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Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a novel Vision-based Outlier Detection (VOD) approach, leveraging computer vision models to spot outliers in the building energy records. The models are trained to identify outliers by analyzing the load shapes in 2D time series plots derived from the energy data. The VOD approach is tested on four years of workday time-series electricity consumption data from 290 commercial buildings in the United States. Two distinct models are developed for different usage purposes, namely a classification model for broad-level outlier detection and an object detection model for the demands of precise pinpointing of outliers. The classification model is also interpreted via Grad-CAM to enhance its usage reliability. The classification model achieves an F1 score of 0.88, and the object detection model achieves an Average Precision (AP) of 0.84. VOD is a very efficient path to identifying energy consumption outliers in building operations, paving the way for the enhancement of building energy data quality, operation efficiency, and energy savings. [ABSTRACT FROM AUTHOR]

Investing in renewable energy is of utmost importance, especially in the context of addressing climate change. However, while numerous studies have explored the role of renewable energy in achieving carbon neutrality, there is a noticeable gap in the literature regarding how uncertainties affect the impact of renewable energy on electric power CO2. To bridge this gap, the present study employs recently developed nonparametric techniques, namely multivariate quantile-on-quantile regression (MQQR) and time-varying quantile causality (TVQC), to investigate the relationship between renewable energy and electric power CO2 in the presence of uncertainties. The study utilizes monthly data spanning from January 1988 to May 2023. The bivariate results reveal that renewable energy and low uncertainties contribute to improving ecological quality by reducing electric power CO2. Furthermore, the multivariate quantile-on-quantile regression results highlight the substantial influence of low uncertainties on the impact of renewable energy in lowering electric power CO2. Additionally, the TVQC analysis demonstrates that both renewable energy and uncertainties possess predictive power regarding electric power sector CO2. The study discoveries showcase that the influence of renewable energy on electric power CO2 is subject to external moderation. The suggested policy framework in this study is structured to assist the United States in accomplishing the goals outlined in SDG 7. [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]

Electricity and water systems in the Western US (WUS) are closely connected, with hydropower comprising 20% of total annual WUS generation, and electricity related to water comprising about 7% of total WUS electricity use. Because of these interdependencies, the threat of climate change to WUS resources will likely have compounding electricity impacts on the Western Interconnect grid. This study describes a WUS‐wide water system model with a particular emphasis on estimating climate impacts on hydropower generation and water‐related electricity use, which can be linked with a grid expansion model to support climate‐resilient electricity planning. The water system model combines climatically‐driven physical hydrology and management of both water supply and demand allocation, and is applied to an ensemble of 15 climate scenarios out to 2050. Model results show decreasing streamflow in key basins of the WUS under most scenarios. Annual water‐related electricity use increases up to 4%, and by up to 6% during the summer months, driven by growing agricultural demands met increasingly through a shift toward energy‐intensive groundwater to replace declining surface water. Total annual hydropower generation changes by +5% to −20% by mid‐century but declines in most scenarios, with decreases in summer generation by up to nearly −30%. Water‐related electricity use increases tend to coincide with hydropower generation declines, annually and seasonally, demonstrating the importance of concurrently evaluating the climate signal on both water‐for‐energy and energy‐for‐water to inform planning for grid reliability and decarbonization goals. Plain Language Summary: Electricity and water systems in the Western United States (WUS) have a strong dependency. Water fuels hydropower generation and electricity is used to pump, transport, treat, heat, and dispose of water. Climate change poses a serious threat to water availability in the WUS and is likely to also affect hydropower generation and electricity use related to water. This study develops a WUS water system model to evaluate the impact of a set of climate change scenarios on the dynamic interplay between water availability and demand and hydropower generation and electricity use by the water sector by 2050. We find that in many key basins, streamflow decreases under the climate scenarios. At the same time, reliance on groundwater increases to meet growing agricultural water demand. Hydropower generation shows decreases in most cases while energy use related to water increases. Because these hydropower generation declines tend to occur during periods when electricity demand for water grows, electricity grid planners in the Western Interconnect will benefit from this type of study by informing future grid buildouts that maintain reliability and decarbonization goals. Key Points: Interactions among hydro‐climate, hydropower, and water's energy use are critical for evaluating Western US (WUS) water‐energy climate risksUnder climate change, our WUS water model finds declining streamflows, growing agricultural demands, and increased groundwater useWith warming and drying, water‐related energy use grows while hydropower generation decreases under most climate scenarios by mid‐century [ABSTRACT FROM AUTHOR]

Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 264 suitability layers for use with 56 power plant technologies in a harmonized format that can be easily ingested by geospatially-enabled modeling software allowing for customization to robustly address science objectives when evaluating varying future conditions. [ABSTRACT FROM AUTHOR]

Many utility companies in the United States have introduced time-of-use (TOU) rates for homeowners with the goal of regulating electricity consumption during peak hours. The electrical appliances in homes include various thermostatically controlled devices, such as air conditioners (AC) for thermal comfort, and nonthermostatically controlled devices such as clothes washers. As a result, homeowners face the complicated challenge of economically operating multiple electrical appliances in their homes while maintaining comfort and convenience. This is usually due to the lack of an explicit understanding of the correlation between cost saving and the users' comfort. To understand the correlation, this article is designed to construct a framework by integrating three major components: a multi-objective optimization method accommodating multiple competing goals with different weights, a learning-based system modeling approach describing the dynamics and thermal coupling effects of appliances, and a novel comfort index method differentiating preferred and acceptable thermal comfort. Our proposed framework can allow the indoor air temperature to fall into the "preferred" range with a marginal cost increase. The simulation result shows that an additional 8 h for the preferred thermal comfort can be achieved with a cost increase of only 1.77%. [ABSTRACT FROM AUTHOR]

The western United States has experienced severe drought in recent decades, and climate models project increased drought risk in the future. This increased drying could have important implications for the region’s interconnected, hydropower-dependent electricity systems. Using power-plant level generation and emissions data from 2001 to 2021, we quantify the impacts of drought on the operation of fossil fuel plants and the associated impacts on greenhouse gas (GHG) emissions, air quality, and human health. We find that under extreme drought, electricity generation from individual fossil fuel plants can increase up to 65% relative to average conditions, mainly due to the need to substitute for reduced hydropower. Over 54% of this drought-induced generation is transboundary, with drought in one electricity region leading to net imports of electricity and thus increased pollutant emissions from power plants in other regions. These drought-induced emission increases have detectable impacts on local air quality, as measured by proximate pollution monitors. We estimate that the monetized costs of excess mortality and GHG emissions from drought-induced fossil generation are 1.2 to 2.5x the reported direct economic costs from lost hydro production and increased demand. Combining climate model estimates of future drying with stylized energy- transition scenarios suggests that these drought-induced impacts are likely to remain large even under aggressive renewables expansion, suggesting that more ambitious and targeted measures are needed to mitigate the emissions and health burden from the electricity sector during drought. [ABSTRACT FROM AUTHOR]

Written during the transition of Bitcoin mining power to the United States in the wake of China's Summer 2021 Bitcoin ban, this article explores how United States policymakers could incentivize Bitcoin miners to help, rather than hinder, United States climate and electricity goals. While Bitcoin mining's high electricity consumption has traditionally been seen as problematic from an environmental and grid reliability perspective, this article proposes a win-win solution for Bitcoin mining, the environment, and grid reliability by synergizing mining operations with renewable energy projects. Where Bitcoin miners require an abundance of inexpensive electricity, remote renewable energy projects—particularly wind farms—have the potential to produce excess electricity that is either unprofitable or risks grid overload. By implementing overlay zoning with the locational, renewable, and demand-response considerations that this article proposes, policymakers can incentivize electricity-intensive Bitcoin mining operations to co-locate near remote renewable energy projects, fostering a mutually beneficial relationship that will support, rather than threaten, United States climate and electricity goals. [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]

Attaining Sustainable Development Goals (SDGs) is important to control the adverse impacts of climate change and achieve sustainable development. Among the 17 SDGs, target 13 emphasizes enhancing urgent actions to combat climate-related changes. This target is also dependent on target 7, which advocates enhancing access to cheap alternative sustainable energy. To accomplish these targets, it is vital to curb the transport CO2 emissions (TCO2) which increased by approximately 80% from 1990 to 2019. Thus, this study assesses the role of transport renewable energy consumption (TRN) in TCO2 by taking into consideration transport fossil fuel consumption (TTF) and road infrastructure (RF) from 1970 to 2019 for the United States (US) with the intention to suggest some suitable mitigation policies. Also, this study assessed the presence of transport environmental Kuznets curve (EKC) to assess the direction of transport-induced growth. The study used the Bayer-Hanck cointegration test which utilizes four different cointegration techniques to decide cointegration along with the Gradual Shift causality test which considers structural shift and fractional integration in time series data. The long-run findings of the Dynamic Ordinary Least Squares (DOLS) test, which counters endogeneity and serial correlation, revealed that the transport renewable energy use mitigates as well as Granger causes TCO2. However, transport fossil fuel usage and road infrastructure enhance TCO2. Surprisingly, the transport EKC is invalid in the case of the US, and increased growth levels are harmful to the environment. The association between TCO2 and economic growth is similar to a U-shaped curve. The Spectral Causality test revealed the growth hypothesis regarding transport fossil fuel use and economic growth connection, which suggests that policymakers should be cautious while decreasing the usage of transport fossil fuels because it may hamper economic progress. These findings call for revisiting growth strategies and increasing green energy utilization in the transport sector to mitigate transport emissions. [ABSTRACT FROM AUTHOR]

The U.S. electricity sector has undergone a series of technological and political economic transformations between 1995 and 2020. Some, but not all, states have moved toward the provision of electricity through wholesale electricity markets, while others have remained traditionally regulated. In addition, electricity is increasingly generated from natural gas and renewable energy, and while total electricity consumption is stagnant in the United States, electric utility revenues have continued to grow. In combination with a growing emphasis on increasing shareholder value, the result of these changes has been the entry of a range of new electricity industry, finance, and regulatory actors, as well as a shift in the ways in which existing actors are seeking to realize surplus value. In light of these changes, in this paper, I outline the contemporary contours and contradictions of the U.S. electricity industry and detail five accumulation strategies being used in the industry. Building on scholarship in energy geography and geographies of finance, and based on the analysis of industry documents and financial analyst reports, I detail how the U.S. electricity has been reshaped by the introduction of new types of technologies, financial risk and investment opportunities, and changing modes of regulation. The resulting accumulation strategies are contested, partial, and incomplete, and are therefore a messy set of overlapping and at times contradictory strategies being deployed in an industry in flux. [ABSTRACT FROM AUTHOR]

In this study, the relationship between electricity and growth of the economy is investigated by applying the newly-developed bootstrap autoregressive-distributed lag test with a Fourier function to examine both the causality and cointegration for China, India, and the United States (US). While it is not possible to detect a long-term cointegration relation among the economy's electricity and growth, the study findings demonstrate the contingency of the causality. The ensemble method in machine learning performs better than conventional methods as electricity is an independent indicator for forecast economics. Concerning the US, previous electricity consumption has a positive impact on the current nature of economic growth. In contrast, the consumption of electricity is negatively affected by the development of the economy. However, for China and India, positive and negative feedback can be observed, respectively. Due to the increased awareness of the environment's adverse effects, China should promote technologies that conserve energy and boost energy efficiency to achieve sustainable development in both environmental and economic terms. In India's context, broadening access to electricity has significance for residents in rural areas and enhances economic growth. It is recommended that policy-makers promote innovative technologies in the US, as the abundant natural and human resources can make valuable contributions to the society and development of the economy. [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]

The transportation sector produces a large portion of greenhouse gas emissions in the United States. Meeting ambitious reductions in greenhouse gasses requires large-scale adoption of battery electric vehicles and has led to several policies and laws aimed at incentivizing their sales. While electric vehicles comprise a small percentage of the overall fleets of vehicles, the expected production of electric vehicles is soon expected to be in the millions. This will create challenges in providing an adequate charging infrastructure, as well as the ensuing management of the overall electricity demand at the grid level. In this work, a novel smart-charging protocol for battery electric vehicle charging at workplace parking structures is proposed. The Octopus Charger-based Mixed Integer Linear Programming protocol allows octopus chargers (i.e., charging stations with multiple cables) to independently schedule charging periods for their assigned vehicles. The proposed protocols can manage a parking structure demand load while reducing the number of installed charging stations. Driving patterns from the National Household Travel Survey were used to perform simulations, to verify and quantify the effectiveness of the proposed protocol. The proposed protocol resulted in improved peak load reductions for all simulated smart-charging scenarios when compared with uncontrolled charging. Critically, the assignment algorithm resulted in a number of required chargers close to the theoretical minimum. [ABSTRACT FROM AUTHOR]

To predict the electricity consumption demand accurately, based on the data set of PJM company in the United States, the deep autoregressive recurrent networks (Deep AR) time-series model was utilized to predict the electricity consumption of Commonwealth Edison Company at a certain 12-hour interval in the future. Based on the distribution parameters of the data in the long short term memory network (LSTM), the predicted value was obtained by sampling in the distribution. Mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used as evaluation indexes for predicting short-term electricity consumption, and the model was compared with the time-series model of autoregressive integrated moving average (ARIMA) algorithm model and the Prophet algorithm model. The results show that the three performance indexes of MAE、RMSE and MAPE of the Deep AR algorithm model in predicting short-term electricity consumption are respective 1 070.01, 1 279.31 and 6.12% with high prediction accuracy. The proposed algorithm can not only predict electricity consumption in the future, but also can predict the probability distribution for further describing the globality of events. [ABSTRACT FROM AUTHOR]

Problem definition: By providing an environmentally friendly alternative to traditional vehicles, electric vehicles will transform urban mobility, particularly in smart cities. In practice, after an electric vehicle is plugged in, the charging station completes charging as soon as possible. Given that the procurement cost of electricity and associated emissions vary significantly during a day, substantial savings can be achieved by smart charging—delaying charging until the cost is lower. In this paper, we study smart charging as an innovative business model for utility firms. Academic/practical relevance: Utility firms are already investing in charging stations, and they can achieve significant cost savings through smart charging. Methodology: We consider a mechanism design problem in which a utility firm first announces pairs of charging price and completion time. Then, each customer selects the pair that maximizes their utility. Given the selected completion times, the utility firm solves the optimal control problem of determining the charging schedule that minimizes the cost of charging under endogenous, time-varying electricity procurement cost. We assume that there are ample parking spots with chargers at the charging station. Results: We devise an intuitive and practically implementable policy for scheduling charging of electric vehicles under given completion times. We prove that this policy is optimal if all customers arrive at the station simultaneously. We also characterize the optimal pairs of charging price and completion time. By using real electricity demand and generation data from the largest electricity market in the United States, we find that cost and emissions savings from smart charging are approximately 20% and 15%, respectively, during a typical summer month. Managerial implications: In contrast to the current practice of charging vehicles without delay, we show that it is economically and environmentally beneficial to delay charging some vehicles and to set charging prices based on customers' inconvenience cost of delays. We also find that most of the savings from implementing smart charging can be achieved during peak-demand days, highlighting the effectiveness of smart charging. [ABSTRACT FROM AUTHOR]

To achieve environmental sustainability, it is important to change the energy pattern from dirty fossil fuels to renewable-clean energy sources. For this transition, countries should use their financial resources effectively and efficiently. Against this background, this study aims to examine the impact of financial development on renewable energy consumption in the United States over the period 1980–2019, considering urbanization, economic structure, and economic growth as control variables. The study contributes to the existing literature by testing the effect of six sub-indicators, namely efficiency, depth, and accessibility of financial markets and institutions on renewable energy consumption. This is done using the novel Fourier quantile causality test with wavelet transforms. The empirical results show that financial development encourages renewable energy consumption at high quantiles in the medium- and long-run. Moreover, depth and access to financial markets are the two most important factors that promote renewable energy consumption. Based on the overall findings, the study suggests that the United States government should adopt policies that improve the depth and access to financial markets rather than financial institutions to support green growth. [Display omitted] • Is there any impact of financial sector on renewable energy consumption in the USA? • A Fourier and wavelet based quantile causality analysis is used. • Financial development stimulates renewable energy consumption at higher quantiles. • Improving financial depth and access is crucial for renewable energy consumption. [ABSTRACT FROM AUTHOR]

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The debate about the threshold effect of sectoral renewable energy consumption on economic growth has attracted extreme attention, but only limited evidence supports this issue. Therefore, the objective of this paper is to study the non-linear relationship between economic growth, capital, labor and sectoral renewable energy consumption for the case of the United States during the period 1981Q1 and 2021Q1. Using the threshold detection method proposed by Hansen (2002), the effect of sectoral renewable energy consumption on economic growth is positive and significant only if the electric power, industrial, residential, commercial, and transport sectors exceed a renewable energy consumption threshold lnREC >7.095; lnREC >6.138; lnREC >4.897; lnREC >3.212; and lnREC >2.849, respectively. Indeed, for the U.S. to achieve positive economic growth from its renewable energy investments, it must exceed a certain threshold of renewable energy consumption for each sector. However, if the United States use renewable energy below a certain threshold, the effect of sectoral renewable energy consumption on economic growth is negative. These results have important implications for both policymakers and investors. [Display omitted] [ABSTRACT FROM AUTHOR]

Financial Transmission Rights (FTRs) are financial derivatives in wholesale electricity markets that are sold in auctions. The revenue collected from FTR auctions is passed through to electricity customers to reimburse them for transmission congestion payments they make in the spot energy market. On average, electricity customers’ congestion payments greatly exceed auction reimbursements in electricity markets across the United States. We study the issue of auction revenue deficiency through the lens of Auction Revenue Rights (ARRs), which is the predominant mechanism used in U.S. electricity markets to distribute auction revenue to electricity customers. We demonstrate how the ARR process influences fundamental supply conditions in the FTR auction market and show how divergent auction equilibria emerge under different ARR decision-making regimes. Using market data from PJM, we find empirical evidence that variation in ARR management strategies helps explain differences between an FTR’s auction price and its realized ex post value. [ABSTRACT FROM AUTHOR]

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation's energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. This paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events. [Display omitted] • A systematic summer droughts generating capacity assessment framework is developed. • Capacity derating models for various generation technologies are presented. • The impacts of summer droughts on generating fleet in PJM and SERC are studied. • Impact of extreme summer drought on usable capacity of generators is significant. [ABSTRACT FROM AUTHOR]

Ethylene is one of the largest greenhouse gas emitters and the most diversly used commodity chemicals globally. Electrocatalytic reduction of CO 2 to ethylene received great attention from the research society to decarbonize the ethylene production. In this study, a life-cycle analysis is conducted using the Greenhouse Gases, Regulated Emissions, and Energy use in Technologies (GREET) model on the three electrocatalytic CO 2 -reduction pathways (or "e-ethylene" pathways): i) cascade conversion via carbon monoxide intermediate; ii) single-step conversion in membrane electrode assembly (MEA); and iii) single-step conversion in alkaline flow cell. The results showed that the electricity consumption is the lowest for the cascade pathway (164 MJ/kg), thus resulting in the lowest cradle-to-gate carbon intensity [18 kgCO 2 e/kg with United States (US) average grid)] among the three pathways followed by the single-step MEA (32 kgCO 2 e/kg) and then by the single-step alkaline (56 kgCO 2 e/kg). However, all three e-ethylene pathways were significantly more carbon-intensive than their fossil-based counterpart (1.1 kgCO 2 e/kg) due to their excessive energy consumption with the current state of technology. With renewable electricity, all three pathways yielded negative carbon intensity: from −3.1 kgCO 2 e/kg to −1.6 kgCO 2 e/kg depending on the source of CO 2. The threshold carbon intensity of electricity (TCIE), defined as the upper bound of the carbon intensity of electricity to achieve lower carbon intensity for e-ethylene compared to fossil-based ethylene, is calculated for both current and future state of e-ethylene technologies. The cascade pathway had the highest TCIE out of the three e-ethylene pathways for both current (92 gCO 2 e/kWh) and future (124 gCO 2 e/kWh) state of technologies. However, the carbon intensity of average US grid (i.e., 467 and 303 gCO 2 e/kWh for current and future projections) were higher than the TCIEs of the corresponding timeline. Thus, reducing electricity requirement for e-ethylene pathways and bringing low-carbon generation mix in the United States (US) grid faster than the current projection are both essential to decarbonize ethylene and its downstream chemicals/polymers. [Display omitted] • Life cycle analysis is conducted on three e-ethylene pathways. • Different sources of CO 2 and electricity are compared. • E-ethylene is more carbon-intensive than fossil-ethylene with current US grid. • Reducing energy consumption and carbon intensity of electricity is necessary. Synopsis: Electrocatalytic reduction of CO 2 to chemicals received great attention from the research society to tackle climate change. This article compares the environmental impacts of e-ethylene to conventional ethylene production. [ABSTRACT FROM AUTHOR]

Consumers can procure renewable energy via subscribing to green electricity from their utility for a price premium (or virtual option) or installing residential solar (or physical option). From economic theory, these options can be framed as substitutes (choosing between) or complements (choosing both). To test for this relationship, this study estimates how subscriptions to green electricity influence demand for residential solar installations using data from 21 towns served by Cape Light Compact, one of the oldest Community Choice Aggregation (CCA) programs in the United States. Regression analysis suggests that virtual and physical options are treated as substitutes. An increase in the relative price of solar installation is correlated with fewer solar installations and a higher demand for green electricity. However, the price of green electricity does not explain residential solar installations. This has implications for the marketing of renewable procurement options and long-term planning for distributed versus utility-scale renewables. [ABSTRACT FROM AUTHOR]

This research attempts to examine the relationship between renewable and non-renewable electricity consumption, carbon dioxide (CO 2) emissions and economic policy uncertainty for the United States. For empirical analysis, the study employs monthly data for the period of 1985 M1 to 2020 M12 and used Bootstrap Rolling approach. The empirical findings revealed that, for full sample Granger causality test, there is a unidirectional causality running from electricity consumption to economic policy uncertainty and bidirectional causality between CO 2 emissions and economic policy uncertainty. The estimated parameters are statistically unstable for three models. Overall, the conclusion of study supports the sustainable development goals (SDGs: 7, 10, 13) of the United States. The study argues that policymakers and political leadership of US should be aware of climate change consequences in order to fulfill carbon neutrality target and should develop economic policies accordingly in-line with energy security, clean and greener energy for all and sustainable cleaner production objectives. Further, the uncertainty issues should be considered while designing the environmental regulations. [ABSTRACT FROM AUTHOR]

This paper evaluates changes in electricity generation costs caused by the introduction of market mechanisms to determine production in the United States. I use the staggered transition to markets from 1999 to 2012 to estimate the causal impact of liberalization using a differences-in-difference design on a comprehensive hourly panel of electricity demand, generators' costs, capacities, and output. I find that markets reduce production costs by 5 percent by reallocating production: gains from trade across service areas increase by 55 percent based on a 25 percent increase in traded electricity, and costs from using uneconomical units fall 16 percent. (JEL L51, L94, L98, Q41, Q48) [ABSTRACT FROM AUTHOR]

Summary: Many electricity consumers around the world are billed electric peak load charges. In the United States, these demand charges can contribute up to 70% of a consumer's electricity bill. Research has shown that the financial impact of these charges to the consumers can be reduced by acting on the intelligence provided by peak electric load day (PELD) forecasts. Unfortunately, published research detailing PELDs forecasting methodologies for facilities adopting behind the meter renewable electricity generation (BTMREG) is non‐existent. This paper contributes: (a) a PELD forecasting methodology applicable to consumers with BTMREG. A case study demonstrates the methodology's ability to achieve 93% of the potential savings in kW, translating to demand charge reductions of approximately US$ 142 129 per year. (b) The first side‐by‐side performance comparisons of electric load and PELD forecasting models for scenarios with and without BTMREG. (c) The first PELD forecasting model savings comparison for scenarios with and without BTMREG. Experimental results suggest that counterintuitively, BTMREG adoption can translate into higher peak load savings for electricity consumers. [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]

We examine the relationship between renewable and non-renewable energy consumption and economic growth in the United States. While the regime-dependent Granger causality test results for the non-renewable energy consumption and economic growth suggest bi-directional causality in both regimes, we cannot validate any causality between renewable energy consumption and economic growth. The US meets its energy demand from non-renewable sources; as such, renewable energy consumption does not seem to affect economic growth. Given the efficiency and productivity of renewable energy investments, we conclude that it is worthwhile to consider renewable energy inputs to replace fossil fuels given potential benefits in terms of global warming and climate change concerns. In this regard, increasing the R&D investments in the renewable energy sectors, increases in productivity and profitability of renewable energy investments are likely to accrue benefits in the long run. [ABSTRACT FROM AUTHOR]

Despite its economic prosperity, the United States has encountered challenges in promoting ecological quality. Therefore, it is imperative for the United States to acknowledge that maintaining its economic accomplishments requires concurrent efforts to safeguard its ecological quality. To solve this challenge, this study aims to scrutinize the impact of green electricity, globalization (in terms of trade and finance), and high technology on the United States' load capacity factor. Employing quarterly frequency data spanning from 1990 to 2020, this research utilizes cutting-edge quantile-based econometric techniques, including wavelet quantile regression, which effectively captures interrelationships across various periods and quantiles. Additionally, it employs quantile causality and quantile-on-quantile regression methods, robust in handling nonlinearity, to assess the ecological repercussions of these variables. The overarching findings reveal that, high tech, financial development, economic growth, and globalization (both in trade and finance) lessen ecological quality. Conversely, green electricity consumption consistently promotes ecological quality. Furthermore, the quantile causality results unveil evidence of feedback causality between the load capacity factor and its drivers, underpinning the need for policy formulation based on these findings. [Display omitted] [ABSTRACT FROM AUTHOR]

Long-term temperature change and variability are expected to have significant impacts on future electric capacity and investments. This study improves upon past studies by accounting for hourly and monthly dynamics of electricity use, long-term socioeconomic drivers, and interactions of the electric sector with rest of the economy for a comprehensive analysis of temperature change impacts on cooling and heating services and their corresponding impact on electric capacity and investments. Using the United States as an example, here we show that under a scenario consistent with a socioeconomic pathway 2 (SSP2) and representative concentration pathway 8.5 (RCP 8.5), mean temperature changes drive increases in annual electricity demands by 0.5-8% across states in 2100. But more importantly, peak temperature changes drive increases in capital investments by 3-22%. Moreover, temperature-induced capital investments are highly sensitive to both long-term socioeconomic assumptions and spatial heterogeneity of fuel prices and capital stock characteristics, which underscores the importance of a comprehensive approach to inform long-term electric sector planning. Temperature changes as a result of climate change are expected to impact electric capacity and investment. Here, the authors show that in the United States under socioeconomic pathway 2 and RCP 8.5 mean temperature rises will drive increased electricity demand (0.5-8%) by 2100, along increases in capital investments by 3-22%. [ABSTRACT FROM AUTHOR]

It is tempting to attribute variations in support for nuclear power to prominent accidents such as Three Mile Island in the United States or Fukushima in Japan. To illuminate how such attribution can be problematic, the authors discuss the historical context of the Three Mile Island accident in the United States. They point out that the US nuclear industry faced major challenges even before the 1979 accident: Forty percent of all US reactor cancellations between 1960 and 2010, they write, occurred before the accident in Pennsylvania. While safety concerns were undoubtedly a driver of public aversion to new nuclear construction in the United States, the nuclear industry already faced substantial economic and competitiveness obstacles, much like the nuclear industry worldwide before Fukushima. [ABSTRACT FROM AUTHOR]

Providing peaking capacity could be a significant U.S. market for energy storage. Of particular focus are batteries with 4-h duration due to rules in several regions along with these batteries' potential to achieve life-cycle cost parity with combustion turbines compared to longer-duration batteries. However, whether 4-h energy storage can provide peak capacity depends largely on the shape of electricity demand. Under historical grid conditions, beyond about 28 GW nationally the ability of 4-h batteries to provide peak capacity begins to fall. We find that the addition of renewable generation can significantly increase storage's potential by changing the shape of net demand patterns; for example, beyond about 10% penetration of solar photovoltaics, the national practical potential for 4-h storage to provide peak capacity doubles. The impact of wind generation is less clear and likely requires more detailed study considering the exchange of wind power across multiple regions. • Storage can reduce the net demand for electricity and replace conventional peaking capacity. • The practical potential for 4-h storage as peaking capacity in the U.S. is about 28 GW. • This potential increases greatly with increased deployment of solar photovoltaics. • This potential is not significant impacted by wind potential. [ABSTRACT FROM AUTHOR]

High air temperatures are a public health threat, causing 1300 deaths annually in the United States (US) along with heat-related morbidity and increased electricity consumption for air-conditioning (AC). Increasing tree canopy cover has been proposed as one way to reduce urban air temperatures. Here, we assemble tree cover and developed land-cover information for 97 US cities, housing 59 million people, and use regression relationships to analyze how much current urban tree cover reduces summer (JJA) air temperatures and associated heat-related mortality, morbidity, and electricity consumption. We find that 78% of urban dwellers are in neighborhoods with less than 20% tree cover. Some 15.0 million people (25% of total) experience a reduction of 0.5–1.0°C from tree cover, with another 7.9 million (13% of total) experiencing a reduction of greater than 1.0°C. Current relationships between temperature and health outcomes imply that urban tree cover helps avoid 245–346 deaths annually. Heat–mortality relationships in the 1980s, when a smaller fraction of US households had AC, imply a greater role in the past for urban tree cover in avoiding heat-related mortality. As AC availability has increased, the value of tree cover for avoiding heat-related mortality has decreased, while the value of tree cover for reducing electricity consumption likely has increased. Currently, for the 97 cities studied, the total annual economic value of avoided mortality, morbidity, and electricity consumption is an estimated $1.3–2.9 billion, or $21–49 annually per capita. Applying our results to the entire US urban population, we estimate urban tree cover annually supplies heat-reduction services worth $5.3–12.1 billion. [ABSTRACT FROM AUTHOR]

In the United States, and in most countries, the real Gross Domestic Product grows at a faster pace than the real household income, thus the two indicators could potentially pose dissimilar information. Hence, the study considers the suitability of modeling the United States real disposable personal income (per capita) with the renewable energy consumption across the main sectors: electric and power, industrial and transportation, and residential and commercial. By employing the dynamic Autoregressive Distributed Lag (ARDL) approach, there is a significant and long-run increase in income (real disposable income) growth caused by a percent increase in the renewables consumed by the industrial sector during the examined period 1973:Q1-2018: Q2. The growth observed in the real growth disposable income with respect to the respective increase in renewable energy consumption in residential and commercial, electric and power is significantly more than the income growth. Similarly, the short-run dynamic is significant, like the Granger causality with feedback between income growth and industrial consumption. Granger causality also exists from the real disposable personal income to electric and powers sector consumption of renewable energy with feedback. The study proffers sustainable social and renewable energy mix policies for the United States. [ABSTRACT FROM AUTHOR]

• Across NYS, a wealth of low-value land exists to support utility-scale solar PV. • Solar generation can reduce summer peak demand by up to 9.6% in certain regions in New York. • Regional ramping requirements may increase by up to 151% during sunny winter days. • PV performance during system peak should be retained in capacity valuation of solar. Rapidly growing utility-scale solar photovoltaic (PV) holds promise for addressing the energy and environmental challenges posed by high electricity demand days (HEDDs). We assessed the implications on strategic planning of future solar development in an emerging solar market, New York State (NYS) in the U.S., by synthesizing information on electrical infrastructure, tax assessment, geographical constraints and measured meteorological data. Considering three solar PV penetration scenarios (4500 MW, 6000 MW, and 9000 MW), we examined the impact of distributed utility-scale solar farms on peak demand reduction and ramping requirements during historical peak events. Our results reiterate that, across NYS, a wealth of low-value land exists to support utility-scale solar PV and that generation from these resources can reduce peak demand by up to 9.6% under the 9000 MW scenario. In addition, peak demand reduction displays locational and temporal dependency. Contingent upon local meteorology, a solar farm can reduce demand during the peak hour by anywhere between 10 and 74% of its rated capacity during summer HEDDs. However, the highest ramping requirements are more likely to occur during winter than summer. Furthermore, because developers cannot predict performance during the annual system peak, current capacity valuation methodologies for solar projects may not be adequate to promote a healthy competitive market for solar. Incorporating a broader spectrum of peak demand conditions into variable resource capacity valuation would improve strategic planning, not only in NYS, but across growing solar markets worldwide. [ABSTRACT FROM AUTHOR]

Purpose: Most life cycle inventories (LCIs) of electricity consumed in different regions of the USA disregard the power that is being traded between these regions, while the US EPA's eGRID database does not contain scope 3 or noncombustion emissions. The relevance of these omissions has not yet been comprehensively quantified in the literature. This paper aims to close this knowledge gap. Methods: The parameterized inventory models that underlie all publicly available grid mix datasets in the GaBi databases were configured to create 26 eGRID subregional LCIs based on data from EPA's eGRID2014 database and the Federal Energy Regulatory Commission's FERC-714 report on power trade for the same reference year. Results were calculated regarding climate change, acidification, eutrophication, particulate matter, smog formation, and blue water consumption per kilowatt hour of electricity and compared for direct plant emissions only, the cradle-to-gate production mix (including scope 3), and the cradle-to-gate consumption mix (including scope 3 and power imports). Results and discussion: Results showed that the scope 3 burdens represent the most significant data gap if not accounted for. Specifically, the GWP results show median increases of around + 18% for GWP100 and of + 30% for GWP20 when scope 3 GHG emissions are added to the direct plant emissions. Other impact categories showed even larger increases when compared to direct plant emissions, with a maximum increase of + 573% in eutrophication potential for the subregion CAMX. While the median deviations of the consumption mixes compared to the production mixes are moderate, individual subregions show significant increases in environmental burdens for selected metrics. However, power trade seems less of an issue for the impact category of climate change as only the CAMX subregion shows an increase larger than 10% when power trade is accounted for. Conclusions: A cradle-to-gate production mix can be a suitable proxy of the environmental burdens of electricity consumed in many subregions, but it can also lead to significant errors for others depending on the environmental metric(s) one is trying to assess. Power plant emissions by themselves (scope 2) that exclude both scope 3 emissions and power trade cannot be recommended for use in any kind of carbon accounting or other environmental analysis because they suffer from severe data gaps and may skew the comparison of the environmental burdens associated with power consumption in the different subregions significantly. [ABSTRACT FROM AUTHOR]

Buildings represent one of the most signiflcant sources of energy consumption in the United States and other countries in the world. One of the most signiflcant factors affecting buildings' energy performance is the behavior and actions of their occupants. Monitoring, understanding, and decoding occupant's activities are fundamental to identify energy waste and for proposing strategies to reduce excessive energy consumption in buildings. In this paper, we present an approach for automatic detection and proactive monitoring of energy waste caused by occupants' behaviors. We flrst introduce a mathematical formalism to model states and trajectories arising in buildings in the context of energy consumption by occupants. Then, we present a set of easy to implement algorithms that used sensing information to detect wasteful states and trajectories. We also describe and implement a prototype of a non-invasive, sensor network consisting of inexpensive temperature, light, and distance sensors, as well as electricity consumption plug monitors that capture data related to occupancy behaviors in energy consumption. By combining occupancy counts, sensing information, and energy expenditures in different regions of a building, we can estimate how occupancy behavior is affecting energy use in a non-invasive way. Our ideas are tested experimentally in a study case in a residential building. [ABSTRACT FROM AUTHOR]

Google Trends is a service based on big data that shows the frequency of global searches in real time. As an index of individual interest, Google Trends has been used in various research fields. This study estimates the relationship between household electricity consumption and individuals' interest in energy based on Google Trends data. In particular, from the viewpoint of renewable energy, we compare the relationship between electricity consumption and the “renewable” Google Trends keyword with that between electricity consumption and other keywords. A model is constructed to examine the effect on household consumption of substituting electricity with renewable energy. We find that household electricity consumption decreases by 16.017 million kWh for every one unit increase in search of the “renewable” keyword. This study therefore illustrates that Google Trends enables the estimation of driving factors that are difficult to uncover when analyzing with various economic indicators. [ABSTRACT FROM AUTHOR]

The article reports on nuclear power growth and energy waste in the United States as projected by the Energy Research and Development Administration(ERDA). It examines the intensive electrification scenarios on which ERDA projections on U. S. nuclear power growth are based revealing that the high projection from ERDA of the future electric energy growth pushes the country toward plutonium economy. It states that the use of electric resistance heating as a major industrial process heat source is just one of the wasteful and uneconomic use of electricity resulted from the shift toward all electric economy. INSET: Energy Wastes in the COAT Scenario.

In the fall of 1979, the United States Department of Energy implemented the Low Cost/No Cost Energy Conservation Program in six New England states. Results of a consumer experiment show that the use of incentives significantly increased consumer response. [ABSTRACT FROM AUTHOR]