The impacts of COVID-19 on clean energy labor markets: Evidence from multifaceted analysis of public health interventions and COVID-health factors.

COVID-19 pandemic has affected clean energy labor market. Using real-time job vacancy data, this study analyzes the impacts of the pandemic on the U.S. clean energy labor market in 2020, including biomass, energy efficiency (EE), electric vehicle (EV), power/microgrid, solar, and wind industries. This study identifies how COVID-health factors and public health interventions influence clean energy job availability during the early COVID pandemic. Overall, California had the most energy jobs and experienced a significant decrease in April 2020. EV and solar had the highest percentages of job vacancies during the pandemic in general. Still, lockdowns had the most severe influence on EE and wind jobs. Stay-at-home orders negatively affected clean energy job vacancies in biomass, EV, power/microgrid, and wind. Social-gathering restrictions, however, did not have much influence. Increased COVID tests at the state level had the strongest and most positive influence on clean energy job postings, indicating the importance of a state's ability to manage public health infrastructure or crisis issues. COVID hospitalizations negatively influenced the job vacancies in biomass and wind but did not affect the other four sectors; conversely, as COVID death numbers increased, the number of jobs in biomass, EV, power grid, solar, and wind decreased, but not in EE jobs.

Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network.

BACKGROUND: This study presents an intelligent table tennis e-training system based on a neural network (NN) model that recognizes data from sensors built into an armband device, with the component values (performances scores) estimated through principal component analysis (PCA). METHODS: Six expert male table tennis players on the National Youth Team (mean age 17.8 ± 1.2 years) and seven novice male players (mean age 20.5 ± 1.5 years) with less than 1 year of experience were recruited into the study. Three-axis peak forearm angular velocity, acceleration, and eight-channel integrated electromyographic data were used to classify both player level and stroke phase. Data were preprocessed through PCA extraction from forehand loop signals. The model was trained using 160 datasets from five experts and five novices and validated using 48 new datasets from one expert and two novices. RESULTS: The overall model's recognition accuracy was 89.84%, and its prediction accuracies for testing and new data were 93.75% and 85.42%, respectively. Principal components corresponding to the skills "explosive force of the forearm" and "wrist muscle control" were extracted, and their factor scores were standardized (0-100) to score the skills of the players. Assessment results indicated that expert scores generally fell between 60 and 100, whereas novice scores were less than 70. CONCLUSION: The developed system can provide useful information to quantify expert-novice differences in fore-hand loop skills.

Modernizing our way out or digging ourselves in? Reconsidering the impacts of efficiency innovations and affluence on residential energy consumption, 2005-2015.

This study scrutinizes the impacts of efficiency innovations as well as affluence on residential energy consumption, which is a major driver of greenhouse gas emissions. The study draws on the ecological-modernization perspective, which is optimistic about how technological innovations and affluence can help societies overcome environmental challenges associated with production and consumption, and the political-economy perspective, which raises doubts about whether these factors are beneficial to the environment, given their tendency to drive more consumption. Analysis of nationally representative longitudinal data reveals mixed relationships between efficiency innovations and residential energy consumption: while some measures of efficiency innovations, generally those not requiring human-technology interactions, are negatively related to residential energy consumption, others are either unrelated to it or drive more consumption. These findings suggest efficiency innovations offer only minimal opportunities for conserving energy, and may depend on the nature of the innovation. Raising doubts about the potential for rising affluence to promote environmental protection, this study reveals positive relationships between our measures of affluence and residential energy consumption.

Collective privacy recovery: Data-sharing coordination via decentralized artificial intelligence.

Collective privacy loss becomes a colossal problem, an emergency for personal freedoms and democracy. But, are we prepared to handle personal data as scarce resource and collectively share data under the doctrine: as little as possible, as much as necessary? We hypothesize a significant privacy recovery if a population of individuals, the data collective, coordinates to share minimum data for running online services with the required quality. Here, we show how to automate and scale-up complex collective arrangements for privacy recovery using decentralized artificial intelligence. For this, we compare for the first time attitudinal, intrinsic, rewarded, and coordinated data sharing in a rigorous living-lab experiment of high realism involving >27,000 real data disclosures. Using causal inference and cluster analysis, we differentiate criteria predicting privacy and five key data-sharing behaviors. Strikingly, data-sharing coordination proves to be a win-win for all: remarkable privacy recovery for people with evident costs reduction for service providers.

When concentrated disadvantage happens: Exploring the nexus of energy and internet insecurities among vulnerable households.

Digital divide and energy insecurity are pervasive issues among underserved communities, issues that become prounoued during the COVID-19 lockdowns. These disparities underscore the critical need to address them promptly to narrow socio-economic gaps. Our study, based on an online survey of 2,588 respondents in the United Kingdom, explores how concentrated socio-economic disadvantage exacerbates insecurities relating to energy and internet access. Our findings reveal that marginalized groups including low-income households, women, renters, ethnic minorities, and individuals with lower educational attainment are disproportionately affected. Our research extends beyond financial implications to explore the broader social and psychological effects such as trust in utility and internet providers. The study also demonstrates how heightened burdens from energy and internet costs adversely affect the quality of indoor environments, underscoring the interconnected nature of these challenges. Based on these insights, we advocate for policy interventions that adopt comprehensive social justice frameworks to tackle these intersecting inequalities effectively.

Localized energy burden, concentrated disadvantage, and the feminization of energy poverty.

Energy burden directly influences households' health and safety. Amid a growing literature on energy, poverty and gender remains relatively understudied. We evaluate socioeconomic, geographic, and health factors as multidimensions of concentrated disadvantage that magnify energy burden in the United States over time. We show that the energy burden is more pronounced in disadvantaged counties with larger elderly, impoverished, disabled people, and racialized populations where people do not have health insurance. Neighborhoods with households headed by women of color (especially Black women) are more likely to face a high energy burden, which worsened during the COVID-19 pandemic. Although energy costs are often regarded as an individual responsibility, these findings illustrate the feminization of energy poverty and indicate the need for an intersectional and interdisciplinary framework in devising energy policy directed to households with the most severe energy burden.

Beyond technology adoption: Examining home energy management systems, energy burdens and climate change perceptions during COVID-19 pandemic.

The COVID-19 pandemic has rapidly changed our lives. While the global impacts of the pandemic are shocking, the implications for energy burdens, climate policy, and energy efficiency are salient. This study examines income differences in the acceptance of and willingness to pay for home energy management systems during the COVID-19 pandemic among 632 residents in New York. Additionally, this study examines energy profiles, energy burdens, climate change issues, risk perceptions, and social-psychological factors. Compared with low-income households, our findings suggest that high-income households use more energy, have higher utility bills during quarantine mandates, perceive a higher risk of COVID-19 infection, and perceive climate change issues to be better than before. Low-income households, however, experience the highest energy burdens. Regarding HEMS acceptance, high-income households are more willing to adopt energy and well-being-promoting features of HEMS and more willing to pay a higher monthly fee for all the features than other income groups. Overall, participants were more willing to pay a higher price for the energy features than the well-being-promoting features. Low-income households indicate lower social norms, personal norms, and perceived behavioral control over adopting HEMS; they also perceive HEMS to be more difficult to use and less useful. Higher-income households express a higher trust in utilities than low-income households. Surprisingly, cost concerns, technology anxiety, and cybersecurity concerns relating to HEMS do not differ across income groups. This paper addresses the interactions among technology attributes and social-psychological and demographic factors, and provides policy implications and insights for future research.

How limitations in energy access, poverty, and socioeconomic disparities compromise health interventions for outbreaks in urban settings.

Low-income households (LIHs) have experienced increased poverty and inaccess to healthcare services during the COVID-19 pandemic, limiting their ability to adhere to health-protective behaviors. We use an epidemiological model to show how a households' inability to adopt social distancing, owing to constraints in utility and healthcare expenditure, can drastically impact the course of disease outbreaks in five urban U.S. counties. LIHs suffer greater burdens of disease and death than higher income households, while functioning as a consistent source of virus exposure for the entire community due to socioeconomic barriers to following public health guidelines. These impacts worsened when social distancing policy could not be imposed. Health interventions combining social distancing and LIH resource protection strategies (e.g., utility and healthcare access) were the most effective in limiting virus spread for all income levels. Policies need to address the multidimensionality of energy, housing, and healthcare access for future disaster management.

Coronavirus comes home? Energy use, home energy management, and the social-psychological factors of COVID-19.

This study explores the dynamics of energy use patterns, climate change issues and the relationship between social-psychological factors, with residents' acceptance of and willingness to pay (WTP) for home energy management systems (HEMS) during the COVID-19 pandemic in New York. The results of our survey suggest that there were no longer morning or evening usage peaks on weekdays, and a significant portion of respondents are experiencing higher or much higher electricity use than average. Most residents' perception of climate change issues during COVID-19 remained unchanged. Attitude, perceived behavioral control, and social norms are overall the strongest predictors of adoption intention and WTP for HEMS. Regarding WTP for specific well-being features, attitude was the strongest positive predictor of telemedical and home security features, and social norms are the strongest positive predictor of elderly assistance and job search. Technology anxiety, surprisingly, positively influences WTP for the well-being features. Trust in utilities is not related to adoption intention, but is positively associated with WTP for the well-being features. Although cybersecurity concerns are positively associated with HEMS adoption intention for energy and well-being features, this relationship is not significant in WTP. Residents who had moderate perceived risk of getting COVID-19 are willing to pay more than the high- and low-risk groups. This paper addresses the interactions among technology attributes, and users' social-psychological and demographics factors. Additionally, this study provides insights for further research in examining technology adoption and energy dynamics during times of crises, such as the COVID-19.

Validity of energy social research during and after COVID-19: challenges, considerations, and responses.

Measures to control the spread of coronavirus disease 2019 (COVID-19) are having unprecedented impacts on people's lives around the world. In this paper, we argue that those conducting social research in the energy domain should give special consideration to the internal and external validity of their work conducted during this pandemic period. We set out a number of principles that researchers can consider to give themselves and research users greater confidence that findings and recommendations will still be applicable in years to come. Largely grounded in existing good practice guidance, our recommendations include collecting and reporting additional supporting contextual data, reviewing aspects of research design for vulnerability to validity challenges, and building in longitudinal elements where feasible. We suggest that these approaches also bring a number of opportunities to generate new insights. However, we caution that a more systemic challenge to validity of knowledge produced during this period may result from changes in the kinds of social research that it is practicable to pursue.