This essay presents the concept of sustainability intelligence as a possible response to the current unsustainable course of society. We expound on the three components shaping this concept – naive intelligence, native intelligence, and narrative intelligence – and argue why they could thus serve as inspiration and key reference points for rising to our collective sustainability challenge. The essay ends with a brief exploration of the wider practical, policy and political implications of the concept.
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Expectations are high for digital technologies to address sustainability related challenges. While research into such applications and the twin transformation is growing rapidly, insights in the actual daily practices of digital sustainability within organizations is lacking. This is problematic as the contributions of digital tools to sustainability goals gain shape in organizational practices. To bridge this gap, we develop a theoretical perspective on digital sustainability practices based on practice theory, with an emphasis on the concept of sociomateriality. We argue that connecting meanings related to sustainability with digital technologies is essential to establish beneficial practices. Next, we contend that the meaning of sustainability is contextspecific, which calls for a local meaning making process. Based on our theoretical exploration we develop an empirical research agenda.
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The concept of business sustainability has been investigated, reviewed, and criticized by a plethora of scholars. What constitutes the essence of business sustainability—and its relationship to the actual state of our planet—is still an integral part of the discourse on business-society relations. Recently, Dyllick and Muff (Organization & Environment, 29:156–174, 2016) have reviewed literature in order to uncover what constitutes ‘true’ business sustainability, explaining the apparent absence of a coupling between corporate sustainability initiatives and the state of the planet and explore how this coupling can be strengthened. As such, the authors provide many relevant pointers for answering the question: when is business truly sustainable? This paper aims to respond both critically and constructively to Dyllick and Muff’s article by addressing three points: the somewhat confusing conception of what actually comprises ‘true’ business sustainability, the authors’ choice not to address the underlying economic model and the model of consumer behavior, and the types of sustainability intelligence that, in our view, business needs to develop to truly become a force for spurring sustainable development. We use the Sustainable Development Goals (SDGs) as a case in point to illustrate our argument. In doing so, this paper aims to add to a firm-centered conceptualization of the business-society interface in a constructive way to stimulate further discourse on the concept, and to make a theoretical contribution with respect to coupling mechanisms in the realm of business sustainability.
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PBL is the initiator of the Work Programme Monitoring and Management Circular Economy 2019-2023, a collaboration between CBS, CML, CPB, RIVM, TNO, UU. Holidays and mobility are part of the consumption domains that PBL researches, and this project aims to calculate the environmental gains per person per year of the various circular behavioural options for both holiday behaviour and daily mobility. For both behaviours, a range of typical (default) trips are defined and for each several circular option explored for CO2 emissions, Global warming potential and land use. The holiday part is supplied by the Centre for Sustainability, Tourism and Transport (CSTT) of the BUas Academy of Tourism (AfT). The mobility part is carried out by the Urban Intelligence professorship of the Academy for Built Environment and Logistics (ABEL).The research question is “what is the environmental impact of various circular (behavioural) options around 1) holidays and 2) passenger mobility?” The consumer perspective is demarcated as follows:For holidays, transportation and accommodation are included, but not food, attractions visited and holiday activitiesFor mobility, it concerns only the circular options of passenger transport and private means of transport (i.e. freight transport, business travel and commuting are excluded). Not only some typical trips will be evaluated, but also the possession of a car and its alternatives.For the calculations, we make use of public databases, our own models and the EAP (Environmental Analysis Program) model developed by the University of Groningen. BUAs projectmembers: Centre for Sustainability, Tourism and Transport (AT), Urban Intelligence (ABEL).
Dutch Cycling Intelligence (DCI) embodies all Dutch cycling knowledge to enhances customer-oriented cycling policy. Based on the data-driven cycle policy enhancement tools and knowledge of the Breda University of Applied Sciences, DCI is the next step in creating a learning community between road authorities, consultants, cycling industry, and knowledge institutes with their students. The DCI consists of three pilars:- Connecting- Accelerating knowledge- Developing knowledgeConnecting There are many stakeholders and specialists in the cycling domain. Specialists with additional knowledge about socio-cultural impacts, geo-special knowledge, and technical traffic solutions. All of these specialists need each other to ensure a perfect balance between the (electric) bicycle, the cyclist and the cycle path in its environment. DCI connects and brings together all kind of different specialists.Accelerating knowledge Many bicycle innovations take place in so-called living labs. Within the living lab, the triple helix collaboration between road authorities the industry and knowledge institutes is key. Being actively involved in state-of-the-art innovations creates an inspiring work and learning environment for students and staff. A practical example of a successful living lab is the cycle superhighway F261 between Tilburg and Waalwijk, where BUAS tested new cycle route signage. Next, the Cycling Lab F58 is created, where the road authorities Breda and Tilburg opened up physical cycling infrastructure for entrepreneurs in the bicycle domain and knowledge institutes to develop e-cycling innovation. The living labs are test environments where pilots can be carried out in practice and an excellent environment for students to conduct scientifically applied research.Developing knowledge Ultimately, data and information must be translated into knowledge. With a team of specialists and partners Breda University of applied sciences developed knowledge and tools to monitor and evaluate cycling behavior. By participating in (inter)national research programs BUAS has become one of the frontrunners in data-driven cycle policy enhancement. In close collaboration with road authorities, knowledge institutes as well as consultants, new insights and answers are developed in an international context. By an active knowledge contribution to the network of the Dutch Cycling Embassy, BUAS aims to strengthen its position and add to the global sustainability challenges. Partners: Province Noord-Brabant, Province Utrecht, Vervoerregio Amsterdam, Dutch Cycling Embassy, Tour de Force, University of Amsterdam, Technical University Eindhoven, Technical University Delft, Utrecht University, DTV Capacity building, Dat.mobility, Goudappel Coffeng, Argaleo, Stratopo, Move.Mobility Clients:Province Noord-Brabant, Province Utrecht, Province Zuid-Holland, Tilburg, Breda, Tour de Force
Organisations are increasingly embedding Artificial Intelligence (AI) techniques and tools in their processes. Typical examples are generative AI for images, videos, text, and classification tasks commonly used, for example, in medical applications and industry. One danger of the proliferation of AI systems is the focus on the performance of AI models, neglecting important aspects such as fairness and sustainability. For example, an organisation might be tempted to use a model with better global performance, even if it works poorly for specific vulnerable groups. The same logic can be applied to high-performance models that require a significant amount of energy for training and usage. At the same time, many organisations recognise the need for responsible AI development that balances performance with fairness and sustainability. This KIEM project proposal aims to develop a tool that can be employed by organizations that develop and implement AI systems and aim to do so more responsibly. Through visual aiding and data visualisation, the tool facilitates making these trade-offs. By showing what these values mean in practice, which choices could be made and highlighting the relationship with performance, we aspire to educate users on how the use of different metrics impacts the decisions made by the model and its wider consequences, such as energy consumption or fairness-related harms. This tool is meant to facilitate conversation between developers, product owners and project leaders to assist them in making their choices more explicit and responsible.
