Energy Information Administration Research Articles

Optimal Gasoline Price Predictions: Leveraging the ANFIS Regression Model

This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with an emphasis on its implications for policy-making and strategic decisions in the energy sector. The model leverages a comprehensive dataset from the U.S. Energy Information Administration, spanning over 30 years of historical price data from 1993 to 2023, along with relevant temporal features. By combining the strengths of fuzzy logic and neural networks, the ANFIS approach can effectively capture the complex, nonlinear relationships present in the data, enabling reliable price predictions. The dataset’s preprocessing involved decomposing the date into year, month, and day components to enhance the model’s input features. Our methodology entailed a systematic approach to ANFIS regression, including data preparation, model training with the inclusion of the previous week’s prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. The results indicate that incorporating previous prices significantly enhances the model’s accuracy, as reflected by improved scores and correlation metrics. The findings have significant implications for the energy sector, where stakeholders can leverage the ANFIS model’s insights for strategic decision-making. Accurate gasoline price forecasts are instrumental in devising pricing strategies, managing risks associated with price volatility, and guiding policy formulation. The model’s predictive capability enables energy companies to optimize resource allocation, plan for future investments, and maintain competitive advantage in a market influenced by fluctuating prices. Moreover, policymakers can utilize these predictions to assess the impact of energy policies on market prices and consumer behavior, ensuring that regulatory measures align with market dynamics and sustainability goals. In addition to the ANFIS model, we also employed Vector Autoregression (VAR) and Autoregressive Integrated Moving Average (ARIMA) models to validate our approach and provide a comprehensive understanding of time series forecasting within the energy sector. Notably, the ANFIS model achieves a score of 0.9970 and a robust correlation of 0.9985, demonstrating its ability to accurately forecast gasoline prices based on historical data and features. The integration of these traditional techniques with advanced ANFIS modeling offers a robust framework for accurate and reliable gasoline price prediction, which is vital for informed policy-making and strategic planning in the energy industry.

Reduction in Emissions by Massive Solar Plant Integration in the US Power Grid

Fossil fuels, the predominant energy source in the United States, have been identified as major contributors to environmental pollution through the release of harmful emissions. As a countermeasure, there has been an increasing focus on the exploration and development of cleaner energy alternatives to alleviate the environmental degradation caused by fossil fuels and to satisfy the growing energy needs. This study conducted scenario analyses to evaluate the impact of integrating solar energy into specific US power grids on reducing carbon emissions. The analysis encompassed electrical systems within California, New England, New York, and the Southwest, utilizing datasets from the Energy Information Administration and National Renewable Energy Laboratory. The Energy Information Administration dataset includes information on net generation according to each source and carbon emissions according to fuel type, whereas the National Renewable Energy Laboratory dataset provides hourly projections for 6000 theoretical photovoltaic installations and detailed solar energy output data every five minutes over a year. Our findings indicated a notable decrease in carbon dioxide emissions following the introduction of solar power facilities. The most significant reductions were observed in the Southwest and California, attributed to solar plant integration. Conversely, New York and New England were identified as regions requiring additional policy measures and incentives to meet the emission reduction goals.

Estimating carbon emissions from thermal power plants based on thermal characteristics

Carbon emissions from thermal power plants (TPPs) are a significant source of anthropogenic carbon and a priority for carbon reduction efforts. The accurate estimating of carbon emissions from TPPs is essential research in the field of carbon reduction. In this study, we take TPPs located in the United States as examples to discuss the relationship between the thermal characteristics of the power plant and its annual carbon emissions. Our data consists of land surface temperature (LST) product generated from Landsat 8 thermal data, LST product from ERA5-Land reanalysis dataset, and carbon emission data reported by the U.S. Energy Information Administration (EIA). Based on pertinent research of urban heat islands, we propose the concept of thermal characteristic index (TCI), which can provide a quantitative description of power plant thermal characteristics. Furthermore, we convert TCI to annual thermal characteristic index (A-TCI) by annual end-point interpolation and Simpson integration of the TCI series to match the annual carbon emission data and then establish a TCI-based annual carbon emission estimation model (TACEE-Model). Using the TACEE-Model, we predict the annual carbon emission values and evaluate the model’s performance. Experimental results indicate that the TACEE-Model exhibits great predictive capability, with the mean absolute percentage error (MAPE) of 0.0325, and the average prediction percentage error of 3.24%. The application of annual end-point interpolation and Simpson integration can significantly enhance the model’s prediction accuracy, resulting in a 51.42% reduction in MAPE. Overall, this research presents a new method for verifying annual carbon emissions from TPPs, which can contribute to the supervisory oversight of carbon emission reduction from TPPs.

Fueling the Future: A Comprehensive Analysis and Forecast of Fuel Consumption Trends in U.S. Electricity Generation

The U.S. Energy Information Administration (EIA) provides crucial data on monthly and annual fuel consumption for electricity generation. These data cover significant fuels, such as coal, petroleum liquids, petroleum coke, and natural gas. Fuel consumption patterns are highly dynamic and influenced by diverse factors. Understanding these fluctuations is essential for effective energy planning and decision making. This study outlines a comprehensive analysis of fuel consumption trends in electricity generation. Utilizing advanced statistical methods, including time series analysis and autocorrelation, our objective is to uncover intricate patterns and dependencies within the data. This paper aims to forecast fuel consumption trends for electricity generation using data from 2015 to 2022. Several time series forecasting models, including all four benchmark methods (Mean, Naïve, Drift, and seasonal Naïve), Seasonal and Trend Decomposition using Loess (STL), exponential smoothing (ETS), and the Autoregressive Integrated Moving Average (ARIMA) method, have been applied. The best-performing models are determined based on Root Mean Squared Error (RMSE) values. For natural gas (NG) consumption, the ETS model achieves the lowest RMSE of 20,687.46. STL demonstrates the best performance for coal consumption with an RMSE of 5936.203. The seasonal Naïve (SNaïve) model outperforms the others for petroleum coke forecasting, yielding an RMSE of 99.49. Surprisingly, the Mean method has the lowest RMSE of 287.34 for petroleum liquids, but the ARIMA model is reliable for its ability to capture complex patterns. Residual plots are analyzed to assess the models’ performance against statistical parameters. Accurate fuel consumption forecasting is very important for effective energy planning and policymaking. The findings from this study will help policymakers strategically allocate resources, plan infrastructure development, and support economic growth.

Carbon Footprint of Total Intravenous and Inhalation Anesthesia in the Transcatheter Aortic Valve Replacement Procedure

ObjectiveWe aimed to quantify and compare the emissions for deep sedation with TIVA and general anesthesia with inhaled agents during the transcatheter aortic valve replacement procedure. Designa retrospective study. Settingat a tertiary hospital in Boston. ParticipantsThe anesthesia records of 604 consecutive patients who underwent the TAVR procedure between January 1, 2018 and March 31, 2022 were reviewed and analyzed. InterventionsData were examined and compared in the following two groups: general anesthesia with inhaled agents and deep sedation with TIVA. Measurements & Main ResultsThe gases, drugs, airway management devices, and anesthesia machine electricity were collected and converted into CO2e. The carbon emissions of intravenous medications were converted with the CO2e data for anesthetic pharmaceuticals from Parvatker et al. study. For inhaled agents, inhaled anesthetics and oxygen/air flow rate were collected at 15-minute intervals and calculated using the anesthetic gases calculator provided by the Association of Anesthetists. The airway management devices were converted based on life-cycle assessments. The electricity consumed by the anesthesia machine during general anesthesia was estimated from the manufacturer's data (Drager, GE, USA) and local Energy Information Administration data. The data were analyzed in the Chi-squared test or Wilcoxon rank-sum test. There were no significant differences in the patient's demographic characteristics, such as age, gender, weight, height, and BMI. The patients who received general anesthesia with inhaled agents had statistically higher total CO2e per case than deep sedation with TIVA (16.188 vs. 1.518 kg CO2e, P < 0.001), primarily due to the inhaled agents and secondarily to airway management devices. For deep sedation with TIVA, the major contributors were intravenous medications (71.02%) and airway management devices (16.58%). A subgroup study of patients who received sevoflurane only showed the same trend with less variation. ConclusionsWe found that the patients who received volatile anesthesia had a higher CO2e per case. This difference remained after a subgroup analysis evaluating those patients only receiving sevoflurane and after accounting for the differences in the duration of anesthesia. Data from this study and others should be collectively considered as a health care profession aims to provide the best care possible for our patients while limiting the harm we cause to the environment.

Addressing energy insecurity: Policy Considerations for enhancing energy assistance programs

Household energy consumption is crucial for a productive and safe life. Despite its importance, 33.7 million U.S. households experienced energy insecurity in 2020. This paper examines the prevalence, correlates, and effects of energy bill assistance programs, which aim to alleviate the hardship. This analysis relies on logistic regressions and the 2020 Residential Energy Consumption Survey, a nationally representative survey administered by the Energy Information Administration. Results reveal 16 percent of the energy insecure population received assistance to pay its energy bill in 2020. Several socioeconomic and housing characteristics are associated with receipt of assistance; however, logistic regression estimates suggest prior participation in energy assistance and receipt of a disconnection notice from a utility provider are indicators substantively and significantly associated with energy assistance participation that warrant attention from scholars and practitioners. Lastly, outcomes generally indicate previous participation in energy assistance does not impact the odds a household will experience energy insecurity. Based on findings, I offer three policy recommendations: 1) increase resources spent on energy assistance to align with demand; 2) enhance communication between utility providers and low-income households regarding available assistance opportunities; and 3) prioritize engagement with populations that never participated in energy assistance to facilitate successful application processes.

Sustainable utilization of biogas technology: A promising solution to combat the energy crisis in India

India is facing a significant energy crisis due to its rapidly growing population, which is not being met by an equivalent increase in energy supply. According to the US Energy Information Administration, India's electricity consumption is projected to grow by 3.3% annually until 2035. Biogas technology, which converts waste into clean energy, offers a promising solution to this problem and accounts for 9% of global energy consumption. Despite being introduced in 1981, biogas technology's potential in India remains largely untapped, with only 7.34 MWe of off-grid projects generated by 2020. However, Tamil Nadu and Karnataka have significant potential for biogas generation. The National Biogas and Manure Management Program (NBMMP) have installed 5,056,139 biogas plants between 1981-82 and 2020-21, with Maharashtra being the top performer. India's agrarian economy provides ample raw materials for biogas generation, making it a crucial renewable resource for the country's energy crisis. Given the pressing need to address global warming, greenhouse effects, depleting fossil fuels, and pollution, biogas technology is essential for a cleaner, more sustainable future. Focusing on biogas technology can help India meet its increasing energy demands and contribute to a cleaner and more sustainable future. From 2018-19 to 2020-21, the NBMMP received a total allocation of INR 1,439,249,000.

Introducing graphene‐enhanced technologies to the regulated market to mitigate carbon emissions

AbstractAmbitious net zero carbon emissions goals have been set by various climate change agencies and government officials, including President Biden. One of the most prominent emissions goals aims to reach net zero carbon emissions globally by 2050. Unfortunately, the United States is not on track to achieving this goal and the United Nations has announced that several nations have fallen short of their carbon emissions agreements. Increases in global temperatures, deadly natural disasters, and species extinction have heightened the pressure facing scientists and policymakers to convince nations to stay on course to reach net zero emissions within the next few decades. Last year, the largest contributor to carbon dioxide emissions in the United States was “U.S. motor gasoline and diesel (distillate) fuel consumption” (U.S. Energy Information Administration, Frequently Asked Questions), making it clear that changes must be made in the energy sector to mitigate this. Recent innovations in the battery industry have shown exciting promise for the future of clean energy, though regulatory barriers must be overcome to fully establish these new technologies. One such technology is the graphene‐enhanced lithium‐ion battery which has five times the energy density of regular lithium‐ion batteries due to the high thermal conductivity of graphene. However, graphene is difficult to produce on a large scale, and many investors and stakeholders have hesitated to participate in the graphene supply chain due to the lack of regulation and health and safety information on graphene. The EPA (Environmental Protection Agency), along with other standardization organizations, has worked to understand nanomaterials like graphene over the past decade, but they seem to be behind in producing substantial data on the material's properties. Furthermore, current graphene manufacturers have varying ways of classifying and labeling graphene products, creating additional confusion in the marketplace. To accelerate the process of researching and regulating graphene, the EPA's adherence to predetermined deadlines for the creation of regulations for up‐and‐coming materials must be mandated, a standardized graphene labeling system should be adopted, universities and federal agencies should be incentivized to collaborate on research related to graphene production methods, and a mandatory “battery passport” system should be implemented to ensure the widespread availability of battery information. These recommendations are aimed at increasing trust and collaboration within the graphene industry and standardizing graphene materials to reduce ambiguity within the supply chain. By implementing these policy changes, the use of graphene‐enhanced lithium‐ion batteries in electric vehicles and grid energy storage systems can become more widespread, and allow for higher efficiency. As improvements are made to clean energy alternatives, the use of fossil fuels can be reduced, thus, decreasing overall carbon emissions.

Urbanization, Energy Consumption and Environmental Quality in Asian Countries

The rate of urbanization has enhanced around the globe. By moving the production from less to more energy-intensive sources, increasing the rate of urbanization leads to more energy consumption. The major threat to the environment of the world is the increasing rate of greenhouse gas emissions and global warming. The main purpose of this study is to elaborate on the relationship between urbanization, energy consumption, and CO2 emission in Asian countries. In this study, CO2 is used as a dependent variable while urban population, population density, renewable and non-renewable energy consumption, GDP per capita growth, the square of GDP per capita growth, and trade are used as the independent variables. We have obtained data from 1980 to 2020 from 32 Asian countries. Data have been taken from the sources of the Energy Information Administration (EIA) and World Development Indicators (WDI). We have used the panel ARDL technique for the long-run and short-run estimation of variables. The findings of this study reveal that non-renewable energy has a positive but insignificant whereas per capita GDP growth, urban population, population density, and trade have a positive effect; and per capita square of GDP growth and renewable energy consumption has a negative and significant effect on CO2 emission. The findings of Granger causality show that there is uni-variate causality running from non-renewable energy consumption towards CO2 emission and from urban population to renewable energy. There is bi-variate causality running from renewable energy to CO2, non-renewable to renewable energy consumption, and from urban population to non-renewable energy consumption. There is no causality exists between the urban population and CO2 emission. This study suggests that the Government may adopt more energy conservation policies to reduce CO2 emissions. The government may suggest using the public transportation system rather than the private transportation system to mitigate CO2 emissions.

Regardless of its Merits, Carbon Capture Faces Several Challenges

A theme I have struck multiple times in this column is the reality that energy policy designed to eliminate atmospheric carbon as quickly as possible must—to be genuinely sustainable through political cycles and reliability challenges—recognize that natural gas will be here and play an important role in our energy mix for a very long time to come. Even the Biden Administration, with its stated objective of phasing out all fossil fuels, has recognized this reality, through the projections of its own U.S. Energy Information Administration (EIA). In the 2023 Annual Energy Outlook prepared by EIA, one of its cases in assessing the future energy mix includes one in which non‐hydro resources, primarily solar and wind, become low‐cost enough to be competitive leaders in the marketplace. Figure 1 depicts EIA’s projection, which does see renewables surpassing natural gas in market share by the late 2040s but still leaves natural gas with a 25 percent share of domestic energy consumption, down only 8 percent from current levels. Of course, when exports of liquefied natural gas (LNG) are added to this, as the largest exporter of LNG in the world, the natural gas line is much flatter.

Georectified polygon database of ground-mounted large-scale solar photovoltaic sites in the United States

Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MWdc) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON.

Assessing the energy efficiency of fossil fuel in ASEAN

The world's industries, transportation systems, and households rely heavily on fossil fuels despite their limited availability and high carbon content. Therefore, it is of the utmost importance to improve fossil fuel energy efficiency in order to facilitate the shift towards a sustainable energy system with reduced greenhouse gas emissions. This paper employs a slacks-based measure network data envelopment analysis model with undesirable outputs to assess the efficiencies of fossil fuel energy in the Association of Southeast Asian Nations (ASEAN) countries during a span of seven years, specifically from 2015 to 2021. The inclusion of undesirable outputs in this study is important because it allows for a more realistic assessment of efficiency by considering factors like CO2 emissions, which are undesirable outcomes associated with fossil fuel use. The datasets utilised in this study are sourced from the U.S. Energy Information Administration and the open data website of Our World in Data. Based on the findings, it can be observed that Singapore and the Philippines have demonstrated outstanding performance in maximising the utilisation of fossil fuels. In contrast, Myanmar exhibits the lowest level of efficiency in this analysis. By identifying top-performing countries in terms of fossil fuel efficiency, it is possible to implement measures to boost efficiency in under-performing countries. This can be achieved through the promotion and adoption of cleaner energy alternatives, specifically renewable energy sources that exhibit a low or negligible carbon footprint. These findings offer significant contributions to policymakers exploring sustainable energy usage, environmental stewardship, and the formulation and execution of comprehensive strategies that aim to mitigate carbon dioxide emissions arising from the consumption of fossil fuels in the ASEAN region.

Location is a major barrier for transferring US fossil fuel employment to green jobs

The green energy revolution may displace 1.7 million fossil fuel workers in the US but a Just Transition to emerging green industry jobs offers possibilities for re-employing these workers. Here, using 14 years of power plant data from the US Energy Information Administration, job transition data from the Census Bureau, as well as employment and skills data from the Bureau of Labor Statistics, we assess whether people employed in fossil fuel resource extraction today are co-located and have the transferable skills to switch to expected green jobs. We find that these workers could leverage their mobility to other industries and have similar skills to green occupations. However, today’s fossil fuel extraction workers are not co-located with current sources of green energy production. Further, after accounting for federal employment projections, fossil fuel extraction workers are mostly not located in the regions where green employment will grow despite attaining the appropriate skillsets. These results suggest a large barrier to a Just Transition since fossil fuel extraction workers have not historically exhibited geospatial mobility. While stakeholders focus on re-skilling fossil fuel extraction workers, this analysis shows that co-location with emerging green employment will be the larger barrier to a Just Transition.

Intraday Return Predictability in the Crude Oil Market: The Role of EIA Inventory Announcements

We study the impact of the announcements released by the US Energy Information Administration (EIA) on crude oil storage every Wednesday at 10:30 ET (the beginning of the third half-hour interval) on intraday return predictability, that is, intraday momentum. Our results indicate that returns on the third half-hour on EIA announcement days can significantly and positively predict the returns in the last half-hour, whereas, on non-EIA announcement days, only returns in the first half-hour have significant predictability. The dominant source of prediction in the first half-hour return mainly comes from the overnight component. EIA announcements contribute to intraday momentum because they attract more informed traders and because the period surrounding their release is often associated with a reduction in liquidity. Substantial economic gains can be made by using efficient intraday predictors as trading signals.

Crude Oil Cost Forecasting using Variants of Recurrent Neural Network

Crude oil cost plays very important role in the country’s economic growth. It is having close impact on economical stability of nation. Because of these reasons it is very important to have accurate oil forecasting system. Due to impact of different factors oil cost data is highly nonlinear and in fluctuated manner. Performing prediction on those data using data driven approaches is very complex task which require lots of preprocessing of data. Working on such a non-stationary data is very difficult. This research proposes recurrent neural network (RNN) based approaches such as simple RNN, deep RNN and RNN with LSTM. To compare performance of RNN variants this research has also implemented Naive forecast and Sequential ANN methods. Performance of all these models are evaluated based on root mean square error(RMSE), mean absolute error(MAE) and mean absolute percentage error(MAPE). The experimental result shows that RNN with LSTM is more accurate compare to all other models. Accuracy of LSTM is more than 96% for the dataset of U.S. Energy Information administration from March 1983 to June 2022. On the basis of experimental result, we come to the conclusion that RNN with LSTM is best suitable for time series data which is highly nonlinear.

Exploring Performance and Cost Trade-Offs in Electrochemical CO2 Separation Processes

Increasing global energy demand coupled with a continued reliance on carbon-intensive fuels has contributed to the rise in anthropogenic greenhouse gas emissions that are adversely impacting the global climate.1,2 While the expansion of low-cost renewable electricity (often coupled with energy storage) offsets fossil-fuel-based generation, complete mitigation of carbon emissions will likely require the capture, utilization, and/or storage of carbon dioxide (CO2) at scale. Today’s state-of-the-art separation technologies rely on thermochemical processes to extract CO2 from industrial waste streams, post-combustion flue gases, or directly from the atmosphere. Such approaches are energetically intensive and typically rely on fossil-fuel-derived heat to release CO2 and regenerate the sorbent, ultimately limiting process effectiveness.3 Electrochemical approaches to CO2 capture and concentration are increasingly seen as a viable pathway to reducing carbon emissions, as these processes enable several advantages including direct integration with renewables, modular deployment, operation near ambient conditions, and high energetic efficiencies.4 Despite this promise, these concepts are at an early-stage and the relationships between cost and performance are not well-understood at the molecular-, cell-, or system-level.In this presentation, we will discuss how techno-economic modeling can be used to inform design criteria for cost-competitive electrochemical CO2 separation systems. We will specifically focus on a 4-stage system with soluble redox-active species for direct binding / debinding with CO2. First, we describe and integrate all process units (i.e., electrochemical reactor, absorber, desorber) via thermodynamic, chemical, and electrochemical relationships and then estimate capital, fixed, and variable costs via a levelized cost of capture model. Second, we investigate key scaling correlations and cost sensitivities for the 4-stage system, including the role of resistive losses in the electrochemical reactor and binding affinity in the absorption column. Third and finally, we connect system-level performance and cost targets to molecular / system properties and operating envelopes. While our focus is on a particular system, the framework is generalizable and can be extended to different electrochemical and thermochemical approaches to CO2 separation, potentially enabling technology comparisons on a common basis. Acknowledgements This work was supported by the Alfred P. Sloan Foundation. K.M.R. gratefully acknowledges the support of the National Defense Science and Engineering Graduate (NDSEG) Fellowship. References Ritchie, H., Roser, M. & Rosado, P. CO2 and Greenhouse Gas Emissions. Our World in Data (2020). Annual Energy Outlook 2021. U.S. Energy Information Administration (2021).Khan, F. M., Krishnamoorthi, V. & Mahmud, T. Modelling reactive absorption of CO2 in packed columns for post-combustion carbon capture applications. Chemical Engineering Research and Design 89, 9, 1600–1608 (2011).Renfrew, S. E., Starr, D. E. & Strasser, P. Electrochemical Approaches toward CO2 Capture and Concentration. ACS Catal. 10, 21, 13058–13074 (2020).

Determinant of Forex Rolling Spot Contracts on Indonesia Commodity andDerivative Exchange (ICDX)

One of the derivative futures contract types is the Forex Rolling Spot Contracts (FRSC) traded in the Indonesia Commodity and Derivatives Exchange (ICDX). So far, no research has been conducted on this product type, which can be used as an investor reference. This study aims to find empirical evidence of how far macroeconomic variables affect the returns of ten FRSC investment instruments: AUD/USD, EUR/USD, GBP/USD, USD/JPY, USD/CHF, GBP/JPY, EUR/JPY, AUD/NZD, NZD/USD, and USD/CAD traded at ICDX during the period 2021 - 2022. The statistical analysis method used in this research is quantitative with descriptive explanations. Data collection techniques were obtained from secondary data through library research. Secondary data was obtained through the internet sites of ICDX, Indonesia Stock Exchange (IDX), Bank Indonesia, London Bullion Metal Association, Bloomberg, and the US Energy Information Administration. The analytical method used in this study is panel data analysis. The study type was an explanatory study with data processing using the STATA application. The study results showed that the determinant of interest rate and inflation did not affect FRSC return; JCI return and gold price return had a significant positive on FRSC return; bond index return and crude oil price return had a negative effect on FRSC return.

Are dynamic tariffs effective in reducing energy poverty? Empirical evidence from US households

Statistics from the United States (US) Energy Information Administration and the US Census Bureau reveal that a growing number of households are threatened by energy poverty. Both argue that energy poverty is becoming more common than traditional poverty. Recently, dynamic pricing programmes have been made possible by the increasing use of smart meters and other devices which enable consumers to play a more active role in electricity systems. This study aims to determine if dynamic pricing programmes can reduce the energy burden on households and diminish the number suffering from energy poverty. Data from the Annual Electric Power Industry Report and the American Community Survey were merged and analysed by state and year to assess the impact of dynamic pricing on energy poverty for 51 US states from 2013 until 2021. The results show that time-of-use and critical-peak-pricing tariffs can reduce the number of households suffering from energy poverty. Conversely, CPP and RTP increase the number of households in energy poverty. These findings indicate that policymakers and retailers need to develop pricing programmes that will encourage consumers to modify their consumption habits and enhance the integration of intermittent renewable energy sources and reduce energy poverty.

Towards a Green Economy in China? Examining the Impact of the Internet of Things and Environmental Regulation on Green Growth

The idea of green growth stresses the necessity for economic expansion while resolving environmental issues, notably climate change. The Internet of Things (IoT) and environmental regulations have the potential to support green growth. Therefore, this study intends to examine the empirical link between the IoT, environmental regulations, and green growth in China by utilizing the autoregressive distributed lag (ARDL) and quantile autoregressive distributed lag (QARDL) methods to analyze data from 1997 to 2021. Data are obtained from reputable local and international sources like the Organisation for Economic Co-operation and Development (OECD), World Development Indicators (WDI), the Energy Information Administration (EIA), and the National Bureau of Statistics of China. Findings derived from the baseline ARDL model prove that the IoT, environmental regulations, renewable energy consumption, and research and development (R&amp;D) encourage long-run green growth. Likewise, the robust model also highlights that the internet, environmental policy stringency, renewable energy consumption, and R&amp;D help encourage green growth. In the short run, environmental policy stringency and the internet are favorably linked to green growth in the robust model, and renewable energy consumption is favorably linked to green growth in the baselines model; however, environmental regulation is negatively linked to green growth. The findings from the QARDL analysis show that the impact of the IoT on promoting green growth is significant across all quantiles. On the other hand, the effects of environmental regulation are more pronounced at higher levels of green growth. These findings imply that policymakers should try to increase the role of digitalization in society by promoting the IoT and the internet to decouple economic growth and environmental pollution. Moreover, the digitalization policy should be supported by implementing strict environmental laws and regulations.

Petroleum++: The Expanded Scope of the Industry and Our Role in It

The expanded scope of our industry as it evolves means that members of the petroleum engineering profession will enjoy a long, rewarding career. At the same time, we need to continue developing our competencies to enable expected contributions in producing new forms of energy resources and mitigating climate change concerns, as well as the constant changes and advancements in our traditional role of producing oil and natural gas. Based on the projections of multiple groups including the US Energy Information Administration (EIA), the International Energy Agency (IEA), and several international and national oil companies, it is clear the world energy demand will continue to grow into the 2040s. Lower-carbon sources will be the fastest-growing among the various sources of energy, demand for oil will continue to increase at least into the 2030s, and demand for natural gas will continue to grow into the 2040s. The latest projection from IEA (October 2022) illustrates these points. For example, as I am writing this column, Norway approved more than $18 billion investments to develop 19 oil and gas fields. Consensus also appears to indicate that policy will influence the speed of change of the energy landscape, that energy transition will vary by region, and that the pace of the energy transition will depend on costs, infrastructure, project execution, and geopolitics. This indicates that multiple solutions are needed to satisfy energy demand, oil and gas will remain essential for decades, and technological developments will continue to be needed in all sources of energy and may end up changing the mix of energy sources in the future. Data and projections also show two important points. The required levels of investment to provide the world with its energy needs are lagging, and there will be a shift in the regional demand of energy. Needed investment in oil and gas should be about $900 billion annually. Following the COVID-19 pandemic, when investment dipped to $600 billion, investments have gradually increased but are not yet to the desired levels. For the new energy sources to reach the level needed for IEA’s Net Zero 2050 target, annual investment in the range of $2,600 billion is needed. Right now, this investment is around $1,700 billion. Projections for the regional share of the world economy indicates that Asia will have about 43% of the world economy by the year 2040 (currently at 35%) followed by North America at about 24% (currently at 26%) and the EU and the UK at about 17% (currently at 20%). Shares of Latin America and the Middle East will stay around the current levels of 7% and 4%, respectively, while Africa’s share will increase from about 4 to 5%. The tables show the changes in global primary energy demand as well as the changes in different regions. I obtained the information from the sources cited earlier. However, I converted the values from exajoules (1e18 joule, a measurement unit I cannot relate to) to BOE using a conversion factor of 1 exajoule = 163.45e6 BOE.