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This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

This study aims to analyze the relationship between expectations of change after a crisis, such as that generated by COVID-19, people's levels of happiness and the propensity for pro-sustainable behavior.

Surveys were conducted using 1,130 observations. The aim was to measure expectations of change during the pandemic. Furthermore, the relationship between these expectations, happiness levels, and attitudes towards sustainability was analyzed for various elements of daily life. Statistical techniques such as principal component analysis (PCA), cluster analysis, variance analysis and correlation analysis were utilized to explore the underlying patterns and relationships.

Optimistic expectations for post-pandemic change are associated with a greater likelihood of engaging in environmentally beneficial behaviors, leading to higher levels of life satisfaction. This correlation is particularly significant when individuals envision broader transformations in the collective behavior of humanity. These findings suggest a strong link between beliefs in positive societal transformations and both pro-environmental actions and personal well-being.

The unique circumstances of the pandemic, characterized by heightened media consumption, likely influenced the expectations of individuals, particularly fostering pessimistic outlooks in critical situations. Clear and solid variables were utilized despite not employing validated scales to measure expectations. However, it has been proven that there is a clear link between change expectations, happiness, and the propensity for a more sustainable daily life.

This study identifies guidelines that strengthen brands' communication strategies based on individuals' sustainability profiles and visions of the future.

There is a need to target skeptical, change-resistant segments of the population with a more convincing and solid discourse to promote sustainable consumption and behavior.

This is the first study to simultaneously address the relationship between individuals' expectations of change following traumatic events such as the pandemic, their engagement in sustainable behavior, and their increased levels of happiness.

This work aims to assess the potential of a Bio-District as a model for applying the circular economy concerning the waste scope. It aims to understand the capability of organic farms to manage waste with a circular perspective, starting with the use of indicators that directly or indirectly impact the waste scope.

This study is based on previous work that identified and systematised the circular indicators of the agri-food sector within a dashboard. With this research as a basis, the indicators within the waste scope in the dashboard were extracted. Cross-linked indicators with an indirect connection to the waste scope were also systematised and tested in a case study. Primary and secondary data were used for the study. The primary data came from a semi-structured interview, and the secondary data were from official databases.

The work highlights two important results. The first allows the definition of a subclassification of indicators by product and organisation, extracting those with a cross-linked characteristic concerning the waste scope. Secondly, the indicators' application shows the farm's circular and waste valorisation potential within the Bio-District. The study also made it possible to test a new indicator, the “Potential Energy Biomass Recovery”, to measure the farm's potential to produce energy from waste.

This research proposes a new circular economy approach to evaluate waste management in the agri-food sector.

This paper is part of the Special Issue series Improving Life on Planet Earth – A Call to Action for Service Research to Achieve the Sustainable Development Goals (SDG). This paper aims to provide the groundwork for Service Research Theme 2 – services that provide OPPORTUNITY for all humans. Service Research Theme 2 comprises SDG4, quality education; SDG5, gender equality; and SDG10, reduced inequalities and seeks to mobilize ServCollab’s aspirations to reduce human suffering, improve human well-being and enable well-becoming.

A scoping review appraising existing service research related to SDG4, SDG5 and SDG10 was conducted, establishing interlinkages, identifying patterns within each SDG and then assembling a research agenda for service researchers.

The scoping review identifies 18 patterns in service research (six patterns per SDG) pertaining to Service Research Theme 2. Common patterns among the SDG4, SDG5 and SDG10 included underrepresentation, consumer-centricity, the absence of explicit SDG linkages, the predominance of one theoretical anchor and the preference for quantitative studies, particularly surveys. Overall, the scoping review found that service research related to Service Research Theme 2 is patchy in that it is overdeveloped in some topics, methodologies and methods yet underdeveloped or silent in others.

The high-level research problem of Service Research Theme 2 is as follows: How have services provided OPPORTUNITY for all humans? This paper analyzes patterns in service research and, from these patterns, assembles a research agenda that sparks and guides further research.