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The sustainability challenge requires experimentation with innovations, followed by an upscaling process towards a broader regime change in the long term. In Europe we observe various regional hotspots for sustainability experimentation which suggests that there are favorable spatial contexts. Little is known about why different kinds of experiments flourish or fail in various spatial contexts. In this paper we explore these contexts by using the habitat concept. A habitat is regarded as the configuration of favorable local and regional context factors for experimentation. To capture the diversity of these habitats we have constructed archetypical experimentation patterns. These patterns are built up of five dimensions: knowledge, governance, informal institutions, regional innovation advantages, and social learning. In a comparative case study in four city regions in Europe we find a large contrast in habitats. Countercultures play an important role, as they shape a beneficial context for experimentation through alternative ideas and lifestyles. We also find indications that it is important that a combination of several habitat factors is present, and that these factors have aligned and evolved over several years of experimentation, thus leading to a more mature habitat. The research suggests that regional stakeholders can positively influence most of the habitat factors shaping future upscaling. However, there are also some important factors, such as regional knowledge and skills, which have a path-dependent nature and are more difficult to improve in the short term.

What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’ open innovation process. It conceptually establishes a direct and autonomous learning interaction model. The key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies.

To support progress towards the transition to a circular economy, the ability to measure circularity is essential. The consideration of the role biobased products can play in this transition is however still largely lacking in the current development of circularity monitoring approaches. The first step in coming to a suitable monitoring framework for biobased products is to define circular economy principles. In this paper, specific characteristics of biobased products were considered in defining six circular economy principles for biobased products: (1). Reduce reliance on fossil resources, (2). Use resources efficiently, (3). Valorize waste and residues, (4). Regenerate, (5). Recirculate and (6). Extend the high-quality use of biomass. In order to evaluate the circularity performance of biobased products with respect to these principles, what needs to be measured was defined considering both intrinsic circularity and impact of this circularity. The intrinsic indicators provide a measure of success in implementation of these circularity principles, and the latter impacts of circularity, i.e., impact of closing the loops on accumulation of hazardous substances and impact of circularity on sustainability (environmental, economic and social). Yet, to unlock the potential of a sustainable circular bioeconomy, strong accompanying measures are required.

The Sustainable Development Goals (SDGs) are offered as a comprehensive strategy to guide and encourage sustainable development at multiple scales both nationally and internationally. Furthermore, through the development of indicators associated with each goal and sub-goal, the SDGs support the notion of monitoring, evaluation and adaptive management, underpinned by the aspirations of social justice, equity and transparency. As such, the ethical intention of the SDGs is well founded. However, possible conflicts and trade-offs between individual SDGs have received little attention. For example, SDGs relating to poverty (SDG 1), inequality (SDG 10), food security (SDG2), economic development (SDG 8) and life in water and on land (SDGs 14 and 15), are potentially competing in many circumstances. In a social–ecological context, policy support and formulation are increasingly adopting systems approaches, which analyse the complex interactions of system elements. Adopting such an approach in this work, the above SDGs are analysed for coastal Bangladesh. This demonstrates multiple potential trade-offs between the SDGs, including agricultural farming approaches in the light of poverty reduction, and between economic growth and environmental integrity as well as equity. To develop coherent and policy relevant socio-ecological strategies, appropriate decision frameworks need to be co-developed across the range of stakeholders and decision-makers. Integrated models have great potential to support such a process.

This paper proposes a multi-size Split-diesel generator (Split-DG) model with three different sizes of DGs and more switching configurations compared to the existing split-DG models. The proposed multi-size Split-DG system is examined for optimal sizing of remote microgrids with and without renewable-battery system. As a novel concept, multi-size Split-DG is used to reduce contamination, cost, and dumped power by using multiple small DGs to replace the single-size large DG. As another contribution of this study, a practical model is developed by considering the capacity degradation of components, spinning reserve, as well as DG’s and fuel tank’s constraints. The optimization problem is solved using a variable weighting particle swarm optimization (VW-PSO) algorithm. The effectiveness of the proposed Split-DG systems, optimized by the developed VW-PSO, is verified by comparing the results with conventional single-size DG system and the system optimized by conventional PSO. While the formulated optimization problem is general and can be used for any remote microgrids, an aboriginal community in South Australia is examined in this study. For this purpose, realistic data of load and weather, as well as technical and economic data of components, are used. It is found that the Split-DG-PV-WT-BES system has the lowest electricity cost compared to the systems without BES, or without PV and WT.

Sustainability is a multidimensional concept that goes beyond environmental protection to economic development and social equity. This change drives entrepreneurs introducing new sustainability-derived products and services to help sustain nature and communities. Entrepreneurship is increasingly being cited as a significant conduit for bringing about a transformation to sustainable products and processes. This research uses the term sustainability entrepreneurs for those who introduce innovative sustainability-derived products, processes, and services for gain, where gain includes economic and non-economic gains to individuals, the economy, and society. The research design was a qualitative exploratory study. The study focuses on a sample of sustainability entrepreneurs as the target respondents who take a commitment to sustainability via sustainability entrepreneurship. Interviews were based around open-ended questions prompting the respondents to engage in a storytelling approach to describe their journey from consumer concern to sustainability entrepreneurship. The data were analyzed using thematic analysis. A set of themes emerged after analysis, reflecting experience as sustainability entrepreneurs. The themes were: Concern as a consumer for sustainability living; Entrepreneurship is not just for money or identity fulfilment but also for altruism; Values underpinning sustainability entrepreneurship: Fairness, responsibility, altruism, and integrity; and Collaboration not competition. This research contributes to an understanding of sustainability entrepreneurs as those who emerge from consumer concern to introduce innovative sustainability derived products, processes, and services for gain, where gain includes economic and non-economic gain to individuals, the economy, and society.

Clean energies are being incorporated into the energy mix in numerous countries. Through a spatial survey of maritime trade, restricted military maritime areas, marine planning, and the presence of fauna and flora along the Ligurian Sea, locations for possible investments in wave energy harvesting were identified in the Northern Thyrrenian Sea, along the Ligurian coast. Previous studies in this region have demonstrated, at a lower spatial resolution, the wave energy potential that can be captured and its variation over time. However, the optimization of wave energy exploitation under the criteria of the functionality and safety of converter devices has not yet been evaluated in the Ligurian Sea. The purpose of this study is to identify the optimal wave energy converter from an economic and technical perspective at several selected locations in the Ligurian Sea. This study involves the scaling of the employed power matrices to obtain the optimized capacity factors of wave energy converters.