Transdisciplinary approaches to local sustainability: aligning local governance and navigating spillovers with global action towards the Sustainable Development Goals

In an evolving world, effectively managing human–natural systems under uncertainty becomes paramount, particularly when targeting the United Nations 2030 Agenda for Sustainable Development Goals (SDGs). The complexity in multi-actor decision-making and multi-sectoral settings, coupled with intricate relationships and potential conflicting management approaches, makes understanding the local implications of progressing towards the global SDGs challenging. We used a transdisciplinary approach for knowledge co-production with local stakeholders to assess the impact of local action to boost sustainability in the Goulburn–Murray region, Victoria, Australia, and its alignment with global action towards the SDGs. Together, we co-developed 11 local actions geared towards achieving four locally important environmental and socioeconomic SDGs, with a particular emphasis on addressing potential ‘spillovers’—unintended effects that influence SDGs across scales. Through system dynamics modelling, we evaluated the interplay between these local actions and global scenarios, emphasising their synergies, trade-offs, and the resulting impact on SDG indicators. Key findings indicate a predominant synergy between global and local actions across most SDG indicators. However, certain areas like dairy production, riverine algal blooms, and agricultural profit displayed trade-offs. Local actions significantly impacted indicators, such as crop production, dairy output, agricultural land use, and agricultural profitability. Findings highlighted the need for complementary actions in areas, such as water availability management, skilled workforce, and salinity control. This study underscored the importance of harmonising local initiatives with global sustainability objectives and can inspire local governance to champion resilience policies that harmoniously integrate local actions with global sustainability goals, adapting to evolving uncertainty scenarios

VirtuWind: Virtual and Programmable Industrial Network Prototype Deployed in Operational Wind Park

With anticipated exponential growth of connected devices, future industrial networks require an open solutions architecture facilitated by standards and a strong ecosystem.VirtuWind aims to develop and demonstrate an SDN and NFV ecosystem, based on an open, modular and secure framework.A prototype of the framework for intra-domain and inter-domain scenarios will be showcased in real wind parks,as a representative use case of industrial networks. Validate the economic viability of the demonstrated solution is paramount for VirtuWind. This paper details this vision and explains steps forward

Why community-based disaster risk reduction fails to learn from local knowledge? Experiences from Malawi

This contributing paper aims to investigate the extent to which community-based disaster risk reduction (CBDRR) in practice really takes into account local knowledge (LK). It is often taken as given that CBDRR serves as a mechanism for the inclusion of local knowledge (LK) in disaster risk reduction (DRR). But the reality from the ground suggests that this increased attention does not result in practical inclusion of communities nor their LK in DRR. Through in-depth empirical qualitative data from Malawi, the paper explores the dynamics between the inadequate inclusion of LK and approaches to DRR. This study argues that LK is underutilised in CBDRR and finds that current practice provides a limited opportunity for the inclusion of LK, due to five prime obstacles: i) current approach to community participation, ii) financial constraints and capacity of external stakeholders, iii) the donor landscape, iv) information consolidation and sharing, and v) external stakeholders attitudes towards LK. In CBDRR, a strong dichotomy between local and scientific knowledge is maintained, and further re-examination of community-based approaches in practice is needed to make them truly transformative

Early Warning Systems and Their Role in Disaster Risk Reduction

In this chapter, we introduce early warning systems (EWS) in the context of disaster risk reduction, including the main components of an EWS, the roles of the main actors and the need for robust evaluation. Management of disaster risks requires that the nature and distribution of risk are understood, including the hazards, and the exposure, vulnerability and capacity of communities at risk. A variety of policy options can be used to reduce and manage risks, and we emphasise the contribution of early warnings, presenting an eight-component framework of people-centred early warning systems which highlights the importance of an integrated and all-society approach. We identify the need for decisions to be evidence-based, for performance monitoring and for dealing with errors and false information. We conclude by identifying gaps in current early warning systems, including in the social components of warning systems and in dealing with multi-hazards, and obstacles to progress, including issues in funding, data availability, and stakeholder engagement

Why does community-based disaster risk reduction fail to learn from local knowledge? Experiences from Malawi

It is often taken as given that community-based disaster risk reduction (CBDRR) serves as a mechanism for the inclusion of local knowledge (LK) in disaster risk reduction (DRR). In this paper, through in-depth qualitative analysis of empirical data from Malawi, we investigate the extent to which CBDRR in practice really takes into account LK. This research argues that LK is underutilised in CBDRR and finds that current practice provides a limited opportunity for the inclusion of LK, due to five prime obstacles: i) current approach to community participation, ii) financial constraints and capacity of external stakeholders, iii) the donor landscape, iv) information consolidation and sharing, and v) external stakeholders attitudes towards LK. In CBDRR, a strong dichotomy between local and scientific knowledge is maintained, and further re-examination of community-based approaches in practice is needed to make them truly transformative

Exploring the integration of local and scientific knowledge in early warning systems for disaster risk reduction: a review

The occurrence and intensity of some natural hazards (e.g. hydro-meteorological) increase due to climate change, with growing exposure and socio-economic vulnerability, leading to mounting risks. In response, Disaster Risk Reduction policy and practice emphasize people-centred Early Warning Systems (EWS). Global policies stress the need for including local knowledge and increasing the literature on integrating local and scientific knowledge for EWS. In this paper, we present a review to understand and outline how local and scientific knowledge integration is framed in EWS, namely: (1) existing integration approaches, (2) where in the EWS integration happens, (3) outcomes, (4) challenges, and (5) enablers. The objective is to critically evaluate integration and highlight critical questions about assumptions, goals, outcomes, and processes. In particular, we unpack the impact of power and knowledges as plural. We find a spectrum of integration between knowledges in EWS, mainly with dichotomy at the start: focus on people or technology. The most popular integration approaches are participatory methods such as ‘GIS mapping’ (technology) and methods that focus on ‘triangulation’ (people). We find that critical analysis of power relations and social interaction is either missed or framed as a challenge within integration processes. Knowledge is often seen as binary, embedded in the concept of ‘integration’. It is important to know what different knowledges can and cannot do in different contexts and acknowledge the hybrid reality of knowledge used for EWS. We argue that how we approach different knowledges in EWS has fundamental implications for the approaches to integration and its meaning. To this end, attention to the social processes, power dynamics, and context is crucial

B cell and/or autoantibody deficiency do not prevent neuropsychiatric disease in murine systemic lupus erythematosus

Background: Neuropsychiatric lupus (NPSLE) can be one of the earliest clinical manifestations in human lupus. However, its mechanisms are not fully understood. In lupus, a compromised blood-brain barrier may allow for the passage of circulating autoantibodies into the brain, where they can induce neuropsychiatric abnormalities including depression-like behavior and cognitive abnormalities. The purpose of this study was to determine the role of B cells and/or autoantibodies in the pathogenesis of murine NPSLE. Methods: We evaluated neuropsychiatric manifestations, brain pathology, and cytokine expression in constitutively (JhD/MRL/lpr) and conditionally (hCD20-DTA/MRL/lpr, inducible by tamoxifen) B cell-depleted mice as compared to MRL/lpr lupus mice. Results: We found that autoantibody levels were negligible (JhD/MRL/lpr) or significantly reduced (hCD20-DTA/MRL/lpr) in the serum and cerebrospinal fluid, respectively. Nevertheless, both JhD/MRL/lpr and hCD20-DTA/MRL/lpr mice showed profound depression-like behavior, which was no different from MRL/lpr mice. Cognitive deficits were also observed in both JhD/MRL/lpr and hCD20-DTA/MRL/lpr mice, similar to those exhibited by MRL/lpr mice. Furthermore, although some differences were dependent on the timing of depletion, central features of NPSLE in the MRL/lpr strain including increased blood-brain barrier permeability, brain cell apoptosis, and upregulated cytokine expression persisted in B cell-deficient and B cell-depleted mice. Conclusions: Our study surprisingly found that B cells and/or autoantibodies are not required for key features of neuropsychiatric disease in murine NPSLE

A Reactive Security Framework for Operational Wind Parks Using Service Function Chaining

The innovative application of 5G core technologies, namely Software Defined Networking (SDN) and Network Function Virtualization (NFV), can help reduce capital and operational expenditures in industrial networks. Nevertheless, SDN expands the attack surface of the communication infrastructure, thus necessitating the introduction of additional security mechanisms. A wind park is a good example of an industrial application relying on a network with strict performance, security, and reliability requirements, and was chosen as a representative example of industrial systems. This work highlights the benefit of leveraging the flexibility of SDN/NFV-enabled networks to deploy enhanced, reactive security mechanisms for the protection of the industrial network, via the use of Service Function Chaining. Moreover, a proof of concept implementation of the reactive security framework for an industrial-grade wind park network is presented. The framework is equipped with SDN and SCADA honeypots, modelled on (and deployable to) an actual, operating wind park, allowing continuous monitoring of the industrial network and detailed analysis of potential attacks, thus isolating attackers and enabling the assessment of their level of sophistication

Participatory Modeling for Analyzing Interactions Between High‐Priority Sustainable Development Goals to Promote Local Sustainability

Achieving the Sustainable Development Goals (SDGs) is challenging given the complex interactions between different SDGs and their spillover effects. We developed a system dynamics model—the Local Environmental and Socio-Economic Model (LESEM)—to analyze and quantify context-based SDG interactions at the local scale using a participatory model co-design process with local stakeholders. The LESEM was developed for the Goulburn-Murray Irrigation District in Victoria, Australia, to assist policymakers in analyzing local issues with a more integrated and holistic approach to sustainable development at the local scale. The process of participatory systems dynamics modeling facilitates integrated and strategic decision-making and can help local policymakers identify and quantify potential trade-offs and synergies that benefit multiple SDGs, which eventually leads local communities toward sustainability. We present an illustrative application of the model that quantifies SDG interactions across four high-priority SDGs, namely clean water and sanitation (SDG 6), zero hunger (SDG 2), economic growth (SDG 8), and life on land (SDG 15). We illustrate the use of the model in assessing key SDG indicator trajectories under a business-as-usual (BAU) scenario from 2010 to 2050. Under the BAU, agri-food production increased despite a decline in water resource availability, with gains driven by intensification and increased agricultural productivity. This boosted local prosperity and reduced the amount of agricultural land required to meet future agri-food demand, thereby reducing pressures on terrestrial ecosystems and creating the space for ecological restoration and carbon storage in soils and biomass. However, agricultural intensification impacted water quality through increases in algal blooms and river salinity