What Drove Saudi Arabia’s 2020 Fall in CO2 Emissions?

In June 2021, the energy data provider Enerdata released its initial estimates for Saudi Arabia’s 2020 carbon dioxide (CO2) emissions. The data indicate that the Kingdom’s CO2 emissions from fuel combustion decreased by 3.3%, from 508.3 million tonnes of CO2 (MtCO2) in 2019 to 491.8 megatonnes of CO2 (MtCO2) in 2020.

A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

Construct Dimensionality of Personal Energy at Work and Its Relationship with Health, Absenteeism and Productivity

Personal energy at work has become a popular topic among HRM scholars and practitioners because it has proven to impact performance. Based on the outcomes of previous research and the call for further exploration of the construct of personal energy at work, we executed this quantitative study. We explored the factor structure of the construct and its relationships with health and productivity by examining the construct that addresses four dimensions: physical, emotional, mental and spiritual energy. Data were collected from 256 employees in an international health tech company and used to analyze construct dimensionality and relationships with health, absenteeism and productivity. The results provided support for the four-dimensional structure of personal energy at work and show that the construct of personal energy at work is related to the outcomes of health, absenteeism and productivity. Implications for theory and practice, as well as directions for future research, are discussed.

Compression Techniques of Electrical Energy Data for Load Monitoring: A Review

In recent years, the electric grid has experienced increasing deployment, use, and integration of smart meters and energy monitors. These devices transmit big time-series load data representing consumed electrical energy for load monitoring. However, load monitoring presents reactive issues concerning efficient processing, transmission, and storage. To promote improved efficiency and sustainability of the smart grid, one approach to manage this challenge is applying data-compression techniques. The subject of compressing electrical energy data (EED) has received quite an active interest in the past decade to date. However, a quick grasp of the range of appropriate compression techniques remains somewhat a bottleneck to researchers and developers starting in this domain. In this context, this paper reviews the compression techniques and methods (lossy and lossless) adopted for load monitoring. Selected top-performing compression techniques metrics were discussed, such as compression efficiency, low reconstruction error, and encoding-decoding speed. Additionally reviewed is the relation between electrical energy, data, and sound compression. This review will motivate further interest in developing standard codecs for the compression of electrical energy data that matches that of other domains.

Brief communication: Lower-bound estimates for residence time of energy in the atmospheres of Venus, Mars and Titan

The residence time of energy in a planetary atmosphere, τ, which was recently introduced and computed for the Earth's atmosphere (Osácar et al., 2020), is here extended to the atmospheres of Venus, Mars and Titan. τ is the timescale for the energy transport across the atmosphere. In the cases of Venus, Mars and Titan, these computations are lower bounds due to a lack of some energy data. If the analogy between τ and the solar Kelvin–Helmholtz scale is assumed, then τ would also be the time the atmosphere needs to return to equilibrium after a global thermal perturbation.