Advances and New Trends in Environmental Informatics

Software affects society and environment in a variety of ways. In addition to the energy and resource requirements driven by software products, social factors are to be mentioned as sustainability-relevant aspects. In order to support software developers in creating products that can be positively assessed from a sustainability point of view, the question of practically usable assistance arises in this context. With this aim, in this paper the methodological approach of design patterns is examined. It is discussed how sustainability patterns can be described in a structured way and how they can be incorporated into practical software engineering processes. Particular attention is paid to aspects of completeness and consistency of the descriptions as well as the explicit inclusion of human action. It is recommended to provide pattern descriptions with a high formalization degree in order to enable verifiability for logical plausibility and completeness and to allow further machine-assisted processing. Prospectively appropriate pattern repositories could be set up to support practical software product development processes in the future.

With the increasing digitalization of all industry sectors, information systems are becoming more and more complex. At the same time, thanks to their crucial societal role, they have the potential to help (or hinder) organizations in their ambition to contribute to sustainability goals. In our work, we aim at helping them by identifying the important concerns that shape their information systems and that are found to influence complexity in certain sustainability aspects. To this aim, we perform a study in a mid-sized bank, and build a taxonomy of concerns that emerge from real projects and experience, and blend both complexity and sustainability.

This article presents a software architecture based on the onion architecture that uses the concept of application microservices in order to integrate data backends with dashboard applications. Its main goal is to reduce the complexity in the architecture’s frontend and therefore to increase the performance of the application for the user. The concept of the added application layer as well as its interaction with the other parts of the architecture is described in detail. Then an evaluation of its advantages is presented which shows the benefits of the concept regarding performance and simplicity using a real-world use case in the energy and environmental domains.

Compared to conventional power plants, Virtual Power Plant is a new electrotechnical and information technology concept that is used for the interdisciplinary merging of electrical engineering and information technology for the central control and monitoring of decentralized renewable energy systems and components. A formal presentation of definitions and consensus building is seen as an instrument to specify the practice-oriented design of Virtual Power Plants. The present paper focuses on the Virtual Power Plants as key factor for sustainable energy—especially in its common understanding of terms. This compilation serves as a mutual knowledge base and function because of the “Shared Conceptualization” for the specialist user within the knowledge domain “Information technology” and “Electrical engineering.” An Event-driven Process Chain is created for this purpose. In order to obtain such an Event-driven Process Chain, a qualitative analysis based on semi—systematic literature review led to a selection of suitable term definitions that are contained in the scientific databases. This investigation finally leads to the compilation of entities and their mutual relations, which are required to create the model. The conception of an Event-driven Process Chain is seen in this context as a contribution to the specification and structuring of the discussion about Virtual Power Plants. A potential contribution is, the ability to map a field of research, synthesize the state of knowledge, and create an agenda for further research in context of information systems and information security research.

Artificial intelligence (AI) is one of the pioneering driving forces of the digital revolution in terms of the areas of application that already exist and those that are emerging as potential. On the technical side, this paper deals with the energy requirements of artificial intelligence processes. It also identifies efficiency approaches in this sector. Increases in productivity often lead to an increased demand for energy, which is contrary to sustainability in terms of reducing CO₂ emissions. Therefore, it will be examined to what extent rebound effects can reduce the savings potential for energy in relation to methods of artificial intelligence and what the main factors of CO₂ emissions are.

Photovoltaic (PV) modules can make a huge contribution to achieve the Sustainable Development Goals of the United Nations. To be able to make that contribution, regular check-ups and evaluation of installed PV modules are necessary as they can develop faults and degenerate over time. In this project, we improve the dark IV string curve method used for on-site fault detection and module evaluation. We do so by training artificial intelligence (AI) models to predict the maximum power point and the bright IV curve of PV modules given the weather-independent dark IV string curve. We present some background on this topic, describe the data used for training and the developed models. The results are illustrated graphically. To make the models available for PV experts in practice and to support their decision-making process, we also developed the web-database-application iPVModule for storing historical PV Module data and integrated the AI-models.

This research presents a practical approach towards resources and energy efficiency in production systems in small and medium enterprises (SMEs). The approach is based on mapping the production line using the modelling tool UMBERTO® Efficiency + for the representation of the material and energy flows. A case study from a bottle-to-bottle recycling plant in Egypt is selected. Material flow bottle-necks are spotted and energy hotspots are identified. Optimization is implemented in the model and the new values for savings in material, energy and carbon emissions are calculated. The optimized model estimates yearly savings by around 8% in materials and 10% in electricity consumption and consequently in costs and CO₂ emissions. The modelling tool UMBERTO Efficiency + facilitated the simple and time efficient assessment of the production sustainability as well as the prediction of the expected savings upon implementation of the modifications which is a practical and convenient approach for the industrial field.

The aim of this study was to determine the gaseous pollutant concentrations resulting from the natural gas-powered central heating at the Aristotle University of Thessaloniki (AUTh) main campus, on the basis of limited emission and meteorological data. For this reason, a methodology was compiled addressing emissions and concentration levels as a function of a set of meteorological scenarios: Emissions were estimated based on campus operational conditions and concentration levels were calculated by employing Gaussian plume model approach via an in-house implementation in Python. The necessary climatic conditions were used to compile a sum of 1080 different weather–dispersion model scenarios. Obtained results allowed for the adequate analysis of the geospatial distribution of pollutants. Concentration levels were estimated to be below relevant limit values but dependent on the prevailing meteorological conditions.

Human mobility has been recognized as one of the critical factors determining the spread of contagious diseases, such as SARS-CoV-2, a highly contagious and elusive virus. This virus disrupts the normal lives of more than half of the global population in one way or another, claiming the lives of millions. In such cases, mobility should be managed via the imposition of certain policies. This proposed study presents a newly developed spatial platform aimed at simulating and mapping the spread of infectious diseases and mobility patterns under different scenarios based on different epidemiological models. In addition to the "business as usual" scenario, other response scenarios can be defined to reflect real-world situations, taking into consideration various parameters, including the daily rise in infections and deaths, among others. The developed system provides insights to decision-makers about strategies to be implemented and measures for controlling the spread of the virus.

A human behavior model describing the actions of pedestrians in public pedestrian-only space was constructed on the basis of high-precision trajectory data gathered using a laser scanner sensor system. First, a route selection model that could be used to describe macroscopic trajectories was constructed. Next, a walking model that includes the psychological stress imposed by the presence of walls, columns, and other hindrances to motion. These models were then combined to create the human behavior model. Next, using laser scanner sensors, highly precise measurements were taken of the trajectories of pedestrians in the reception area of a hospital. The observational data were employed to estimate unknown parameters in the human behavior model, and to note the characteristics (sex, patient or staff, mobility aid usage) of the pedestrians as they varied with pedestrian attributes. Finally, we proposed a procedure for evaluating the comfort and efficiency of public pedestrian-only space. Using simulation analysis, we demonstrated that it is possible to uniquely estimate the spatial distribution of comfort and efficiency at any given location from the frequency of passages at that location.

The complex nature of threats to large cities residents requires a rethinking of the existing methods for the vulnerability assessment of the population to various kinds of them. Moscow concentrates about 9% of the Russian population, and with the Moscow oblast—about 12%. The high level of spatial concentration of the population and its active dynamics determine the increased level of natural and man-made risk in the city. The purpose of this study is to assess the vulnerability of the Moscow population to natural and man-made hazards, taking into account the actual population size and its movement in the city within different time cycles (daily and weekly-seasonal). Trying to find an appropriate solution, authors used alternative sources of statistical information—mobile operators data. The use of these data made it possible to obtain more detailed information on the state of socio-geographical systems, to overcome barriers associated with the incompleteness and ‘static’ nature of official statistical information (data provided by Rosstat). Mobile operators data allow obtaining more reliable depictions of the localization of its users at a certain point in time, which made it possible to adjust and clarify the current ideas about the distribution of the population in Moscow. As a result of the study, it was shown that for the center of Moscow and for New Moscow, the population vulnerability level is much higher than reflected in official documents. On the contrary, in the peripheral areas of Old Moscow, the potential risks are reduced because the real population density is significantly lower than it is estimated in the calculations provided by Rosstat.

This paper presents the concept, and the prototype of a platform that enables citizens from local communities to share their experiences with historic disasters online as open educational resources for teachers, community builders, urban planners, or volunteers. Key focus of the platform is to highlight the impact of disasters on citizens’ everyday lives. Based upon an overview of previous research, a requirements analysis, personas and related use cases, we present the insights into the prototypical implementation of the platform. We also provide a brief outlook upon possible practical uses of the platform, and its materials.

During to the recent growth of e-commerce and as the number of shipments is rising last mile services are facing many challenges and hurdles especially in relation to sustainable action. Within the project NaCl measures were tested to address and to positively impact the three dimensions of sustainability on the last mile. Based on the usage of electric cargo bikes and a crowd logistics approach shipments of a regional logistics service provider and beyond of regional stationary retailers were realized during a three-month pilot phase. The project's activities and objects were evaluated regarding positive effects on social and ecological sustainability while including the economic aspects of the transportation system. The aim of the evaluation was to be able to make a fundamental statement regarding the usability of the crowd logistics approach and the sustainable Customer Relationship Management approach and, if necessary, to identify optimization potential and concrete suggestions for improvement. Another objective of the evaluation was to determine the needs of different participants such as retailers and shippers as well as of receivers and deliverers while considering sustainable goals. Various data collection and analysis methods were adopted to evaluate the project objectives in quantitative and qualitative ways. The evaluation audited the approach’s ability to have positive impacts on different dimensions of sustainability and showed further possibilities for improvement.

During the last few years, “Mobility as a Service” (MaaS) has been conceptualized and researched as a platform for integrated, mixed-mode mobility. While some hope it will lead to environmental benefits, its real effects are still unclear. Here, we explore how MaaS is related to, and can be combined with, the established “Avoid-Shift-Improve” transport planning approach (ASI). We see that the MaaS concept described in research does not support “Avoid”-ing unnecessary transport. We combine learnings from MaaS research with learnings from a living lab, where mobility services can be booked in combination with a local co-working hub for commuters. In both literature and living lab, we especially examine the role of public authorities for ASI in MaaS. We conclude that more research is needed on how MaaS can be guided by ASI, and suggest that non-travel accessibility services, such as co-working hubs, could be part of the MaaS concept to support “Avoid”-ing unnecessary transport. Furthermore, we suggest that urban form needs to be considered in MaaS research. We also see that public authorities have an important role to play in ensuring that MaaS serves ASI and sustainable mobility.

Information and communication technology (ICT) provides unprecedented opportunities to reduce greenhouse gas (GHG) emissions from passenger transport by avoiding, shifting or improving transport. Research on climate protection through ICT applications in passenger transport mainly focuses on theoretical potentials, is assuming that digital mobility services replace GHG-intensive transport modes (e.g. car travel), and does not specify the conditions under which decarbonization potentials will materialize. It is known that digital mobility services can also take a complementary (as opposed to substituting) role in travel or replace non-motorized travel, which can increase GHG emissions. Based on existing literature, we develop a conceptual framework to guide qualitative and quantitative assessments of the relationship between ICT use, passenger transport and GHG emissions. The framework distinguishes three types of effects: (1) First-order effects, GHG impacts of producing, operating and disposing the ICT hardware and software, (2) second-order effects, impacts of ICT on properties of transport modes, transport mode choice and travel demand, and (3) third-order effects, long-term structural changes due to ICT use (e.g. residential relocation). We qualitatively demonstrate the framework at the example of automated driving and discuss methodological challenges in assessments of ICT impacts on passenger transport such as the definition of system boundaries, consideration of socio-demographic characteristics of individuals and the inference of causality. The framework supports researchers in scoping assessments, designing suitable assessment methods and correctly interpreting the results, which is essential to put digitalization in passenger transport at the service of climate protection.

Digital platforms promote the shared consumption of a variety of material and immaterial goods. As they grow and appeal to more consumers, the new practices and patterns of sharing can have both desirable and undesirable impacts from a sustainability point of view. The present paper takes a closer look at the various impacts of shared consumption practiced in the Digital Sharing Economy (DSE) with the aim to propose a guideline for assessing the DSE’s sustainability impacts. The guideline builds on a typology of shareable goods and a classification of different positive and negative environmental impacts of sharing. By considering different consumption scenarios (based on consumers’ behavioral responses, where applicable), the study develops a conceptual framework for the DSE’s impacts on the environment. This is also an extension of the general impact categories of Information and Communication Technology (ICT) which already exist in the literature. In addition to the impact of consumption, the concepts of the impact of provision, impact of access, and impact of maintenance are introduced as the four main evaluations comprising the sustainability assessment of sharing platforms. The considerations addressed can be helpful in delineating further evaluations of the sustainability of various instances of digital sharing systems.

Climate change is one of the biggest threats to humanity in the near future. Almost all different scenarios of climate change involve large-scale disasters and hazards. In order to define goals to cities, regions and countries in regards to mitigating climatic change, we first need to understand which the important key performance indicators (KPIs) are, how they can be measured and which values they take. Then, each country can calculate its performance based on these KPIs, setting realistic goals for better performance in the near future. This paper performs a large survey to identify and list 63 relevant KPIs, together with suggested units and metrics associated with them, divided in eight different thematic areas. It can be considered as an important contribution in the global efforts to understand climatic change, shaping policies and setting goals associated with it.