Shaping Tomorrow Today – SDGs from multiple perspectives 3658383186, 9783658383183

Table of contents :
Foreword
Contents
Editors and Contributors
Introductory Overview
A World of Multi-Dimensional Crises
The Sustainable Development Goals
Sustainable Development and Universities in Austria
The Faculty of Environmental, Regional and Educational Sciences and the Aim of This Edited Volume
How this Volume was Organized
A. Sustainability Challenges in Cities, Communities and Regions
B. Sustainability Learning in Schools and Other Educational Institutions
C. Actors for Learning and Implementing Sustainability
References
Sustainability Challenges in Cities, Communities, and Regions
The Challenge of Providing Information About Regional Climate Change
Introduction
The Context of Regional Climate Research
Foundations and Sources of Climate Information
Observations
Limitations of Observational Data
Role of Observations
Climate Model Simulations
How do Climate Models Work?
Climate Model Experiments
Projection Uncertainties
Process Understanding
Generating and Communicating Relevant Climate Information
User Collaboration
Information Construction
Organisation of Regional Climate Research
Conclusions
References
Littering in Municipal Public Places: The Role of Pers
List of Abbreviations
Introduction
Method
Results
Descriptive Statistics
Multiple Regression Analyses
Personal Intention to Avoid Littering
Intention to Prevent Other Important People from Littering
Discussion
Limitations
General Observations
Contributions to the Academic Debate
Future Directions
Implications for the Sustainable Development Goals (SDGs)
Conclusion
References
Everyday Disasters in Everyday Lives—Rethinking SDG 11
Introduction
Outlook: Corona Care Revolution without Further Crises and Categories
References
Correlates of Active Commuting in Austrian Adults: Doe
Introduction
Methods
Study design and participants
Measures
Commuting Mode
Potential Correlates
Statistical Analysis
Results
Participants and Descriptive Data
Association Between Commuting Mode and Personal and Environmental Correlates
Discussion
Strengths and limitations
Conclusion
Appendix A. Supplementary data
References
Sustainability Learning in Schools and Other Educational Institutions
How Closing Small Schools in Rural Regions Affects Com
Introduction
Maintaining the Community’s Vigour
Primary school: Saint Anna at Schwanberg
Method
Sample
Evaluation of Sequences and Results
Discovery
Dream
Design
Destiny
Discussion of the Results
School Life Goes on—Conversion from a Small School to a Larger Primary School
Research Questions
Method
Sample
Survey Instrument
Evaluation of the Data
Evaluation of the Questions and Results
Discussion of the Results
Summary
References
Reaching the ‘Hard to Reach’: Implementing “Education for All” by Participative Action Research and
Inclusive Education and Participation in Schools
The Inclusive Inquiry
Aims and Research Questions
Method
Sample
Instrument
Procedure
Data Analysis
Results
Research Question 1: Students’ Perception of the Research Lessons and Perceived Changes
Research Question 2: Students’ perceived involvement in Planning and Participation during the Research Question
Research Question 3: Wishes for Future Lessons
Conclusion
References
Addressing Teacher Shortages to Achieve Inclusive and
Introduction
The Supply of and Demand for Qualified Teachers: Factors that Influence Policy
Policy Initiatives to Balance Teacher Supply and Demand
Methods
Findings
Teacher Supply and Demand in Austria
Teacher Education Reforms
Alternative Pathways to Teacher Qualifications
Strengthening School Autonomy
Teacher Supply and Demand in South Africa
Teacher Education Reforms
Alternative Pathways to Teacher Qualifications
Strengthening School Autonomy
Concluding Remarks
References
Urban Disparities in Inclusive and Sustainable School
Introduction
Sustainable Development Goals and Relevance for Education
Policy Framework for the Implementation of SDGs in the Austrian Education System
Studies on SDGs in the Austrian Education System
Methods
School Sample
Content Analysis
Quantitative Data Analysis
Results
Conclusions
References
Learning for Democratic Sustainability in Migration S
Introduction
Active Citizenship in Migration Societies/cities
Active Citizenship
Urban Citizenship
Learning Through and for Active (Urban) Citizenship
Migration and Learning in the Context of the SDGs
Development and Main Characteristics of SDGs
Migration in the SDGs
Critical Approaches of Education for Sustainable Development (ESD) in the Context of Migration
Conclusion
References
Interdisciplinary Practical Trainings as a Contributi
Introduction
Goal, Aim, and Research Questions
Method
Theoretical Background
Education for Sustainable Development (ESD)
Transdisciplinary, Cross-Boundary, Action, and Mode-2 Learning
Transformative Learning
The Third Mission of HEI
Social Engagement as a Third Mission Activity
Service-Learning as an Activity of HEI Social Engagement
Raising Sustainability Awareness Among the General Public
Practical Insights
Basics of Interdisciplinary Practical Trainings (IPs) as a Research Entity
Evaluation of the Case Study
Sustainability Issues Addressed and Real-World Context
Knowledge and Competences Acquired by the Students
Community Service and Broadening of Public Mindset
Student Integration and Interaction
Action-Orientation
Inter-/transdisciplinary Approaches
Discussion and Conclusion
References
Actors for Learning and Implementing Sustainability
Social Work Organisations as Sustainable Actors: Characteristics and Perspectives of Ecologically
Introduction
Sustainable Development and Social Work
Defining ECO-WISE
Research Approach
Characteristics of ECO-WISE
Social Dimension
Economic Dimension
Ecological Dimension
Institutional Dimension
Challenges and Perspectives
Need for Inclusive Conditions
Need for Sufficient Financing Conditions for Social Enterprises
Need to Establish existing Business Areas and Develop New Ones
Cross-over-policy
Conclusion
References
Sustainability Aspects and Educational Relevance on S
Introduction
EKo-K.I.S.S. Project
Sustainable Development Goals
‘Quality Education’—Ensuring Inclusive and Equitable Quality Education and Promoting Lifelong Learning Opportunities for All (SDG4)
Initial Situation
Insights into Survey Results
Outlook on Pedagogical Practice
‘Gender Equality’—Achieve Gender Equality and Empower All Women and Girls (SDG5)
Initial Situation
Insights into Survey Results
Outlook on Pedagogical Practice
“Good Health and Well-Being”—Ensure Healthy Lives and Promote Well-Being for All at All Ages (SDG3)
Initial Situation
Insights into Survey Results
Outlook on Pedagogical Practice
“Responsible Consumption and Production”—Ensure Sustainable Consumption and Production Patterns (SDG12)
Initial Situation
Insights into Survey Results
Outlook on Pedagogical Practice
Closing Remarks
References
Social Inclusion and Competence Acquisition Through Volunteering with Disadvantaged Children, Adol
Introduction
Theoretical Approaches and Definitions
Social Inclusion
Competence Acquisition
Volunteering
Volunteering in the Context of Youth Welfare: Empirical Study Results and Implications for SDGs
Research Design and Methods
Reasons for and Areas of Volunteering
Tutoring
Mentorships
Acquisition of Competences Through Volunteering
Goal-Orientation & Self-Initiative
Commitment & Endurance
Creativity & Flexibility
Self-Reflection & Willingness to Learn
Open-Mindedness & Curiosity
Empathy & Healthy Boundaries
Communication & Cooperation Skills
Responsibility & Reliability
Willingness to Take Risks
Problem-Solving Skills & Use of Knowledge and Information
Analytical Ability & Judgement Skills
Use of Personal Contacts
Professional Skills
Discussion of Results and Implications for SDGs
Conclusion
References
Digitalisation at Workplaces: Challenges, Contextual
Introduction
Key Terms
Digitalisation, Digital Media, and Technologies
Media, Digital, and Web Literacies
Innovation
Research Design
Discussion of Key Findings
Understanding and Acceptance of Digitalisation
Digital Literacies of Employees
Implementation of Digital Tools: Barriers and Potentials
Intensification of Work and the Blurring of the Private and Professional Life
Conclusion and Outlook
References
Re-/Searching for “A Better Life for Everyone”: Overc
Introduction
Forum Theatre as a Method of Participatory Dramatic Research
Project Examples
Quality of Life in Urban Environments: The “ZusammenSpiel” Project
Avoiding Poverty and Reducing Inequality: “Kein Kies um Kurven Kratzen”
Acting in an Environmentally Friendly Way: What is Preventing us from Doing so?
Reflection and Prospect
References

Citation preview

Lernweltforschung

Sandra Hummel · Philipp Assinger · Christian Bauer · Thomas Brudermann · Andrea Jany · Martin Jury · Romana Rauter · Mireille van Poppel   Editors

Shaping Tomorrow Today – SDGs from multiple perspectives

Lernweltforschung Band 39 Series Editors Heide von Felden, Johannes-Gutenberg-Universität Mainz, Mainz, Germany Rudolf Egger, Karl-Franzens-Universität Graz, Graz, Austria

Ausrichtung und Zielsetzung Innerhalb der derzeit dominierenden gesellschaftlichen Entwicklungen wird der Stellenwert der individuellen Handlungsfähigkeit der sozialen Akteure in den Vordergrund gerückt. Schlagworte wie „Wissensgesellschaft“ oder „Civil Society“ weisen auf die zentrale Bedeutung von Lern- und Bildungsprozessen für die politische, ökonomische und kulturelle Entwicklung hin. Diese Entwicklung schlägt entsprechend auf die einzelnen Biografien durch. Mit dem in dieser Reihe entfalteten Programm der Lernweltforschung werden diesbezüglich die hier eingelagerten Vielschichtigkeiten und Eigenwilligkeiten, die überraschenden Umgestaltungen und Suchbewegungen von Subjekten in Lern- und Bildungsprojekten untersucht. Die hier sichtbar werdenden eigensinnigen Aneignungsprozesse werden innerhalb der je konkreten Situationen und Strukturen analysiert. Lernwelten werden dabei zumindest in einer doppelten Bedeutung sichtbar: Sie sind Rahmen und Rahmungen zugleich, Blick und Gegenblick, in denen Erfahrungen (im Rückgriff auf ein System von Regeln) bewertet, als Bestandteile der sozialen Welt durch subjektive Bedeutungszuweisung (re-)konstruiert werden, und in denen auch das „Aneignungssystem“ selbst und der Prozess der Erfahrungsaufschichtung zur Disposition stehen.

Sandra Hummel · Philipp Assinger · Christian Bauer · Thomas Brudermann · Andrea Jany · Martin Jury · Romana Rauter · Mireille van Poppel Editors

Shaping Tomorrow Today – SDGs from multiple perspectives

Editors Sandra Hummel Institute of Educational Sciences ­University of Graz Graz, Austria

Philipp Assinger Institute of Educational Sciences ­University of Graz Graz, Austria

Christian Bauer Institute of Geography and Regional ­Science, University of Graz Graz, Austria

Thomas Brudermann Institute of Environmental Systems ­Sciences, University of Graz Graz, Austria

Andrea Jany Institute of Geography and Regional ­Science, University of Graz Graz, Austria

Martin Jury Wegener Center for Climate and Global Change, University of Graz Graz, Austria

Romana Rauter Institute of Environmental Systems ­Sciences, University of Graz Graz, Austria

Mireille van Poppel Institute of Human Movement Science Sport and Health, University of Graz Graz, Austria

ISSN 2512-1081 ISSN 2512-109X  (electronic) Lernweltforschung ISBN 978-3-658-38318-3 ISBN 978-3-658-38319-0  (eBook) https://doi.org/10.1007/978-3-658-38319-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Responsible Editor: Stefanie Laux This Springer VS imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH, part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Foreword

Shaping Tomorrow Today—The Faculty of Environmental, Regional and Educational Sciences What is and can a “faculty” represent today in the differentiated everyday scientific life of a university beyond an organisational structure for research and teaching tasks? What is the answer to the structure of science specified therein and what “message” does it give to the researchers, teachers, and students involved in it? And how is this idea linked to relevant problems of our society, to the dynamics of the scientific community, to the development of young scientists, or to the interests of students? How can the interaction between the “scientific subjects” and the researchers be supported? Which standards and benchmarks are used to evaluate scientific achievements and burdens, and with which consequences? All of these (and many more) are questions about the self-image of a form of organisation of science: the faculty. In order to answer these questions, debates on strategy are certainly necessary, whose standardised goals and competitive procedures are fundamentally needed, but, in my opinion, even more important for this seems to be patience and the trust in the people working here. Also, sufficient resources, lively freedom, the renunciation of prohibitions on thinking and the support of even risky projects are most likely to enable those forms of self-reflection in science that are characterised by a responsibility to society and robust wisdom. The URBI (Environmental, Regional and Educational Sciences) faculty, founded in October 2007, brings together the four branches of Education Sciences, Sports Sciences, Geography, and Environmental Systems Sciences—and tries to support the demand for its approximately 6000 students, six institutes and two scientific centres. As can be seen in the mission statement, in dealing with social challenges, the focus is on a critical examination of one's own scientific practice in relation to the explanatory and transformative knowledge acquired here. The fact that this can be done successfully has been clearly demonstrated in V

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recent years by the development of the faculty—in teaching and in research. Nevertheless, it is always appropriate to reflect carefully on the tasks and possibilities of the faculty as a living organism to define further developments and the advantages of such an organisational form. The science-specific aspect is probably the most common one because it fundamentally defines the work at universities. At the same time, however, science takes place in a social and life-world space that also influences the most stringent hierarchy of scale levels. How colleagues experience each other in their concrete everyday work, which forms of exchange with co-researchers and co-learners are dominant, determines to a great extent how the “freedom of science” can be combined with the aspiration for freedom for everyone. Now, the idea of a “free science” that follows only its own epistemic rules is an ancient one, but this freedom of science must always be formulated in a concrete and present way. Although the historically grown consensus that scientific actors, their tasks, and (self-controlled) mechanisms in research and teaching are generally familiar to the public, is still alive. Complaints from the scientific community about excessive economisation through New Public Management control models or a withdrawal from the perspective of “Bildung durch Wissenschaft” [education through science] to a pure competence-driven system are also clearly evident. All these elements have a great impact on the “identification of success” of scientific outcomes. Integrated into the university-wide performance and quality assurance systems, the URBI faculty is committed to a philosophy that only the most possible freedom, personal liability, and independence of responsible employees within low-level hierarchies can create quality as an expression of creative solutions to problems. For this reason, it is essential for a faculty management to empower the personal autonomy of all colleagues. Scientific action (besides the subject-specific elements) also has a lot to do with social intelligence, with ideas of self-efficacy, and the creative power to produce (internationally) presentable results. Only the daily noticeable scope for decision making of the individuals, within socially responsible autonomy, can determine science as “a problem-solving community”. The variously developed joint projects and goals must therefore be secured on a voluntary basis within the framework of equal participation. A faculty in this sense can be described as a specific form of an “Allmende”. An “Allmende” describes a kind of “common property”, where the “neighbours” collectively cultivate this field. To use this term to characterise an organisational form such as a faculty emphasises that there is no clear separation between the individual branches of science because every form of creating and communicating knowledge is based on conceptualisations and its communication. This “core business” of scientific and critical thinking emphasises–beyond any discipline-specific boundaries–the

Foreword

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creation of analytical tools that allow every scientist and all interested persons to name, to see, and to understand something that only thereby becomes visible and communicable. On the one hand, each faculty remains a specific mixture of theories, methodologies, and methods that prove their potential in the disciplines. On the other hand, however, there is also the chance to “generate” something that is connectable for all, something that could never be achieved by central planning or by simply gathering the strengths of the individuals. Such a concept of an “Allmende”, of the commons, goes far beyond the idea of a “homo oeconomicus” and leads straight into the direction of a dynamic form of a “scientific community”. The papers presented here try to illustrate such a common process and to create a framework for it within the faculty. The intention of this collection is grouped around the idea that a way out of today's political, economic, cultural, social, and ecological crises can only be found by learning processes. Political-technical strategies affect too rarely and systematically the everyday behaviour of people. The dominant social and environmental developments cannot therefore be managed without increasing the individual capacity of social actors. Learning and educational processes play an essential role because life depends regionally and globally on the fact that unavoidable learning requirements can no longer be denied. The topics addressed in this book currently play an important role at the URBI faculty. For the future it will be necessary to bring these complex questions more into joint research, communication, and development tasks without changing existing structures at the institutes. The main aim is to develop the “branding” of the URBI faculty at the university. Key questions for this are: Which social forms and narratives support people in learning in order to deal with cumulative risks? How can the current social transformations be converted into concrete scenarios of action so that it becomes clear who is how concerned in the processes? What kind of scientifically oriented “storytelling” can make these transformations and their diverse motivations for action more understandable? How can the relationship between real visible problems and the opening of possibilities be established without immediately falling into the continuous strategies of risk shifting through pseudo-national behaviour in the sense of a “Floriani principle” or through apocalyptic “... Aufregungsschäden im Sinne einer ökologischen Tugenddiktatur” (excitement damage in the sense of an ecological virtue dictatorship) (Luhmann 1986, p. 21)? How do people learn to interpret the epidemical, technical, and scientific risk expertise in a challenging way and how do they transform global hazards into regional and life-world contexts? How can social skills support empowerment processes and how can scientific expertise be used to establish substantial connections between the various fields of knowledge and relevant references to individual and social life?

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In a first step, this publication should help to improve the interaction between the individual research groups and make the points of common interest more visible. Subsequently, it should be discussed which formats can broaden the exchanges at the faculty and which thematic guidelines can facilitate concrete references. At the same time, it could also be considered how such a systematic approach to connect our topics can also be used to integrate these interdisciplinary views into the teaching processes to allow students to be more interested in research. To answer the questions asked at the beginning of this article, the major asset of this faculty, for me, is to create a space for as many people (students, teachers, and researchers) as possible, in which they can develop their different potentials in the sense of “Bildung durch Wissenschaft”. This can only be successful, if scientists are also prepared to be aware of the world problems that surround them, of economic and political events, and their commitment to social responsibility. How this can be realised is clearly shown in the following articles written by members of the faculty of Environmental, Regional and Educational Sciences. I would like to thank all URBI staff for their engagement in these activities! Rudolf Egger Dean Faculty of Environmental, Regional and Educational Sciences University of Graz Austria

Contents

Introductory Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Philipp Assinger and Sandra Hummel Sustainability Challenges in Cities, Communities, and Regions The Challenge of Providing Information About Regional Climate Change. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Douglas Maraun Littering in Municipal Public Places: The Role of Personal Factors and Intentions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Julia Neumann and Thomas Brudermann Everyday Disasters in Everyday Lives—Rethinking SDG 11.5 in Times of Multiple Crises. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Nicolas Schlitz, Andrea Jany, Rivka Saltiel and Anke Strüver Correlates of Active Commuting in Austrian Adults: Does Personality Matter?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Matteo C. Sattler, Tanja Färber, Katharina Traußnig, Gottfried Köberl, Christoph Paier, Pavel Dietz and Mireille N. M. van Poppel Sustainability Learning in Schools and Other Educational Institutions How Closing Small Schools in Rural Regions Affects Community Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Filippina Risopoulos-Pichler, Judith Pizzera and Bärbel Hausberger

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Reaching the ‘Hard to Reach’: Implementing “Education for All” by Participative Action Research and Students’ Voices. . . . . . . . . 141 Edvina Bešić, Lisa Paleczek, Julia Ladenstein and Barbara Gasteiger-Klicpera Addressing Teacher Shortages to Achieve Inclusive and Equitable Education for All: Policies for the Supply of and Demand for Qualified Teachers in Austria and South Africa. . . . . . . . . . . . . . . . . . . 161 Vasileios Symeonidis and Irma Eloff Urban Disparities in Inclusive and Sustainable School Cultures in Graz, Austria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Melina Tinnacher, Heike Wendt and Nora Luschin-Ebengreuth Learning for Democratic Sustainability in Migration Societies? Aspects of Citizenship Education in Urban Contexts. . . . . . . . . . . . . . . . . 215 Annette Sprung, Brigitte Kukovetz and Petra Wlasak Interdisciplinary Practical Trainings as a Contribution of Higher Education to Raising Sustainability Awareness Among the Public . . . . . . 235 Ulrike Gelbmann and Christian Pirker Actors for Learning and Implementing Sustainability Social Work Organisations as Sustainable Actors: Characteristics and Perspectives of Ecologically Oriented Work Integration Social Enterprises. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Maria Anastasiadis Sustainability Aspects and Educational Relevance on Social Media Influencers for Children and Adolescents. . . . . . . . . . . . . . . . . . . . . 281 Lisa Mittischek and Ines Waldner Social Inclusion and Competence Acquisition Through Volunteering with Disadvantaged Children, Adolescents, and Families. . . . . . . . . . . . . . 301 Elias Schaden Digitalisation at Workplaces: Challenges, Contextual Factors and Innovation Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Sabine Klinger, Romana Rauter and Susanne Sackl-Sharif Re-/Searching for “A Better Life for Everyone”: Overcoming Habits and Exploring New Behaviours and Solutions for Crises and Problems by Using Participatory Dramatic Research Methods. . . . . . . . . . . . . 355 Michael Wrentschur

Editors and Contributors

About the Editors Sandra Hummel is an educational scientist in the research area ‘Empirical Learning World Research and Higher Education Didactics’ at the Institute for Educational Sciences at the University of Graz. Her work focuses on higher education didactics as well as on learning and teaching in the context of educational innovations. She is the coordinator of several EU projects that aim at the learnercentred development of educational technologies. Philipp Assinger  works at the Department of Educational Sciences at the University of Graz as an Assistant Professor for Continuing Vocational Education and Training. His research is concerned with the development of vocational competencies in and outside the workplace environment. He has also been working on validation of prior learning and competence development in the Austrian wood processing industry. Christian Bauer is Senior Lecturer for Physical Geography at the University of Graz, Institute of Geography and Regional Science. His research focuses on geomorphology and geomorphometry in karst terrains, applied geomorphological topics using GIS (natural hazards) as well as human-environment interactions. His interdisciplinary research experience involves different disciplines, including Geosciences, System Science and History. Thomas Brudermann  is an associate professor at University of Graz. He has been working for various national and international research institutions, such as International Institute for Applied System Analysis, Vienna University of Economics and Business, National Institute for Environmental Studies (Japan) and the Asian Institute of Technology (Thailand). He works on various topics in the XI

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interdisciplinary field of sustainability and innovation research, with a focus on decision making and decision analysis. Andrea Jany  is a researcher in the field of housing. She holds a Ph.D. in Architecture of Graz University of Technology in Austria. Her research focuses on resident satisfaction and housing requirements in combination with participatory concepts. She participated in various interdisciplinary national research projects financed by e.g. Climate and Energy Fund, FFG, and state government. Currently she is contributing leadauthor for housing for the APCC Special Report 2022 and part of the scientific advisory board of the 1st Austrian Climate Council.  Martin Jury  is a climate scientist at the Wegener Center for Climate and Global Change at the University of Graz. His research spans from climate and climate impact model evaluation, the quantification of climate change impacts on and the communication of them to society. He participated in several national and international research projects. Recently, he served as Chapter Scientist for the IPCC AR6 WGI Chapter 10: Linking global to regional climate change. Rauter Romana  is Associate Professor on Sustainability and Innovation Management at the Institute of Systems Sciences, Innovation and Sustainability Research at University of Graz. In her research she explores ways of how companies can advance on sustainability which includes, amongst others, managing and measuring sustainability innovations or developing new and sustainable business models. Romana has (co)-authored numerous scientific publications in these fields and is Co-Chair of the International New Business Models Conference Series. Mireille N. M. van Poppel  is professor in Physical Activity and Public Health at the Institute of Human Movement Science, Sport and Health at the University of Graz. Her work focuses on the role of physical activity in maintaining and improving health in different target groups. This includes understanding the determinants of physical activity and the development and evaluation of interventions for the promotion of physical activity.

Editors and Contributors

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Contributors Maria Anastasiadis  Institute for Educational Science, University of Graz, Graz, Austria Philipp Assinger Institute of Educational Science, University of Graz, Graz, Austria Edvina Bešić  Institute for Secondary Teacher Education, University College of Teacher Education Styria, Graz, Austria Thomas Brudermann  Institute of Systems Sciences, Innovation & Sustainability Research, University of Graz, Graz, Austria Pavel Dietz  University Medical Center, University of Mainz, Mainz, Germany Irma Eloff  University of Pretoria, Pretoria, South Africa Tanja Färber Institute of Psychology, University of Bamberg, Bamberg, Germany Barbara Gasteiger-Klicpera  Institute of Education Research and Teacher Education, Inclusive Education Unit, University of Graz, Graz, Austria Ulrike Gelbmann  Institute of Systems Sciences, Innovation and Sustainability Research, University of Graz, Graz, Austria Bärbel Hausberger  Institute of Geography and Regional Science, University of Graz, Graz, Austria Sandra Hummel Institute of Educational Sciences, University of Graz, Graz, Austria Andrea Jany  RCE Graz-Styria—Centre for Sustainable Social Transformation, University of Graz, Graz, Austria Sabine Klinger Institute of Educational Sciences, University of Graz, Graz, Austria Brigitte Kukovetz  Institute for Educational Science, Department ‘Migration— Diversity—Education’, University of Graz, Graz, Austria Gottfried Köberl  Weizer Energie-Innovations-Zentrum GmbH, Weiz, Austria Julia Ladenstein  Institute of Education Research and Teacher Education, Inclusive Education Unit, University of Graz, Graz, Austria

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Editors and Contributors

Nora Luschin-Ebengreuth  Institute for Early Childhood and Primary Teacher Education, University of Teacher Education Styria, Graz, Austria Douglas Maraun  Wegener Center for Climate and Global Change, University of Graz, Graz, Austria Lisa Mittischek Institute of Educational Sciences, University of Graz, Styria, Austria Julia Neumann Institute of Systems Sciences, Innovation & Sustainability Research, University of Graz, Graz, Austria Christoph Paier  Institute of Human Movement Science, Sport and Health, University of Graz, Graz, Austria Lisa Paleczek  Institute of Education Research and Teacher Education, Inclusive Education Unit, University of Graz, Graz, Austria Christian Pirker Institute of Systems Sciences, Innovation and Sustainability Research, University of Graz, Graz, Austria Judith Pizzera Institute of Geography and Regional Science, University of Graz, Graz, Austria Mireille N. M. van Poppel Institute of Human Movement Science, Sport and Health, University of Graz, Graz, Austria Romana Rauter Institute of Systems Sciences, Innovation & Sustainability Research, University of Graz, Graz, Austria Filippina Risopoulos-Pichler Institute of Geography and Regional Science, University of Graz, Graz, Austria Susanne Sackl-Sharif  University of Music an Performing Arts Graz, Graz, Austria Rivka Saltiel  Institute of Geography and Regional Sciences, University of Graz, Graz, Austria Matteo C. Sattler Institute of Human Movement Science, Sport and Health, University of Graz, Graz, Austria Elias Schaden Work Area Social Pedagogy at the Department of Educational Science, University of Graz, Graz, Austria

Editors and Contributors

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Nicolas Schlitz Institute of Geography and Regional Sciences, University of Graz, Graz, Austria Annette Sprung Institute for Educational Science, Department for Migration, Diversity and Education, University of Graz, Graz, Austria Anke Strüver Institute of Geography and Regional Sciences, University of Graz, Graz, Austria; RCE Graz-Styria—Centre for Sustainable Social Transformation, University of Graz, Graz, Austria Vasileios Symeonidis  University of Graz, Graz, Austria Melina Tinnacher  Institute of Education Research and Teacher Education, University of Graz, Graz, Austria Katharina Traußnig  Institute of Psychology, University of Graz, Graz, Austria Ines Waldner  University Collage of Teacher Education Styria, Styria, Austria Heike Wendt  Institute of Education Research and Teacher Education, University of Graz, Graz, Austria Petra Wlasak  Institute for Educational Science, Department ‘Migration—Diversity—Education’, University of Graz, Graz, Austria Michael Wrentschur Institute for Educational Sciences, University of Graz, Graz, Austria

Introductory Overview Philipp Assinger   and Sandra Hummel  

A World of Multi-Dimensional Crises At the time of writing this introduction during late summer and fall of 2021, major parts of the world were in crisis mode. The COVID-19 pandemic was at the beginning of its fourth wave spreading in most parts of the world and putting pressure on health care systems while further contributing to increasing unemployment, inequality and poverty around the world; unprecedented forest fires caused by heat and arson were raging in the South of Europe, Russia, Africa, South America and Australia; thunder storms and heavy rain hit Germany, Austria and China taking away homes from dozens of families; warmonger regimes in countries like Afghanistan were seizing power, oppressing civilians and making migration a heavily-discussed topic in Europe and countries in the Middle-East and Asia. There are certainly more events which currently characterise the world. If we take a step back and abstract from this snapshot of single events and contextualize them, we can say that there are several long ranging developments and complex issues confronting the global community as well as regional and local communities with significant challenges. To name just a few of them: climate change, demographic change and migration, globalisation and regionalisation, political governance and democracy, social divides and inclusion/exclusion, digitalisaP. Assinger · S. Hummel (*)  Institute of Educational Sciences, University of Graz, Graz, Austria e-mail: [email protected] P. Assinger e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 S. Hummel et al. (eds.), Shaping Tomorrow Today – SDGs from multiple perspectives, Lernweltforschung 39, https://doi.org/10.1007/978-3-658-38319-0_1

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tion and work, education and lifelong learning. These developments and issues are largely interdependent, defining the existing circumstances of crises as multidimensional. To address these multi-dimensional crises, the Sustainable Development Goals (SDGs) were defined by the United Nations (UN) and have been promoted for implementation world-wide. The SDGs themselves, and measures attaining to the intention of the SDGs, mark the starting point and define the framework of this edited volume.

The Sustainable Development Goals The international community has addressed global challenges such as those mentioned above on numerous occasions throughout the last fifty years. For instance, in 1972, the Stockholm Declaration by the UN, the first to raise awareness for environmental issues, and the well-known Club of Rome publication The Limits to Growth set a direction, which was continued a decade later in the so-called Brundtland Report. This report contained the first definition of “sustainable development” as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (World Commission on Environment and Development, 1987). Thirteen years later, in 2000, the Millennium Development Goals (MDGs) were adopted by the UN Member States. While the MDGs achieved considerable success, it is argued repeatedly that they failed to capture the interconnections between the areas of ecological, economic and social sustainability that are commonly implied when talking about sustainable development (Independent Group of Scientists, 2019, p. 3). In 2012, at the United Nations Conference on Sustainable Development, the UN Member States decided to define new priorities to spur up action towards a more sustainable world. On September 25, 2015, at the United Nations General Assembly, a Resolution entitled Transforming our world: the 2030 Agenda for Sustainable Development (United Nations General Assembly, 2015) was finally adopted and came into effect on January 1, 2016. The resolution presented seventeen Sustainable Development Goals (see Fig. 1) with 169 sub-targets intended to guide political decisions over a period of fifteen years until 2030. To overcome the “thematic silos” (Independent Group of Scientists, 2019, p. 3), that were considered the weak spot of the MDGs, it was stated that the SDGs “are integrated and indivisible, global in nature and universally applicable, taking into account different national realities, capacities and levels of development and respecting national policies and priorities.” Moreover, to further stimulate

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Fig. 1   Overview of the 17 Sustainable Development Goals

political implementation, it was agreed that “targets are defined as aspirational and global, with each government setting its own national targets guided by the global level of ambition but taking into account national circumstances.” (United Nations General Assembly, 2015, p. 13). Both, the Austrian national government and the regional government of Styria have both adopted their own measures in order to contribute to the SDGs.1 To get an overview of national and regional initiatives, a report by the Klimabündnis Österreich includes several best-practice examples at the municipal or regional

1 For

Styria e.g. https://www.nachhaltigkeit.steiermark.at/

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level for each of the 17 SDGs.2 In addition to the political initiatives, a significant effort is made by civil society initiatives trying to raise awareness and implement concrete projects, for instance in the area of sustainable lifestyle. A very successful example from Graz is the Verein Nachhaltig in Graz.3 What has been criticised by social scientists about the approach taken in strategies for sustainable development, such as in the case of the SDGs, is the often too narrow focus on political decisions and political programmes. While political decisions and programmes are without a doubt of great importance, the cultural aspects of sustainable development, those activities and behaviours, which are situated in the everyday life of the people, are actually at the heart of what enables or inhibits progress. John Clammer, a sociologist involved in cultural anthropology and development sociology, holds that “all specific dimensions of sustainable practice (…) have deeply cultural characteristics since all require behavioral [sic] changes and involve re-orientations of notions of self-relationships, daily lifestyle decisions, travel patterns, and such obviously cultural processes (…)” (Clammer, 2016, p. 6). From the standpoint of universities with their threefold mission of research, teaching and public knowledge transfer, the view on culture, behaviour and daily lifestyle helps to reconceptualize sustainable development in a way that puts individual learning, educational intentions but also institutional arrangements at the center of attention. Education and learning, institutionalised and formalised in schools, universities or adult education centres, as well as occurring in the workplace, in third-sector projects or in civil society initiatives can be understood essentially as a social practice “in which the production, transformation, and change in the identities of persons, knowledgeable skill in practice, and communities of practice are realized [sic] in the lived-in world of engagement in everyday activity” (Lave & Wenger, 1991, p. 47).

Sustainable Development and Universities in Austria Universities are powerful actors on the path to a sustainable society. They have multiple functions as research and educational institutions including those as knowledge creators and multipliers, consumers and producers of resources,

2 See: 3 See:

https://www.klimabuendnis.at/sdg-broschuere-lokal-gemeistert https://nachhaltig-in-graz.at/

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employers and role-model institutions, or actors with a significant regional impact (Zimmermann & Risopoulos-Pichler, 2016). To contribute towards sustainable development, universities actively promote inter- and transdisciplinary as well as system-oriented and transformative approaches to research and teaching so that the multi-dimensional character of sustainability issues can be addressed. Universities in Austria have taken up the responsibility to contribute to a knowledge base and systematically analyze and develop solutions to economic, social and ecological problems. Section 1 of the Austrian Universities Act 2002, which was reaffirmed in 2020 in the Manifest for Sustainability of the Universities Austria, calls for universities to hold themselves responsible for their actions towards society and the environment. For instance, building on the Austrian Universities Act, universities have joined together to form the Alliance for Sustainable Universities in a commitment towards a comprehensive, global and intergenerational understanding of sustainable development. Sustainability is considered one of the basic ethical concerns of universities and encompasses in this conceptualisation ecological, social, economic and cultural dimensions, the latter making a significant contribution to the networking and integration of the other three.4 The University of Graz operates a sustainability policy, which provides guidelines not only for research and teaching but also in relation to organizational areas such as waste and sanitary management, renewable resources, mobility of staff and students, or communication and cooperation with students and with partners outside the university.5 We must not forget that, after all, a university that wants to change society to the better, might also think “beyond sustainability” as a matter that lies outside the university, as the philosopher of higher education Ronald Barnett put it (2018, pp. 42–55). Discussing the university as an ethical institution in a self-conscious and self-reflective way is, thus, as important as the provision of solutions to societal problems since it reminds us that universities are themselves part of and intertwined in the multi-dimensional crises.

4  See

the Memorandum of Understanding of the Alliance for Sustainable Universities: http://nachhaltigeuniversitaeten.at/wp-content/uploads/2020/12/Memorandum-of-Understanding-der-Allianz-inkl.-NH-Verstaendnis_2020_Final.pdf 5 See the Sustainability Guidelines of the University of Graz: Umweltleitlinien – EMAS: Umweltmanagement an der Universität Graz (uni-graz.at).

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The Faculty of Environmental, Regional and Educational Sciences and the Aim of This Edited Volume In 2007, the Faculty of Environmental, Regional and Educational Sciences (German: Umwelt- Regional- und Bildungswissenschaften, URBI) was established at the University of Graz. This interdisciplinary faculty has brought together the branches of Educational Science, of Geography, of Sports Science, and of Environmental Systems Science including several research centres like the Centre for the Professionalization of Early Childhood Education, the Centre for Sustainable Social Transformation, the Regional Center for Didactics of Geography and Economics, the Wegener Centre for Climate Research, the Center for Digital Teaching and Learning or the Center for Teaching Competence. The URBI faculty has projected its work on an overarching approach, in which scientific analyses and the development of practice-oriented solutions complement each other in the analysis of problematic social issues.6 Considering the complexity emerging from such multi-dimensional crises as sketched out in the beginning, it seems rather obvious that unidimensional research approaches and hierarchical technocratic strategies are bound to fall short since they can hardly account for the multitude of intertwined issues. Also, as was mentioned, people, their behaviour and their habits are at the source of  many problems. Therefore, it seems imperative to take people, their multifaceted nature and the necessary learning and educational processes into account when intending to create sustainable solutions and move towards a better life for everyone. The broad thematic agenda and the variety of scientific approaches represented at the URBI faculty amount to a spectrum which offers a unique view on today’s – political, economic, cultural, social and ecological – multi-dimensional crises, their interplay, and on possible solutions manifesting in institutional, educational, learning, and knowledge transfer arrangements. A first joint project, collecting contributions from members of all URBI institutions, was published in 2010 under the title Interdisziplinarität. Wissenschaft im Wandel [Interdisciplinarity. Science in Transformation] (Lenz, 2010). The intention at that time was to show the potential of interdisciplinarity in research, teaching and administration. Ten years later, in early 2020, a group of researchers from all URBI institutions came together to discuss a further joint publication.

6 See

the Mission Statement of the URBI faculty: Mission Statement – Faculty of Environmental, Regional and Educational Sciences (uni-graz.at).

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The group formed the URBI editorial board and agreed to devise a concept using the SDGs as a starting point for a joint investigation of the commonalities of the various thematic, theoretical, and methodological approaches represented at the faculty. It was intended to lay grounds for common projects and interdisciplinary approaches that could possibly be pursued in the future. The following guiding questions were, thus, defined and communicated to the faculty staff as a thematic framework for submissions to this edited volume: • Which kinds of measures (e.g., political, educational, administrative, civil society) can support and facilitate broader individual participation in democratic practise and economic development for sustainable societies? • What could be the contribution of public institutions and private organisations of all kinds (e.g., national, regional, municipal governments and administration, NGO’s/NPO’s, industrial organisations, voluntary associations) to manage multi-dimensional crises? • Which topical issues (e.g., climate change, human rights, digitisation, consumption, sustainable economies) and which form of knowledge, skills and competencies are to be promoted in the discourse concerning sustainable development? • What kind of research and teaching methodologies (e.g., participatory research, service learning) could better involve critical stakeholders and are suitable from the perspective of research/teaching quality and stakeholder needs? The aim of the book is to foster and promote a shared understanding of research at the faculty, including among the faculty leaders, the research and teaching staff as well as the students and the partner institutions. This edited volume is therefore addressed to a broad academic and non-academic audience. Contributions are explicitly interdisciplinary and multi-methodological, and discuss issues of interest to readers with a background in the natural and social sciences as well as in the humanities and in educational science. This edited volume is particularly intended for students enrolled in the URBI faculty and taking courses in the faculty’s compulsory introductory module. Students are introduced to a variety of topics, research approaches, methodologies and theories incorporated in field of environmental, regional and educational sciences and worked on in profound way during the various Bachelor and Master Programs. As for the research community, the edited volume is intended to serve researchers of all disciplines con-

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cerned with a broad perspective on sustainable development, perhaps leading to cooperative projects in the future.

How this Volume was Organized To account for the scientific quality of the contributions and to assure that the contributions meet criteria of good scientific practice, the editorial board decided to have all contributions  went through a double-blind review process. This review process consisted of two stages: In the first stage (completed in June 2020), URBI faculty staff was invited to submit extended abstracts (max. 2 pages) addressing the four broad research questions. The submissions were reviewed by all members of the editorial board. Authors, then, received feedback and authors who provided abstract that met the submission criteria were invited to prepare a full chapter. In the second stage, full chapters were reviewed anonymously by at least two members of the editorial board (completed in October 2021). Given the interand multidisciplinary nature of the project, the review process not only focused on methodological soundness and compliance with scientific standards in the respective discipline(s), but also on readability from the perspective of a broader audience. Therefore, one reviewer from the same discipline as the author(s) of a chapter was selected to assess the disciplinary perspective of the contribution, while the other reviewer was from a different discipline focusing on the aspects of readability and intelligibility. The editorial board received a great variety of contributions. This variety, however, presented us with the challenge of clustering the contributions in way that is coherent with and adequate to the thematic framework and guiding questions of this edited volume. Finally, we decided on three broad thematic clusters. The clusters are: (A) sustainability challenges in cities, communities and regions, (B) sustainability learning in schools and other educational institutions, and (C) actors for learning and implementing sustainability. A short notice concerning the visual presentation of the edited volume and the contributions: The icons of the seventeen SDGs are placed at the cover page of each thematic cluster whereby all SDGs addressed in the contributions of each cluster are colored while those not addressed are grey. The same was applied in a smaller version to the first page of each contribution to make the reader immediately aware of the SDGs referred to in the contribution.

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A. Sustainability Challenges in Cities, Communities and Regions The volume opens with a chapter by Douglas Maraun. He discusses the challenges arising from the provision of information concerning regional climate change to users such as decision makers, engineers, companies, NGO’s or farmers. Successful climate action depends on the collaboration among scientists, and between scientists producing evidence and users who make the decisions. As Maraun emphasizes, such collaboration needs to be sustained across the whole process of research and implementation of evidence-based measures. Littering, that means misplacing solid waste in public spaces, is a problem of global scale. Peoples’ intentions to avoid littering at municipal public places are addressed by Julia Neumann and Thomas Brudermann. Reporting results from a quantitative survey conducted in Frankfurt am Main, the authors explore what role personal factors play for such intentions to avoid littering. Results are provided to support municipalities in dealing with littering-behavior so that its negative impacts could be reduced. The active commuting behavior of the population is of great importance for mobility and health policies. Encouraging people to cycle or walk positively affects individual health and the environment. But is there are relationship between personality traits and the commuting behavior? This question is addressed by Matteo Sattler and colleagues, who use a multivariable model to analyze how strong a predictor of commuting behavior personality traits actually are. A critical perspective onto the goal of reducing the number of people affected by disasters is presented by Nicolas Schlitz, Andrea Jany, Rivka Saltiel and Anke Strüver. This chapter picks up on the interrelations between the Covid-Pandemic and the crisis regarding private care work. Arguing that the Covid-Pandemic has further aggravated the privatization of reproductive work and the commodification of care work, the authors suggest reconceptualizing basic categories of the Sustainable Development Goals. Learning for democratic sustainability in migration societies is the topic by Brigitte Kukovetz, Annette Sprung and Petra Wlasak. The authors explore how Active Citizenship Education and Education for Sustainable Development can contribute to participation and civic engagement in urban communities among both, people shortly arrived in the city and those, who have lived there for many years. In addition to that, the authors critically reflect on the concept of civic learning as raised in the Sustainable Development Goals.

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B. Sustainability Learning in Schools and Other Educational Institutions Filippina Risopoulos-Pichler, Judith Pizzera and Bärbel Hausberger present results from a study, which applied a mixed-methods research design to inquire what consequences the closing of schools in small rural municipalities in the south-west of Styria had on the quality of community life and on the well-being of the children commuting to schools away from their home. The authors make a strong case for the importance of schools as centers of community building in rural areas. Based on estimations that globally around 69 million teachers need to be recruited within the next ten years to guarantee quality education, Vasileios Symenonidis and Irma Eloff explore how national policies in South Africa and in Austria address this massive teacher shortage. Positive developments in both cases are according to the authors somewhat outweighed by the fact that policies are narrowly focused on national solutions. The tendency to concentrate on the provision of teachers rather than on a comprehensive reconceptualization of education is also addressed critically. Edvina Bešić, Lisa Paleczek, Julia Ladenstein and Barbara Gasteiger-Klicpera introduce the „Inclusive Inquiry”. This approach brings together primary school teachers and students in a cooperative and participative research process to support the creation of inclusive school lessons. Data on the “Inclusive Inquiry” collected through 18 focus group interviews with 72 primary-school students was analyzed to learn about how students perceived the cooperatively created lessons and how their ideas and wishes were considered by the teachers in the lesson planning process. The research presented by Melina Tinnacher, Heike Wendt and Nora LuschinEbengreuth looked at school websites and how activities concerning diversity, inclusion and sustainability are made visible on the websites. While in general, activities reported on the websites are few, the research still allows for substantive conclusions concerning the promotion of diversity, inclusion and sustainability in the everyday school life as well as concerning future research on the public communication of schools’ activities. Ulrike Gelbmann and Christian Pirker ask how universities can raise awareness for sustainability issues among the general public. Interdisciplinary Practical Trainings as a method of service learning are presented for student training and for raising awareness among the public within a cooperative framework. Based on data gathered in the Master’s Program Global Studies, Gelbmann and Pirker highlight the importance of an inter- and transdisciplinary approach used to address real world problems within higher education didactics.

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C. Actors for Learning and Implementing Sustainability Within Austrian labor market measures, an underestimated contribution is made by Ecologically Oriented Work Integration Social Enterprises (ECO-WISE). They not only give people at risk of permanent labor market exclusion a meaningful work and support their social integration but they often operate in business areas of ecological relevance such as waste management, organic food production or recycling and repairing of consumer goods. Within this context, Maria Anastasiadis discusses the relation between social work, which is a core activity within ECO-WISE, and the achievement of the Sustainable Development Goals. Another area that is often underrepresented in the scientific discourse is that of voluntary work. Elias Schaden explores the potential of formal volunteering in youth and welfare organizations and how it relates to the social inclusion of disadvantaged shares of the population on the one hand and to competence acquisition of volunteers and beneficiaries on the other hand. Based on a study conducted in Graz, Stuttgart and Rosenheim, Schaden points out avenues for further research and for pragmatic action on the macro-, meso- and micro-level of the social welfare system. The digitalization of the workplace in for-profit and non-profit organizations is at the center of the chapter by Sabine Klinger, Romana Rauter and Susanne Sackl-Sharif. Executives and employees’ perspectives were inquired by the means of quantitative and qualitative research methods to learn more about what determines the usage of digital media and technologies in the workplace and what needs to be done to successfully implement digital technology in workplace settings. Social media influencer have become a global phenomenon with an astonishing presence as marketing multipliers. Lisa Mitischek and Ines Waldner take this phenomenon as the starting point for asking how influencers affect the reality of young people’s lives in terms of nutrition and consumer literacy, and which challenges arise from this with regard to intersectional media didactics. Preliminary results from a project involving school-children and teachers as well as university students and lectures are summarized to answer this question. In the final contribution, Michael Wrentschur recounts methodological principles and examples from dramatic research projects in the areas of life in cities, poverty and social inequality, climate protection and sustainable production and consumption. Dramatic research methodology, such as applied within Forum Theatre projects, is presented as an option to participatory research involving and supporting people directly affected by social problems to acquire knowledge and creative skills needed for a “good life”.

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References Barnett, R. (2018). The Ecological University. A Feasible Utopia. London, New York: Routledge. Clammer, J. (2016). Cultures of Transition and Sustainability. Culture after Capitalism. New York: Palgrave Macmillan. Independent Group of Scientists (2019). Global Sustainable Development Report 2019: The Future is Now – Science for Achieving Sustainable Development. New York: United Nations. Lave, J., Wenger, E. (1991). Situated learning. Legitimate peripheral participation. Cambridge: Cambridge University Press. Lenz, W. (2010). Interdisziplinarität. Wissenschaft im Wandel. Wien: Löcker. United Nations General Assembly (2015). Transforming our world: the 2030 Agenda for Sustainable Development. Online: https://sustainabledevelopment.un.org/post2015/ summit [13.12.2021] World Commission on Environment and Development (1987). Our Common Future. Oxford: Oxford University Press. Zimmermann, F. M., Risopoulos-Pichler, F. (2016). Bildung und Forschung für nachhaltige Entwicklung – eine Notwendigkeit im 21. Jahrhundert. In: Zimmermann, Friedrich M. (Hg.): Nachhaltigkeit wofür? Berling, Heidelberg: Springer Fachmedien.

Philipp Assinger  works at the Department of Educational Sciences at the University of Graz as an Assistant Professor for Continuing Vocational Education and Training. His research is concerned with the development of vocational competencies in and outside the workplace environment. He has also been working on validation of prior learning. Sandra Hummel is an educational scientist in the research area ‘Empirical Learning World Research and Higher Education Didactics’ at the Institute for Educational Sciences at the University of Graz. Her work focuses on higher education didactics as well as on learning and teaching in the context of educational innovations. She is the coordinator of several EU projects that aim at the learner-centred development of educational technologies.

Sustainability Challenges in Cities, Communities, and Regions

The Challenge of Providing Information About Regional Climate Change Douglas Maraun Introduction It is unequivocal that human influence has warmed the atmosphere, ocean and land. Widespread and rapid changes in the atmosphere, ocean, cryosphere and biosphere have occurred (IPCC, 2021). Because of the inertia of the climate system, these changes will continue over the next decades. Global warming beyond 2050 will strongly depend on future anthropogenic greenhouse gas emissions (IPCC, 2021). The impacts of climate change are mostly felt at the regional scale. Trends towards higher mean temperatures have already emerged from internal climate variations, precipitation trends in many regions. Many wet regions have experienced wetting, many dry regions a drying (Gutièrrez et al., 2021). Also extreme events have changed and will continue to change (Seneviratne et al., 2021). These range from large-scale events such as the 2003 European heat wave or the 2018 European drought, to local rainfall extremes, which in turn may trigger flash floods or landslides. Climate change poses future risks for ecosystems and many societal and economic sectors such as agriculture and forestry, transport, productivity and human health. The impacts of climate change are particularly strong in the global south, which is both vulnerable and exposed to strong climatic changes. For instance,

D. Maraun (*)  Wegener Center for Climate and Global Change, University of Graz, Graz, Austria e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 S. Hummel et al. (eds.), Shaping Tomorrow Today – SDGs from multiple perspectives, Lernweltforschung 39, https://doi.org/10.1007/978-3-658-38319-0_2

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climate change will amplify existing water stress and very likely reduce cereal crop productivity in Africa (Niang et al., 2014). Under strong global warming (RCP8.5), some subtropical regions will regularly experience heatwaves with temperatures and humidity beyond physiological limits (Newth and Gunasekera, 2018; Pal and Eltahir, 2016). Successful adaptation to climatic changes is thus key to meeting the United Nations sustainable development goals (SDGs, https://sdgs.un.org/): the goals of zero hunger, good health, no poverty, life below water and life on land are directly affected by the impacts of climate change (Allen et al., 2018). It has been shown that climate change affects women stronger than men (UN Women Watch, 2009) and the poor stronger than the rich (IPCC, 2014), such that also the goals of gender equality and reduced inequality within and among countries are impeded. Ultimately, conflicts arising from climate change may threaten the goal of peace (IPCC, 2014). To limit the impacts of climate change—in particular beyond the limits of adaptability—the Paris agreement has been adopted by 196 parties at COP21 in December 2015. The SDGs on climate action, clean energy, responsible consumption and production and sustainable cities all contribute to mitigating climate change and thus meeting the Paris agreement (Allen et al., 2018). Physical climate research—understanding the climate system, the causes of past variability and trends, and projecting its future evolution—is a formidable and exciting endeavour. A range of grand challenges has been identified (https://www. wcrp-climate.org/grand-challenges/grand-challenges-overview): understanding the interplay between clouds, circulation and climate sensitivity, which crucially determines how strong the climate responds to changes in greenhouse gas concentrations; melting ice and its global consequences; regional sea level rise; carbon feedbacks, which control the carbon-budget and thus the atmospheric CO2 concentrations; near term climate predictions of the next season to decade; changes in the water cycle; and extreme weather and climate events in a warming climate. Other questions such as how weather changes with climate, and how climate change affects habitability may be added to this list (Marotzke et al., 2017). These challenges are interesting and exciting from a point-of-view of purely curiosity-driven science alone, but all have a high societal relevance as well and link directly to the listed SDGs. For several reasons, climate research is in a special situation compared to many other scientific disciplines, although none of its peculiarities is unique to climate science: 1. Climate research targets the complex and global climate system, its internal interactions, and interactions with other systems such as the biosphere. As

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such, the possibility to conduct experiments is very limited. While ­individual local aspects can be studied by field or even laboratory experiments, it is impossible to conduct controlled experiments of how the climate system responds to external forcings. Climate science has this aspect in common with other earth and planetary sciences and many branches of complex system sciences. 2. Many questions of climate change are still a matter of basic research, but at the same time strongly driven by societal demands. Thus climate research inherently requires trans-disciplinary collaboration. Climate research is under pressure to operationalise its outcome, which in many cases still may have a preliminary character. Therefore, it is not a value-free arena, and climate scientists often find themselves in an ethical communication and provision dilemma. This setting has been referred to as post-normal, where facts are uncertain, values in dispute, stakes high and decisions urgent (Funtowicz and Ravetz, 1993). Here, climate research is similar to other disciplines such as demonstrated recently for virology, epidemiology and mathematical modelling during the COVID19 pandemic. 3. Projections of future climate, a key outcome of climate research, are out-ofsample projections and consider centennial-scale time-horizons. But they are required by stakeholders in the near future and therefore fundamentally cannot be verified before being issued. Thus climate research requires approaches to generate trust in climate projections, and to assess and communicate their limitations. Again, a close similarity exists with research on the COVID19 pandemic, which provided a basis for political decision making during the pandemic. The aim of this chapter is to lay out the process of climate research using regional climate change research as example. Several of the challenges listed above are related to regional climate change, climate risk assessments, and adaptation to climate change. But while we have relatively good knowledge to support mitigation decisions, our understanding of regional changes and the regional impacts of climate change is still very limited. Thus, regional climate research is a great example to discuss the challenges and peculiarities of climate research in a nutshell. After presenting the context of regional climate change research, I will discuss the construction of climate information. Then I will lay out the challenges in stakeholder collaboration to produce and communicate relevant climate information, and finally give an overview of the organisation of climate research from the local to the international level.

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The Context of Regional Climate Research Regional climate is mainly determined by solar insolation, that is geographical latitude, the circulation cells of the atmospheric general circulation, ocean currents, the distribution of oceans and land masses, and orographic features such as mountain ranges (Goosse, 2015; Hartmann, 2015; Maraun and Widmann, 2018). Latitude explains the main temperature gradients between equator and poles. The circulation cells define regions of rising air—and thus rain—and regions of sinking air—and thus dryness. For instance, in the Hadley cell, solar heating along the thermal equator drives convection—upward motion causing strong thunderstorms and heavy rain—and thereby creates the Inner Tropical Convergence Zone (ITCZ). The rising air diverges polewards, is diverted by the Coriolis force, and sinks over the sub-tropics, thereby creating the big deserts such as the Sahara. In the mid-latitudes, the strong meridional temperature gradients create the polar jet and the storms in the west wind belt. Fluctuations in the jet stream are responsible for much of our weather variability such as persistent blocking high pressure systems that bring heatwaves and drought in summer (Woollings, 2010). Ocean currents such as the wind-driven Gulf stream transport heat towards high latitudes, whereas the equatorward Humboldt or Benguela currents carry cold water masses towards low latitudes. Regions close to the sea are characterised by a mild and wet—maritime—climate, whereas regions further inland experience hotter summers, colder winters and less rain, i.e., a continental climate (Goosse, 2015). Mountain ranges cause phenomena such as orographic precipitation on the windward side, dryness in the leeside, temperature gradients with height, Föhn winds and local wind systems on slopes and in valleys (Barry, 2008). Regional climate variability is influenced by large-scale modes of variability such as the North Atlantic Oscillation, the El Nino/Southern Oscillation, the Atlantic Multidecadal Oscillation (AMO) or the Pacific Decadal Oscillation (PDO), which are internal random fluctuations of the climate system, and their teleconnections, i.e., their remote influences (Goosse, 2015). Local feedbacks, e.g., between temperature and soil moisture or snow, albedo and temperature, can modulate climate variability (Goosse, 2015; Hall et al., 2008; Seneviratne et al., 2010). Regional climate change is determined by thermodynamic and dynamic effects, and their interaction (Shepherd, 2014). Thermodynamic effects are directly controlled by rising temperatures, such as snow melt or increasing

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a­ tmospheric moisture. Dynamic effects are related to changes in the atmospheric circulation patterns and are themselves driven by differential warming signals, e.g., in the Tropics and the Arctic (Manzini et al., 2014; Zappa and Shepherd, 2017). Feedbacks and local effects again modulate regional climatic changes (Goosse, 2015; Hall et al., 2008; Seneviratne et al., 2010). Also regional forcings play an important role, such as changes in aerosol concentrations or land-use changes (de Noblet-Ducoudré et al., 2012; ⁠Boé et al., 2020; Gutiérrez et al., 2020; Mathur and AchutaRao, 2020). Information about past and future regional climate changes may be relevant for many different types of users. These range from other scientists assessing the impacts of climate change, to engineers using the information to design critical infrastructure, to companies or farmers affected by weather variability, to NGOs working in development aid, to regional planners in charge of preparing adaptation measures, to the actual decision makers. The information may be used in different contexts. Understanding the causes of past changes, in particular the attribution of particular extreme events to anthropogenic global warming, can be important for questions of liability and compensation (Otto et al., 2017). Projections of regional climate change are the basis for climate risk assessments. Again, changes and extreme events causing the highest risks are of particular interest (Sutton, 2019; Weaver et al., 2017). Many countries and the European Union have developed adaptation strategies (e.g. European Commission, 2013). Climate proofing is now mandatory for many new development, infrastructure and construction projects (Asian Development Bank, 2009; ClimateAdapt, 2020; Hjerp et al., 2012). The type of information and the level of detail required varies between different stakeholders and decision contexts. Whereas often qualitative information may suffice, planners of critical infrastructure require precise information, such as how likely a dam may be flooded within a certain period (Weaver et al., 2017). Also the relevant time scales differ between contexts (Doblas-Reyes et al., 2021). A vintner may be interested in information for the next 30 years, whereas forest management or, in some cases, city planners may require information for the next 70 to 100 years. For some stakeholders, climate change may be a key stressor, whereas for others it may only be one out of many (Räsänen et al., 2016). Finally, the values of stakeholders—e.g. their risk averseness or tolerance; or their focus on economic or health issues—may vary considerably (Parker and Lusk, 2019).

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Foundations and Sources of Climate Information The classical textbook ideal of scientific discovery is an interplay of theory and experiment. The theory can be used to make a prediction, and an experiment would be designed in such a way that it could in principle falsify the prediction. Many successful predictions would corroborate the theory (Popper, 1959). The general public sometimes perceives climate research as being based solely on simulations with climate models and thus as being unscientific. While climate projections indeed crucially depend on climate models, credibility in these projections can only be built by complementing them by additional foundations and sources of information (Doblas-Reyes et al., 2021). These mainly encompass observations and theoretical (or process) understanding (Baumberger et al., 2017). Elements from all these foundations and sources may then be combined into different lines of evidence. In particular at the regional scale, knowledge of the local population may be a very useful additional sources of information (Rosenzweig and Neofotis, 2013).

Observations Climate has been systematically monitored over the last decades with a generally improving network of in situ measuements as well as remote sensing. Climate monitoring is overseen by the World Meteorological Organisations (WMO's) Global Climate Observing System (GCOS). The Ocean Observing System comprises in situ measurements on commercial ships, drifting and moored buoys and gliders. In situ measurements of the atmosphere comprise station observations, measurements by commercial ships and aircrafts and weather balloons. Remote sensing mainly relies on weather radars, lidars and satellite soundings (Karl et al., 2010; https://gcos.wmo.int/).

Limitations of Observational Data As observational data are obtained via measurement devices, often involving substantial processing, they cannot be considered pure facts, but come along with a range of issues. Station observations are first of all limited by their availability. For instance, even in the US and Europe, where rain gauges are generally densely distributed, their mean distance is typically much larger than the spatial decorrelation length of daily precipitation, such that much of the spatial variations are not recorded

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(Isotta et al., 2014; Maraun and Widmann, 2018). In particular thunderstorms may simply be missed (Hiebl and Frei, 2018). Long records of at least 60 years at daily resolution are still relatively sparse, only few stations provide data at a subdaily resolution (Lewis et al., 2019). Also variables other than temperature and precipitation are often not recorded (Karl et al., 2010). Remote and mountainous regions are generally not well covered (Frei and Schär, 1998). To enable a sensible comparison between observational data and climate model output, which is generated on a grid, often in the form of grid-cell area averages, observational data have to be gridded, i.e., transformed to a grid. Typical methods for the gridding are interpolation or kriging (Diggle and Ribeiro, 2007; Shepard, 1984). A key problem is the fact that station density is often so coarse that many grid cells do not cover a station. In particular in mountainous terrain this poses problems, as climate variables such as temperature and precipitation vary strongly in space. Here complex models such as PRISM accounting for topographical features are used (Daly et al., 1994). In general, gridded observational data do not perfectly represent area averages (Herrera et al., 2019). Station data often suffer from weather dependent biases (Maraun and Widmann, 2018). For instance, strong winds may cause turbulence around rain gauges, which blows a substantial fraction of the falling rain or snow over the gauge. These issues may at least partly be corrected by empirical formulae. A serious problem in climate monitoring are so-called inhomogeneities (Trewin, 2010; Venema et al., 2012): a measurement device may be replaced by a new device, measuring slightly different values. Or the surroundings of a weather station may change over time. For instance, trees may grow or may be cut, and buildings may be constructed. Such changes cause jumps or artificial trends in time series, distorting the climatic trends of interest. The WMO therefore recommends to record any such changes as station metadata, and to record with old and new measurement devices in parallel for a sufficiently long period, to be able to identify and correct such inhomogeneities. But in particular before these recommendations were issued, inhomogeneities were usually not flagged. A substantial amount of research efforts has been invested to develop statistical methodologies to identify and remove inhomogeneities (HOME, Venema et al., 2012). For temperature, these methodologies are rather successful, but for precipitation—because of its strong spatial variability—a homogenisation at the daily scale is essentially impossible (Venema et al., 2012). Similar issues have been identified for remote sensing data. Radar data, for instance, utilise reflected radar signals to derive 3-dimensional information about precipitation by inverse modelling techniques (Rogers and Yau, 1989). The resulting data are thus highly processed data products subject to a range of limitations.

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In particular in mountainous terrain, weather radars have difficulties recording rainfall in valleys (Haiden et al., 2011). To combine their respective advantages, products integrating station and radar data have been produced (e.g. Haiden et al., 2011). To overcome the limitation of sparse observations, a growing number of reanalysis datasets has been produced (Kalnay, 2003). These assimilate observational data into weather forecasting models in such a way that the model closely follows the real weather sequence. The advantage is that the model produces a physically consistent time evolution of a broad range of climatic variables on a regular grid at a high temporal resolution. The quality of reanalyses depends on the chosen assimilation scheme and forecasting model, and the availability and quality of observations over the region of interest (Bengtsson et al., 2007).

Role of Observations Observational data can be used for several purposes in the context of regional climate research (Doblas-Reyes et al., 2021). • Simply for monitoring purposes, i.e., to record changes in temperature, rainfall and other variables on long time scales. • To test hypotheses about climatic processes and to quantify climatic phenomena. For instance, statistical approaches can be used to identify and quantify the remote influences of the so-called North Atlantic Oscillation on European climate (Trigo et al., 2002). In fact, several statistical approaches have been co-developed by climatologists and statisticians, such as the Yule-Walker equations (Katz, 2002). • To predict regional climate several months into the future. For instance, predictions of the El Nino/Southern Oscillation either use observational data to initialise climate model predictions, or they employ statistical prediction models trained on observational data (Latif et al., 1998). • To evaluate climate model simulations (see below). Model evaluation based on observational data may seem straight forward, but for some variables and regions it is highly non-trivial. For instance, rainfall data over some regions of the world may be so sparse and unreliable that the difference between different derived observational datasets may be as large as between a climate model and observations (Gibson et al., 2019; Kotlarski et al., 2019).

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Climate Model Simulations As laid out in the introduction, conducting real world experiments in climate science is very limited. Moreover, the physical laws governing the climate system are complex and allow for analytical, closed solutions only for rare idealised cases (Holton and Hakim, 2012). A hierarchy of different climate models has been developed to close these gaps. On the one hand, models represent our theoretical understanding via numerical integrations of physical equations (up to computational constraints). On the other hand, climate models serve as a computer-generated laboratory to conduct experiments. In that sense, climate models are similar to computer models in other scientific disciplines such as astrophyiscs (Parker, 2020). The double role of climate models has to be kept in mind to avoid circular reasoning. To avoid that climate model simulations are prematurely interpreted as real behaviour, they have to be evaluated against our theoretical understanding and observational evidence. Thus, climate science crucially relies on the combination of theory, observations and models (Flato et al., 2014, Doblas-Reyes et al., 2021).

How do Climate Models Work? The first mathematical models of the climate system have been energy balance models, used to understand paleo-climatic variations and the greenhouse effect (Edwards, 2011). Since then, many different types of climate models have been developed. For the purpose of this Chapter “Introductory overview” will focus on those models typically used to generate regional climate information, that is so-called general circulation models (GCMs), dynamical regional climate models (RCMs) and selected statistical approaches. GCMs describe the circulation of either atmosphere or ocean, and are usually coupled together and with other components of the climate system. They are intended to numerically represent atmosphere and ocean, land-surface, cryosphere, and vegetation, and their interactions, at the space and time scales relevant for climate studies. The central components are the dynamical cores of the atmosphere and ocean models (Neelin, 2010; Trenberth, 1993). Here, I will describe the atmospheric component only; the ocean component is structurally very similar (see the previous references for details). The dynamical core of a GCM numerically integrates the physical equations governing fluid dynamics (air is a fluid) and thermodynamics: the Navier–Stokes equations on a rotating sphere, representing Newton's second law; the conservation of mass; the conservation of energy, which describes the changes of

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t­emperature in response to exchanges of radiative, conductive and internal energy exchanges; and the conservation of humidity. These equations are partial differential equations which are highly nonlinear and have to be solved for the whole globe. Therefore they cannot be solved analytically and have to be integrated numerically using finite differences. To this end, the atmosphere is represented by a three dimensional grid, and temporal and spatial derivatives in the equations are approximately represented at each grid point by finite differences between points in space and time, respectively. Certain criteria on the relative spatial and temporal grid spacing and regarding the choice of numerical schemes have to be fulfilled to avoid instability (growing numerical errors) and other numerical artefacts (Neelin, 2010; Trenberth, 1993). A finite grid trivially cannot capture effects below the grid box resolution. Thus, a standard GCM with a horizontal resolution of, say, 100 km, cannot represent phenomena such as convection (localised motion) and the associated cumulus clouds and precipitation. But such sub-grid effects often influence processes on the resolved scales. For instance, local convective cells redistribute heat and humidity throughout the troposphere, and, in case of the Inner Tropical Convergence Zone (ITCZ), fuel the planetary scale Hadley circulation (Bony et al., 2015). Likewise, solar and long-wave radiation and their interaction with greenhouse gases and clouds control the energy budget of the Earth (Goosse, 2015). Often, it is sub-grid variables such as precipitation that are relevant for users of climate information (Rössler et al., 2019). To overcome this limitation, a plethora of so-called parametrisations has been developed. These are sub-grid models, describing how large-scale variables influence small scale behaviour and feed back into the large-scales (e.g., how the large-scale temperature and humidity distribution initiates convection and thereby causes vertical motion, redistribution of temperature and humidity, the formation of clouds and precipitation). These models can be simple ad-hoc models derived from plausibility arguments, to rather complex 1-dimensional climate models (Stensrud, 2007). They are often built from observational data from field campaigns and calibrated to produce realistic simulations. Parameterisations capture a long list of processes, such as shallow and deep convection, turbulence and drag; cloud formation and precipitation; radiative transfer; and exchanges of mass and momentum between ocean and atmosphere (see Fig. 1). Computational limitations imply a trade-off between model complexity and model resolution. Coupled GCMs explicitly simulating many components of the climate system such as the cryosphere and biosphere and the carbon cycle are called Earth System Models (ESMs). Because of their complexity, their horizontal resolution is usually limited to 100 km. Standard coupled atmosphere–ocean

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Fig. 1   Grid and selected parameterised processes in a coupled ocean–atmosphere general circulation model (GCM)

GCMs are now, in dedicated projects, run at a horizontal resolution as fine as 25 km (Haarsma et al., 2016; Roberts et al., 2018). Over recent years, experimental GCM simulations of only a couple of months have been conducted at a horizontal resolution of about 1 km to explicitly resolve convection and its feedbacks into the large-scale circulation (Satoh et al., 2019; Stevens et al., 2019). To better represent regional climate while keeping computational costs at a reasonable level, dynamical downscaling with RCMs has been proposed (Giorgi, 1990). These models are structurally similar to GCMs, but run over a limited domain only. At the domain boundaries they receive input from coarser resolution climate models or reanalysis data (see Observations). Over recent years, these models have been run at a horizontal resolution of about 1 km to explicitly represent convection (Coppola et al., 2020; Prein et al., 2015). Not long ago, RCMs have been atmospheric models only (coupled to a land-surface model), but recently coupled Atmosphere–Ocean RCMs have been developed to better

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­represent atmosphere–ocean interactions (Kjellström et al., 2005; Somot et al., 2008). Usually, RCMs do not feed back into the coarse resolution driving simulations. A complementary approach to dynamical downscaling is statistical downscaling (Maraun and Widmann, 2018). Here empirical relationships between large and local scales are derived from observational data and applied to large-scale GCM output to represent local climate variability. Many different statistical models representing these relationships exist, but all of them rely on the key assumptions to be valid: the statistical model has to represent all large-scale influences that may change; the model has to correctly extrapolate to the new climate state; and the driving GCM has to credibly simulate the changes in large-scale influences (Maraun and Widmann, 2018). Most climate models are biased compared to observed climate: long term averages of simulated temperatures, precipitation and other variables often slightly, sometimes strongly deviate from long-term observed averages (Flato et al., 2014; Kotlarski et al., 2014, see below). For impact studies, these differences may be crucial: for instance, a model that is just one degree too warm, may simulate a wrong snow melt and thus risk of flooding. A pragmatic technique to navigate this problem is bias adjustment (Maraun, 2016; Maraun and Widmann, 2018): relevant statistical aspects of the model output are adjusted to match observed climate. To be applicable to future climate simulations, and to avoid statistical artefacts, several assumptions have to be fulfilled (Maraun et al., 2017; Maraun and Widmann, 2018): the climate models to be adjusted should resolve the climate phenomenon of interest; the models should not fundamentally misrepresent processes controlling the regional changes of interest; and the statistical model used for the adjustment should not introduce artificial trends.

Climate Model Experiments Climate models are computer laboratories to conduct numerical experiments. Here, I will present a selection of experiment types relevant for regional climate studies. The most prominent climate model experiments are so-called future projections (Goosse, 2015): centennial scale climate model simulations are performed with time-varying external forcings. These forcings are based on scenarios and typically comprise plausible future changes in greenhouse gas concentrations, but also land-use and aerosole changes. The idea of these experiments is not to predict a specific state of the climate system at a given time (or over a given time period) in the future, but rather to simulate the range of possible climate states for certain assumed future forcings. These simulations can then, e.g., inform

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d­ ecisions about reducing carbon dioxide emissions by illustrating the effects of different climate policies on climate. Detection and attribution experiments are designed to identify whether climate has been changing, what the causes of observed changes were, and whether anthropogenic climate change contributed to the severity of observed extreme events (Hasselmann, 1997; Bindoff et al., 2013; Stott et al., 2016). In principle, simulations with different combinations of forcings, for instance no external changes at all, only changes in solar insolation and volcanic eruptions, only greenhouse gas emissions and combinations thereof. These simulations are then statistically compared to the observed trends. Such simulations have demonstrated that temperature increases over the last 50 years in many regions of the world cannot be explained by natural forcings alone, but only by a combination of natural and anthropogenic forcings, in particular greenhouse gas emissions. Other types of attribution studies have shown that, e.g., the severe Sahel drought in the 1970s and 80 s was mainly driven by a natural fluctuation of North Atlantic sea surface temperatures (Giannini, 2003). Similar sensitivity experiments have highlighted the role of low soil moisture for the severity of the 2003 European heatwave (Fischer et al., 2007), and the influence of Black Sea warming on a devastating rainfall event in Russia (Meredith et al., 2015). Closely related experiments can be used to translate observed events into a warmer climate (Shepherd et al., 2018; Sillmann et al., 2020). For instance (Lackmann, 2015) investigated how severe Hurricane Sandy could be in 2100, and Maraun et al. (2021, under review) studied how a severe rainfall-triggered landslide event in Austria would unfold in a 3 °C warmer climate.

Projection Uncertainties Projections of future climate are uncertain because of mainly three sources: forcing uncertainty, climate response uncertainty and internal climate variability (Chen et al., 2021). Forcing uncertainty is related to our limited ability to predict changes in forcings external to the climate system such as solar radiation (which has decreased over recent decades and is predicted to further decrease over this century, Steinhilber and Beer, 2013), aerosols, land-use changes and greenhouse gas concentrations. In particular human-induced changes in the latter three forcings are in principle unpredictable because humans can respond to what they experience (human reflexive uncertainty; Dessai and Hulme, 2004). Therefore, a range of scenarios is simulated by climate models representing different greenhouse gas emissions, land-use and aerosol changes (Eyring et al., 2016). These scenarios are not predictions, but what-if scenarios. The implementation of current climate

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policies would result in a warming of about three degrees relative to pre-industrial climate, a reversal of current policies would cause a stronger warming, and much stronger mitigation actions would be required to reach the Paris agreement (Hausfather and Peters, 2020). Climate response uncertainty, also called model uncertainty, refers to our limited understanding of how strong the climate system responds to external forcings and our limited ability to simulate the climate system. An important factor is the role of clouds: high clouds warm the climate, whereas low clouds cool it (Goosse, 2015). The strength of global warming thus depends crucially on how the distribution of clouds changes in a future climate. It is well established that high tropical clouds will become more abundant, but changes in low clouds are less well understood (Goosse, 2015). Also, tropical clouds fuel the general circulation of the atmosphere, their changes thus also affect regional weather patterns (Bony et al., 2015). To represent these uncertainties as comprehensively as possible, simulations of many different climate models, so-called multi-model ensembles are considered (e.g. Eyring et al., 2016; Jacob et al., 2020). Uncertainties related to clouds are related to the fact that standard GCMs cannot explicitly simulate deep convection in thunderstorms, and the parameterisations necessary to represent deep convection generate hugely different changes. Here, simulations at the kilometer scale with GCMs are expected to greatly reduce uncertainties. Internal variability is linked to modes of variability as discussed in Sect. Observations. It can be considered random (weather) fluctuations on longer time scales ranging from weeks to centuries. In principle this variability is predictable some time into the future, depending on the phenomenon. The actual weather is predictable only for a week into the future. Seasonal to decadal climate predictions are in principle possible because of slowly varying components of the climate system such as sea surface temperatures, sea ice or soil moisture (several months) and the deep ocean circulation (decades). In practice, however, predictability is further limited by incomplete starting data and imperfect models (Meehl et al., 2009). On time scales beyond a decade, internal variability therefore has to be treated as random fluctuations around the expected climate change. To represent internal variability, many simulations with varying initial conditions (state of atmosphere and ocean), so-called single model initial condition ensembles (SMILES), have been conducted (Deser et al., 2014). Internal variability is particularly relevant for precipitation and at regional scales (Deser et al., 2012; Maraun, 2013, Doblas-Reyes et al., 2021). Forcing and model uncertainty grow with projection lead time, whereas uncertainties due to internal variability remain roughly constant with lead time. Thus,

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the relative role of internal variability decreases with lead time (Hawkins and Sutton, 2009). In principle these uncertainties are not only important for climate projections, but the relative importance of these sources might be very different for other types of climate experiments. For example, past greenhouse gas concentrations are relatively well known (Since the 1960s from direct measurements, before that from ice cores (Barnola et al., 1987), such that the associated uncertainties are relatively small for attribution experiments.

Process Understanding Even though climate models are intended to represent our theoretical understanding of the climate system, they are—as any model—simplifications of reality and may represent some phenomena in an implausible manner. In particular parametrisations (their structure, their parameters, which are often only weakly constrained by observations, and their interplay) may lead to unrealistic simulations of some phenomena and implausible projections. Thus it is crucial to test model simulations against our theoretical understanding (Baumberger et al., 2017; Doblas-Reyes et al., 2021). Regional climate change can broadly be explained by thermodynamic and dynamic changes, and the interaction thereof (Shepherd, 2014). Thermodynamic changes are directly linked to changes in the Earth's radiative balance, such as rising temperatures, melting of sea ice and snow, increasing saturation vapour pressure (via the law of Clausius-Clapeyron) and thus the potential for higher atmospheric humidity and precipitation intensities. Their direct link to the thermodynamic balance of the climate system, and their distinct fingerprint, gives us high confidence into thermodynamic changes. Dynamic changes are linked to changes in the large-scale circulation, such as changes in the tracks and frequency of storms and the persistence of blocking highs (Woollings et al., 2018). Dynamic changes are often a complex response to competing thermodynamic drivers and difficult to distinguish from internal variability. Confidence in dynamic changes is therefore comparably low (Shepherd, 2014; Woollings, 2010). For instance, changes in global precipitation are well constraint by the global energy budget. Yet regional changes in precipitation depend on both thermodynamic and dynamic changes and are thus in some regions highly uncertain (Fig. 2).

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Fig. 2   Projections of seasonal mean precipitation based on 39 models from the CMIP5 multi-model ensemble (Taylor et al., 2012) according to the RCP8.5 scenario (2081–2100 vs. 1986–2005). Hatching indicates regions where the change is within internal climate variability (multi-model mean change is less than one standard deviation of internal variability). Stippling indicates regions where the change is large compared to internal variability and robust across models (multi-model mean change is greater than two standard deviations of internal variability and where at least 90% of models agree on the sign of change). Adopted from Fig. 12.22, Collins et al. (2013). Used with permission

Generating and Communicating Relevant Climate Information Barsugli et al. (2013) coined the practitioner's dilemma: a plethora of different sources of climate change information exists, but users (see Sect. Observations) are usually left alone in selecting the relevant and credible sources. In particular, many different model simulations exist, from different generations of different GCMs and RCMs, often bias-adjusted with various methods, and from a range of statistical downscaling methods (Barsugli et al., 2013; Hewitson et al., 2017). There is, hence, a growing insight that it is necessary to distil the credible and relevant information from these different sources of information (Hewitson et al., 2014). Climate information distillation has essentially two aspects: first, the collaboration with users, and second the construction of information (Doblas-Reyes et al., 2021).

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User Collaboration The traditional, and arguably still the most common way of providing data to users is via a linear supply chain: GCM projections, potentially dynamically downscaled and/or bias adjusted, are provided via a data portal. Users then download the variables of interest of their target domain and post-process these data. The choice of data is often determined by convenience and availability (Rössler et al., 2019). This approach, however, has serious limitations: • First, some climate models may simulate the relevant phenomena very well, while others may not. Also, some impacts may require very specific nonstandard model output. Thus it is important for climate data providers to understand the user context to be able to select a model and model output that serves the specific modelling purpose. Even more, it may be impossible, or not sensible, to answer some user questions, for instance because of limited modelling capabilities. Then it has to be identified how the user problem may be addressed from a different viewpoint. • Second, user values may play an important role in the information provision (Parker and Lusk, 2019). Consider a user concerned with the highest risks from future El Nino events. Latif et al. (2015) found super El Niños of very high intensity under strong warming in the climate model KCM. Whether the model is an outlier or simulating a plausible worst case cannot be clearly decided based on our current physical understanding. A risk averse user, acting according to the precautionary principle, may consider this model in their risk assessment. A different, more risk tolerant user may reject this model as implausible. Thus, it is more and more recognised that climate information provision benefits from an early collaboration with users, although the optimal organisation and depth of this collaboration may vary from case to case (Berkhout et al., 2013; Doblas-Reyes et al., 2021).

Information Construction The different sources of information may provide not directly comparable or even contradictory evidence. To seriously quantify the uncertainties of climate projections and to build trust in these projections, a key task in the provision of climate

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information is therefore the integration and potential reconciliation of different sources of information (Doblas-Reyes et al., 2021): do different models differ in their projections just because of random internal variability; because one model is wrong (or both); or because both model simulations are not directly comparable (e.g. one model may simulate the broad regional change, whereas the other resolves a local feature)? Or do we have to consider both simulations, because our limited understanding does not allow us to resolve the contradiction? Similar issues arise for the comparison of models and observations. Information construction therefore has to. 1. quantify the role of internal variability as much as possible, for instance by SMILES (e.g. Deser et al., 2014); 2. assess the adequacy or fitness of different models for the given purpose (Parker, 2009); 3. Select a manageable subset of well performing models that span climate projection uncertainties as comprehensively as possible. 4. Check the plausibility of the resulting changes and associated uncertainties against our process understanding. The concept of adequacy-for-purpose is emerging as an important issue in climate science (Parker, 2009, Chen et al., 2021; Doblas-Reyes et al., 2021) and merits additional discussion. Even if no climate model (or type of climate model) will perfectly represent a certain relevant climate phenomenon, the performance of different models may vary substantially and some may simulate the phenomenon reasonably well. Often, a useful first guess can be made already because of structural arguments: is the resolution sufficiently fine to resolve the phenomenon? Are the necessary model components included and interactively coupled? Are the relevant external forcings included? In particular statistical downscaling and bias adjustment models are usually designed to represent a very limited range of aspects only, such that a first selection is straight forward (Maraun and Widmann, 2018). Such a priori considerations should be complemented by a processoriented evaluation against observations and a plausibility check of projected changes against theory (Baumberger et al., 2017; Maraun and Widmann, 2018).

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Organisation of Regional Climate Research The provision of decision-relevant regional climate information not only requires collaboration with users, but also among climate scientists. Maintaining a global monitoring system as well as generating ensembles of multiple climate models requires a close collaboration between scientific institutions across the globe, as well as capacity building in developing countries (Hewitson, 2015). Global climate research is mainly coordinated within the World Climate Research Programme (WCRP), founded in 1980 under the joint sponsorship by the World Meteorological Organisation (WMO), the International Council for Science (ICSU) and the Intergovernmental Oceanographic Commission (IOC) of the UNESCO. The work of WCRP is organised into core projects, addressing the global atmosphere, ocean, cryosphere and the land-surface. Focus is given to the grand challenges listed above in the introductory section. Different task teams lead specific activities, such as the coupled model intercomparison project (CMIP) that generates state-of-the-art GCM ensembles (currently CMIP6, Eyring et al., 2016). The Coordinated Regional Downscaling Experiment (CORDEX, Giorgi et al., 2009) is a WCRP endorsed project to coordinate the generation of regional climate model ensembles across many regions of the world. For instance, simulations for Europe and the Mediterranean are conducted by EUROCORDEX (Jacob et al., 2020) as well as MED-CORDEX (Ruti et al., 2016). Currently, the WCRP is undergoing a major restructuring (WCRP Joint Scientific Committee (JSC), 2019). The Regional Information for Societies (RIfS) is a new umbrella for all regional climate research activities including CORDEX. Socalled Lighthouse Activities are currently under development to spark new ideas. One of these activities will address climate risk. In 2012 the international program Future Earth has been established. Future Earth is governed by ICSU, the Belmont Forum and a range of United Nations organisations. Its scope is much broader than the physical science focussed WCRP and addresses all major global sustainability challenges. Close collaborations with WCRP exist, e.g., via the knowledge action network on Emergent Risks and Extreme Events. Global climate monitoring is organised via the Global Climate Observing System (GCOS), co-sponsored by the WMO, IOC, United Nations Environment Programme (UNEP) and ICSU. In 1988, the WMO and UNEP established the Intergovernmental Panel on Climate Change (IPCC) to assess the state of climate research to inform policy makers. A key element of the IPCC's mission is to publish reports that are

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policy-relevant but not policy-prescriptive. Since 1990, the IPCC has published 5 major assessment reports and a long list on special reports. IPCC reports are written by an author team, specifically selected for each report, that does not conduct research itself, but rather assesses the relevant published scientific literature. These reports undergo a complex review process with hundreds of expert and government reviewers. For transparency reasons all reviewer comments and responses by IPCC authors are published along with the report. Recently, the Working Group I contribution (focusing on the physical science basis) of the sixths assessment report has been published (Masson-Delmotte, 2021, forthcoming). For the first time, this report has a focus on regional climate, addressed in the Chapters “Linking global to regional climate change” (Doblas-Reyes et al., 2021), “Weather and climate extreme events in a changing climate” (Seneviratne et al., 2021), “Climate change information for regional impact and for risk assessment” (Ranasinghe et al., 2021) and the Regional Atlas (Gutièrrez et al., 2021). To improve the management of the risks of climate variability and change and adaptation to climate change, the WMO has implemented the Global Framework for Climate Services (GFCS, (Hewitt et al., 2012). The GFCS enables the development and application of climate services to assist decision-making at national, regional and global levels.

Conclusions Human influence has already changed regional climates and will continue to do so. Information about possible future changes at the regional scale is requested by decision makers for adaptation planning. But providing credible regional climate change information is still an issue of basic research and not possible without addressing several of the grand challenges introduced above. Climate models play a key role in generating regional climate information: they represent our theoretical understanding of the climate system and are a test bed for conducting experiments. But models are always a simplification of reality. According to a famous quote by the statistician G.E.P. Box, “All models are wrong, but some are useful” (Box, 1976). Box made this statement in the context of statistical modelling, but it applies to modelling in general. An important amendment by Box, which is rarely quoted, is: “Is the model good enough for this particular application?” (Box et al., 2009). This is nothing else than the adequacy-for-purpose discussion in climate science. As discussed, further lines of evidence are thus needed to assess the adequacy of a model or type of models for a given purpose, and to build trust in regional climate projections: comparisons

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of different models and model types (e.g., ensembles of different resolutions; different generations of models); to evaluate the simulation of key processes against observations; and to check the plausibility of model results against our theoretical understanding. In particular the evaluation against observations is non-trivial, as observations themselves are affected by substantial limitations and uncertainties. New model generations sometimes substantially improve the quality of previous projections, but often the improvements are incremental or insignificant (Flato et al., 2014). Gaining trust in new model projections requires a substantial amount of analyses. Thus, climate science has to find a useful balance between the production of new simulations and the analysis of existing simulations. Collaboration with users is key to provide relevant information: climate scientists have to understand, which questions are relevant in a given context, and users need to understand which questions can sensible be posed. To optimally inform climate risk assessment and adaptation decisions, the user values have to be taken into account in the research design. For different contexts, different approaches exist with varying levels of user-interaction. An important, but still unresolved, challenge in the development of climate services is to generate userrelevant information when only limited resources are available to collaborate with a very high number of potential users. A key reason for the success of climate research is the strong international collaboration: maintaining a global climate monitoring system, generating coordinated model ensembles, and assessing the global state-of-the-art of climate research with unprecedented rigour is only possible because thousands of scientists worldwide work together in international initiatives. Ultimately this work may contribute to achieving many of the sustainable development goals.

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Douglas Maraun  is a climate scientist and leads the regional climate research group at the Wegener Center for Climate and Global Change at the University of Graz. His research spans all aspects of regional climate variability and change with a focus on extreme weather and climate events. Maraun chaired the international VALUE network on the evaluation of statistical regionalisation approaches and contributes to activities of the World Climate Research Programme. Recently, he served as lead author to the 6th Assessment Report of the Intergovernmental Panel on Climate Change.

Littering in Municipal Public Places: The Role of Personal Factors and Intentions Julia Neumann and Thomas Brudermann   Introduction Sustainable Development Goal 11 aims to transform cities towards inclusion, safety, resilience, and sustainability (United Nations, 2020). Littering, i.e. misplacing solid waste, is associated with negative environmental, social, and economic consequences and appears to be a problem worldwide. Efforts to reduce littering represent one piece of the puzzle in the transition towards sustainable cities and communities. The German Environment Agency (Umweltbundesamt) estimated that one single person in Germany consumes a hot beverage on average 34 times per year in a plastic or plastic-coated paper cup. These cups are

List of Abbreviations ANOVA Analysis of Variance CLT Central Limit Theorem NAT Norm Activation Theory PCA Principal Component Analysis TPB Theory of Planned Behaviour VBN Value-Belief-Norm Theory

J. Neumann · T. Brudermann (*)  Institute of Systems Sciences, Innovation & Sustainability Research, University of Graz, Graz, Austria e-mail: [email protected] J. Neumann e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 S. Hummel et al. (eds.), Shaping Tomorrow Today – SDGs from multiple perspectives, Lernweltforschung 39, https://doi.org/10.1007/978-3-658-38319-0_3

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not only difficult for waste processing companies to recycle, but often are not even disposed of properly. Instead of finding their way into a waste bin, they are often carelessly discarded (Umweltbundesamt, 2019). A similar pattern can be observed with cigarette butts. Every day, 204 million cigarettes are smoked in Germany. In many cases, the cigarette butts, which contain carcinogenic substances and heavy metals, are thrown away and end up on the ground or in water bodies (Deutsche Umwelthilfe, 2019). Such acts of misplacing solid waste are referred to as littering and are associated with environmental, social, economic, and aesthetic problems (Schultz et al., 2013). Littering can lead to a contamination of soil and water in the environment (Cingolani et al., 2016). It can also pose additional threats to humans if it results in safety, fire, or human health hazards. Litter on the ground is also perceived as unsightly and incurs high waste disposal costs (Schultz et al., 2013). Why some people litter and others do not has been a topic of interest within the scientific community for decades: Overall, personal, social, and situational factors seem to play roles and often interact with one another. For instance, Cialdini et al. (1990) found that people littered more often when litter was already present around them. The role played by the physical environment in combination with social factors is further supported by Keizer et al. (2008) who found that littering is also more likely in an otherwise disorderly setting. In studies that analysed more individual factors, certain demographic characteristics were found to be related to littering, such as gender, education, and age (Hartley et al., 2018a; Schultz et al., 2013). However, as Schultz et al. (2013) pointed out, the evidence regarding demographic attributes of people who litter is inconclusive. Similarly ambiguous findings are reported in the literature regarding personal or psychological factors that are associated with littering behaviour. In this paper, we understand personal factors as psychological influences on an individual’s littering behaviour, as described by Gifford and Nilsson (2014). Several theories of human behaviour have been applied by researchers to the context of littering in order to identify the most relevant personal factors. Several theories have been frequently used or discussed according to a literature review conducted as part of this study, including the Theory of Planned Behaviour (TPB, as described by Ajzen, 1991), the Norm-Activation Theory (NAT) developed by Schwartz and Howard (1981; as cited in Klöckner, 2013), the Value-Belief-Norm Theory (VBN) by Stern (2000), and the Focus Theory of Normative Conduct by Cialdini et al. (1990). The TPB states that behaviour is preceded by an intention, which, in turn, is shaped by attitudes, subjective norms, i.e., the social pressure a person feels to display a form of behaviour, and perceived behavioural control, i.e., how easy or difficult it is to implement the behaviour (Ajzen, 1991). The TPB is one of

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the most frequently used theories to explain environmental behaviour in general (for an overview, see e.g. Hu et al., 2019; Vagias et al., 2014), but has also been subject to criticism due to its lack of predictive power and debatable adequacy (Hu et al., 2019), sometimes weak (e.g., Juvan and Dolnicar 2014) and sometimes significant (Nigbur et al. 2010) links between behavioural intentions and actual behaviour, and the fact that it seems to have little power to predict repetitive behaviour (e.g., Klöckner, 2013; Klöckner & Blöbaum, 2010). The NAT or parts of it have also been frequently applied to the context of littering (e.g., Gusmerotti et al., 2016; Hu et al., 2019). The NAT contains a core concept of a personal norm, i.e., ‘the reflection of the personal value system in a given situation’ (Klöckner, 2013, p. 1030), that needs to be activated to influence a person’s behaviour. As environmental behaviour is often considered to belong to the moral domain, the application of the NAT seems justified (Thøgersen, 1996; as cited in Klöckner, 2013). The VBN is basically an extension of the NAT. This theory states that a person’s behaviour is directly shaped by their personal norms (Klöckner, 2013) and that the pre-conditions for action are ranked in a certain way. The concept of an ecological worldview, i.e., eco-centric views and the acknowledgment of limited resources and human harm to the environment, has been included in the theory as a factor related to the awareness of consequences. Klöckner (2013) criticised both the VBN and the NAT for their lack of power to explain repetitive behaviours. The Focus Theory of Normative Conduct (Cialdini et al., 1990) introduced the notions of descriptive and injunctive norms, distinguishing between behaviours that are actually performed (descriptive norms), and behaviours those of which others would (dis)approve (injunctive norms). These norms may overlap, but they also contradict each other. Cialdini et al. (1990) found support for the role of and the distinction between injunctive and descriptive norms in the context of the littering. They also emphasised the fact that norms can only influence people’s behaviour when activated, meaning ‘made salient or otherwise focused on’ (Cialdini et al., 1990, p. 1015). Over time, more researchers have adopted the theories above and extended them by adding more factors, because they regarded the initial theories as insufficient to address littering and other environmentally significant behaviours. For instance, some authors emphasised the effects of environmental practice and environmental theory knowledge on the extent of littering (e.g., Hu et al., 2018; Vagias et al., 2014). Others have introduced the idea of a factor called concern about consequences (Hartley et al., 2018a) or have pointed out the influence of a person’s past littering behaviour on their current or future behaviour (e.g., Hu et al., 2019). Furthermore, some authors have reported differences regarding the

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social acceptance of certain types of litter and littering situations (e.g., Torgler et al., 2012). Eventually, whether a person identifies with the area they live in may have an influence on how they perceive the local environmental conditions (Meloni et al., 2019). As a consequence, a whole series of personal factors have been assumed to have an influence on people’s (non-)littering behaviour. However, the relevance of each of these factors varies across studies and contexts. For instance, in the study by Cialdini et al. (1990), the participants littered handbills less often when an anti-littering injunctive social norm (what others think should be done) was salient. However, in a study by Hartley et al. (2018a), the perceived injunctive norm–here referring to the opinion of family and friends–could not be applied to predict the participants’ intentions to act against marine litter. Littering studies that analyse personal factors focus commonly on marine (e.g., Beeharry et al., 2017; Hartley et al., 2018a, b) and/or touristic litter, respectively, littering in nature conservation areas (e.g., Brown et al., 2010; Hu et al., 2018, 2019). However, although litter from cigarettes has often been studied (Basto-Abreu et al., 2016; Rath et al., 2012), other everyday acts of littering that can happen anywhere and anytime seem to have been investigated and related less frequently to personal factors. Members of the scientific community, municipalities, and city governments all have an interest in the causes of and means to reduce littering. The municipal initiative #cleanffm has implemented various measures in recent years in an attempt to raise awareness about the topic and prevent people from littering. Their overall goal is to achieve a high quality of living, so that people will enjoy living or staying in the city of interest – Frankfurt am Main, Germany (#cleanffm, 2020). The current study was conducted in cooperation with the coordinators of #cleanffm. The research aims were (1) to contribute to the scientific body of the literature on littering and (2) to generate new insights for the coordinators of the municipal cleanliness campaign #cleanffm. Given the uncertainties associated with personal factors and littering behaviour in different situations, the study was carried out to investigate the roles of selected personal factors in a specific and apparently less frequently researched context: municipal public places. We collected data by conducting a cross-sectional, online survey with citizens of Frankfurt am Main. We analysed the data by performing descriptive statistics and multiple regression analyses to identify personal factors that predict the intention to avoid littering.

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Method A quantitative, empirical case study including explanatory and descriptive elements was conducted. No causal relationships were investigated due to the use of a non-experimental design without randomisation or experimental manipulation. All measurements used were either based on those found in other similar studies or on theoretical considerations cited in the literature. The following personal factors identified during the literature review were eventually selected for the case study based on a consideration of pros and cons (e.g., regarding their practicability or reasonableness): attitude towards littering, subjective norms, perceived behavioural control, personal norms, descriptive and injunctive norms, awareness of consequences, concern, city identity, acceptance of littering in specific situations, and (self-reported) past behaviour. In addition, several demographic aspects were considered (gender, age, education, and smoking status). Following a recommendation by Döring and Bortz (2016), concept specifications of the personal factors were created, since personal factors are latent variables, i.e., not directly observable. Primary data were generated by conducting a cross-sectional online survey (using the software LimeSurvey) in the summer of 2019 over a period of one month after conducting a pre-test with ten participants. Participants were recruited via social media, the Internet, and newsletters by #cleanffm. The items chosen to measure the selected personal factors were taken from other littering studies but had had to be adjusted more or less dramatically due to the differing contexts or for the participants’ convenience. These items were phrased in the form of questions and statements followed by five-point response scales ranging from I agree to I do not agree, following the recommendations of Döring and Bortz (2016). The items were presented in blocks, whereby items in one block were presented in a random order. Control questions were included to check for the people’s level of attention and accuracy. The participants were people who, according to a control question, were spending a lot of time in the city of Frankfurt am Main when the survey took place. The sample was a convenience sample, and the participants self-selected to join the study. In total, 648 people participated in the survey, although only 537 responses could be used due to drop-outs or other exclusion criteria. The sample included a notably high share of academics (55 %) and women (67 % of the participants). Missing data were dealt with by using listwise or pairwise deletion (for a discussion of this method, see van Buuren, 2018). Certain quality criteria (objectivity, reliability, and validity) were checked. While the participants’ objectivity

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was assumed due to the standardised online procedure, their reliability, wherever applicable, was assessed using Principal Component Analyses (PCAs) and calculations of Cronbach’s alpha. Based on the results of the PCAs, several items had to be excluded from further analyses. Some item scales were deemed inadequate and converted into single-item measures instead. Validity was addressed in terms of content validity. We assumed that all conceptual facets of the personal factors were addressed in the respective items, although not addressed comprehensively. The data were analysed both descriptively and using multiple regression analyses. The five-point response scales were treated as being of interval level, taking a common but controversial approach (Field et al., 2012). With regard to the Central Limit Theorem (CLT), we applied the assumption that ‘the sampling distribution tends to be normal anyway – regardless of the shape of the data we actually collected’ (Field et al., 2012, p. 169) for large samples, i.e. n  ≥  30 or 40 (Elliott & Woodward, 2007; Field, 2009). This was to justify the use of parametric tests and the mean as a measure of central tendency without having obviously normally distributed data. In addition, five-number summaries (minimum and maximum value, 25th, 50th, and 75th percentile) are reported. To conduct the multiple regression analyses, the variables were chosen based on theoretical grounds, i.e., based on the literature review results and the adjustments were made due to the investigation of the quality criteria. According to the PCA results, the intention to avoid littering as the intended outcome variable (and likewise past anti-littering behaviour as one predictor) was shown to have two facets in this study: a personal intention to avoid littering (i.e., ‘I try to avoid misplacing my waste’) and an intention to prevent important other people from littering (i.e., ‘I try to encourage important others, e.g., friends and family, to avoid misplacing their waste too’). The personal factors selected as predictor variables are summarised in Table 1 in the Results section. Gender, age, education, and smoking status were selected as sociodemographic control variables. Following the recommendation of Field (2009), initial regression analyses (one for the sociodemographic aspects, where dummy variables had to be used for most variables, and one for the personal factors) were run to identify the predictors that made substantial contributions. Eventually, two multiple regression analyses (one hierarchical) were run including the factors identified as important. Further diagnostic tools were applied to check for the predictor’s quality and extreme cases. Assumptions made in the multiple regression analyses were considered but, following the recommendation of Field et al. (2012), regarded as negligible, since no generalisation could take place either way due to the convenience sample. Conclusions can only be drawn about the present sample.

1.05 1.26 3.09 4.52

537 532 534 514 534 534

Perceived behavioural control regarding littering (PBC)

Acceptance of littering of food wrappings (ACCfw)

Acceptance of littering of cigarette butts (ACCcig)

Descriptive norm for littering of food wrappings (DENfw)

Descriptive norm for littering of cigarette butts (DENcig)

Acceptance of placing waste next to overstuffed bins (ACCob)a

1.43

2.51

2.35

2.09

519 479

Subjective norm regarding littering (SUN)

2.85

1.14

2.37

City identity (CIT)

509

Injunctive norm regarding littering (INN)

1.82

529 533

Concern about negative consequences of littering (CON)

1.19

Awareness of problems, resp. negative consequences associated with 532 littering (PRA)

Personal norm regarding littering (PEN)

1.5

500 536

Past attempts to prevent littering of important others (BEHior)

Attitude towards littering (ATT)

1.71 1.13

517 498

Intention to prevent littering of important others (INTior)

1.07

x̄

Personal past anti-littering behaviour (BEHsr)

534

n

1.35

0.86

0.96

0.69

0.27

1.26

0.95

0.90

1.15

0.64

1.14

0.70

0.88

1.51

0.39

1.21

0.21

SD

Descriptive Statistics

Personal intention to avoid littering (INTsr)

Personal Factor

Table 1   Descriptive Statistics of all Personal Factors Selected for Data Analysis

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Min

5

5

5

5

5

5

5

5

5

5

5

5

5

5

4

5

2.5

Max

1

4

2.5

1

1

1

1.33

1

2

1

1

1

1

1

1

1

1

25th

2

5

3

1

1

2

2

1

3

1

1

1

1

2

1

1

1

50th

4

5

3.75

1

1

3

2.67

2

4

1

2

1

2

3

1

2

1

75th

(continued)

Percentile

Littering in Municipal Public Places: The Role … 53

528 474

Descriptive norm for placing waste next to overstuffed bins (DENob)a

Descriptive norm for throwing apple cores into bushes (DENac)a 3.73

3.96

3.41

x̄

1.00

1.04

1.35

SD

Min

1

1

1

Max

5

5

5

3

3

2

25th

4

4

4

50th

Percentile

5

5

5

75th

Note. n refers to the sub-sample size. The column labelled with x̄ contains the means. SD stands for standard deviation. Min. and Max. refer to the minimum, resp., the maximum values observed for a variable’s distribution (decimal values, also in case of the percentiles, occurred, since some factors were based on scales, i.e., averaged values stemming from several items). The 50th percentile is, at the same time, the median. The 25th and the 75th percentile represent the boundaries of the interquartile range. A value of 1 represents a strong position against littering, resp., a strong identification with the city (CIT) or no acceptance, resp., a strong perception that the behaviour is not common (a). A value of 5 represents a weak anti-littering position, resp., a weak identification with the city (CIT) or a strong acceptance, resp., a strong perception that the behaviour is common (a) aThis variable was only investigated descriptively and not used for the multiple regression analyses.

534

n

Descriptive Statistics

Acceptance of throwing apple cores into bushes (ACCac)a

Personal Factor

Table 1   (continued)

54 J. Neumann and T. Brudermann

Littering in Municipal Public Places: The Role …

55

Results Descriptive Statistics Table 1 shows the means, standard deviations, and five-number summaries for all personal factors. Figure 1 provides a rough graphical overview of the means of the personal factors and their confidence intervals. For most personal factors, the participants in this sample provided strong or rather strong anti-littering responses on the survey. The measures of central tendency indicated a weak position against littering only in case of descriptive norm

Fig. 1   Means and Confidence Intervals of the Personal Factors Selected for Data Analysis. Note. Abbreviations were used for the personal factors. The long versions of the names can be found in Table 1. The grey bars indicate the means of the variables. The error bars represent the 95 % confidence intervals of the means. The meaning of the values 1 to 5 differs slightly for all variables. A value of 1 usually represents a strong position against littering (or, resp., a strong identification with the city). A value of 5 usually represents a weak position against littering (or, resp., a weak identification with the city). In case of the light grey bars, the meaning of the values 1 to 5 is different, since the variables may not represent actual littering, but an acceptance of and norms related to littering.

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for littering of cigarette butts. The littering of cigarette butts was perceived apparently as a descriptive norm. Only two cases (injunctive norm regarding littering and descriptive norm for littering of food wrappings) could neither be clearly assigned to a strong position nor to a weak position against littering.

Multiple Regression Analyses Personal Intention to Avoid Littering Regarding the sociodemographic control variables, only the dummy variables DummyG2 (males vs. females), DummyA3 (19–24-year-olds vs. 45–54-yearolds), and DummyS1 (smokers vs. non-smokers) were used to predict the personal intention to avoid littering. This decision was based on a preliminary multiple regression analysis of all dummy control variables, with the exception of several sociodemographic categories which could not be represented due to sample sizes that were too small: R2 = 0.09; F(9, 500) = 5.65, p