Knowledge and practice in Jordan regarding the outbreak and prevention of COVID-19: a questionnaire-based study

Purpose: To investigate public awareness and source of information regarding the management of the 2019 Coronavirus (COVID-19) pandemic among Jordanians. Methods: A descriptive cross-sectional online survey was conducted in Jordan during the COVID-19 outbreak from March 25th to April 29th, 2020. A validated online questionnaire addressing participants ' current awareness about COVID-19 pandemics, source of information, and perspectives of their role. Data were analyzed using Statistical Package for Social Science (SPSS) software. Descriptive analysis data were reported as mean and standard deviations for continuous variables and percentages were used for qualitative variables. P-values <= 0.05 were considered significant. Results: This study involved 409 participants that had a mean age of 26.2 +/- 8.7 years and 76.3 % were females. Nearly 67.7 % of the participants obtained their information about COVID-19 from social media, and 16.6 % from governmental agencies. Furthermore, 70.7 % of participants believed that wearing a medical face mask is not necessary to protect against COVID-19, about 95.6 % of the participants agreed to take COVID-19 detection tests when they suffer from symptoms related to COVID-19 infection, and 98.8 % agreed to visit the hospital if they have the infection. Conclusion: This survey has shown the importance of public awareness in the prevention and control of pandemic diseases. Most Jordanian participants have good knowledge of COVID-19 as a deadly disease that spreads rapidly among the population in a community. Furthermore, the people have awareness of drugs that enhance the immune system. This public awareness made Jordan one of the countries with reduced number of weekly recorded cases of COVID-19 at the beginning of the pandemic.

Social Media for Public Health: Framework for Social Media-Based Public Health Campaigns.

The pervasiveness of social media is irrefutable, with 72% of adults reporting using at least one social media platform and an average daily usage of 2 hours. Social media has been shown to influence health-related behaviors, and it offers a powerful tool through which we can rapidly reach large segments of the population with tailored health messaging. However, despite increasing interest in using social media for dissemination of public health messaging and research exploring the dangers of misinformation on social media, the specifics of how public health practitioners can effectively use social media for health promotion are not well described. In this viewpoint, we propose a novel framework with the following 5 key principles to guide the use of social media for public health campaigns: (1) tailoring messages and targeting them to specific populations-this may include targeting messages to specific populations based on age, sex, or language spoken; interests; or geotargeting messages at state, city, or zip code level; (2) including members of the target population in message development-messages should be designed with and approved by members of the community they are designed to reach, to ensure cultural sensitivity and trust-building; (3) identifying and addressing misinformation-public health practitioners can directly address misinformation through myth-busting messages, in which false claims are highlighted and explained and accurate information reiterated; (4) leveraging information sharing-when designing messages for social media, it is crucial to consider their "shareability," and consider partnering with social media influencers who are trusted messengers among their online followers; and (5) evaluating impact by measuring real-world outcomes, for example measuring foot traffic data. Leveraging social media to deliver public health campaigns enables us to capitalize on sophisticated for-profit advertising techniques to disseminate tailored messaging directly to communities that need it most, with a precision far beyond the reaches of conventional mass media. We call for the Centers for Disease Control and Prevention as well as state and local public health agencies to continue to optimize and rigorously evaluate the use of social media for health promotion.

Kalakip: A Short Animation Film about Depression in Young Adults during COVID-19 pandemic

This paper highlights the functionality of producing a 2-Dimensional animation as a visual supporting material to raise awareness about depression. The study explored the significance of United Nations Sustainable Development Goal 3: Good health and well-being, wherein the discussion emphasized the importance of managing the mental health, particularly raising public awareness about depression among young adults during the COVID-19 pandemic lockdown. The researcher followed the conceptual framework of multimedia production cycle in creating a short-Animated film. The creation of story line and visual assets described the notion of depressive disorder for audiences to have a sense of urgency, awareness, and learnings on how the young adult students become vulnerable in depression throughout the pandemic. © 2022 IEEE.

Public Awareness and Sentiment Analysis of COVID-Related Discussions Using BERT-Based Infoveillance

Understanding different aspects of public concerns and sentiments during large health emergencies, such as the COVID-19 pandemic, is essential for public health agencies to develop effective communication strategies, deliver up-to-date and accurate health information, and mitigate potential impacts of emerging misinformation. Current infoveillance systems generally focus on discussion intensity (i.e., number of relevant posts) as an approximation of public awareness, while largely ignoring the rich and diverse information in texts with granular information of varying public concerns and sentiments. In this study, we address this grand challenge by developing a novel natural language processing (NLP) infoveillance workflow based on bidirectional encoder representation from transformers (BERT). We first used a smaller COVID-19 tweet sample to develop a content classification and sentiment analysis model using COVID-Twitter-BERT. The classification accuracy was between 0.77 and 0.88 across the five identified topics. In the sentiment analysis with a three-class classification task (positive/negative/neutral), BERT achieved decent accuracy, 0.7. We then applied the content topic and sentiment classifiers to a much larger dataset with more than 4 million tweets in a 15-month period. We specifically analyzed non-pharmaceutical intervention (NPI) and social issue content topics. There were significant differences in terms of public awareness and sentiment towards the overall COVID-19, NPI, and social issue content topics across time and space. In addition, key events were also identified to associate with abrupt sentiment changes towards NPIs and social issues. This novel NLP-based AI workflow can be readily adopted for real-time granular content topic and sentiment infoveillance beyond the health context. [ABSTRACT FROM AUTHOR] Copyright of AI is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using green finance to promote global growth in a sustainable way

Green finance is gaining traction as a public policy objective. This paper summarises global and Indian progress in the field of green finance. Also, it examined the development in the field of the green finance in India, roadmap of the green finance sustainability, requirement for the significance of the green finance and Initiatives taken by the Government of India. Using a variety of data sources, we analyse public awareness (Google Trends) and financial possibilities (bank loans and bond issuances) for green initiatives. While public awareness and financing options have improved in India, our findings indicate that reducing asymmetric information through improved information management systems and increased collaboration among stakeholders could pave the way for more environmentally friendly and sustainable long-term economic growth. The need for the supply of sustainable financing to grow faster, already demonstrated by the financial gap for the sustainable development goals, has been reinforced from post-COVID19 environment, with escalation of climate issue. For this, investors, investees, middlemen and policymakers must discover methods to collaborate more effectively and with a greater sense of urgency.

Pandemic Effect of COVID-19: Identification, Present Scenario, and Preventive Measures Using Machine Learning Model

Mutation in viruses is known to be an unavoidable phenomenon. But at times, it may become a life-threatening pandemic just like in the case of the 2019 novel coronavirus, formally named as SARS-CoV-2, which consumed around 36,405 lives out of 750,890 infections as per the data available with the World Health Organization as of the end of March 2020. Found to be from the family of earlier known outbreaks (SARS and MERS) of the twenty-first century, it has now become a public health emergency of international concern (PHEIC). Countries around the world have spent millions of dollars to get a positive sign of finding vaccines, but still it remains an unsolved mystery. Even though there is implementation of strict lockdown measures from several affected countries around the globe, the trend line of COVID-19 epidemic is still increasing exponentially. Being in this scenario, this paper deals about the outbreak of 2019-nCoV and its structure, growing stages, global statistics, transmission modes, and most possible precautionary methods and also its emphasis on creating public awareness by answering few key clarifications about novel beta coronavirus disease. The machine learning method used in this study was taught using records from COVID-positive tests. Results from a week were included in the testing set (individuals who were confirmed to have COVID-19). This proposed model predicted the COVID-19 lab findings with high accuracy by only using eight numeric data, age 60, knowing contact with an infected individual, and the existence of five early clinical signs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Information Technology and Digital Sufficiency for Building the Sustainable Circular Economy

Information technologies possess the significant potential to improve the efficiency of resources and optimize energy usage, as well as make a significant contribution to the sustainable circular economy (CE). The concept of digital sufficiency provides a framework for understanding how information technology can be part of significant achievements in the circular economy, especially when embraced by business companies. Moreover, the possibility of the implementation of closed-loop resources has become possible with the development of digital manufacturing technologies. However, the research of establishing the CE in SMEs, especially in fossil-energy-abundant countries, such as the Russian Federation, is quite limited. Our paper fills in this gap by studying the adoption of CE practices as well as the investments for promoting CE in Russian SMEs through such factors as the existence of R&D, bank loans, and access to grants at the national and international level. It achieves this based on the data sample of 314 managers of Russian SMEs. Our results demonstrate that the investment or existence of R&D in SMEs and knowledge of CE as well the governmental funding and access to wider markets all together tend to have a significant and positive effect on implementing and investing into CE in SMEs, while the administrative barriers yield a small but negative effect. These results might be helpful for the relevant stakeholders in order to identify factors catalyzing attention from both the SMEs engaged in CE transitions, as well as help the decision makers wishing to foster the transformation of the SMEs to a circular economy. We can conclude that supporting SMEs (both financially and via increasing their public awareness) to make their own transitions towards CE has a societal effect that can speed up a greener transition and significantly contribute to increasing energy efficiency.

TECHNICAL BRIEFING:ANTIMICROBIAL FINISHES Protecting against pathogens, viruses and bacteria

The Covid-19 pandemic has sparked global fretfulness. The concern is that the virus is spreading too far, too fast and medical scientists can't seem to find a way to contain it. Juan Dumois, a pediatric infectious-diseases physician at Johns Hopkins All Children's Hospital in St. Petersburg, Florida reported that "coronavirus in general will last a lot longer on a solid, nonporous surface compared to porous fabrics". He also suggested they would survive for longer on artificial fibres such as polyester rather than cotton, which is one of the mediums for spreading infection. In concurrence with an increasing public awareness of infectious diseases, the textile industry, including Sarex, would like to re-introduce two of its effective antimicrobial agents which are effective against a broad spectrum of microbes, pathogens and viruses. In this study, Sarex have treated various textile substrates - cotton, polyester and polyamide fabrics - with these anti-microbial agents and have tested them for durability using the AATCC100 test method. The results are very encouraging and can help in controlling the spread of the infections, thus contributing to the well-being of humankind. © 2020 World Textile Information Network. All rights reserved.

Impact of media messages on containment of Coronavirus pandemic in Nigeria.

Background: Different countries adopted various measures to stop the spread of COVID-19. In Nigeria, the federal government, through the Presidential Task Force on the pandemic and some non-governmental organizations, mounted vigorous public enlightenment and education campaign through the media to contain the spread of the disease. Objective: This article examined the impact of that effort by assessing the level of public awareness, perception, and satisfaction the campaign generated. Method: A cross-sectional design and purpose sampling technique were used for the study. Questionnaires were distributed online through personal and group platforms on Whatsapp and Telegram applications. This technique ensured that only the users of these applications responded to the questionnaire. The national survey returned 359 responses. Results: The results indicated a high level of public awareness from the media messages as 89.08% of respondents heard about COVID-19 from the media messages, 87.74% believed that media messages about the pandemic increased their awareness of it and 90.81% of respondents got influenced by the media messages to adjust to safety protocols against the disease. Majority of the respondents (75.49%) were satisfied with the overall performance of the media in their sensitization campaign. While 49.03% benefitted to a very large extent from the media messages, 44.01% benefitted to a large extent. Conclusion: The results showed that the impact of the media awareness messages on COVID-19 was high and that Nigerian media contributed immensely to reducing the spread of the disease in the country.

Health awareness of rural households towards COVID-19 pandemic in India: Evidence from Rural Impact Survey of the World Bank.

This paper aims at analysing the level of awareness of the symptoms and the methods of protection from COVID-19 based on the Rural Impact Survey of the World Bank, collected from 5200 households belonging to six states in India that is, Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh. Data has been analysed using chi-square test and regression analysis. Results of the analysis indicate that about 70.8% rural households are aware of the symptom of coronavirus, and 81.9% are aware of the preventive measures for controlling the spread of COVID-19. Analysis indicates a significant association between awareness level on symptoms and prevention of COVID-19 and socio-demographics and location. The study further analyses the key determinants of awareness of COVID-19 symptoms and preventive measures using the logistics regression model, indicating that age, gender, education, income, poverty status, access to information, cash relief and medical services are the determining factors of health awareness on COVID-19 pandemic among rural households in India. Considering the importance of self-protecting measures in fighting the pandemic, this paper highlights the importance of strengthening public awareness for containing the spread of the COVID-19 pandemic.

The Impact of Social Media Shared Health Content on Protective Behavior against COVID-19.

The use of social media has increased during the COVID-19 pandemic because people are isolated and working from home. The use of social media enhances information exchange in society and may influence public protective behavior against the COVID-19 pandemic. The purpose of this study is to identify the factors affecting public protective behavior when relying on COVID-19 pandemic-related content shared on social media. A model based on Protection Motivation Theory (PMT) was proposed and validated using a quantitative survey approach. A questionnaire was distributed to random respondents, and 488 responses were received and analyzed using Smart-PLS software. The findings showed that perceived risk, e-health literacy, public awareness, and health experts' participation influence public protective behavior when using social media to share COVID-19-relevant content. The outcomes of this study can enhance government agencies' and public health care authorities' understanding of how to use social media to raise awareness and reduce panic among the public.

Adopting Haar Cascade Algorithm on Mask Detection System Based on Distance

COVID-19 which has hit almost the whole world, including Indonesia, which has become an epidemic in early 2020. Many cities and districts have enforced to comply with health protocols by using masks. All cities and regencies in South Sumatra are also required to follow health protocols by wearing masks and maintaining distance. So that the Mask Detection System program is a way to overcome public awareness, especially Bina Darma University that the importance of using masks today. In the case of making this mask detection system program using Python and using the Haar Cascade Algorithm. From experiments using the Haar Cascade method, the results show that this system can detect people who use masks and do not use masks. This test is also done by inputting images or videos. Futhermore, in testing this detection system, the approximate distance and angle also need to be considered because it will be very influential. © 2022 IEEE.

How did the Tokyo Metropolitan Government respond to COVID-19?

Tokyo Metropolitan Area is the most populous metropolitan area in the world. While cities around the world are struggling to cope with COVID-19, the number of new positives and deaths in Tokyo has so far been relatively contained compared to other large metropolitan areas. In Japan, infection control measures do not prohibit people from moving around during a COVID-19 outbreak. However, people are not only refraining from travel and social activities at the request of the government, but are also using their own judgment to avoid risk based on information about the infectious disease. This plays an extremely important role in Japans infection control measures. Expectations are high in Japan for maintaining the health care system and minimizing deaths. It is necessary to steadily respond to these expectations while normalizing social functions.

Evaluating Knowledge, Awareness, and Practices among Pakistani Population regarding COVID-19: An Online Survey-Based Study

Aim The study aims to analyze the knowledge, awareness, and practices among the Pakistani population. Study Method This is an online survey-based study conducted in July 2020 among the general public of Pakistan. Pretested and structured self-administered questionnaire, designed on Google Forms Inc., was used to collect data. The questionnaire included sociodemographic and measurable coronavirus disease 2019 (COVID-19) knowledge data. Assessments on participants' attitudes and practices toward COVID-19 included questions on transmission, symptoms knowledge, and preventive measures. Results Among the survey completers ( n = 962) 61% ( n = 590) were male and 39% ( n = 372) were female. The majority of participants is aged 18 to 25 and belonged to Punjab. The participants had moderate knowledge regarding disease origin, clinical features, symptoms, and prevention. A vast majority of participants had good knowledge of symptoms and prevention measurements. A good number of participants were also practicing precautionary measures. The majority of participants utilize media and government authorities as authoritative sources of information. The population was also satisfied with the information given by the government. Conclusion Participants have moderate knowledge regarding the COVID-19 pandemic, reflected by a positive attitude and safe practices. There are gaps in knowledge of the virus, its origin, and transmission. Rumors affect the psychology of people, which may lead to the worst situation of panic conditions.

An SEM-ANN approach to evaluate public awareness about COVID, A pathway toward adaptation effective strategies for sustainable development.

This study was conducted to evaluate public awareness about COVID with aimed to check public strategies against COVID-19. A semi structured questionnaire was collected and the data was analyzed using some statistical tools (PLS-SEM) and artificial neural networks (ANN). We started by looking at the known causal linkages between the different variables to see if they matched up with the hypotheses that had been proposed. Next, for this reason, we ran a 5,000-sample bootstrapping test to assess how strongly our findings corroborated the null hypothesis. PLS-SEM direct path analysis revealed HRP -> PA-COVID, HI -> PA-COVID, MU -> PA-COVID, PM -> PA-COVID, SD -> PA-COVID. These findings provide credence to the acceptance of hypotheses H1, H3, and H5, but reject hypothesis H2. We have also examined control factors such as respondents' age, gender, and level of education. Age was found to have a positive correlation with PA-COVID, while mean gender and education level were found to not correlate at all with PA-COVID. However, age can be a useful control variable, as a more seasoned individual is likely to have a better understanding of COVID and its effects on independent variables. Study results revealed a small moderation effect in the relationships between understudy independent and dependent variables. Education significantly moderates the relationship of PA-COVID associated with MU, PH, SD, RP, PM, PA-COVID, depicts the moderation role of education on the relationship between MU*Education->PA-COVID, HI*Education->PA.COVID, SD*Education->PA.COVID, HRP*Education->PA.COVID, PM*Education -> PA.COVID. The artificial neural network (ANN) model we've developed for spreading information about COVID-19 (PA-COVID) follows in the footsteps of previous studies. The root means the square of the errors (RMSE). Validity measures how well a model can predict a certain result. With RMSE values of 0.424 for training and 0.394 for testing, we observed that our ANN model for public awareness of COVID-19 (PA-COVID) had a strong predictive ability. Based on the sensitivity analysis results, we determined that PA. COVID had the highest relative normalized relevance for our sample (100%). These factors were then followed by MU (54.6%), HI (11.1%), SD (100.0%), HRP (28.5%), and PM (64.6%) were likewise shown to be the least important factors for consumers in developing countries struggling with diseases caused by contaminated water. In addition, a specific approach was used to construct a goodness-of-fit coefficient to evaluate the performance of the ANN models. The study will aid in the implementation of effective monitoring and public policies to promote the health of local people.

Assessing the spatial variability of raising public risk awareness for the intervention performance of COVID-19 voluntary screening: A spatial simulation approach.

The rapid spread of a (re)emerging pandemic (e.g., COVID-19) is usually attributed to the invisible transmission caused by asymptomatic cases. Health authorities rely on large-scale voluntary screening to identify and isolate invisible spreaders as well as symptomatic people as early as possible to control disease spread. Raising public awareness is beneficial for improving the effectiveness of epidemic prevention because it could increase the usage and demand for testing kits. However, the effectiveness of testing could be influenced by the spatial demand for medical resources in different periods. Spatial demand could also be triggered by public awareness in areas with two geographical factors, including spatial proximity to resources and attractiveness of human mobility. Therefore, it is necessary to explore the spatial variations in raising public awareness on the effectiveness of COVID-19 screening. We implemented spatial simulation models to integrate various levels of public awareness and pandemic dynamics in time and space. Moreover, we also assessed the effects of the spatial proximity of testing kits and the ease of human mobility on COVID-19 testing at various levels of public awareness. Our results indicated that high public awareness promotes high willingness to be tested. This causes the demand to not be fully satisfied at the peak times during a pandemic, yet the shortage of tests does not significantly increase pandemic severity. We also found that when public awareness is low, concentrating on unattractive areas (such as residential or urban fringe areas) could promote a higher benefit of testing. On the other hand, when awareness is high, the factor of distances to testing stations is more important for promoting the benefit of testing; allocating additional testing resources in areas distant from stations could have a higher benefit of testing. This study aims to provide insights for health authorities into the allocation of testing resources against disease outbreaks with respect to various levels of public awareness.

Managing the COVID-19 in Canada: Exploring Twitter Communication by Government and Health Officials

Since the unfolding of the COVID-19 virus as a global health crisis that threatens public health, government and health officials in Canada and in many other countries used Twitter as an instrument for health communication. It has been a relevant mean for informing and raising public awareness about precautionary measures to better mitigate the pandemic. In Canada, governments and public health institutions at federal, provincial and territorial levels have been using Twitter to spread COVID-19 related public health information to their citizens during the first, second and third waves of the pandemic. In this study, we aimed to investigate the use of Twitter by governments and health institutions at the Federal government of Canada, and Canadian provinces and territories. Specifically, our main purpose is to explore insights from Twitter online public discourse harnessed by the government and the public health institutions in Canada through their official accounts. These insights will be studied from three analysis: Activity, engagement, and trends. To do so, we collected 32,198 tweets published from a total of 62 government (i.e. 29 Twitter accounts) and health accounts (i.e. 33 Twitter accounts) including institutions (e.g ministries) and officials (e.g Prime Ministers accounts) Twitter accounts between 01 September 2020 and 31 August 2021. Our results show that the health and government institutions have been more active during the third wave of the pandemic than the second wave. The results also show that among all the Twitter accounts, the federal representatives and the representatives of the provinces of Ontario, Alberta, and British Columbia respectively have been more active. Finally, the results demonstrate that the Twitter users in Canada have been more engaged with the government accounts at the federal level than at the provinces and territories level. © 2022 ACM.

CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

With the proliferation of smart devices and widespread Internet connectivity, social sensing is advancing as a pervasive sensing paradigm where experiences shared by individuals on social platforms (e.g., Twitter and Facebook) are analyzed to interpret the physical world. In this article, we introduce CovidTrak, a vision of social intelligence-empowered contact tracing that aims to scrutinize the knowledge derived using social sensing to track Coronavirus Disease 2019 (COVID-19) infections among the general public. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemes are existent today (e.g., in-person and phone interviews, paper forms, email and web-based questionnaires, and smartphone apps), they often require active user participation and might miss certain cases of social interactions that go off-the-records but still lead to COVID-19 transmission. By contrast, social sensing provides an alternative avenue for spontaneously determining such contacts by harnessing the rich experiences and information conveyed by people on social data platforms (e.g., a group photograph tweeted from a house party with a potential contact). As such, CovidTrak can form a powerful basis to combat the COVID-19 pandemic. The vision of CovidTrak intends to answer the following questions: 1) how to bolster the privacy and security of the online users while determining their contacts? 2) how to collect relevant social signals that indicate in-person encounters among people? 3) how to reliably process the vast amount of noisy data from social platforms to identify chains of transmission? 4) how to handle the scarcity of location metadata in the incoming data? 5) how to effectively communicate crucial contact information to concerned individuals? and 6) how to model and handle the responses of the common people toward contact information? We envision unexplored opportunities to leverage multidisciplinary techniques to address the above questions and develop effective future CovidTrak schemes.

Mode Choice Modeling for Sustainable Last-Mile Delivery: The Greek Perspective

As the private sector is under heavy pressure to serve the ever-growing e-commerce market, the potential of implementing new disruptive mobility/logistics services for increasing the level of the current last-mile delivery (LMD) services, is emerging. Vehicle automation technology, characterized by high-capacity utilization and asset intensity, appears to be a prominent response to easing this pressure, while contributing to mitigation of the adverse effects associated with the deployment of LMD activities. This research studied the perceptions of Greek end-users/consumers, regarding the introduction of autonomous/automated/driverless vehicles (AVs) in innovative delivery services. To achieve this, a mixed logit model was developed, based on a Stated Preferences (SP) experiment, designed to capture the demand of alternative last-mile delivery modes/services, such as drones, pods, and autonomous vans, compared to traditional delivery services. The results show that the traditional delivery, i.e., having a dedicated delivery person who picks up the parcels at a consolidation point and delivers them directly to the recipients while driving a non-autonomous vehicle—conventional van, bike, e-bike, e-scooter—remains the most acceptable delivery method. Moreover, the analysis indicated that there is no interest yet in deploying home deliveries with drones or AVs, and that participants are unwilling to pay extra charges for having access to more advanced last-mile delivery modes/services. Thus, it is important to promote the benefits of innovative modes and services for LMD, in order to increase public awareness and receptivity in Greece.

RES Market Development and Public Awareness of the Economic and Environmental Dimension of the Energy Transformation in Poland and Lithuania

Energy transformation in the European Union countries is progressing. Its scope is defined by formal and legal regulations and its effectiveness by the position of decision-makers, legitimised by public support for a particular type of challenge. Both issues are the focus of this article. The promotion of environmental protection measures is currently strongly promoted globally. Hence the widespread acceptance in principle of the changes associated with the implementing of the Green New Deal in the energy sector is not surprising. However, to what extent is knowledge of the solutions constituting the mainstream transition (renewable energy sources) ingrained among communities? Does the level of public awareness influence individual consumer choices, modelling the market? The threads outlined above inspired deliberations focused on analysing the assumptions behind energy transition in the EU, with particular reference to the countries directly bordering the line of the ongoing conflict in Ukraine (Poland, Lithuania), in the light of the resulting and escalating restrictions exacerbating the energy crisis. The immediate neighbourhood of the adopted countries, and their similar socio-economic conditions, provided the basis for comparisons and conclusions. The motivation for the choice of the issue and research area was to fill the clear information gap in this study area, strictly in relation to the adopted configuration of these countries. The research proceedings in the outlined area were primarily based on the methodology appropriate for capture and analysis of economic phenomena, enriched with the results of our own findings (questionnaire survey regarding general knowledge of the ZE market and consumer preferences), in order to assess the economic and environmental dimensions of energy transition in Poland and Lithuania and to assess the level of public awareness in this respect in the countries under study. The presented research is an important complementary element of the authors’ series of studies devoted to the analysis of the development of the renewable energy market in Poland and the Baltic States, related to the individual dimensions of RES. Their results give rise to the conclusion that increased social awareness in these countries determines the popularisation of RES solutions in individual use, regardless of their type, stimulating the progress of the energy transformation process.