Risk-based evaluation and management of cyber-physical-social systems during pandemic crisis

This research focuses on risk-based evaluation and management of cyber-physical-social systems during a pandemic crisis. The ongoing novel coronavirus (COVID-19) epidemic has caused serious challenges for the world’s countries. The health and economic crisis caused by the COVID-19 pandemic highlights the necessity for a deeper understanding and investigation of the best mitigation policy. While different control strategies in the early stages, such as lockdowns and school and business closures, have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses and some controversial impacts on social justice. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative socio-economic impact of control strategies. To maximize the effectiveness of the controlling policies during a major crisis like a pandemic, we propose two mathematical frameworks which optimize the contorting policy while considering three sets of important factors, including the epidemiologic, economic, and social impact of the pandemic. Each formulation quantifies the epidemiologic impact using a modified SIRD (susceptible-infected-recovered-deceased) model which captures the number of infected, recovered, immune, and deceased populations. The two formulations propose different approaches for measuring the social and economic impacts of the pandemic. In the first formulation, the economic impact is a twofold measure, first the unmet demand because of supply perturbation (due to industry closure), and the second is the local business shrinkage because of demand perturbation (due to the state closure). The modified SIRD model is combined with a multi-commodity maximum network flow problem (MNFP) in which the unmet demand is measured in a network of states and industries. The proposed formulation is implemented on a dataset that includes 11 states, the District of Columbia (including the states in New England and the mid-Atlantic), and 19 industries in the US. In the second formulation, the economic impact is measured using the supply side multi-regional inoperability input-output model, accounting for the inoperability of each industry to satisfy the demand of final consumers and other industries, due to its closure. Also, the second formulation measures the social impact of the pandemic policy, by incorporating the vulnerability of social communities to get infected due to state opening or to lose their job due to the closure of the state. We test the efficacy of proposed formulations on the real data set of COVID-19 applicable to 50 states, the District of Columbia, and 19 industries. Both formulations are multi-objective mixed integer programming with three objectives which are solved using the augmented ε-constraint approach. The final pandemic policy is selected from the set of Pareto-optimal solutions based on the least cubic distance of the solution from the optimal value of each objective. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiologic impact change in the opposite direction, and it is more effective to close most states while keeping the majority of industries open. For each Pareto optimal solution, the unmet demand and the propagation of inoperability to the industries and states can be tracked down. This will give a holistic view of the impact of the pandemic policy on the health, economy, and social justice aspects of the country

Outlook on biofuels in future studies

Foresight methods are useful for long-range planning such as strategic energy management, energy policy, and renewable and sustainable energy planning to manage uncertainties. Futures studies may affect the anticipation and speculation of future and emerging technologies. In this paper, biofuels futures are explored based on a critical evaluation of the literature to draw the state-of-the-art for the future-oriented biofuel research. A six-fold typology mapping from two main futures studies methodologies is used. (i) descriptive scenarios, forecasts, and statistical scenarios as descriptive methods; (ii) roadmaps, visions, and backcasts as prescriptive methods. The expectations embodied in the literature are then explored through deriving research challenges about the future of biofuels: (1) the main motives and driving forces in a biofuel era; (2) the main obstacles or difficulties confronting a biofuel era; (3) the plausibility and importance of each of different scenarios; (4) key technological breakthroughs for the bioeconomy; (5) details about development, maturity and flourish; (6) biofuel era's significant achievement. The literature explains a wide range of plausible futures, from centralized systems related to technological breakthroughs to decentralized systems based on small-scale renewable. Fundamental technological elements are uncovered, and a plausible biofuel economy is drawn along with the necessary pathway to reach it. The review shows a general agreement that a biofuel economy would develop gradually, and a prompt shift to biofuels would require powerful governmental support coupled with significant disruptions such as changes in environmental principles of countries, technology breakthroughs, higher oil prices, and urgency of climate change.Peer reviewe

Improving renewable energy policy planning and decision-making through a hybrid MCDM method

Shifting from fossil to clean energy sources is a major global challenge, but in particular for those countries with substantial fossil-fuel reserves and economies depending on fossil-fuel exports. Here we introduce an improved framework for renewable energy planning and decision-making to help such countries to more effectively harness their abundant renewable energy resources. We use Iran as a case for the analysis. The framework includes identifying and removing barriers that prevent the use of renewables. It is based on combining two models: Benefit, Opportunity, Cost, Risk (BOCR) and Analytic Network Process (ANP) models. In the analyses, the mutual weight of strategic criteria is employed such as technology, economy, energy vulnerability, security, global effects, and human wellbeing. Using the integrated model, we find that solar energy would be the preferential renewable energy source for Iran. Also, the role of infrastructures, policies, and administrative structures in renewable energy to facilitatetheir development was analyzed. The renewable energy policy-making framework presented is applicable to other countries as well.Peer reviewe

Performance evaluation of complex electricity generation systems

To evaluate the performance of complex electricity generation systems, a new dynamic network-based data envelopment analysis (DNDEA) approach is presented. Past data envelopment analysis (DEA) studies on energy system efficiency have often ignored the dynamics of each process of the system individually. Here a network-based DEA method is built, which considers the interrelationships of the operations to determine the efficacy of the system. For assessing the performance over successive periods, with time-based dependencies between the successive periods, a dynamic DEA (DDEA) model is proposed. In DDEA, a linear combination of the efficiencies in successive periods is used as the complement of the system. The network-based and dynamic features of the created model enable measuring the performance of each sub-system process and the entire system in multi-period planning horizons simultaneously. These features make the DEA model identify changes in system efficiencies so much better than the current approaches. The created model is comprehensively implemented in the Iranian electricity sector using real data. Based on the findings, the efficiencies of power generation and transmission sectors are decreasing while the distribution performance is increasing. The proposed model could be applied to electricity generation systems in other countries as well.Peer reviewe

A comparative assessment of air quality across European countries using an integrated decision support model

Funding Information: The European Union (EU) has been active in creating a cleaner society by controlling air pollutants within different sectors such as transportation [13] and industrial activities [14]. Air pollution control in the EU and tracking of emission commitments of its member states is administered by the European Environment Agency [15], supporting policies aiming at enhancing green economics, sustainable industries, high environmental development, sustainable and smart societies, and cleaner agricultural activities [16]. Publisher Copyright: © 2021 Elsevier LtdReducing air pollution including greenhouse gas emissions originating from extensive use of fossil fuels is critical for European countries aiming at improving their environment and at carbon neutrality by the middle of this century. To optimally reduce the air pollutants and mitigate the climate change, not only national or European Union level regulation need to be considered, but also international agreements such as the Sustainable Development Goals, Kyoto Protocol, and Paris Climate Agreement need to be included in these strategies. Managing such a complex framework would benefit from reliable multi-criteria decision-making approaches. Current models to enhance air quality often concentrate on one criterion at a time and focus on momentary improvements only, unable to offer longstanding enhancement. Therefore, comparative analysis of emissions of several air pollutants simultaneously is highly relevant empowering decision-makers with better tools for policy development. The focus of this study is on a decision support model based on the Best-Worst Method and the Measurement of Alternatives and Ranking According to Compromise Solution method to comparatively analyze air pollutants of 22 European countries. This study is the first in its kind to develop an integrated decision model for air quality assessment considering six air pollutants. Extensive sensitivity analyses were performed to highlight the impacts from different scenarios on the decision-making. The results indicate that Sweden, Latvia, France, Lithuania, Hungary, and Italy ranked as the top six countries with the lowest emission. However, Finland, Poland, the Czech Republic, Luxembourg, and Estonia had the lowest overall ranking and the highest per capita emissions. The proposed methodology and evaluation framework can provide a helpful tool for developing regional and national strategies to minimize air pollutants and to improve environmental sustainability.Peer reviewe