We consider the Neumann initial boundary value problem associated to the chemotaxis system(⋆){ut=((u+1)m−1ux−u(u+1)mvx)xin (0,1)×(0,∞),vt=vxx−v+u,in (0,1)×(0,∞), where m∈R is a given parameter. The relation between diffusion and taxis sensitivity is critical since the ratio u(u+1)m/(u+1)m−1 grows like u2/n for large u with n=dim((0,1))=1.Nonetheless, we show that there is no critical mass phenomenon if m≤−1; that is, in that case all solutions emanating from suitably regular initial data are globally bounded. For certain parabolic–elliptic simplifications of (⋆), we obtain the same conclusion for all m∈(−∞,−1]∪(0,∞) and even for all m∈R if the initial datum is additionally assumed to be monotone.This stands in contrast to critical mass phenomena known to occur for critical quasilinear Keller–Segel systems considered in higher-dimensional domains. Accordingly, we make use of several special features of the one-dimensional setting such as the boundedness of the energy functional from below, the embedding W1,n↪L∞, and the fact that the mass accumulation function solves a spatially non-degenerate parabolic equation.
Artificial intelligence (AI) is increasingly integrated in sustainability governance, yet most applications remain oriented towards optimisation and prediction, reducing complex social-ecological issues to technical problems. This narrow focus neglects plural values, lived experiences, and democratic judgement essential for transformative change. We advance a reflexive AI governance approach that treats AI as a socio-technical assemblage shaping problem framing, knowledge legitimisation, and authority distribution. Synthesising material, technical, epistemic, and ethico-political challenges, the paper draws on Aristotelian notions of techne, episteme, and phronesis to outline three reflexivity dimensions: design, epistemological, and engagement. Using a four-phase governance cycle and a protected area management scenario, we show how reflexivity can help align AI with plural, justice-oriented transformation pathways. Reflexive AI governance grounded in sustainability's visions fosters deliberation, inclusivity, and ecological sufficiency, enabling democratic capacities over whether and how AI should be used, including the legitimate possibility of non-use, restriction, or withdrawal.
Abstract Farming systems face an increasing sustainability challenge requiring integrated solutions to minimize the trade‐offs between the social, ecological and economic dimensions of sustainability. Yet, most research remains siloed, limiting interdisciplinary understanding of farming systems as coupled social‐ecological systems (SESs) and precluding holistic solutions. Addressing this gap, we employed an interdisciplinary knowledge co‐production approach in Kilimanjaro, integrating social, ecological and economic data to understand the multidimensionality of the farming systems. We first surveyed 306 farmers and used multivariate analysis to categorize the farming systems. Based on data from semi‐structured interviews with 15 farmers and field observations of agricultural plots they own or manage, we conducted a cost–benefit analysis of each farming system. Through thematic analysis, we revealed the perceived impacts of farming practices on biodiversity, soil and yields. We triangulated these findings with those from ecological studies conducted on the same agricultural plots. We finally identified challenges to and solutions for sustainable farming, complemented by five key informant interviews. We found a heterogeneous agricultural landscape characterized by maize‐bean, homegarden and coffee farming systems. All systems were estimated to be profitable but sensitive to environmental and market‐related shocks. Farmers and key informants identified four major challenges, largely similar across farming systems: biophysical constraints, production costs and market constraints, infrastructure limitations and gaps in agricultural extension support. Proposed solutions included farm‐level interventions such as manuring, biological pest control and planting drought‐resistant crops, which were low‐cost practices and perceived to offer multiple benefits. Notably, using high‐cost agrochemicals, particularly in the maize‐bean system, was perceived to have long‐term negative environmental impacts. These perceptions were consistent with the findings from the ecological studies. Institutional‐level solutions involved leveraging cooperatives and improving agricultural information, while policy and government‐level solutions focused on subsidies, incentives for organic produce, market regulation and tax reductions. By integrating social, ecological and economic dimensions in an interdisciplinary knowledge co‐production approach, we identified context‐specific challenges and actionable solutions that account for local practices and structural support needs. This approach, which values farmers' experiential knowledge alongside ecological and economic insights, has the potential for wider utility in guiding sustainability in farming systems beyond Kilimanjaro. Read the free Plain Language Summary for this article on the Journal blog.
The transition towards a circular economy necessitates coordinated, systemic change across value chains. Yet the role of anchor firms in initiating and orchestrating nascent circular ecosystems remains underexplored. Using an inductive, qualitative single-case design, we analyse a German mid-sized entrepreneurial firm, drawing on 21 interviews and over 1000 pages of archival material. We develop a five-phase process model of early anchor-led orchestration and a capability bundle centred on establishing circular intentions, setting foundational structures, aligning stakeholders around a circular value proposition, activating the ecosystem through experimentation and preparing for scaling. We highlight the importance of addressing deep cultural-cognitive structures and building orchestration capabilities to overcome circular paralysis. We specify what makes circular ecosystems distinct from generic business or entrepreneurial ecosystems: higher dependence on policy-making, reverse flows and intensive legitimacy work. The study theorises orchestration capabilities and offers actionable strategies for managers and policymakers to design and scale circular ecosystems.
Advancements in artificial intelligence (AI), particularly in generative AI and agentic AI, have intensified challenges related to transparency and explainability. While explainable AI (XAI) research has evolved to facilitate human interaction with such complex, black-box systems, research and practice lack clarity on the causal chain linking explanations, user perceptions, and real-world outcomes, a relationship that remains conceptually fragmented. To address this gap, we conducted a systematic literature review of 107 experimental user studies on XAI. We developed a conceptual framework guided by the stimulus-organism-response-consequences (S-O-R-C) model to systematize current human-XAI research and examine how users respond to explanations. Our study contributes to the literature by clarifying how explanations shape user interactions and downstream effects in real-world settings. We propose five research directions to help navigate the challenges of emerging AI systems (e.g., LLMs, AI agents) and evolving human-AI delegation.
