• Energy Research
• 13. Climate action close
• 7. Clean energy close
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The social confinement resulting from the COVID-19 crisis temporarily reduced greenhouse gas emissions. Although experts contend that the decrease in pollution rates was not drastic, some surveys detect growth in social concern about the climate. In this new climate-conscious environment, municipalities and local governments are promoting a new way of living and caring for cities, even before they can regain national and international freedom of movement. This work analyzes the connections between new climate awareness arising from the COVID-19 crisis, proposals of sustainable citizenship around the world, and its communication on Twitter to educate the new eco-conscious audience. The methodology mixes quantitative and qualitative analysis, using the Twitonomy Premium tool and the Twitter research tool with data extracted at the end of December 2020. Among the top ten most influential and active accounts, the results show educational institutions, local institutions, companies, neighborhoods, associations, and influencers. The impossibility of living in the city has not prevented citizen education and commitment to make real change for when that city and its citizens return to normality. However, this new normality must be different: more ecological, more responsible, more sustainable, and practiced from early childhood.

This paper analyzes the co-movements of prices of fossil fuels, energy stock markets and EU allowances. This analysis is conducted in order to identify the spillover effect of volatility and correlation among these financial markets, and to provide a scientific basis that shows the interest of incorporating sustainable assets in the design of minimum risk strategies of investment. To achieve this goal, we have used a Vector Autoregressive-Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (VAR-DCC-GARCH) model that also incorporates a stock index of industrial companies as a leading indicator of the level of economic activity. In addition, the paper conducts an impulse response analysis to determine how unexpected shocks to prices are propagated along time, and, in particular, how they affect prices of the others, both in mean, variance and correlation. Therefore, the results of this one- and two-dimensional analysis allow for the study of short and long run dynamics of the relationship among those prices, thus, providing greater meaning and information for investors, which has implications for building their portfolios. The analyzed period was from January 2010 to February 2021, so that the data include half of phase II, full phase III and the onset of phase IV of the EU ETS, as well as the COVID-19 outbreak in the European context. We also analyzed whether the EUA price impulses the demand of clean energy stocks, which has important implications for the objective of triggering the investment in clean energy. Our results show the transmission mechanism of all of those prices, which are relevant not only for investors but also for policymakers to construct an early-warning system, revealing the most important transmission channels. Moreover, from an investment viewpoint, we observe a decline in dirty energies and a rise in the clean energy market, which might be an indication of the progress towards the energy transition to renewables sources within a circular economy perspective. Therefore, this shows that the EU ETS is achieving its goals, and that clean energy companies, aligned with their role towards socially responsible initiatives, are also gaining acceptance in terms of investments, which would be beneficial for the environment.

In the near future, the year 2050, agricultural production should expand to fulfill the needs of approximately 9.7 billion inhabitants. Such an objective should be harmonized with social, economic, and environmental sustainability aspects to maintain safe food production and food security worldwide. For more than a year, the COVID-19 pandemic has raised and is still strongly disrupting the agro-livestock production sector, similar to several other economic sectors. In this sector, the relationships between suppliers, producers, and consumers should always continue to maintain the activity of the production chain, which are impaired by social distancing decisions taken following the emergence of the COVID-19 pandemic. In this study, a global cross-sectional survey (translated into four languages: Arabic, English, French, and Spanish) was shared with people belonging to the agricultural sector to identify: (1) the role of the agricultural information and communication technologies (ICTs) in agro-livestock farming systems sustainability during the period of COVID-19 pandemic, (2) the need for such technologies in the agricultural sector, and (3) the factors that affect the use of such technologies. The results showed that the most frequently used agricultural ICTs were social media (Facebook and/or WhatsApp; 27.3%) and online platforms and Internet services (26.3%), whereas robotic vehicles and/or drones (6.6%) were less frequently used. During the emergence of the pandemic, the major reasons impacting agro-livestock farming systems’ sustainability were social distancing (30.0%), shortage of labor (17.7%), maintaining precision farm management (14.8%), product marketing (14.2%), access production inputs (7.2%), and others (16.1%). Applying agricultural ICTs solved many obstacles related to the production process, such as maintaining precision farm management (25.6%), product marketing (23.6%), and access production inputs (16.1%). The subgroup analyses of the results considering the degree of country advancement, size of agribusinesses, and role/position of respondents in the farm highlighted the importance of supporting the use, availability, and awareness of agricultural ICTs at least for some groups of people such as those belong to developing countries, laborers, and small-scale agri-business holders. This cross-sectional study highlights the urgent need to turn to and to expand the use of new agricultural ICTs to meet the growing demand for food production in the world and to ensure the resilience and sustainability of farming systems, specifically under unexpected and extreme conditions.

The Covid-19 crisis has had a major impact on electricity markets, affecting power plant input and output prices. In this paper Spanish electricity and natural gas prices and international carbon prices are used to calculate the variable margin of natural gas combined cycles (NGCC), i.e. the Clean Spark Spread (CSS). The stochastic behavior of the CSS is modeled using an Ornstein-Uhlenbeck (OU) process because of its properties. The expected first semester 2020 CSS results based on the fitted model with daily 2016-2019 data, taking the end of 2019 as a starting point, are compared with the actual figures for the same period. In the first half of 2020 electricity and natural gas prices are significantly lower than expected at the end of 2019, but carbon allowance prices have decreased less in percentage terms. The monthly CSS values in the first half of 2020 are significantly lower than expected for March-May 2020, with the April value being -€4.15/WWh lower figure than the €7.16/MWh expected. This work calculates distributions of daily and monthly CSS values. This research is supported by the Basque Government through the BERC 2018–2021 programme and by the Spanish Ministry of the Economy and Competitiveness (MINECO) through BC3 María de Maeztu excellence accreditation MDM-2017-0714 . Further support is provided by the project MINECO RTI 2018-093352-B-I00 .

The electricity sector was negatively impacted by the coronavirus disease (COVID-19), with considerable declines in consumption in the initial phase. Investors were in turmoil, and stock prices for these companies plummeted. The aim of this paper is to demonstrate the significant negative influence of the pandemic on abnormal returns for the electricity sector, specifically for traditional and renewable companies and the influence of ESG scores, using the event study approach and multi-variate regressions. Our results show that the pandemic indeed had a negative impact on the electricity sector, with renewable electricity companies suffering a sharper decline than traditional ones. Moreover, we find that ESG pillar scores affected electricity companies differently and are sector-specific. For renewable electricity companies, the returns were positively influenced by the environmental ESG scores and negatively by governance ESG scores.

[EN] Fighting against climate change and global warming is one of the biggest challenges faced by the Maritime Industry nowadays to make the supply chain greener and environmentally sustainable. Cutting greenhouse gases (GHG) emissions and decarbonizing the international shipping has been a paramount activity for the International Maritime Organization (IMO) since the first set of international mandatory measures to improve ships' energy efficiency and reduce CO2 emissions per transport work, as part of the International Convention for the Prevention of Pollution from Ships (MARPOL) released in 2011. Besides that, changes in consumption habits around the globe (i.e., digitalization and growth of e-commerce) plus disruptive events like the COVID-19 or the blocking of the Suez Canal, to name only a few, have also highlighted the need for building more resilient maritime transport networks. In this work, a pragmatical analysis of the principal machine learning algorithms has been carried out to provide a qualitative prediction of the Estimate Time of Arrival (ETA) of container vessels applied to short-sea shipping where the distance between ports is reduced. By exploiting both, the Automatic Identification System (AIS) and meteorological data gathered over a desired area of interest, the developed approach delivers a model capable of predicting the ETA of ships where the reaction time of the stakeholders involved in the management of the Port Call is very reduced (i.e., less than two hours of sailing between ports) and therefore, tolerance for error is low. Very positive results were obtained for the training dataset collected under real conditions for more than a year. The best results were obtained by the RF model with a Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 11.31 and 19.56 minutes respectively. This work has been developed under the framework of the COSIBAS project (funded by CDTI research and innovation programme under grant agreement No.EXP 00110912/INNO20181033).

Since the emergence of the virus that causes COVID-19 (the SARS-CoV-2) in Wuhan in December 2019, societies all around the world have had to change their normal life patterns due to the restrictions and lockdowns imposed by governments. These changes in life patterns have a direct reflection on energy consumption. Thanks to Smart Grid technologies, specifically to the Advance Metering Infrastructure at secondary distribution network, this impact can be evaluated even at the customer level. Thus, this paper analyzes the consumption behavior and the impact that this crisis has had using Smart Meter data. The proposed approach includes the selection and normalization of features, automatic clustering, the obtaining of the estimated consumption without considering the crisis (at short and mid-terms) and the impact evaluation. The proposed approach has been tested on a case with a real Smart Meter infrastructure from Manzanilla (Huelva, Spain). The results of this use case showed that residential customers have increased their consumption around 15% during full lockdown and 7.5% during the reopening period. In contrast, globally, non-residential customers have decreased their consumption 38% during full lockdown and 14.5% during the reopening period. However, referring to non-residential customers, five different consumption profiles were found with different short-term and mid-term behaviors during the COVID crisis. The different behavior found shows customers who have maintained their normal consumption during the lockdown, others who have reduced it (to a greater or lesser extent) and have not recovered it after the removal of the restrictions, and others who have reduced the consumption but then they recovered it when the restrictions were removed. The metadata of the customers in each behavior cluster found are highly correlated to the restrictions imposed to control the spread of the virus. This study shows evidence about the proposed approach usefulness to analyze the behavior and the impact at customer level during the COVID-19 crisis.