rao's F tests, the evidence of a unidirectional causality running from militarization to CO emissions, from energy consumption to CO emissions, and from militarization to energy consumption all without a feedback was found. Further, the results determined that 26% of the forecast-error variance of CO emissions was explained by the forecast error variance of militarization and 60% by energy consumption. [ABSTRACT FROM AUTHOR]

Existing buildings, or those constructed before 2000, make up approximately 90% of the commercial building stock in the U.S. Existing buildings represent a large opportunity for reducing energy consumption in the U.S., because they account for approximately 16% of the country's energy consumption each year. This paper presents a design process, in the form of a set of recommended practices, for achieving successful, deep energy retrofits (those that reduce energy consumption by 30% or more) in commercial buildings. These practices include forming an integrated design team, developing a process map for the design phase of the retrofit project that facilitates collaboration and communication, and routinely evaluating the energy performance of the project. This paper focuses on the predesign, schematic design, design development, and construction documents phases of a retrofit project and proposes tasks required in each of these phases to achieve deep energy savings in a retrofit project. [ABSTRACT FROM AUTHOR]

To what extent is energy performance determined by interactions between occupants, behaviour and buildings systems, as well as building and climate characteristics? The purpose of this paper is to establish which factors, and to what extent, affect the energy performance of a housing stock. The US residential sector is used to test the relative roles of socio-economic and behavioural aspects of occupants, as compared with climatic and physical building characteristics. Household heating and cooling energy consumption is investigated using an existing extensive survey, the Residential Energy Consumption Survey (RECS) by the US Department of Energy. This data set consists of information on actual energy consumption along with detailed energy-related characteristics of the housing units and their occupants. The paper presents a statistical analysis of the housing stock and assesses the relative roles of variables on energy use. Regression models and path analysis are used to determine the significance of parameters and their interrelationships. Results reveal that apart from climate, occupant behavioural aspects and socio-economic aspects are critical, partly directly and also significantly indirectly through their influence on choices and decisions about the physical characteristics of buildings and systems. Models are created for the analysis of heating and cooling energy that can be used to explore energy-related policies. Dans quelle mesure la performance energetique est-elle determinee par les interactions entre les occupants, le comportement et les systemes des batiments, ainsi que par les caracteristiques de la construction et du climat? L'objet de cet article est de determiner les facteurs qui ont une incidence sur la performance energetique d'un parc de logements, et ce, dans quelle mesure. Le secteur residentiel americain est utilise pour tester les roles respectifs des aspects socioeconomiques et comportementaux des occupants, par comparaison avec les caracteristiques climatiques et physiques des batiments. La consommation energetique domestique utilisee pour chauffer et refroidir les logements est etudiee en utilisant une vaste enquete existante du Ministere de l'Energie americain, l'Enquete sur la Consommation d'Energie du Secteur Residentiel (RECS). Ce jeu de donnees consiste en des informations portant sur la consommation reelle d'energie, ainsi que sur les caracteristiques detaillees, liees a l'energie, des unites d'habitation et de leurs occupants. L'article presente une analyse statistique du parc de logements et evalue les roles respectifs des variables sur l'utilisation d'energie. Des modeles de regression et une analyse des pistes causales sont utilises afin de determiner l'importance des parametres et leurs interrelations. Les resultats revelent que, le climat mis a part, les aspects comportementaux et les aspects socioeconomiques des occupants sont cruciaux, pour partie directement, mais egalement indirectement, de maniere significative, par leur influence sur les choix et les decisions touchant aux caracteristiques physiques des batiments et des systemes. Il est cree des modeles permettant d'analyser l'energie servant au chauffage et au refroidissement, qui peuvent etre utilises afin d'etudier les politiques energetiques pouvant etre menees. Mots cles: comportement, parc bati, consommation d'energie, logement, habitant, residentiel, Etats-Unis [ABSTRACT FROM AUTHOR]

Summary: A sustainable, balanced energy portfolio is necessary for a country's continued economic growth. This portfolio must collectively be able to provide reliable, resilient electricity at stable, affordable prices. Nuclear energy is an important contributor to global clean energy supply, both as a primary source and by complementing and enabling other clean energy sources. As we look to the design and operation of future energy systems, we see an increasing need to think differently about how we utilize our energy resources to meet all of our energy needs—not just electricity but also industrial and transportation demands. Resource utilization in light of a broader desire to reduce environmental impacts leads us to consider transforming how we use nuclear energy, which currently provides more than half of the nonemitting electricity generated in the United States. A paradigm shift is required to develop optimal energy generation and use configurations that embrace novel approaches to system integration and process design. The US Department of Energy (DOE) Office of Nuclear Energy (NE) program on Integrated Energy Systems (IES)—formerly the Nuclear‐Renewable Hybrid Energy Systems (N‐R HES) program—was established to evaluate potential options for the coordinated use of nuclear and renewable energy generators to meet energy demands across the electricity, industrial, and transportation sectors. These formerly independent sectors are becoming increasingly linked through technology advances in data acquisition, communications, demand response approaches, and control technologies. Advanced modeling and simulation tools can be employed to design systems that better coordinate across these sectors. Implementation of integrated multi‐input, multi‐output energy systems will allow for expanded use of nuclear energy beyond the grid in a manner that complements the increased build‐out of variable renewable energy generation. These integrated systems would provide enhanced flexibility while also providing energy services and supporting the production of additional, nonelectric commodities (eg, potable water, hydrogen, and liquid fuels) via excess thermal and electrical energy from the nuclear system. Increased flexibility of traditionally baseload nuclear systems will support energy security, grid reliability, and grid resilience while maximizing the use of clean energy technologies. This paper provides an overview of current efforts in the United States that assess the potential to increase utilization of nuclear energy systems, in concert with renewable energy generation, via the IES program. Analysis tools and approaches and preliminary analysis results are summarized, and planned experimental activities to demonstrate integrated system performance are introduced. [ABSTRACT FROM AUTHOR]

This paper examines the actions and the factors driving those actions to reduce energy consumption and enhance energy efficiency taken by United States cities. While not much empirical evidence is available on why governments pursue practical sustainability actions, we attempt to shed more light on this important topic by empirically identifying factors that contribute to concrete actions toward sustainability policies. We adopt political market theory as a basic theoretical framework with policy-making applied to city energy consumption. Using the 2010 ICMA (local government sustainability policies and program) data, this study expands the focus of analyses to evaluate the effect of the form of government on energy consumption and energy efficiency by using multiple regression analysis. The findings show that at the city level, the mayorcouncil form of government are negatively associated with governments' efforts to reduce energy consumption. However, cities with at-large elections and municipal ownership are more likely to adopt sustainability actions. We also find that a large-scale economy has significant effects on the effort to reduce city energy consumption and improve energy efficiency. This shows that environmental policies are directly connected to locally relevant affairs, including housing, energy use, green transportation, and water. Thus, local level administrators could take an executive role to protect the environment, encourage the development of alternative energy, and reduce the use of fossil fuel and coal energy. These efforts can lead to important environmental ramifications and relevant actions by municipal governments. [ABSTRACT FROM AUTHOR]