• Energy Research
• 7. Clean energy close
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As COVID-19 spreads worldwide, governments have been implementing a wide range of measures to contain it, from movement restrictions to economy-wide shutdowns. Understanding their impacts is essential to support better policies for countries still experiencing outbreaks or in case of emergence of subsequent pandemic waves. Here we show that the cumulative decline in electricity consumption within the 5 months following the stay-home orders ranges between 3% and 12% in the most affected EU countries and USA states, except Florida, which shows no significant impact. Italy, France, Spain, California, Austria, and New York have recovered baseline consumption by the end of July, whereas Great Britain and Germany remain below baseline levels. We also show that the relationship between measures stringency and daily decline in electricity consumption is nonlinear. These results illustrate the severity of the crisis across countries and can support further research on the effect of specific measures.

The residential sector in Thailand has been a fast-growing energy consumption sector since 1995 at a rate of 6% per year. This sector makes a significant contribution to Thailand’s rising electricity demand especially during the COVID-19 pandemic. This study projects Thailand’s residential electricity consumption characteristics and the factors affecting the growth of electricity consumption using a system dynamics (SD) modeling approach to forecast long-term electricity consumption in Thailand. Furthermore, the COVID-19 pandemic and the lockdown can be seen as a forced social experiment, with the findings demonstrating how to use resources under particular circumstances. Four key factors affecting the electricity demand used in the SD model development include (1) work and study from home, (2) socio-demographic, (3) temperature changing, and (4) rise of GDP. Secondary and primary data, through questionnaire survey method, were used as data input for the model. The simulation results reveal that changing behavior on higher-wattage appliances has huge impacts on overall electricity consumption. The pressure to work and study at home contributes to rises of electricity consumption in the residential sector during and after COVID-19 pandemic. The government and related agencies may use the study results to plan for the electricity supply in the long term.

The German Federal Climate Change Act includes binding annual greenhouse gas (GHG) emission targets for the economic sectors energy, industry, buildings, transport, agriculture and waste. For sectors that fail to meet their targets, climate policy measures have to be implemented immediately. However, some sectors may only have achieved their targets thanks to the COVID-19 pandemic, while others may have missed their targets because of COVID-19. For policy making, it is therefore important to disentangle the effects of the COVID-19 pandemic from the effects of policy and other drivers on emissions. In this paper, the effects of the pandemic on GHG emissions in Germany in 2020 are estimated at the national level and at the levels of the economic sectors. Counterfactual emissions are estimated here based on autoregressive econometric models, thereby distinguishing between different factors of emissions based on decomposition analysis. The findings at the national level suggest that the COVID-19 pandemic lowered GHG emissions in 2020 in Germany by about 41 Mt CO2-eq (5.15%) compared to counterfactual emissions. Accordingly, about 58% of the reduction in emissions between 2019 and 2020 in Germany may be attributed to the pandemic. The findings at the sectoral level imply that without the COVID-19 pandemic, all sectors with the exception of the transport sector, would have met their emissions target in 2020 as set in the Federal Climate Change Act. The transport sector would have missed its emission target in 2020 without COVID-19, suggesting that existing policies were not strong enough to bend the curve of emissions trends sufficiently down. In contrast, the buildings sector failed to meet its target in 2020 because of COVID-19, prompting immediate climate policy measures in this sector. The COVID-19 pandemic lowered greenhouse gas (GHG) emissions in Germany in 2020 by 5.5% compared to counterfactual emissions and accounted for about 58% of total estimated reductions between 2019 and 2020.Without COVID-19, all sectors but the transport sector would have met their emissions targets.Climate policy response should take into account the effects of extraordinary events like COVID-19 suppressing GHG emissions based on counterfactual emissions because failure to implement measures early on may turn out to be costly in the medium term once the transitory emissions reductions no longer occur.Counterfactual emissions should be considered prior to crediting emissions surplus or deficit to subsequent years. The COVID-19 pandemic lowered greenhouse gas (GHG) emissions in Germany in 2020 by 5.5% compared to counterfactual emissions and accounted for about 58% of total estimated reductions between 2019 and 2020. Without COVID-19, all sectors but the transport sector would have met their emissions targets. Climate policy response should take into account the effects of extraordinary events like COVID-19 suppressing GHG emissions based on counterfactual emissions because failure to implement measures early on may turn out to be costly in the medium term once the transitory emissions reductions no longer occur. Counterfactual emissions should be considered prior to crediting emissions surplus or deficit to subsequent years.

One of the three most important components of the effective work of an organization or enterprise in the energy sector is properly organized staff work. Thus, all the risks arising in this process should be given proper consideration. Conventional methods can hardly be of any assistance, which is why this paper proposes the innovative rapidly adaptive model for the evaluation of existing risks and the forecasting of emerging ones due to the COVID-19 pandemic in modern personnel management. The model employs a probability theory and applies it to four major risk groups, which are related to many factors. It also describes the methods of mitigating these risks. This made it possible to improve personnel management efficiency, thus offsetting the adverse consequences of the COVID-19 pandemic.

For the Ukrainian population, there are factors surrounding the choices and preferences one must consider when changing permanent residence. Smart residential areas should be built according to Ukrainian legislation and global innovations, which would reduce administrative dislocations, the load on megalopolises, and negative anthropogenic impacts, and should be based on the increase in energy efficiency and reducing waste. We analyzed the core principles of designing smart residential areas and concluded that constructing cutting-edge residential areas should involve private investments in order to avoid shadow schemes and irrational use of funds (in the Ukraine, the share of the shadow market is more than 50%). Research shows that, as humans inhabit a three-dimensional space, it is possible to predict migration and other permanent residence/behavioral responses, the analysis of which allows controlling migration flows and improves the conditions of Ukraine’s small residential areas based on decarbonization. We conclude that energy saving systems can reduce consumption in a city by 60% and improve Ukraine’s ecosystem. Research also shows that reducing “dislocation”, in terms of population density, by creating open, innovative, eco-friendly environments based on green economy principles, can provide innovative development maps and economic, social, and cultural population growth, decreasing the load on big cities/regional economies, and encourage the restoration of sales markets and production after the COVID-19 pandemic. We developed a model to assess the innovativeness of residential areas, apply alternative methods of energy generation, and analyze the impact of the energy production and consumption market in Europe (with recommendations for the Ukraine). This article estimates energy intensity indicators of the gross domestic product in the Ukraine and in Europe, offering methods to decrease energy dependence and increase energy efficiency in the Ukraine, by adopting alternative energy sources (e.g., biohydrogen out of residues, air, and solar energy), and enhancing environmental legislation.

We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based on a novel online forecast combination of multiple point prediction models. It contains four steps: i) data cleaning and preprocessing, ii) a new holiday adjustment procedure, iii) training of individual forecasting models, iv) forecast combination by smoothed Bernstein Online Aggregation (BOA). The approach is flexible and can quickly adjust to new energy system situations as they occurred during and after COVID-19 shutdowns. The ensemble of individual prediction models ranges from simple time series models to sophisticated models like generalized additive models (GAMs) and high-dimensional linear models estimated by lasso. They incorporate autoregressive, calendar, and weather effects efficiently. All steps contain novel concepts that contribute to the excellent forecasting performance of the proposed method. It is especially true for the holiday adjustment procedure and the fully adaptive smoothed BOA approach. A winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm is presented.

The overcoming of the issues on energy crisis and inequality have become the priorities as far developing as developed countries are concerned. Moreover, energy inequality has increased due to the shortage of natural gas and rising energy prices in retaliation to the economic recovery affected by the COVID-19 pandemic. This study aims to verify the linkage between the growth of renewable energy consumption and the country’s economic advancement. In this context, this paper determines the main driving forces of renewable energy consumption in European countries during 2000–2018. The annual data for panel regression analysis are retrieved from the OECD. Stat and World Bank Open Data. This empirical analysis employed a set of estimation procedures such as the panel unit root test (Levin, Lin & Chu; Im, Pesaran, Shin W-Stat; ADF-Fisher Chi-square; and PP-Fisher Chi-square methods), the Pearson correlation, fixed- and random-effects models, generalized method of moments (GMM), Hausman and the robustness tests. The results from the Hausman test ratified that the fixed-effects regression model is more suitable for involved panel balanced data. The results of fixed-effects regression and GMM identified the statistically significant and positive relationship between the share of renewable energy consumption of total final energy consumption, GDP per capita, and CO2 emissions per capita for the overall sample. In turn, the total labor force, the gross capital formation, and production-based CO2 intensity are inversely related to renewable energy consumption. The identified effects could provide some insights for policymakers to improve the renewable energy sector towards gaining sustainable economic development.

The coronavirus pandemic (COVID-19) has led to a massive collapse in economic activity and energy demand, with the result of significant emissions reductions at a global scale. However, the existing literature investigating abatement from COVID-19 mainly overlooked the overwhelming emissions reduction in Europe's power sector. We address this by assessing the intricate relationship between electricity demand shocks and heterogeneous generation technologies in the power sectors of 16 major European economies during January to March 2020. We apply an econometric model in an instrumental-variables framework. In a first step, we assess the impact of COVID-19 infections on electricity demand, and in a second step how this translates into emissions abatement. We find that, during full lockdown, COVID-19 reduced electricity demand by 19% and carbon emissions by an astonishing 34% per hour, whereas there is severe country heterogeneity depending on the electricity supply structure and demand shock intensity. From our estimates, we predict that power sector emissions fell by 18.4% in 2020. Our results reveal the importance of a carbon price, so that a demand reduction can offset large amounts of emissions by displacing coal at the margin. We derive several policy implications from our analysis to draw lessons from the pandemic.