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All these changes transformed several human paradigms, among the recent changes of the contemporary age, we found: secular education, and virtual education, the latter became potentialized thanks to a global event. Thanks to this event, digital tools were essential for the intellectual and economic development of the world, among those used was the e-learning platform that allowed teachers and students to interact with each other. [...]there coexisted countless homes in which buying a technological device or hiring internet service was not possible, either because of the economic situation or geographical location, they could not access virtual classes, which led them to make monetary sacrifices such as: loans or mobilizing to places where the signal could reach. Keyword: Digital tools;virtual education;digital platforms;social networks. 1.Introducción Desde el comienzo de la historia de la humanidad, nuestra especie se caracterizó por la capacidad del racionamiento, lo que nos diferencia de los otros animales por el hecho de saber interpretar y preguntamos constantemente el porqué de las cosas.
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The current COVID-19 pandemic has caused a significant decline in human mobility during the past three years. This may lead to reconfiguring future tourism flows and resulting transformations in the geographic patterns of economic activities and transportation needs. This study empirically addresses the changes in tourism mobility caused by the pandemic. It focuses on the yet unexplored effects of the destination type on tourism volume change. To investigate this, 1426 metropolitan, urban/resort and dispersed destinations were delimited based on Airbnb offers. Airbnb reviews were used as the proxy for the changes in tourist visits in 2019–2022. Linear mixed-effects models were employed to verify two hypotheses on the differences between the effects of the pandemic on three kinds of tourism destinations. The results confirm the tourism de-metropolisation hypothesis: metropolitan destinations have experienced between −12.4% and −7.5% additional decreases in tourism visits compared to secondary cities and resorts. The second de-concentration hypothesis that urban/resort destinations are more affected than dispersed tourism destinations is not supported. The results also confirm that stricter restrictions and destination dependence on international tourism have negatively affected their visitation. The study sheds light on post-pandemic scenarios on tourism mobility transformations in various geographic locations.
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Since 2019, the year of the COVID-19 outbreak, many businesses have been shut down across different industries and geographical locations, and the construction industry is one of those that has suffered more than any other due to its volatile nature. This study emphasizes the impact of COVID-19 on project management within construction companies and the ways it has affected the completion of projects. The literature review was provided with effective theoretical information regarding the research topic by highlighting key concepts, theories, and models after identifying crucial aspects that resulted in cost and time overruns in the context of UK construction projects. This study has been conducted as a primary qualitative method to gather firsthand data employing interviews with five construction project managers from the UK. The researcher has discussed interpretivism research philosophy, the inductive research approach, and explanatory search design and has conducted purposive sampling. In addition, primary qualitative data collection and thematic data analysis have been discussed in this study. The research has identified 10 types of themes focused on the transcript, which were developed through interviews. Further, a detailed comparative discussion was made on three top themes, which were based on cost overrun, and time extension of construction projects during COVID-19. The interviewees have outlined the significant impact of COVID-19 on supply chain management and the labor force. The multimethod approach helps in understanding the diverse point of view across different countries and geographical regions and finally reaching a conclusion with a comparative approach.
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PurposeThe main objective of this paper is to provide a systematic literature review (SLR) and structured insight into last mile delivery, ultimately identifying gaps in current knowledge and proposing a framework for future research direction in terms of sustainability in the area.Design/methodology/approachThis paper identifies and synthesizes information from academic journals and examines "Journals and Publishing place,” "Geographic location,” "Year of Publication,” "University and Author Affiliation,” "Themes and Sub-themes,” "Theory,” "Research Design, Methods and Area” and "Industry Involvement.” A collection of online databases from 2005 to 2020 were explored, using the keywords "Last mile delivery,” "Last mile logistics,” "Last mile transportation,” "Last mile fulfillment,” "Last mile operations” and "Last mile distribution” in their title and/or and/or keywords. Accordingly, a total of 281 journal articles were found in this discipline area, and data were derived from a succession of variables.FindingsThere has been significant growth in published articles concerning last mile delivery over the last 15 years (2005–2020). An in-depth review of the literature shows five dimensions of the last mile: last mile delivery, transportation, operations, distribution and logistics. Each of these dimensions is interrelated and possess clustered characteristics. For instance, last mile operations, last mile transportation and last mile delivery are operational, whereas last mile distribution is tactical, and last mile logistics possess strategic characteristics. The findings also indicate that even though the sustainability concept can be incorporated into all levels of the last mile, the current literature landscape mainly concentrates on the operational level.Research limitations/implicationsThis review is limited to academic sources available from Emerald Insight, Science Direct, Taylor and Francis, Springer, MDPI and IEEE containing the mentioned keywords in the title and/or /or keywords. Furthermore, only papers from high-quality, peer-reviewed journals were evaluated. Other sources such as books and conference papers were not included.Practical implicationsThis study dissects last mile delivery to produce a framework that captures and presents its complex characteristics and its interconnectedness with various related components. By analyzing last mile delivery in its entirety, the framework also helps practitioners pinpoint which levels of last mile delivery (operation, tactical or strategic) they can incorporate the concept of sustainability.Originality/valueThe research findings enrich the contemporary literature landscape and future work by providing a conceptual framework that incorporates the "economic,” "environmental” and "social” pillars of sustainability in all dimensions of the last mile delivery.
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In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.
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The World Health Organization (WHO) declared the status of coronavirus disease 2019 (COVID-19) to a global pandemic on March 11, 2020. Since then, numerous statistical, epidemiological and mathematical models have been used and investigated by researchers across the world to predict the spread of this pandemic in different geographical locations. The data for COVID-19 outbreak in India has been collated on daily new confirmed cases from March 12, 2020 to April 10, 2021. A time series analysis using Auto Regressive Integrated Moving Average (ARIMA) model was used to investigate the dataset and then forecast for the next 30-day time-period from April 11, 2021, to May 10, 2021. The selected model predicts a surge in the number of daily new cases and number of deaths. An investigation into the daily infection rate for India has also been done. © 2023 The authors and IOS Press.
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A stochastic model for individual immune response is developed. This model is then incorporated in a larger simulation model for the spread of COVID-19 in a population. The simulator allows random transitions between being susceptible, exposed, having mild or severe symptoms, as well as random non-exponential sojourn times in those states. The model is more efficient than others based on geographical location, where the virus spreads according to actual distance between individuals. We are able to simulate much larger populations and vary parameters such as time between vaccinations, probability of infection, and so on. We present an application to study the effects on healthcare as a function of vaccination policies. © 2022 IEEE.
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To compete with the speedy revolution of high technological innovation and restarted economy for the post-COVID-19 period in China, governments and organizations should be active in attracting high-tech talent to enhance independent and indigenous R&D capability. Talent agglomeration effectiveness is the strongest endogenous force pushing competitiveness for regional economy and industrial development. Due to the complexity of high-tech talent agglomeration, there are still considerable gaps to evaluate the incentive factors. This study evaluates the influential indicator system by using a hybrid fuzzy set theory extended Analytic Hierarchy Process (AHP) approach for proximity to reality from individual, organizational and environmental dimensions. The statistical analysis is adopted to verify the results of fuzzy AHP analysis. This research explores the founding that individual incentives are more important than environmental factors, and environmental incentives are more influential than organizational incentives. Job satisfaction, welfare system, and geographical location are the highest ranking factors. High-tech start-ups should give priority to combine geographical location with political support to reserve site selection or firm relocation for a great effectiveness of high-tech talent agglomeration. © 2022 Elsevier Ltd
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In light of global climate change and the biodiversity crisis, making cities more resilient through an adjusted design of urban green and blue spaces is crucial. Nature-based solutions help address these challenges while providing opportunities for nature experiences, and providing cultural ecosystem services that support public health. The COVID-19 pandemic and its associated stressors highlighted the interrelated socio-ecological services provided by nature-based solutions like urban green and blue spaces.This pan-European study therefore aimed to enhance the socio-ecological understanding of green and blue spaces to support their design and management. Using an online survey, green and blue space preferences, usage, and pandemic-related changes in greenspace visit and outdoor recreation frequencies were examined.Greenspace visit and outdoor recreation frequencies were associated with respondents' (N = 584 from 15 countries) geographical location, dominant type of neighbourhood greenspace and greenspace availability during the pandemic, but not greenspace perceptions or sociodemographic background.Greenspace visit and outdoor recreation frequencies were generally high;however, Southern Europeans reported lower greenspace visit and outdoor recreation frequencies both before and during the pandemic than Northern Europeans. Many Southern Europeans also reported having few neighbourhood greenspaces and low greenspace availability during the pandemic.The most common outdoor recreational activity among respondents before the pandemic was walking or running with the most frequently stated purpose of time spent outdoors being restorative in nature (i.e. relaxing or calming down). Most Europeans had positive perceptions of green and blue spaces with preferences for structurally diverse and natural or unmanaged green elements.This highlights the importance of accessible green and blue spaces both in everyday life and during times of crisis. Stakeholders, their preferences, and regional and cultural differences should be included in the co-design of urban green and blue spaces to maximize their potential for both people and nature.Read the free Plain Language Summary for this article on the Journal blog.
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COVID-19 is not the last virus;there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims an HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. The proposed STRENUOUS system discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject's cross-path can be infected and suggest quarantine followed by testing. The proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus.
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It is no secret that Covid-19 is a deadly pandemic. And in a bid to break the chain of transmission, mask-wearing is a widely approved practice outlined by various health experts and the WHO. However, humans often ignore to behave appropriately in response to the pandemic. It has been found that many people do not wear the masks that essentially increases the risk of spreading of the deadly virus in crowded areas. This paper proposes a deep learning-based approach to understand the general behaviour of Indian population across major cities during the onset and continuation of the Covid-19 pandemic. We have proposed a face-mask detection guided method to evaluate the risk factors of various geographical locations (mainly a few important cities of India). Initially, a deep learning-based face mask detection has been proposed to detect the persons without masks, with proper masking behaviour, and with improper masking behaviour. The algorithm takes the image of a public place (e.g. congregated area) as input and detects the human faces in the image with appropriate masking behaviour, We then introduce a new graph-based algorithm to calculate the risk of transmission of the disease based on the mask-wearing behavior of people. In this manner, we intend to keep track of, and analyse, the amount of risk of the spread of Covid-19 over a given time period. Our analysis on several Indian cities show that there are certain links between the daily Covid-19 cases and the mask wearing behaviour. © 2022 IEEE.
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Since the COVID-19 pandemic broke out in early 2020, the global community has been living in fear, stress, and isolation. The COVID-19 vaccine might provide a solution to the ongoing global crisis. This study seeks to monitor the trends of depression that have been discussed on Twitter before and after the COVID-19 vaccine was released and explores whether such differences were universal or geographical. Specifically, this paper investigates the variations in sentiment in different geographic regions and the change of sentiments before and after the vaccine release. We collect tweets containing keywords "COVID-19"and 'depression' and rely on releasing date of the COVID-19 vaccine as a division point. The experiment results reveal that topics related to depression varied significantly across different regions before and after the COVID-19 vaccine was released. For example, tweets posted in America are focused on social lockdown and infection with COVID-19 when referring to depression. In contrast, tweets from European countries discuss more typical depression symptoms and Brexit. The tweets posted in Asian, African, and Oceanian contain more discussions on stress. Our analysis further indicates that Asian users' stress is mainly from the study while Oceanian and African users' stress is primarily from family. Another interesting finding in our paper is that tweets show a common desire for normal social activities after the vaccine release, regardless of geographical locations. © 2022 IEEE.
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Retail is one of the defining elements of urban spaces. The study of commerce is largely based on its evolution and how it relates with urban environments. Currently, with the advent of mass tourism, there has been an adjustment in the commercial fabric of the area’s most sought after by tourists. Among these latter areas, the historical centers of commerce stand out. The first objective of this research is to analyze the modern evolution of the commercial fabric of Lisbon by comparing the city center with the rest of the city. For this goal, I use a quantitative approach through the quotient location for specific retail typologies. The results show dissimilarities that are associated with the geographical location of retail, which vary according to the different retail typologies being analyzed. The second goal is based on the assumption that the mere analysis of the evolution of the retail typologies is limited in the context of tourist cities. Considering this matter, a qualitative method (photo analysis, conceptually supported by the concept of authenticity) is used. The results show the usefulness of the concept of authenticity to apprehend and discuss how retail is reacting to the tourism industry, thereby contributing to the transformation of the city center into a leisure and entertainment destination.
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Waning Immunity is an important and relevant concept during these days as the COVID-19 pandemic is expected to become endemic in the coming months. By definition, Waning Immunity is the loss of protective antibodies over time and hence necessitates booster shots at regular intervals of time. This quantitative study is on proposition of a model for computing a newly defined metric called Waning Immunity Index (WII). The model takes into account the three group of people namely, susceptible, infected and recovered individuals from the COVID-19 infections. The required data can be collected from the Kaggle repository that contains information on infections, recovery, vaccination and booster doses given on the human population while considering a geographical location. The proposed model and its implementation have thrown light on the spread, control and effect of COVID-19 virus. Results of the proposed model and the measurement can help health officials to seamlessly plan the duration of booster doses administered on vaccinated population. A sample data has been prepared for testing the model and the application of the proposed metrics. Based on the results, it is found that vulnerability of the Waning Immunity increases steeply at some duration and gradually steadies in time. © 2022 IEEE.
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After the outbreak of COVID in Wuhan, it has had an impact on all aspects of tourism industry. Tourists' sentiment is an important factor for people to make tourism decisions. The implementation of tourism decisions affects the development of tourism to a certain extent. In order to explore the impact of the COVID-19 on the tourism industry from the micro level of tourist sentiment. Firstly, the text mining algorithm is used to analyze the emotion of tourism microblog text, and the tourism emotion index TSI is constructed. Then combined with the tourism heat index THI, the tourist sentiment TS comprehensive index is constructed. The temporal and spatial differences of the impact of the epidemic on tourists' emotion are analyzed by comparing the tourists' emotion and epidemic data in different regions and stages. From the temporal and spatial distribution of tourist sentiment and epidemic situation, they are not completely parallel related, and there is spatial heterogeneity. Tourist sentiment is affected by multiple factors such as economic level and geographical location. The change of tourists' mood does not only depend on the change of epidemic data, but also related to many factors such as economic level and geographical location. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.
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The popularity of YouTube provides an effective way to propagate epidemic prevention knowledge by analyzing the video preferences of viewers from different locations. However, it is challenging to analyze video preferences due to the dispersed geographical locations of the YouTube viewers and the indistinguishable video categories and subcategories. This paper combines linear regression and neural networks to unravel both geographical and categorical difficulties and improve the accuracy of task-solving models. First, the YouTube dataset and extract variables are preprocessed, including categories, subcategories, countries, number of subscribers, and view counts of each YouTubers. Then, linear regression and neural networks are trained to classify and find the correlation between these variables. Finally, Matplotlib, google chart, and Tableau are utilized to visualize the result based on video categories and geographical locations. The accuracies of linear regression and neural network models are verified through the R-squared estimation. Both linear regression and neural network models show the trending types of videos and a positive correlation between the number of viewers and subscribers. The experimental results show a remarkable user's tendency of watching films and listening to music, a concentration of YouTube users from India and the U.S., and propose targeted Covid-19 prevention propaganda based on the above two characteristics. © 2022 IEEE.
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Bu çalışma salgın haberlerinin uluslararası piyasaların getirileri üzerindeki etkisini kantil regresyon yöntemi kullanarak araştırmaktadır. Analiz için RavenPack veri platformu tarafından sağlanan medyatiklik endeksi, sahte haber endeksi, ülke duyarlılık endeksi, infodemi endeksi ve medya ilgi endeksi kullanılmıştır. Bu araştırmada 22 Ocak 2020’den 17 Nisan 2020’ye kadar günlük verilerle 80 ülkeden 2.996 gözlem kullanılmıştır. Analiz sonuçlarına göre Covid-19 ile ilgili haberlerin piyasa getirileri üzerindeki etkisinin kantiller arasında farklılık gösterdiğini, diğer bir deyişle haberler ve finansal piyasalar arasında asimetrik bir bağımlılık bulunmuştur. Medyada salgın ile haberlerin artmasıyla birlikte piyasa getirileri üzerindeki olumsuz etkisi düşük kantillerden yüksek kantillere doğru düşüş eğilimi göstermektedir. Covid-19’un neden olduğu finansal çöküşü hafifletmek için etkili iletişim kanallarının daha yoğun kullanılması gerekmektedir. Haberlerin finansal piyasalar üzerindeki etkisini yakalamak için, bu çalışma aynı zamanda ülkeleri Morgan Stanley Sınıflandırma Índeksine göre (MSCI, n.d.), gelişmiş, gelişmekte olan, sınır pazarları ve bağımsız pazarlar olarak ve coğrafi konumuna göre Avrupa, Kuzey ve Güney Amerika, Asya ve Ortadoğu ülkeleri olarak sınıflandırmıştır. Sonuçlar önceki bulgular ile tutarlılık göstermiş ve haberler ile finansal piyasalar arasındaki asimetrik bağımlılık sürmüştür.Alternate : This study investigates the effect of pandemic-related news on stock market returns in international markets using the quantile regression method. The media hype index, fake news index, country sentiment index, infodemic index, and media coverage index provided by the RavenPack data platform are used for the analysis. In this research, 2,996 observations from 80 countries, consisting of daily data from January 22, 2020, to April 17, 2020, were used. The results show that the impact of Covid-19-related news on market returns varies among the quantiles of the stock market;in other words, there is an asymmetric dependency between the news and financial markets. With the increase in coverage about the pandemic in the media, the negative impact on market returns exhibits a decreasing trend from low quantiles to high quantiles. More intense use of effective communication channels is required to alleviate the financial crash caused by Covid-19. To capture the effect of the news on financial markets, this analysis also categorized countries according to the Morgan Stanley Classification Index (MSCI, n.d.), such as by developed, emerging, standalone, and frontier markets and by geographical location, including Europe, Africa, North and South America, Asia, and the Middle East. The results are consistent with the previous findings and the dependency between the news and financial markets remains asymmetric.
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The outbreak of the Covid-19 emerged from Wuhan, Hubei province of China, spread geo-spatially in more than 210 countries causing more than 96.7 million people of the global population infected and 2.06 million deaths (as on 20th January 2021) from 25.416 million people infected and 0.851 million deaths (as on 30th August 2020), which is still spreading in geo-spatiotemporal way to the new geographical locations. There are marked variations in the spectrum of daily new cases of covid-19 between different countries. People do not receive sufficient sunlight to retain adequate vitamin D levels during winter in countries situated at the latitude beyond 35°N. Vitamin D is important in preventing the cytokine storm and subsequent acute respiratory distress syndrome that is commonly the cause of mortality. The global spreading of covid-19 caused marked variations in population mortality between different countries situated at different latitudes, which suggest establishing the correlation between latitude and the severity of the covid-19 outbreak. In this paper, geo-spatial big data analysis has been carried out for determining the impact of latitude and the role of vitamin-D on population mortality for 52 countries situated between the latitude 64°N and 35°S, based on population mortality data from 15th April 2020 to 30th June 2021, which shows relatively lower population mortality in countries that lie below the latitude 38°N. This paper explains the variability factor of population mortality from 3rd May 2020 to 30th January 2021 with respect to population mortality on 15th April 2020 for determining the severity of the covid-19, which shows the significant severity of the covid-19 outbreak in the country such as South Africa, Colombia, Russia, Kuwait, India, Mexico and Ukraine during 30th September 2020 to 30th January 2021 and sudden rise of variability factor for Romania, Serbia, Slovenia, Austria and Poland. © ACRS 2021.All right reserved.
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Les industries culturelles et créatives (ICCs) constituent un véritable levier pour le développement économique des pays et contribuent à promouvoir la créativité des sociétés et à offrir des opportunités pour imaginer de nouveaux futurs. Avant de donner un aperçu global des ICCs, il est important d'avoir conscience de leur hétérogénéité eu égard à leur taille, leur secteur d'activité, leur situation géographique et aux cadres de gouvernance dans lesquels elles évoluent. Les ICCs sont aujourd'hui traversées par de profondes transformations mettant à mal leur développement, voire, pour certaines, leur pérennité. La pandémie de Covid-19 a bouleversé le secteur culturel à travers les mesures sanitaires mises en place pour ralentir la propagation du coronavirus. Cette crise a également induit une accélération du poids des plateformes numériques, avec l'émergence de pratiques culturelles alternatives ébranlant de façon structurelle la chaÎne de valeur des ICCs, qui, pour certaines, n'en tirent guère profit. Face à ces défis, il est indispensable d'oeuvrer à la création d'un environnement propice à la créativité, à l'innovation artistique et à la diversité des expressions culturelles, tout en accompagnant au mieux la transition numérique du secteur et en protégeant les libertés fondamentales.Alternate :The cultural and creative industries (CCIs) constitute a real lever for the economic development of countries and contribute to promoting the creativity of societies and offering opportunities to imagine new futures. Before giving a global overview of CCIs, it is important to be aware of their heterogeneity with regard to their size, their sector of activity, their geographical location and the governance frameworks in which they evolve. Today, CCIs are undergoing profound transformations that are challenging their development and, for some, their sustainability. The Covid-19 pandemic has shaken the cultural sector through the sanitary measures put in place to slow down the spread of the coronavirus. This crisis has also led to an increase in the weight of digital platforms, with the emergence of alternative cultural practices structurally shaking the value chain of the CCIs, some of which hardly benefit from it. Faced with these challenges, it is essential to work towards the creation of an environment conducive to creativity, artistic innovation and the diversity of cultural expressions, while providing the best possible support to the sector's digital transition and protecting fundamental freedoms.The cultural and creative industries (CCIs) constitute a real lever for the economic development of countries and contribute to promoting the creativity of societies and offering opportunities to imagine new futures. Before giving a global overview of CCIs, it is important to be aware of their heterogeneity with regard to their size, their sector of activity, their geographical location and the governance frameworks in which they evolve. Today, CCIs are undergoing profound transformations that are challenging their development and, for some, their sustainability. The Covid-19 pandemic has shaken the cultural sector through the sanitary measures put in place to slow down the spread of the coronavirus. This crisis has also led to an increase in the weight of digital platforms, with the emergence of alternative cultural practices structurally shaking the value chain of the CCIs, some of which hardly benefit from it. Faced with these challenges, it is essential to work towards the creation of an environment conducive to creativity, artistic innovation and the diversity of cultural expressions, while providing the best possible support to the sector's digital transition and protecting fundamental freedoms.
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Demographic factors, statistical information, and technological innovation are prominent factors shaping energy transitions in the residential sector. Explaining these energy transitions requires combining insights from the disciplines investigating these factors. The existing literature is not consistent in identifying these factors, nor in proposing how they can be combined. In this paper, three contributions are made by combining the key demographic factors of households to estimate household energy consumption. Firstly, a mathematical formula is developed by considering the demographic determinants that influence energy consumption, such as the number of persons per household, median age, occupancy rate, households with children, and number of bedrooms per household. Secondly, a geographical position algorithm is proposed to identify the geographical locations of households. Thirdly, the derived formula is validated by collecting demographic factors of five statistical regions from local government databases, and then compared with the electricity consumption benchmarks provided by the energy regulators. The practical feasibility of the method is demonstrated by comparing the estimated energy consumption values with the electricity consumption benchmarks provided by energy regulators. The comparison results indicate that the error between the benchmark and estimated values for the five different regions is less than 8% (7.37%), proving the efficacy of this method in energy consumption estimation processes.