Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

Prediction of carbon dioxide (CO2) emissions is a critical step towards a sustainable environment. In any country, increasing the amount of CO2 emissions is an indicator of the increase in environmental pollution. In this regard, the current study applied three powerful and effective artificial intelligence tools, namely, a feed-forward neural network (FFNN), an adaptive network-based fuzzy inference system (ANFIS) and long short-term memory (LSTM), to forecast the yearly amount of CO2 emissions in Saudi Arabia up to the year 2030. The data were collected from the "Our World in Data” website, which offers the measurements of the CO2 emissions from the years 1936 to 2020 for every country on the globe. However, this study is only concerned with the data related to Saudi Arabia. Due to some missing data, this study considered only the measurements in the years from 1954 to 2020. The 67 data samples were divided into 2 subsets for training and testing with the optimal ratio of 70:30, respectively. The effect of different input combinations on prediction accuracy was also studied. The inputs were combined to form six different groups to predict the next value of the CO2 emissions from the past values. The group of inputs that contained the past value in addition to the year as a temporal index was found to be the best one. For all the models, the performance accuracies were assessed using the root mean squared errors (RMSEs) and the coefficient of determination (R2). Every model was trained until the smallest RMSE of the testing data was reached throughout the entire training run. For the FFNN, ANFIS and LSTM, the averages of the RMSEs were 19.78, 20.89505 and 15.42295, respectively, while the averages of the R2 were found to be 0.990985, 0.98875 and 0.9945, respectively. Every model was applied individually to forecast the next value of the CO2 emission. To benefit from the powers of the three artificial intelligence (AI) tools, the final forecasted value was considered the average (ensemble) value of the three models' outputs. To assess the forecasting accuracy, the ensemble was validated with a new measurement for the year 2021, and the calculated percentage error was found to be 6.8675% with an accuracy of 93.1325%, which implies that the model is highly accurate. Moreover, the resulting forecasting curve of the ensembled models showed that the rate of CO2 emissions in Saudi Arabia is expected to decrease from 9.4976 million tonnes per year based on the period 1954–2020 to 6.1707 million tonnes per year in the period 2020–2030. Therefore, the finding of this work could possibly help the policymakers in Saudi Arabia to take the correct and wise decisions regarding this issue not only for the near future but also for the far future.

Exploring Notions of Resilience and Adaptability in the Context of Piloting a Mobile App for Risk Awareness During Covid-19

This paper explores notions of resilience and adaptability in the context of the design, development and pilot of a mobile phone application, COVID-Aware, for enhancing risk awareness during the COVID-19 pandemic. Through an interdisciplinary team approach, we explore the utilization of an information and communications technology platform in supporting resilience and wellbeing at the individual and collective levels among community members. The study integrated data models, that were developed in Jamaica to predict the risk of COVID-19, with existing epidemiological models developed for COVID-19 in different parts of the world. Participants' perspectives on adapting to the use of the app on their mobile devices assisted with exploring ways to share visualisations of this data, and their views of adaptations to health protocols provided feedback for participatory development of the app. The use of the mobile application to support risk awareness, assessment and potential choices, and implications for resilience are discussed. © 2022, IFIP International Federation for Information Processing.

Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology.

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but - for this case study - multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.

The creative industries effects on economic performance in the time of pandemic

Purpose: This paper aims to investigate empirically whether creative industries are boosting the economic performance of the ASEAN countries (Malaysia, Indonesia, Singapore, Thailand, Vietnam and Brunei Darussalam) during the Coronavirus disease (COVID-19) pandemic. Design/methodology/approach: This paper applied a random effect and fixed effect estimation approach to investigate the impact of creative industries’ development (government expenditure on education, export of creative industries, trade openness, innovation index, sukuk issuances) on the economic performance spanning from 2010 to 2020. Findings: The economic performance was proxied by two dependent variables, namely, the gross domestic product and the Misery Index. On top of containment and vaccination measures, the findings demonstrated that creative industries are enhancing economic growth in Association of Southeast Asian Nations (ASEAN) countries, supported by the significant role of the sukuk market as a vital contributor to economic growth. Originality/value: This study is unique because it provides novel and empirical results of the creative industries’ development on economic performance in the ASEAN countries before and during the COVID-19 pandemic. © 2022, Emerald Publishing Limited.

A data-driven understanding of COVID-19 dynamics using sequential genetic algorithm based probabilistic cellular automata.

COVID-19 pandemic is severely impacting the lives of billions across the globe. Even after taking massive protective measures like nation-wide lockdowns, discontinuation of international flight services, rigorous testing etc., the infection spreading is still growing steadily, causing thousands of deaths and serious socio-economic crisis. Thus, the identification of the major factors of this infection spreading dynamics is becoming crucial to minimize impact and lifetime of COVID-19 and any future pandemic. In this work, a probabilistic cellular automata based method has been employed to model the infection dynamics for a significant number of different countries. This study proposes that for an accurate data-driven modelling of this infection spread, cellular automata provides an excellent platform, with a sequential genetic algorithm for efficiently estimating the parameters of the dynamics. To the best of our knowledge, this is the first attempt to understand and interpret COVID-19 data using optimized cellular automata, through genetic algorithm. It has been demonstrated that the proposed methodology can be flexible and robust at the same time, and can be used to model the daily active cases, total number of infected people and total death cases through systematic parameter estimation. Elaborate analyses for COVID-19 statistics of forty countries from different continents have been performed, with markedly divergent time evolution of the infection spreading because of demographic and socioeconomic factors. The substantial predictive power of this model has been established with conclusions on the key players in this pandemic dynamics.