Protecting our oceans and advancing a sustainable blue economy require in-depth understanding of marine systems, driven by robust ocean observation, monitoring and valuation. Yet collecting reliable data remains time- and resource-intensive. This data is vital for scientists, emergency responders, and decision-makers to support early-warning systems and emerging tools like digital twins. Stronger support is therefore needed for data collection and its integration into systemic, innovative, and stakeholder-engaged ocean observation efforts.
https://doi.org/10.1038/s44168-026-00341-xCoastal flooding can result from multiple interacting drivers and can be a complex, challenging topic for learners to grasp. Interactive learning with apps offers new opportunities for improving comprehension and engagement. We present the Floodport app, an educational interactive tool that puts students in the role of coastal risk analysts exploring how natural hazards threaten port safety. Users have to adjust key parameters, including high tides, storm surges, terrestrial rainfall contribution, sea-level rise, and engineered features such as dock height. These forces, individually or jointly, result in water-level rises that may flood the app’s port. The app supports exploration of mitigation designs for the port. Developed in Excel and Python 3.11.4 and deployed as an R/Shiny application, Floodport was used as a classroom game by 153 students with no prior knowledge on coastal flooding concepts. Pre–post survey statistical analysis showed significant learning gains and positively correlation with willingness to engage further. Floodport was found to be a useful tool for basic introduction to flooding concepts. The results indicate strong pedagogical promise and potential for using the app beyond the classroom, in contexts such as stakeholder engagement and training.
https://doi.org/10.3390/hydrology13010028Tourism in the Mediterranean has grown rapidly over recent decades, becoming a major economic pillar for countries. However, this growth is highly seasonal, with visitor flows concentrated in a few warm months and in geographically confined coastal and island nodes (Niavis, 2020; Krabokoukis & Polyzos, 2024). That pronounced seasonality produces short-lived but intense peak loads that stress energy systems (cooling demand, transport fuels, grid flexibility), freshwater supplies (especially on islands and coastal aquifers), waste-management and sanitation services, and local infrastructure for housing and mobility (Milano et Al., 2019; Mihalic, 2020). These concentrated pressures are not clearly depicted in annual totals, and characterize the practical problem of overtourism: when short periods of extreme demand exceed institutional and infrastructural capacity, they make residents’ everyday life more difficult, degrade visitor experience, and threaten the long-run sustainability of destinations (Cheung & Li, 2018; Garau-Vadell et al., 2018). The result is a tightly coupled economy-energy–water–infrastructure nexus in which seasonal tourism peaks amplify operational risks and compound climate vulnerabilities (hotter, drier summers, altered season timing), making the management of peak loads and resource security an urgent policy priority for Mediterranean destinations. Globally, governments and regions have explicitly or implicitly committed to net-zero targets by 2050. National decarbonization plans and National Determined Contributions (NDCs) overwhelmingly emphasize energy-system transformations: rapid deployment of renewables, electrification, and efficiency improvements in supply and demand (Koundouri et al., 2025a; Koundouri et al., 2025b; Fragkos et al., 2024). Those energy-first strategies are necessary but often narrow (Alamanos et al., 2024; Koundouri & Alamanos, 2023): by focusing principally on supply-side technology and aggregate annual emissions they can overlook important cross-sectoral drivers of demand (sectoral composition, fuel type switching, transport and tourism growth) and non-energy resource constraints such as freshwater availability. This gap matters most in places where large, short-lived peaks in activity produces highly asymmetric loads on electricity, fuels and water that are not visible in annual totals (Alamanos, 2024a; Gleick & Palaniappan, 2010; McCabe et al., 2008). Such effects are more evident in the tourism-driven summer surges in the Mediterranean countries (Roson & Sartori, 2014; Angeli et al., 2020). Crucially, tourism tends to receive limited explicit treatment in national decarbonization planning, so potential trade-offs and cross-sectoral feedbacks (e.g., raising electricity needs, peak aviation and maritime fuel demand, pressure on local water supplies) remain under-quantified (Ricard et al., 2023). To address this gap, we apply a holistic, coupled economy–energy–water assessment to account for crosssectoral effects and trade-offs (in line with the broader philosophy of (Alamanos, 2024c), to major Mediterranean tourism hotspots, where we can isolate and quantify the tourism effects and seasonality. The objective is to explore whether and how seasonal peaks undermine or complicate net-zero pathways, to quantify those interactions for the first time in these settings, and to provide transferable guidance for other seasonally stressed destinations worldwide. In order to answer this research and practical question thoroughly, we linked a global Computable General Equilibrium (CGE) model (GTAP-E) which provides sectoral activity drivers, feeding a sectoral energy–emissions model (LEAP) and a purpose-built water module (WaterReqGCH), allowing consistent translation of economy-wide structural change into monthly energy and water demand, under baseline and National Commitments (NC) scenarios.
https://unsdsn.globalclimatehub.org/wp-content/uploads/2026/02/ReportGCH_tourism_final.pdfThe current “multi-crisis” is not a set of separate shocks but a tightly interconnected system of climate and biodiversity pressures, food–energy–water insecurity, macroeconomic fragility, widening inequalities, rapid urbanization, and geopolitical stress. While the SDGs provide an integrated blueprint for action, progress remains insufficient, pointing to a persistent gap between global ambition and operational delivery. This study argues that closing this gap requires a practical Global Commons for implementation, one that can translate policy choices and investments into measurable outcomes, account for cross-sector feedbacks, and support locally feasible pathways. We present the Global Climate Hub (GCH), an AE4RIA–SDSN anchored initiative that combines physical and socio-economic modelling with policy-relevant modelling and participatory co-design. The GCH methodology is organized in three stages: (i) continuous SDG measurement through harmonized data pipelines, spatial diagnostics, and digital twins; (ii) co-designed transformation pathways generated through living labs and coupled model chains (energy, land use, water risk, transport, health, and beyond-GDP welfare and trade outcomes) to quantify synergies, trade-offs, and distributional effects; and (iii) financing, equity, and capacity-building mechanisms that connect pathways to investable roadmaps and strengthen the skills required for sustained implementation. By integrating quantification with stakeholder ownership and open decisionsupport tools, the GCH positions modelling as a practical instrument for policy prototyping, learning, and course correction. The approach is directly relevant to the evidence mandate of the 2027 Global Sustainable Development Report, offering a pathway-oriented basis for accelerating SDG implementation to 2030 and informing longer-term transformation beyond it.
https://wpa.deos.aueb.gr/docs/2601.SystemsTransformations.pdfThe increasing penetration of information and communication technologies in the design, monitoring, and control of water systems enables the use of algorithms for detecting and identifying unanticipated events (such as leakages or water contamination) using sensor measurements. However, data-driven methodologies do not always give accurate results and are often not trusted by operators, who may prefer to use their engineering judgment and experience to deal with such events.
In this work, we propose a framework for interpretable event diagnosis — an approach that assists the operators in associating the results of algorithmic event diagnosis methodologies with their own intuition and experience. This is achieved by providing contrasting (i.e., counterfactual) explanations of the results provided by fault diagnosis algorithms; their aim is to improve the understanding of the algorithm’s inner workings by the operators, thus enabling them to take a more informed decision by combining the results with their personal experiences. Specifically, we propose counterfactual event fingerprints, a representation of the difference between the current event diagnosis and the closest alternative explanation, which can be presented in a graphical way. The proposed methodology is applied and evaluated on a realistic use case using the L-Town benchmark.
https://doi.org/10.1016/j.iswa.2025.200621Water utilities around the world typically use chlorine as the main disinfectant for ensuring high-quality drinking water. Usually, a few fixed sensors monitor water quality by detecting changes in parameters like chlorine residuals, guiding chlorination strategies. However, limited sensor coverage leaves most parts of the network unmonitored. Additionally, rapid urban growth and climate change complicate water quality dynamics, challenging conventional methods for sparse to dense state estimation. In this work, we propose a neural network based surrogate model for efficiently obtaining time-dependent approximations of the chlorine concentration dynamics in a water distribution system. We incorporate this surrogate model into an extended Kalman filter to estimate all chlorine concentration states on the basis of only a few sensors. We perform extensive empirical evaluations on popular benchmark water distribution systems from the literature.
https://doi.org/10.1109/SysTol66549.2025.11267307This study examines the dynamics of citizens' policy attitudes for the allocation of public resources for natural disaster prevention and response, with a focus on the role of experience with extreme environmental events and perceived probability of future events. Through a nationally representative survey currently underway in three US states (California, New York, and Texas), we investigate the influence of geographic and emotional proximity to extreme events in shaping relevant preferences. The preliminary analysis of the first two waves of the study suggests that individuals prioritise resource allocation towards recently experienced shocks, and that this prioritization is not driven by subjective disaster risk assessment alone. This suggests people's potential to prioritise policy actions designed to address climate change when its effects are particularly salient.
https://doi.org/10.1109/SysTol66549.2025.11267299This study investigates the relationship between Environmental, Social, and Governance (ESG) momentum and Sustainable Development Goals (SDG) integration within international equity markets. Leveraging a robust dataset spanning 2002-2023, we identify pronounced ESG momentum effects in stock returns across 63 global markets. Our ESG momentum factor, derived through monthly rebalancing, demonstrates an impressive, annualized Sharpe ratio of 0.7, underscoring its financial viability. Beyond returns, the study highlights the pivotal role of ESG controversies in shaping shortterm financial performance. We advanced the discourse by integrating ESG principles with the SDG framework, proposing a novel model to calculate the SDG footprint of financial portfolios. This alignment between ESG momentum and SDG implementation emerges as a significant tool for investors and policymakers, particularly considering regulatory advancements like the Corporate Sustainability Reporting Directive (CSRD).
https://doi.org/10.1109/SysTol66549.2025.11267373Efficient and sustainable water management is imperative due to the mounting pressure on global water supplies from over-exploitation, desertification, and pollution. Integrated Water Resource Management (IWRM) strategies have demonstrated efficacy in decision support; however, a more comprehensive integration of participatory and economic methodologies is required. The objective of this research is to enhance water resource management through collaborative, stakeholder-driven innovation by integrating experimental economics with Living Labs (LLs). Living Labs offer genuine environments for collaborative creation, enabling scientists and stakeholders to resolve water-related concerns such as supply, demand, and scarcity. These environments establish a connection between controlled experimental conditions and real-world applications, offering a comprehensive understanding of policy formulation and behavioural reactions. We use the Limassol Water Futures Living Lab (LWFLL) as a case study that is dedicated to the creation of a comprehensive, intelligent decision-making framework that will enable the effective management of water resources in the presence of unpredictable climate conditions. LLs can be strengthened and improved by economic methodologies, particularly in water valuation, through integrated frameworks that account for environmental externalities and opportunity costs. Real-time input is provided by technological innovations such as smart meters, desalination technologies or soil moisture sensors, which enables dynamic pricing models to accurately depict the economic and environmental costs associated with water consumption. Experimental economics' external validity is enhanced by the integration of behavioural insights and experimental approaches into LLs, which places interventions in real-world settings.
https://doi.org/10.1109/SysTol66549.2025.11267285Achieving climate-neutrality is a global imperative, requiring interdisciplinarity and science-supported policies across multiple sectors. For the first time, we simulated a scenario representing Greece’s climate-neutrality goals drawing upon its key energy, agricultural and water policies. Using a linked-nexus framework encompassing the FABLE Calculator, the Low Emissions Analysis Platform, the WaterReqGCH, LandReqCalcGCH, and BiofuelGCH tools, we find that Greece’s climate-neutrality policies can deliver large emission reductions by 2050, only under optimistic implementation assumptions. The energy-related emissions can be reduced almost fivefold by 2050, and total energy demand by ~20%. Further improvements can be achieved if domestic biofuel production potential is realized. Sectoral interdependencies and infrastructure constraints are overlooked in existing policies, such as land requirements to meet renewables’ expansion targets, and trade-offs among land-energy-water policies. Our findings indicate that progress towards climate-neutrality depends strongly on full, coordinated policy implementation, and we provide recommendations for a timely, unified and holistic sustainable transition.
https://doi.org/10.1038/s44168-025-00318-2Water-system stress challenges driven by aridification, rapid urbanization and tourism peaks, irrigation-intensive agriculture, pollution, fiscal underinvestment and entrenched social inequities need integrated and adaptive policy responses. We present the Global Climate Hub’s interdisciplinary approach along with an application framework that was developed under the ERC-funded Water Futures project, aiming to tackle such challenges: We couple cross-sectoral modeling (physical and natural systems, water-energy systems, and economics), digital-twin forecasting and real-time monitoring, with experimental-economics, behavioral-economics and Living Labs to allow stakeholders’ feedback and solutions’ co-design. Through regulated sandboxes and randomized trials, the project tests pricing reforms, behavioral nudges and technological pilots (IoT/AI leak detection, decentralized treatment, nature-based solutions), producing robust socio-economic narratives and distributional metrics to inform investment choices. Preliminary policy guidance urges an iterative evidence loop of modelling-valuation- Living Lab validation and solution co-design, supported by open data, toward equitable tariff design, targeted subsidies, matched innovation financing and capacity building to scale proven solutions. The proposed approach translates diverse theories into operational pathways for resilient, efficient and socially just urban drinking-water systems, offering a replicable blueprint for regions facing water scarcity.
https://doi.org/10.1142/S2382624X25710043The Greek maritime sector, one of the largest in the world, faces multiple economic, environmental and development challenges, requiring careful long-term investment decisions. In this paper we present the application of a free, open-source investment decision support tool we have developed, the MaritimeGCH, applied for the Greek fleet. We quantify the effect of two main interventions for a cost-effective carbon abatement, under the recent EU environmental regulations: the implementation of mature on-ship emission reduction technologies and transition scenarios to cleaner fuels. While significant emissions are achievable, even ambitious interventions fall short of fully decarbonizing the sector by 2050. This suggests that a more unified set of policy solutions are needed to achieve the national commitments.
https://doi.org/10.1088/1748-9326/ae114eThe concept of water security has progressed from a narrow emphasis on water supply infrastructure, primarily viewed through an engineering lens, to a comprehensive perspective encompassing technological, economic, environmental, and governance dimensions. The evolution of the water security concept, as evidenced in the relevant literature briefly reviewed in this paper, signifies a significant shift. This shift is toward a more comprehensive consideration of diverse values, stakeholders, and viewpoints by representing in an equitable manner as possible human-centric and ecosystem-based priorities. It also underscores the pressing need for transdisciplinary and more integrated approaches, as the challenges in representing the water security notion more effectively continue to mount. In response to these pressing challenges, the Global Climate Hub (GCH) initiative, operating under the UN Sustainable Development Solutions Network, employs interdisciplinary approaches comprising optimal dynamic combinations of technologies, economic analysis, and policies to devise national and regional water security strategies through inclusion approaches with relevant actors and stakeholders.
https://doi.org/10.3389/frwa.2024.1492698The increasing pressure on global water supplies from overexploitation, drought, and pollution necessitates efficient and sustainable water management. Integrated water resource management strategies have shown effectiveness in decision support, but a deeper integration of economic and participative methodologies is needed. This research reviews the core characteristics and directions of experimental economics and living labs (LLs) and aims to address three research questions, namely, how the participatory, real-world environment of living laboratories can be incorporated into the controlled, hypothesis-driven nature of experimental economics; what is the significance of behavioral insights that are derived from experimental economics in the design and implementation of living labs; and how these two approaches can be merged under one framework. The focus of this review is the improvement of water resource management through collaborative and stakeholder-driven innovation. LLs provide authentic environments for cocreation, allowing scientists and stakeholders to address water-related issues such as supply, demand, and shortage. These environments connect controlled experimental conditions with real applications, providing comprehensive insights into behavioral reactions and policy formulation. LLs can enhance and be strengthened by economic methodologies, particularly in water valuation through integrated frameworks accounting for environmental externalities and opportunity costs. Finally, this article shows that integrating behavioral insights and experimental approaches within LLs improves the external validity of experimental economics by putting interventions in real-world settings.
https://doi.org/10.1146/annurev-resource-013024-033007The EU policy landscape contains several large emission reduction strategies with tools to help us decarbonize. The scale of the transition necessitates an understanding of how different implementation levels of different emission policies impact national energy systems and decarbonization targets.
We utilize the open-source, sector-coupled energy system optimization model Balmorel to analyze detailed European pathways and the impact of three decarbonization scenarios. We consider (1) a European-level carbon budget, (2) a carbon budget broken down to national levels, and (3) a carbon tax policy. The novelty of this paper lies in how these policies affect the European decarbonization pathway in the short term and long term and how they can affect interactions between countries and national emission- and renewable targets.
We demonstrate that the final production mix in 2050 remains near-identical across scenarios, with some variation in biomass sequestration, but differences occur between countries in intermediary years. A European budget causes mismatches between the considered budget and modeled emissions in several countries with more fossil capacity, which can be mitigated by both the country-level budget and a carbon tax. Still, country-level budgets lead to emission displacement, and a carbon tax is significantly more investment-intensive.
https://doi.org/10.1016/j.apenergy.2025.125672Climate change-induced wildfires are increasingly prevalent, particularly during summer periods, with evident consequences in multiple regions worldwide. Wildfires affect and change the condition, functionality, and ecosystem services of the burned sites. Altered hydrologic processes make burnt areas more flood-prone. However, the actual effects of wildfires to flooding, the post-fire protection measures and their economic implications remain still overlooked issues. In this report, we cover these gaps in a multi-disciplinary way. More specifically: PART A: We present a novel, integrated and interdisciplinary computational framework that we have developed for the accurate modelling of post-fire flash-flood events. The 2019 post-fire flood in Kineta, Central Greece is used as a case study-example. The proposed approach assesses the fire impacts (burn extent and severity) with Remote Sensing techniques; ‘recreates’ real storms using the atmospheric model WRF-ARW; simulates the flood using the 2D HEC-RAS hydraulic-hydrodynamic model; and validates the results with remote sensing analysis on the flood extent. We detail the linking of those models, step-by-step, for the first time. We build upon the findings by reviewing, selecting and designing the most appropriate Post-fire Erosion and Flood-protection Treatments (PEFTs), and represent them within a Geographic Information System (GIS), which allows their incorporation to the HEC-RAS hydraulic model. The flood event is simulated under three scenarios: prefire; post-fire (real case, happened in November 2019), and post-fire with the PEFTs protection. Thus, for the first time, we reveal the effects of the fire on flooding (~25%), as well as the effectiveness of the suggested measures to mitigate the flood (completely offsetting the fire’s effect). In order to assess the economic implications of the potential flood protection interventions, we present also a detailed estimation of the: i) Costs of the proposed PEFTs, ii) The flood damage direct costs, which were estimated by a semi-automated AI-based approach using image segmentation and human-checks. The comparison of the costs reveals that protection could have cost just 13.6% of the direct damages. Part B: Drawing from the inaction and poor protection of our real case study, we explore the governance gaps. We performed a knowledge-transfer exercise from similar cases in Australia (climate and governance similarities), based on the VRK (Values-Rules-Knowledge) framework. We provide a detailed stakeholder engagement roadmap targeting changes in anachronistic perceptions about the extreme phenomena, the understanding and application of solutions, and their communication as necessary, multi-benefit and cost-effective measures. These findings are applicable to other case studies, too. Part C: For the facilitation of similar analyses nation-wide, we provide a national Greek inventory of design storms based on the official IDF (intensity-duration-frequency) Curves. For this purpose, we developed a novel tool called Catchment2Storm that provides customized design storms (return periods, durations, time intervals) using just the desired catchment’s location. We comment on the results of its Greek-wide application, highlighting the need for localized design considerations in critical sites such as urban centers, ports, and agricultural areas. 6 Finally, we synthesize all the above into a concrete, agenda-setting list of policy recommendations to foster resilience to combined hazards.
SDSN Global Climate Hub ReportAchieving climate neutrality in Europe requires a collective effort that goes well beyond national energy plans, extending into food systems, land use, and natural resources. While each Member State’s National Energy and Climate Plan (NECP) outlines individual targets, a common assessment addressing diversity in planning horizons, data detail, and resources’ considerations is lacking. This report bridges these gaps by simulating Europe’s 35 NECPs through an integrated, systems-nexus framework that couples energy-emissions, food-land, biofuels and water models under two scenarios: “Business as Usual” (BAU, current trends) and the full implementation of the National Commitments (NC) for net-zero. Our framework links five simulation tools by 2050: i) FABLE Calculator: projects crop and livestock production, dietary shifts, and land-use changes, identifying cost, employment, and GHG impacts under current and CAP-aligned policies; ii) LEAP (Low Emissions Analysis Platform): models energy demand and supply across residential, industrial, transport (terrestrial, maritime, aviation), agricultural and services sectors, calculating multi-pollutant GHG emissions for each fuel type and use; iii) BiofuelGCH: quantifies domestic bioethanol and biodiesel potential, revealing import/export imbalances and highlighting countries that can scale production to serve internal or regional demand; iv) LandReqCalcGCH: translates renewable capacity targets (solar and onshore wind) into land area and investment cost requirements, flagging potential conflicts with agriculture, conservation, and community interests; v) WaterReqGCH: estimates sectoral water withdrawals, comparing them against sustainable supply to flag regional water-stress hotspots and underscore the need for integrated River Basin Management Plan (RBMP) measures. Under BAU, agricultural emissions remain stagnant, energy-use emissions decline only marginally, renewable land expansion is limited, and water stress persists particularly in Southern Europe. In contrast, the NC scenario yields significant GHG emissions reductions across all sectors, and more sustainable food and land projections. Hydrogen and renewable sources replace most fossil fuels in the long-term, but net electricity imports rise in countries with limited domestic capacity. Biofuel production potential and link to sectoral consumptive uses (e.g. agriculture, maritime and aviation sectors) remain underexploited. Major economies (Germany, France, Spain, Italy) remain net biofuel importers, with Europe having an aggregate shortfall. Renewable land-use expansion is an overlooked factor, which however stays at generally feasible levels, with also feasible investment requirements, as long as smart and careful land-use planning is followed. Regarding water supply and demand, Southern countries show severe irrigation deficits. NECPs and RBMPs fail to set enforceable, sector-specific water-use targets, leaving key trade-offs (such as water for hydropower or bioenergy) unresolved. We provide 20 main policy recommendations, addressing sectoral, per-country, and per-policy considerations, ensuring a holistic and equitable approach. Key recommendations include: Sectoral coordinated strategies by aligning building retrofits, renewable rollouts, and urban transport planning to maximize efficiency and emissions reductions in residential, services, industry, and transportation sectors. In industry, detailed roadmaps for steel, cement, and chemicals are needed, combining electrification, renewables, and circular-economy measures. In agriculture, we underline the potential of a shift toward agroecological practices and dietary changes via CAP ecoschemes, and mandate for creative land-use solutions (agrivoltaics, brownfield solar, and agro-pastoral wind) to avoid displacing farmland or forests. Embedding enforceable water-use targets tied to CAP irrigation standards and RBMPs to prevent scarcity is also crucial. Biofuels production uptake and adoption for cross-sectoral consumption should be also encouraged. At the policy level, all NECPs must adopt a unified 2050 horizon, deepen quantitative energy-supply and demand projections, and model cross-border electricity, fuel, and hydrogen trade. Linking NECPs with CAP and RBMPs ensures agricultural, land, and water policies align with climate goals, while cross-border collaboration on grid interconnections and shared renewables fosters a coherent, resilient pathway to net-zero. Finally, equity issues should be addressed by targeting additional financial and technical support to lower-income Member States so they can build infrastructure, adopt clean technologies, and meet stringent targets without disproportionate economic strain.
https://unsdsn.globalclimatehub.org/wp-content/uploads/2025/08/GCHmodels_EU_a4_cover_web-3.pdfAchieving climate-neutrality is a global imperative that demands coordinated efforts from both science and robust policies supporting a smooth transition across multiple sectors. However, the interdisciplinary and complex science-to-policy nature of this effort makes it particularly challenging for several countries. Greece has set ambitious goals across different policies; however, their progress is often debated, and to our knowledge there is no study assessing these policies together, or offering an alternative facilitating their implementation. This report presents an integrated modelling approach, to assess Greek National Commitments for Climate Neutrality, within a holistic framework that includes food-land, water, and cross-sectoral energy systems (residential, industrial, terrestrial, maritime and aviation transportation, and services sectors). This is achieved by combing different models under a scenario of joint implementation of the most important sectoral policies driving the transition to climate-neutrality. In particular, different energy, agricultural, shipping and water policy frameworks are simulated jointly in such a National-Commitment scenario, to provide useful insights on whether these plans can achieve the climate-neutrality goals, and identifying weaknesses that need to be addressed in order to capitalise on the opportunities for a broader sustainability transition. A systems-nexus modelling approach was followed to simulate all sectors, consisting of: the FABLE Calculator simulating the food and land-use systems, and the BiofuelGCH tool for the biofuels’ production potential; the Low Emissions Analysis Platform (LEAP) for the simulation of the energy consumption and the associated GHG emissions of multiple pollutants; the MaritimeGCH model for the shipping sector; the WaterReqGCH accounting tool for the estimation of the water requirements of the studied sectors; and the LandReqCalcGCH tool to estimate the land requirements for any potentially additional renewable energy production units. All the models run under a common simulation period, 2020 to 2050, at an annual time-step. Two scenarios were considered: (a) the ‘current accounts’ or do-nothing scenario (business-as-usual - BAU), which assumes that the current trends will continue applying until 2050; (b) the National Commitments for Climate Neutrality (NCNC), which assumes that the different climate-neutrality relevant policies per sector, are implemented together. The presented modelling approach is tested in Greece, but is replicable for any other country. We find that although specific sectoral plans have the potential to achieve multiple co-benefits, the absence of a unified framework can lead to inefficiencies and missed opportunities for synergies and unintended conflicts among objectives. A key point in transitioning to unified and more integrated approaches is the realization that climate adaptation cannot be seen merely as an emissions reduction effort. It requires a broader sustainability context, involving the improvement of all interconnected sectors. The joint implementation of multiple sectoral policies that are needed for decarbonization however, seems to be currently based on assumptions that often overlook sectoral interdependencies, infrastructure constraints, governance and planning coordination, and social aspects, hindering progress towards a unified and more holistic sustainable transition. Moreover, we present a novel Decision Support System (DSS), that allows policymakers to refine their NCNC, and prioritize investments for measures for implementation, in order to achieve decarbonization in a more efficient way. The DSS is based on MultiCriteria Analysis, uniquely combining Fuzzy AHP and Fuzzy TOPSIS techniques. The Fuzzy AHP is used to weight different energy, emissions, time, and costs considerations (criteria), accounting for preference uncertainty. The Fuzzy TOPSIS offers then a ranking of the different packages of measures (alternative strategies), accounting for data and/or modelling uncertainty. The results of the DSS application consist a refined set of measures, the SDSN scenario, which can lead to decarbonization faster, at lower emissions over the planning horizon, at lower costs and efforts, compared to the NCNC.
https://unsdsn.globalclimatehub.org/wp-content/uploads/2025/06/REPORT_GCHmodels_SDSNscenario_Greece__2.pdfThis paper contributes to addressing the escalating challenge of post-wildfire flood hazards - a growing threat to people and nature under climate change - by integrating advanced flood modelling within a governance framework to support proactive flood-protection planning. The coastal community of Kineta, in Greece, is used as a case study to demonstrate the combined application of the multi-disciplinary modelling approach and the governance assessment framework. The modelling approach analyses post-wildfire floods, and guides the design of post-wildfire erosion and flood protection treatments (PEFTs). It combines remote sensing analyses, atmospheric and hydraulic simulation models like WRF-ARW and HEC-RAS, and the geospatial application of targeted PEFTs, such as log-erosion barriers and wooden check dams. The need to bring such model-driven insights into policies implementing PEFTs, led us to augment the modelling approach with a governance framework followed in Australia, which has many similar hazard and governance characteristics to those of Greece. The governance framework is based on the values-rules-knowledge (VRK) model of decision-making contexts, and identifies key barriers that lead to insufficient flood protection. Robust insights are generated from this process about how to effectively apply integrated modelling approaches within decision-making contexts for knowledge and policy co-production to address institutional, behavioral and knowledge barriers impeding timely investments in flood risk mitigation. The proposed framework is suggested as a comprehensive science-to-policy approach that can support more proactive post-wildfire flood risk management.
http://wpa.deos.aueb.gr/docs/2025.ERL.paper.pdfEnergy systems are facing multiple complex challenges, related to factors such as environmental, climate change, social, economic and market, variations in supply and demand patterns, and infrastructure, to name a few. The Global Climate Hub (GCH) is a research-led initiative operating within the United Nations Sustainable Development Solutions Network framework, and mobilizes nine research units to deliver holistic, equitable, and context-specific energy solutions. These units leverage advanced modelling tools, participatory frameworks, and Open Science principles to support resilient, equitable, and sustainable energy transitions. The integration of new technologies in energy systems planning is key to facilitate the workflows of the GCH, and support a sustainable energy transition. This chapter explores the integration of Artificial Intelligence (AI) in the GCH's transdisciplinary approach to energy systems planning. AI offers transformative capabilities, enabling efficient data analysis, predictive modelling, resource optimization, stakeholder engagement and Open Science. We describe how the major advantages of AI can be integrated in existing approaches developed by the GCH, including climate scenario development, decarbonization pathways design, participatory approaches, and digital applications. While AI's integration into GCH processes is ongoing, the chapter presents a foundational framework for AI-enabled energy planning and invites collaboration to advance global sustainability goals through innovative, inclusive, and scalable solutions.
https://wpa.deos.aueb.gr/docs/2025.Global.Climate.Hub.AI.pdfClimate change–driven wildfires, especially in the Mediterranean, are not only becoming more frequent and severe but also amplifying flood risks by altering catchment hydrology. Yet, post-fire flood risk management remains inadequately addressed. In response, we develop an integrated simulation framework that combines meteorological, hydrological, hydraulic-hydrodynamic models and remote sensing techniques to represent post-wildfire flood hazards and support the design of Post-wildfire Flood Protection Treatments (PFPTs). We utilize the framework to accurately represent a post-wildfire flash flood event in a Mediterranean catchment in Greece. The flood event is simulated under three scenarios: pre-wildfire, post-wildfire without any PFPTs in place (reality), and post-wildfire with PFPTs. The results show that the wildfire's impact on flood extent was around a 24.1 % increase, but the PFPTs could have counterbalanced this impact. Moreover, we present an economic model for estimating the cost of the recommended PFPTs and the flood damage direct costs, combining an accounting and a semi-automated AI-based approach. The cost comparison reveals that the protection would have cost around € 3.45 mill (just the 13.7 % of the flood damage costs, € 25.2 mill) potentially saving € 6.37 mill in flood damage. By filling critical knowledge gaps, our study offers insights into the dynamics of post-wildfire flood events and provides policymakers with valuable insights for timely risk mitigation amidst escalating fire-related disasters.
https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2834/The energy and water sectors face increasing challenges amid sustainability and net-zero transitions, which are integral to meeting the UN Sustainable Development Goals (SDGs). The need for integrated models that connect energy, emissions, and water use is critical for developing holistic and sustainable solutions. This chapter focuses on a water-energy-emissions modelling application for the residential sector, a key development area impacting SDGs related to energy, sustainable urbanization, and environmental management. We apply a combined energy-emissions and water accounting model to assess energy use, emissions output, and water consumption of Greece's residential sector, providing comprehensive, data-driven insights. Such integrated assessments are essential for informed policy evaluation and decision-making. We also analyze Greece's national decarbonization plan to 2050, demonstrating how these models can support policy evaluation and discuss the efficiency of the planned pathways. This approach underscores the importance of cross-sectoral analysis for successful long-term sustainable initiatives.
https://wpa.deos.aueb.gr/wpa_show_paper.php?handle=2554The maritime sector faces multiple techno-economic, environmental and development challenges, requiring careful investment decisions. Several of these challenges and factors are related to the Sustainable Development Goals (SDGs). The need for holistic solutions that can address these considerations simultaneously is becoming increasingly pressing. In this chapter we present the application of a free, open-source Investment Decision Support Tool, called MaritimeGCH, to the Greek fleet as a case study example. The model aims to optimize the fleet composition (based on the minimization of the total costs) under techno-economic, environmental, operational factors and European environmental regulations. After the model description, a presentation on its application, directly and indirectly relevant to various SDGs, including: cleaner fuel mixes (SDG7 on Energy), new ships and technologies (SDG9 on Industry), policies for more environmental-friendly shipping (SDG13 on Climate Action and SDG14 on life below water), and meeting shipping demands (SDG8 on Economic Growth).
https://wpa.deos.aueb.gr/docs/2025.MaritimeGCH.chapter.v3.pdfThe maritime sector faces increasing challenges as part of its ongoing transformation period towards more sustainable shipping: There is a shift in fuel preferences, with a gradual phasing out of high-polluting options in favor of cleaner, more sustainable alternatives, amidst increasingly stringent environmental policies pushing for greenhouse gases (GHG) emissions reduction, on top of the already complex techno-economic considerations for optimal shipping operations. These multifaceted challenges call for sophisticated, holistic solutions that can address economic, environmental, and operational aspects simultaneously. In response, the Global Climate Hub (GCH - an initiative under the UN Sustainable Development Solutions Network) develops integrated models to assess such problems and provide sustainable pathways. Here, we present such a model, the MaritimeGCH, a free, open-source, simple and comprehensive tool to address such challenges of maritime fleet management. MaritimeGCH integrates different techno-economic, environmental, operational factors and recent European environmental policies into a single, comprehensive model, which is at the same time simple and transferable to various scales. The optimization logic is first described for maritime problems; next the detailed mathematical description of MaritimeGCH model is presented; and finally, its potential for policy-relevant scenario analysis is outlined with specific examples. The model is publicly available to encourage similar applications and improvements.
https://wpa.deos.aueb.gr/docs/2025.Sustainable.fleet.operations.pdfEurope is a diverse mix of land cover types, that has experienced significant changes over the past decades. For large scale assessments, it is crucial to understand, capture or even quantify such changes. Measuring and monitoring land cover change is crucial because it directly affects multiple sustainability components, including agricultural management, biodiversity, climate stability, and ecosystem services such as water regulation, soil conservation, and carbon sequestration, to name a few. In this work, publicly available data and open-source software, based on satellite imagery techniques, have been used to estimate key relevant parameters such as land cover change, land productivity, and soil Carbon storage. Next, these parameters are connected to the Sustainable Development Goal (SDG)15: By synthesizing them spatially, the indicator SDG15.3.1 was estimated, quantifying the extents of land improvement, stability, and degradation across Europe, from 2010-2020. The results indicate that a significant proportion of land changes remain under a stable "land sustainability" according to the SDG15.3.1 metric, while there are variations in the 'improved' state countries. Degradation-state land changes account for a smaller percentage in most countries, indicating the need for targeted interventions to address land degradation and restore productivity. The code and results produced per country are publicly available.
https://wpa.deos.aueb.gr/docs/2024.Monitoring.Mapping.SDG15.pdfConsider an event of interest B and another event A which is viewed as information for B. When a decision maker (DM) evaluates the effect of A on B, she evaluates the degree to which she asserts the indicative conditional "if A then B", written as A ---> B. In the context of the standard Bayesian confirmation theory, the degree of assertability, As(A ---> B) is given by DM's subjective conditional probability P(B | A). However, the Bayesian interpretation is not the only rational interpretation of As(A ---> B). An alternative interpretation is that As(A ---> B) goes by the probability that the proposition A ---> B is true, that is by DM's unconditional probability P(A ---> B). It is now widely accepted that there is no interpretation of "--->" that ensures the genral validity of P(A ---> B) = P(B | A). Hence, there are multiple truth-conditional interpretations of "--->" each corresponding to a distinct way of information processing. One of these interpretations, namely the material implication of the Propositional Logic, competes favorably with the Bayesian interpretation on normative grounds. As a result, two decision makers can disagree about their posterior probabilities of B even if they share the same information A and have identical prior probability functions.
https://wpa.deos.aueb.gr/wpa_show_paper.php?handle=2509Achieving climate-neutrality is a global imperative that demands coordinated efforts from both science and robust policies supporting a smooth transition across multiple sectors. However, the interdisciplinary and complex science-to-policy nature of this effort makes it particularly challenging for several countries, both in terms of planning and implementation, especially for countries that were not traditionally used to such holistic governance. There are several examples in the literature coupling different models representing different sectors (e.g. water-energy-food-emissions). However, the use of such integrated models assessing climate-neutrality and decarbonization pathways is rarer.
Greece is an example, and its preparedness to address such challenges is often debated. For the first time, we simulated Greece’s climate-neutrality efforts, within a water-energy-food-emissions nexus context, combining models such as LEAP (energy-emissions), FABLE Calculator (food and land use), and WaterReqGCH (water accounting tool). The Greek National Energy and Climate Plan (NECP) was simulated, to evaluate its expected outcomes, and provide insights into its effectiveness. The goal of this novel approach is to provide useful insights on whether the NECP can achieve the climate neutrality goals, and how we can see it as an opportunity for a broader (nexus-oriented) sustainable transition. The results indicate that the NECP, if implemented, has the potential to significantly reduce carbon emissions across all sectors of the economy (residential, industrial, transport, services, agriculture, and energy production). However, that would require its proper implementation, which in turn requires certain behavioural changes (e.g. adoption of technologies to improve energy efficiency and mixes of cleaner fuels). Moreover, it falls short of achieving a more holistic sustainable transition if not considered as part of a broader suite of policies that can concurrently address agriculture, water resource management, and socio-economic dimensions.
Land use changes, and especially urbanization, significantly impact water and energy systems and the associated GHG emissions. However, studying these dynamics and their effects on coupled water–energy–emissions systems remains underexplored in certain countries. Greece has been slow to integrate these systems into data-driven models assessing their feedback. To fill this gap, this research investigates these dynamics in Greece from 2022 to 2050, combining different modelling approaches for the first time. A Remote Sensing analysis utilizing publicly available data and open-source tools (QGIS) was applied to map and monitor land use changes, including urbanization. Greece is a particularly interesting case study, as simultaneous population decline and increasing urbanization are reshaping key sectors of the developing urban centers, i.e., the residential and services sectors. To capture the complex feedback between urban centers with changing population and their water–energy–emissions responses, we coupled the LEAP (Low Energy Analysis Platform) model with the WaterReqGCH model. Thus, the energy consumption and the associated GHG emissions of the residential and services sectors, along with their water consumption, were simulated. The results reveal critical trends: population decline drives a reduction in overall water and energy consumption, and yet, despite these reducing trends, urban areas claim increasing shares of these resources over time. Similarly, decreasing GHG emissions exhibit shifts in pollutant distribution, with certain emissions holding larger shares in urban contexts. This integrated land–water–energy–emissions analysis underscores the value of holistic assessments in managing these systems sustainably and highlights the need to develop plans considering them as a whole. The provision of detailed information on such evolution patterns and feedback is critical to shaping integrated policies aiming at multiple benefits. By linking urbanization patterns with resource dynamics and environmental impacts, we discuss how our findings can be translated into actionable insights for sustainable urban planning and resource management strategies.
https://sciforum.net/paper/view/23500The terrestrial transportation sector, including passengers, buses, and trains, is becoming an increasingly complex field in terms of decarbonization, requiring science-driven, data-based solutions to address its energy and emissions challenges effectively. Greece exemplifies these challenges, as its transportation sector has been slow in transitioning towards decarbonization, despite the country’s commitments. Factors such as dependence on conventional fuels, infrastructure inefficiencies, and policy gaps exacerbate the situation, highlighting the urgent need for comprehensive modeling and assessment tools. This research presents a detailed assessment of Greece’s transportation sector, focusing on energy demand and associated greenhouse gas (GHG) emissions, per use and per fuel type. Leveraging the Low-Emission-Analysis Platform (LEAP) model, we analyze the sector’s fuel mix across various uses at a national scale, marking, to the best of our knowledge, the first such effort for Greece. The model is tested under Shared Socioeconomic Pathways (SSPs) scenarios: SSP1 (sustainability-focused), SSP2 (moderate progress), and SSP5 (fossil-fueled development), projected to 2050. Our findings reveal critical insights into how different decarbonization pathways could reshape Greece’s transportation sector. The key outcomes discussed include variations in energy consumption, emission trajectories, and the feasibility of achieving national and EU decarbonization targets under diverse socio-economic conditions. This work aims to support policymakers in designing robust, forward-looking transportation strategies aligned with sustainability objectives.
https://sciforum.net/event/SmartSustainableCities2025?section=#session3291Greece features a diverse landscape with significant land cover changes over recent decades, impacting sustainability components such as biodiversity, climate stability, and ecosystem services. Monitoring and mapping these changes are essential for informed land management. This research utilizes freely available satellite data (Remote Sensing) and open-source tools (QGIS and Excel sheets) to assess key metrics, including land cover change, productivity, and soil carbon storage. We also link these metrics to estimate the Sustainable Development Goal (SDG) 15, and the indicator SDG15.3.1, considering sustainable land use changes. The spatial synthesis of these metrics reveals areas of land improvement, stability, and degradation from 2010 to 2020, offering insights into Greece's historical land dynamics. Results highlight that most of the land remains in a stable state of “land sustainability,” but certain regions require targeted interventions to address degradation. Notably, urban expansion and intensive agriculture drive localized declines in ecosystem quality, while forest management and conservation policies contribute to stability and improvement. The methodology emphasizes transparency and replicability, with publicly available code and results tailored for Greece's unique environmental and socio-economic context. By aligning national efforts with SDG targets, this work supports policies for balancing economic growth with ecological resilience, ensuring the sustainable use of terrestrial ecosystems, and enhancing the quality of life for present and future generations in Greece.
https://sciforum.net/paper/view/23501Agricultural systems are becoming increasingly complex, requiring data-driven, science-supported models to address their multifaceted challenges and ensure sustainable management. In Greece, agriculture is a critical sector, contributing significantly to the economy and rural livelihoods, but it also faces pressing challenges such as competing water uses, energy demands, lackluster productivity, and environmental pressures. This study presents a comprehensive multi-model assessment of Greece’s Water–Energy–Food–Ecosystems Nexus, evaluating agricultural production alongside energy and water requirements and quantifying the associated air pollution impacts at the national level. For the first time to our knowledge, we connect the FABLE Calculator (the software of the FABLE Consortium) with LEAP (Low Emissions Analysis Platform, from the Stockholm Environmental Institute) and the WaterReqGCH (a model developed by the Global Climate Hub). The FABLE Calculator provides detailed estimates of agricultural and livestock production, which are then used by LEAP to calculate the respective energy demand and the associated greenhouse gas emissions per fuel type used. The WaterReqGCH model uses the activity levels in FABLE and LEAP in order to estimate the water requirements of the agricultural and livestock sector. The models run based on a current accounts scenario expressing Greece's national commitments to the agri-food, energy, and water sectors according to the Greek Common Agricultural Policy (CAP) Plan, the National Energy and Climate Plan (NECP), and the River Basin Management Plans. The results indicate that the implementation of the CAP Plan, combining higher productivity, together with the NECP, assuming cleaner fuels, can result in a 73.4% decline in Greece's agricultural production GHG emissions despite the slight increase in the sector's energy consumption by 15% in 2050. Agriculture is the dominant consumer of water resources, consistently accounting for 88–89% of the total water consumption over the period 2020-2050. Agricultural water consumption follows a slight increase after 2025 and reaches an average consumption of 8041.12hm³ by 2050, with only minor fluctuations and large uncertainty ranges due to a combination of hydro-climatic and agronomic parameters. The assumed higher productivity of the agricultural sector is likely to also increase its total water consumption. The insights provided by this multi-model approach are useful and holistic evidence for policymaking, highlighting the need for more coordinated approaches.
https://sciforum.net/event/IOCAG2025?subscribe§ion=#session3460Decarbonization is essential to meet the European Union's legally binding climate-neutrality goals, which require a 55% reduction in greenhouse gas emissions by 2030 and complete climate neutrality by 2050. Achieving these targets necessitates coordinated efforts across multiple sectors, supported by robust policies and scientific innovation. However, despite considerable progress in the adoption of renewable sources of energy, Greece still heavily relies on fossil fuels. Most national policies aim to achieve the "minimum requirements" to comply with the EU goals, and this often implies that they follow a least-cost approach, starting from the low-hanging fruits. However, whether a least-cost approach is enough to achieve complete decarbonization by 2050 is questionable, since there are no model-based assessments exploring the trade-offs between different decarbonization strategies, their impact on Greenhouse Gas (GHG) emissions, and the respective implementation costs. We fill this gap by exploring alternative decarbonization strategies by evaluating them based on their outcome (in terms of GHG emissions reduction) and the associated implementation costs. We use the Low Emissions Analysis Platform (LEAP) to explore those strategies by simulating the energy consumption (demand) across several sectors, fuel supply and transformation, and the associated GHG emissions and implementation costs for a planning horizon from 2022 (base year) to 2050 (target year). Our model consists of the following sectors/parameters: • Demand: The demand side of the energy system consists of the Residential,• Supply: The supply side of the system concerns the production of primary energy sources (such as Natural Gas, Lignite, Solar, etc.), the transformation processes (e.g. electricity and heat generation, oil refining, synthetic fuels and hydrogen production) as well as transmission and distribution; • GHG emissions: The GHG emissions are estimated automatically based on the coefficients of the IPCC’s Fifth Assessment Report; • Implementation costs: The implementation costs of alternative strategies are determined based on estimates from the National Energy & Climate Plan and the related literature. Our analysis shows that a least-cost approach is not sufficient for Greece to achieve its climate neutrality goals by 2050. In contrast, a different combination of alternatives, regardless of their implementation costs, is necessary to achieve the desired GHG emissions reduction. Our results for the first time quantify the insufficiencies of such an approach, providing important implications for existing GHG emissions reduction policies.
https://www.researchgate.net/publication/392703184_Exploring_alternative_decarbonization_strategies_for_GreeceThe Global Climate Hub (GCH) has been developed under the United Nations Sustainable Development Solutions Network (UN SDSN). It is an international research-led initiative for tackling complex sustainability challenges. The SDSN GCH develops national and regional pathways (optimal dynamic and spatial mixture of policies, technologies, and fiscal and financial instruments) for the transition to climate neutrality and climate resilience, using a holistic and interdisciplinary methodology: We co-design pathways for climate resilience and neutrality with stakeholders, based on the integration of downscaled climate scenarios with science-based national and regional systems modelling (energy, land and marine use systems, health and socioeconomics systems). The approach is aided by an open-access AI-driven data gathering, aggregation and visualization platform, various innovation accelerators and a training and education unit, aimed at strengthening stakeholder involvement and capacity. The work of the GCH is the result of the coordination of nine distinct research units, covering a wide range of expertise in digital applications, climate science, land, water, food, biodiversity, and marine and maritime systems, energy and decarbonization, land and maritime transport, public health, solutions' application, policy, finance, labour markets, participatory approaches, education and training. The coordinated work of these nine units provides a unique approach of holistically addressing all levels of the human-environmental interface for providing truly sustainable solutions tailored per case study or region. In this presentation, we describe for the first time how maritime operations are seen as a part of a broader sustainability framing of the nine research units of the GCH. First, the importance of “Data, Platforms and Digital Applications” (unit 1) in modelling sustainable maritime operations is outlined. Then, the actual modelling is briefly presented (unit 3), combining the use of climate change projections (unit 2), and the optimal maritime operations, considering energy-fuels-emissions models (unit 4), as well as the economy and finance tools to ensure a just transition (unit 7). Moreover, their interactions and impacts on “environment and public health” (unit 5) are discussed. To bridge science to practical application and policy, and ensure the long-term implementation, we present the role of: the “Transformative and Participatory Approaches” (unit 8) to co-design solutions with stakeholders; the “Innovation/ Acceleration” unit 6, to practically implement these solutions’ and the “Education, Training, Upskilling and Reskilling” (unit 9), to develop the necessary expertise for the stakeholders to own and manage the solutions. This approach comprehensively addresses all aspects of human-environment interaction, providing comprehensive and long-lasting sustainable solutions.
https://www.researchgate.net/publication/392703746_Sustainable_shipping_within_the_Global_Climate_Hub's_models_integrationThe maritime industry is undergoing significant transformation as it grapples with the need for more sustainable shipping practices. This transition involves a shift in fuel preferences, with traditional high-polluting fuels being phased out in favour of cleaner, more sustainable alternatives. The sector is also contending with increasingly stringent environmental regulations, particularly regarding the reduction of greenhouse gas (GHG) emissions. These regulatory demands, coupled with the already complex techno-economic considerations for optimizing shipping operations, present a set of multifaceted challenges that require comprehensive and integrated solutions. In response to these challenges, the Global Climate Hub (GCH) — an initiative under the United Nations Sustainable Development Solutions Network (UN SDSN) — has been actively developing models that offer sustainable pathways for all economic sectors, including the shipping industry. This paper presents such a model: MaritimeGCH, a free, open-source, and comprehensive tool (optimization model) designed to tackle the diverse challenges associated with maritime fleet management. It has been developed in Python, and there can be different variations, depending on the problem being studied and its scale. MaritimeGCH integrates a range of factors, including techno-economic, fuels, environmental, and operational elements, into a single, unified model. It also incorporates recent European environmental policies and penalties, offering a tool that is detailed, flexible, and adaptable to various scales. The model's optimization framework is tailored specifically for maritime challenges, balancing the need for economic efficiencystriving for environmental sustainability. The paper first describes the optimization logic applied to maritime problems, followed by a detailed mathematical breakdown of the MaritimeGCH model. Finally, the model's utility for policy-relevant scenario analysis is discussed. By making MaritimeGCH publicly available, the GCH aims to encourage the broader application of the model while fostering continuous improvements. The model offers significant potential for helping the maritime industry navigate its path toward sustainability while balancing economic and environmental goals in an increasingly complex regulatory landscape.
https://www.researchgate.net/publication/392693937_An_integrated_maritime_optimization_model_considering_constraints_expressing_environmental_regulationsAs the need for more environmentally friendly and energy-efficient operations becomes increasingly urgent, shipping — Shipping, despite being a carbon-efficient mode of transport — faces mounting pressure to adapt. The growing awareness of climate change and its impacts has led to a push for the decarbonisation of maritime transport, an industry responsible for approximately 3% of global greenhouse gas emissions. With international trade largely dependent on shipping, ensuring that maritime operations become more sustainable is essential for achieving broader global climate targets. This transition towards sustainability is especially crucial because of the sector's worldwide magnitude, which is growing in tandem with the surging shipping demand. The industry must fulfil these demands while substantially minimising its environmental impact... Alongside the increase in shipping demand, the transition to net-zero necessitates more environmental restrictions, which are expressed through policies (e.g., Emissions Trading Systems - ETS, etc.). Through a brief review of these new mandates, this paper provides a general overview of the main methods and simulation and optimisation models that have been proposed so far for analysing sustainable shipping scenarios, combining techno-economic and environmental parameters. These models combine techno-economic and environmental parameters to offer a comprehensive understanding of potential pathways for decarbonisation. Optimisation models considering technical shipping, fuels and costs, alternative fuels, transition rates, and various 'what-if' or policy scenarios, have been largely used to provide guidance to policymakers with respect to shipping decarbonisation. Herein, different case studies and scales are considered, in order to provide a more holistic picture of the techno-economic and environmental optimisation modelling approaches in maritime operations. Finally, different scenarios examined by these models are discussed, including different modelling cases related to the economic prices of various parameters, shipping demand, the stringency of environmental policies, and more. The findings of this research provide valuable insights for policymakers, shipping industry stakeholders, and researchers as they explore different models, and develop strategies to balance the need for increased shipping capacity with the imperative of environmental sustainability.
https://www.researchgate.net/publication/392693858_An_overview_of_integrated_maritime_optimisation_approachesCoastal flooding is driven by multiple interacting hazards such as tides, storm surges, heavy rainfall, and sea-level rise. Its complex nature makes it challenging for learners to fully grasp. Interactive learning through technology-enhanced tools offers new opportunities for improving comprehension and engagement. We present the FloodPort, an interactive educational application designed to simulate coastal flood scenarios at a port. Students act as coastal risk analysts, adjusting key parameters such as high tides, storm surge, rainfall runoff, sea-level rise, and the height of engineering structures like the port dock. These drivers can act independently or in combination. The user can explore build-in climate change scenarios or develop its own custom scenario, exploring thus complex hazard interactions and design safer port configurations. Initially developed in Excel and Python, the app has been transformed into a freely accessible Shiny App format (using R), supporting active learning and scenario-based exploration in the classroom.
https://www.researchgate.net/publication/398375456_Floodport_An_interactive_coastal_flood_risk_training_appThe maritime sector faces multiple techno-economic, environmental and development challenges, requiring careful investment decisions. In this paper we present the application of a free, open-source Investment Decision Support Tool, called MaritimeGCH: a least-cost linear optimization model that reflects operational and investment variables and constraints within the shipping industry. The model aims to optimize fleet composition under technoeconomic, environmental, operational factors and European environmental regulations. Through this, we are able to test the effect of different technologies, their respective costs and carbon abatement potential within the Greek shipping fleet. Greece has the second largest fleet globally, with a merchant fleet of about 249 million gross tonnes. Greece ranks first in deadweight tonnage (DWT), accounting for 17.77% of the global capacity, with a fleet of 364 million DWT. This importance stems from a deep-rooted tradition of maritime expertise and a strategic focus on global shipping markets, positioning it as a crucial component of international trade and economic stability (Alexandropoulou et al., 2021; Papandreou et al., 2021).
https://www.haee.gr/media/6481/christopher-deranian_2.pdfReliable and cost-efficient scheduling of pumps is an important task in the daily operations of urban water distribution networks (WDNs). In this work, we address the scheduling of variable-speed pumps using reinforcement learning (RL), which allows network controls to adapt to changes in demand in real-time after a data-driven training phase. Previous contributions have shown the general suitability of RL for control tasks in WDNs [1], [2]. However, most of them assume deterministically known demand patterns (cf. [1]) or consider uncertainty only for valve control (cf. [2]). As RL algorithms can handle uncertain environments, we explore their potential for dynamic scheduling of the network’s pumps under uncertain demand patterns. Our optimisation goal is to train a policy that complies with upper and lower pressure bounds at all nodes in the network while minimising the cost of pumping. To this end, we make use of the Soft Actor-Critic algorithm (SAC) [3]. Data for training and testing is collected using the EPANET simulator for two benchmark networks (Net1 and Anytown) with uncertainties applied to various network parameters. In all setups, the controller is trained without nodal demand information. Our study shows promising results for a pump scheduler that can reduce energy cost by a significant amount while complying with pressure bounds even for unseen scenarios.
https://doi.org/10.15131/shef.data.29921117.v1Publicly accessible and user-friendly benchmarks are crucial for advancing and accelerating research in smart water systems and supporting reproducible studies. Benchmark resources related to Water Distribution Networks (WDNs) are difficult to access, as they are not consolidated within a centralized, standardized interface that could enhance their accessibility, usability, and relevance for researchers. Currently, they are dispersed across various sources, often poorly labelled and lacking clear guidance on their intended use or the research problems they address. In this work, we introduce the WaterBenchmarkHub platform, an open-source and community-driven platform, designed to offer a standardized interface for benchmarking resources. The WaterBenchmarkHub consists of a web interface for browsing the benchmark resources, and a Python package for easy access of those benchmark resources in Python. This project aims to enhance accessibility for a broader range of researchers and ultimately promote and strengthen reproducible research within the community.
https://doi.org/10.15131/shef.data.29921051.v1Ensuring high-quality drinking water is a critical responsibility of water utilities, with chlorine being the main disinfectant typically used. Accurate estimation of chlorine concentrations in the dynamic environment of water distribution networks (WDNs) is essential to ensure safe water supply. This work introduces a comprehensive and carefully created benchmark for training and evaluation of chlorine concentration estimation methodologies in WDNs. The benchmark includes a diverse dataset of 18,000 scenarios of the widely studied ‘Hanoi’, ‘Net1’, and the more recent and complex ‘CY-DBP’ water networks, featuring various chlorine injection patterns to capture diverse physical dynamics. To provide baseline evaluations, we propose and evaluate two neural surrogate models for chlorine state estimation: a physics-informed Graph Neural Network (GNN) and a physics-guided Recurrent Neural Network (RNN).
https://doi.org/10.1007/s42979-025-04008-yIn a desktop exercise, a water utility’s emergency response to suspected wastewater contamination in a drinking water network was compared with a model-based approach using PathoINVEST. This tool simulates contamination scenarios and assists with locating the source of contamination using sampling results.
https://doi.org/10.3390/engproc2024069045Pathogen intrusion in drinking water systems can pose severe health risks. To better prepare in planning and responding to such events, computational models that capture the intrusion and health impact dynamics are needed. This study presents a novel benchmark testbed that integrates current knowledge on pathogen transport and fate in chlorinated systems and can assess infection risk from contamination events.
https://doi.org/10.1016/j.jclepro.2024.143997This paper investigates the fault-tolerant safe control problem in water network systems in the presence of process and actuator faults.
https://doi.org/10.1016/j.ifacol.2024.07.233Quantized signal-driven control for nonlinear systems is of special interest in practice. However, it is nontrivial in the presence of mismatched uncertainties and intermittent denial of service (DoS) attacks. The underlying problem becomes even more complicated when both the input and output signals are attacked, rendering the state variables and the input signal inaccessible or unavailable for the control design. Only the quantized (and thus nondifferentiable) output signal is available in the absence of attack, making regular backstepping design inapplicable. This article introduces a novel adaptive output feedback control method to tackle the aforementioned challenges.
https://doi.org/10.1109/TCYB.2024.3438288This paper studies the problem of secure state estimation of networked switched systems in the presence of denial-of-service (DoS) attacks, as well as disturbances and measurement noise. Firstly, a state transformation rule is designed to partition the original system into two subsystems, facilitating the design of discrete and continuous state observers
https://doi.org/10.1016/j.ejcon.2024.101037In this paper, the problem of actuator fault detection and isolation is investigated for a class of nonlinear interconnected large-scale systems with modelling uncertainty and measurement noise, where each subsystem can have multiple inputs and multiple outputs (MIMO). The main contribution of this work is the derivation of a scheme that is able to diagnose single or multiple actuator faults in one or multiple subsystems.
https://doi.org/10.1080/00207179.2024.2310606Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise, interactive effects of multiple faults and fault propagation. This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems. Actuator and sensor fault isolation are accomplished in two independent modules, that monitor the system and are able to isolate the potential faulty actuator(s) or sensor(s).
https://doi.org/10.1016/j.jai.2024.03.001EPyT-Flow: A Toolkit for Generating Water Distribution Network Data.
https://doi.org/10.21105/joss.07104Supervised online learning relies on the assumption that ground truth information is available for model updates at each time step. As this is not realistic in every setting, alternatives such as active online learning, or online learning with verification latency have been proposed. In this work, we assume that no label information is available after intitial training. We argue that provided we can characterize the expected concept drift as incremental drift, we can rely on a self-labeling strategy to keep updated models. We derive a k-NN-based self-labeling online learner implementing the presented self-supervised scheme and experimentally show that this is an option for learning from incrementally drifting data streams in the absence of label information.
https://doi.org/10.14428/esann/2024.ES2024-49Prototype-based methods constitute a robust and transparent family of machine-learning models. To increase robustness in real-world applications, they are frequently coupled with reject options. While the state-of-the-art method, relative similarity, couples the rejection of samples with high aleatoric and epistemic uncertainty, the technique lacks transparency, i.e., an explanation of why a sample has been rejected. In this work, we analyze the relative similarity analytically and derive an explanation scheme for reject options in prototype-based classification.
https://doi.org/10.14428/esann/2024.ES2024-156Feature selection is one of the most relevant preprocessing and analysis techniques in machine learning, allowing for increases in model performance and knowledge discovery. In online setups, both can be affected by concept drift, i.e., changes of the underlying distribution. Recently, an adaption of classical feature relevance approaches to drift detection was introduced. While the method increases detection performance significantly, there is only little discussion on the explanatory aspects. In this work, we focus on understanding the structure of the ongoing drift by transferring the concept of strongly and weakly relevant features to it. We empirically evaluate our methodology using graphical models.
https://doi.org/10.14428/esann/2024.ES2024-89Especially if artificial intelligence (AI)-supported decisions affect the society, the fairness of such AI-based methodologies constitutes an important area of research. In this contribution, we investigate the applications of AI to the socioeconomically relevant infrastructure of water distribution systems (WDSs). We propose an appropriate definition of protected groups in WDSs and generalized definitions of group fairness, applicable even to multiple non-binary sensitive features, that provably coincide with existing definitions for a single binary sensitive feature. We demonstrate that typical methods for the detection of leakages in WDSs are unfair in this sense. Further, we thus propose a general fairness-enhancing framework as an extension of the specific leakage detection pipeline, but also for an arbitrary learning scheme, to increase the fairness of the AI-based algorithm. Finally, we evaluate and compare several specific instantiations of this framework on a toy and on a realistic WDS to show their utility.
https://doi.org/10.7717/peerj-cs.2317Je mehr KI-gestützte Entscheidungen das Leben von Menschen betreffen, desto wichtiger ist die Fairness solcher Entscheidungen. Dieser Beitrag gibt eine Einführung in die Forschung zu Fairness in KI-Systemen, erklärt die wesentlichen Fairness-Definitionen und Strategien zur Erreichung von Fairness anhand konkreter Beispiele und ordnet die Fairness-Forschung in den europäischen Kontext ein. Weder in der europäischen Gesetzgebung noch in der KI-Forschung kommt es dabei zu einem Konsens, wie Fairness zu definieren und zu erreichen ist. Stattdessen muss für jedes System eine differenzierte und kontextabhängige Betrachtung von möglichen unfairen Ergebnissen und deren Konsequenzen erfolgen. Dieser Beitrag kann dabei unterstützen und richtet sich an ein interdisziplinäres Publikum, indem auf mathematische Formulierungen verzichtet wird und stattdessen Visualisierungen und Beispiele genutzt werden.
https://doi.org/10.1007/978-3-658-43816-6_9Facing climate change, the already limited availability of drinking water will decrease in the future, rendering drinking water an increasingly scarce resource. Considerable amounts of it are lost through leakages in water transportation and distribution networks. Thus, anomaly detection and localization, in particular for leakages, are crucial but challenging tasks due to the complex interactions and changing demands in water distribution networks. In this work, we conceptually analyze the effects of anomalies on the dynamics of critical infrastructure systems by modeling them with Bayesian networks. We then discuss how the problem is connected to and can be considered through the lens of concept drift. This analysis yields our proposal to leverage model-based drift explanations as a tool for localizing anomalies given limited information about the network. The methodology is experimentally evaluated using realistic benchmark scenarios. To showcase that our methodology applies to critical infrastructure more generally, in addition to considering leakages and sensor faults in water systems, we investigate the suitability of the derived technique to localize sensor faults in power systems.
https://doi.org/10.1109/IJCNN60899.2024.10651472Research on methods for planning and controlling water distribution networks gains increasing relevance as the availability of drinking water will decrease as a consequence of climate change. So far, the majority of approaches is based on hydraulics and engineering expertise. However, with the increasing availability of sensors, machine learning techniques constitute a promising tool. This work presents the main tasks in water distribution networks, discusses how they relate to machine learning and analyses how the particularities of the domain pose challenges to and can be leveraged by machine learning approaches. Besides, it provides a technical toolkit by presenting evaluation benchmarks and a structured survey of the exemplary task of leakage detection and localization.
https://doi.org/10.1007/978-3-031-72356-8_11Feature selection is one of the most relevant preprocessing and analysis techniques in machine learning. It can dramatically increase the performance of learning algorithms and at the same time provide relevant information on the data. In the scenario of online and stream learning, concept drift, i.e., changes of the underlying distribution over time, can cause significant problems for learning models and data analysis. While there do exist feature selection methods for online learning, none of the methods targets feature selection for drift detection, i.e., the challenge to increase the performance of drift detectors by analyzing the drift rather than increasing model accuracy. However, this challenge is particularly relevant for common unsupervised scenarios. In this work, we study feature selection for drift detection and drift monitoring. We develop a formal definition for a feature-wise notion of drift that allows semantic interpretation. Besides, we derive an efficient algorithm by reducing the problem to classical feature selection and analyze the applicability of our approach to feature selection for drift detection on a theoretical level. Finally, we empirically show the relevance of our considerations on several benchmarks.
https://doi.org/10.1016/j.neucom.2024.127968The world surrounding us is subject to constant change. These changes, frequently described as concept drift, influence many industrial and technical processes. As they can lead to malfunctions and other anomalous behavior, which may be safety-critical in many scenarios, detecting and analyzing concept drift is crucial. In this study, we provide a literature review focusing on concept drift in unsupervised data streams. While many surveys focus on supervised data streams, so far, there is no work reviewing the unsupervised setting. However, this setting is of particular relevance for monitoring and anomaly detection which are directly applicable to many tasks and challenges in engineering. This survey provides a taxonomy of existing work on unsupervised drift detection. In addition to providing a comprehensive literature review, it offers precise mathematical definitions of the considered problems and contains standardized experiments on parametric artificial datasets allowing for a direct comparison of different detection strategies. Thus, the suitability of different schemes can be analyzed systematically, and guidelines for their usage in real-world scenarios can be provided.
https://doi.org/10.3389/frai.2024.1330257In an increasing number of industrial and technical processes, machine learning-based systems are being entrusted with supervision tasks. While they have been successfully utilized in many application areas, they frequently are not able to generalize to changes in the observed data, which environmental changes or degrading sensors might cause. These changes, commonly referred to as concept drift can trigger malfunctions in the used solutions which are safety-critical in many cases. Thus, detecting and analyzing concept drift is a crucial step when building reliable and robust machine learning-driven solutions. In this work, we consider the setting of unsupervised data streams which is highly relevant for different monitoring and anomaly detection scenarios. In particular, we focus on the tasks of localizing and explaining concept drift which are crucial to enable human operators to take appropriate action. Next to providing precise mathematical definitions of the problem of concept drift localization, we survey the body of literature on this topic. By performing standardized experiments on parametric artificial datasets we provide a direct comparison of different strategies. Thereby, we can systematically analyze the properties of different schemes and suggest first guidelines for practical applications. Finally, we explore the emerging topic of explaining concept drift.
https://doi.org/10.3389/frai.2024.1330258Drinking water is a vital resource for humanity, and thus, Water Distribution Networks (WDNs) are considered critical infrastructures in modern societies. The operation of WDNs is subject to diverse challenges such as water leakages and contamination, cyber/physical attacks, high energy consumption during pump operation, etc. With model-based methods reaching their limits due to various uncertainty sources, AI methods offer promising solutions to those challenges. In this work, we introduce a Python toolbox for complex scenario modeling \& generation such that AI researchers can easily access challenging problems from the drinking water domain. Besides providing a high-level interface for the easy generation of hydraulic and water quality scenario data, it also provides easy access to popular event detection benchmarks and an environment for developing control algorithms.
https://doi.org/10.48550/arXiv.2406.02078Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Several works address the phenomenon of concept drift in the streaming context usually assuming that consecutive data points are independent of each other. To generalize to dependent data, many authors link the notion of concept drift to time series. In this work, we show that the temporal dependencies are strongly influencing the sampling process. Thus, the used definitions need major modifications. In particular, we show that the notion of stationarity is not suited for this setup and discuss an alternative we refer to as consistency. We demonstrate that consistency better describes the observable learning behavior in numerical experiments.
https://doi.org/10.1007/978-3-031-58547-0_7Leakages are a major risk in water distribution networks as they cause water loss and increase contamination risks. Leakage detection is a difficult task due to the complex dynamics of water distribution networks. In particular, small leakages are hard to detect. From a machine-learning perspective, leakages can be modeled as concept drift. Thus, a wide variety of drift detection schemes seems to be a suitable choice for detecting leakages. In this work, we explore the potential of model-loss-based and distribution-based drift detection methods to tackle leakage detection. We additionally discuss the issue of temporal dependencies in the data and propose a way to cope with it when applying distribution-based detection. We evaluate different methods systematically for leakages of different sizes and detection times. Additionally, we propose a first drift-detection-based technique for localizing leakages.
https://doi.org/10.5220/0012361200003654Water distribution systems (WDS) are an integral part of critical infrastructure which is pivotal to urban development. As 70% of the world's population will likely live in urban environments in 2050, efficient simulation and planning tools for WDS play a crucial role in reaching UN's sustainable developmental goal (SDG) 6 - "Clean water and sanitation for all". In this realm, we propose a novel and efficient machine learning emulator, more precisely, a physics-informed deep learning (DL) model, for hydraulic state estimation in WDS. Using a recursive approach, our model only needs a few graph convolutional neural network (GCN) layers and employs an innovative algorithm based on message passing. Unlike conventional machine learning tasks, the model uses hydraulic principles to infer two additional hydraulic state features in the process of reconstructing the available ground truth feature in an unsupervised manner. To the best of our knowledge, this is the first DL approach to emulate the popular hydraulic simulator EPANET, utilizing no additional information. Like most DL models and unlike the hydraulic simulator, our model demonstrates vastly faster emulation times that do not increase drastically with the size of the WDS. Moreover, we achieve high accuracy on the ground truth and very similar results compared to the hydraulic simulator as demonstrated through experiments on five real-world WDS datasets.
https://doi.org/10.1609/aaai.v38i20.30192Long-term water network planning methods need to be adaptive under deep uncertainty.
Reinforcement learning (RL) is a promising approach for decision-making under uncertainty.
We propose the application of reinforcement learning for the design of water networks.
Results show that an RL agent can find feasible solutions to deterministic problems.
This is a first step towards the development of more adaptive planning approaches in the field.
https://virtual.oxfordabstracts.com/#/event/3937/submission/54There is an emerging need for predictive models to be trained on-the-fly, since in numerous machine learning applications data are arriving in an online fashion. A critical challenge encountered is that of limited availability of ground truth information (e.g., labels in classification tasks) as new data are observed one-by-one online, while another significant challenge is that of class imbalance. This work introduces the novel Augmented Queues method, which addresses the dual-problem by combining in a synergistic manner online active learning, data augmentation, and a multi-queue memory to maintain separate and balanced queues for each class. We perform an extensive experimental study using image and time-series augmentations, in which we examine the roles of the active learning budget, memory size, imbalance level, and neural network type. We demonstrate two major advantages of Augmented Queues. First, it does not reserve additional memory space as the generation of synthetic data occurs only at training times. Second, learning models have access to more labelled data without the need to increase the active learning budget and / or the original memory size. Learning on-the-fly poses major challenges which, typically, hinder the deployment of learning models. Augmented Queues significantly improves the performance in terms of learning quality and speed. Our code is made publicly available.
https://zenodo.org/records/7659977In real-world applications, the process generating the data might suffer from nonstationary effects (e.g., due to seasonality, faults affecting sensors or actuators, and changes in the users' behaviour). These changes, often called concept drift, might induce severe (potentially catastrophic) impacts on trained learning models that become obsolete over time, and inadequate to solve the task at hand. Learning in presence of concept drift aims at designing machine and deep learning models that are able to track and adapt to concept drift. Typically, techniques to handle concept drift are either active or passive, and traditionally, these have been considered to be mutually exclusive. Active techniques use an explicit drift detection mechanism, and re-train the learning algorithm when concept drift is detected. Passive techniques use an implicit method to deal with drift, and continually update the model using incremental learning. Differently from what present in the literature, we propose a hybrid alternative which merges the two approaches, hence, leveraging on their advantages. The proposed method called Hybrid-Adaptive REBAlancing (HAREBA) significantly outperforms strong baselines and state-of-the-art methods in terms of learning quality and speed; we experiment how it is effective under severe class imbalance levels too.
https://ieeexplore.ieee.org/document/10022140In our digital universe nowadays, enormous amount of data are produced in a streaming manner in a variety of application areas. These data are often unlabelled. In this case, identifying infrequent events, such as anomalies, poses a great challenge. This problem becomes even more difficult in non-stationary environments, which can cause deterioration of the predictive performance of a model. To address the above challenges, the paper proposes an autoencoder-based incremen-tal learning method with drift detection (strAEm++DD). Our proposed method strAEm++DD leverages on the advantages of both incremental learning and drift detection. We conduct an experimental study using real-world and synthetic datasets with severe or extreme class imbalance, and provide an empirical analysis of strAEm++DD. We further conduct a comparative study, showing that the proposed method significantly outper-forms existing baseline and advanced methods.
https://ieeexplore.ieee.org/document/10191328A significant challenge when attempting to regulate the spatial-temporal concentration of a disinfectant in a water distribution network is the large and uncertain delay between the time that the chemical is injected at the input node and the time that the concentration is measured at the monitoring output nodes. Uncertain time delays are due to varying water flows, which depend mainly on consumer water demands. Existing approaches cannot guarantee that the concentration of the disinfectant will remain within a specified range at the output, even though bounds on time-delay uncertainty may be known. In this work, given bounded water-flow uncertainty, we use the input–output modeling approach to develop a disinfectant scheduling methodology that guarantees a bounded output disinfectant concentration. The proposed methodology creates an input–output model uncertainty characterization by utilizing estimated bounds on water-quality states using the backtracking approach. An optimization problem is formulated and solved to find an input schedule that keeps the disinfectant concentration within predefined bounds for a specified time horizon. Simulation results in two case studies where water demands varied between ±20% of their nominal value show that the proposed scheduler is able to avoid lower bound violations of disinfectant concentration.
https://iwaponline.com/jh/article/26/2/386/99925/Disinfection-scheduling-in-water-distributionThis study delves into the differences between incremental and optimized network design, with a focus on tree-shaped water distribution networks (WDNs). The study evaluates the cost overhead of incremental design under two distinct expansion models: random and gradual. Our findings reveal that while incremental design does incur a cost overhead, this overhead does not increase significantly as the network expands, especially under gradual expansion. We also evaluate the cost overhead for the two tree-shaped WDNs of a city in Cyprus. The paper underscores the need to consider the evolution of infrastructure networks, answering key questions about cost overhead, scalability, and design efficacy.
https://link.springer.com/chapter/10.1007/978-3-031-53503-1_21Water distribution systems are susceptible to contamination events, which can occur due to naturally occurring events, accidents or even malicious attacks. When a contamination event occurs, dangerous substances infiltrating the network may be consumed thereby deteriorating the consumers’ health and possibly affecting the economy. Advances in sensor and actuator technologies are enabling water networks to become smarter and more resilient to these types of events. This paper provides a broad review of the theoretical, modeling, and computational developments in the area of contamination event diagnosis for water distribution systems. Research is segmented into three main tasks, summarized as “Preparedness”, “Event Detection and Isolation” and “Emergency Event Management”. The key research topics from each task are described within a unified systems-theoretic mathematical framework, and their open challenges are discussed.
https://www.sciencedirect.com/science/article/pii/S1367578823000159Water distribution networks (WDNs) with other infrastructures constitute a complex and interdependent multi-utility system. Considering interdependencies between WDNs and other urban infrastructures, this work proposes WDN intervention planning using a dynamic multi-utility approach to tackle the challenges of pressure deficits and cascading failures by the decoupling of different infrastructure systems. For this purpose, the study develops reliability indices representing the hydraulic and decoupled statuses of WDNs with neighbor infrastructures; the hydraulic reliability represents the robustness of the network against the water pressure deficit, and decoupling reliability represents the extent to which WDN elements are decoupled from other assets elements. A multi-objective optimization algorithm is employed to develop rehabilitation strategies by introducing three approaches for WDN upgrade following a phased design and construction method. Evaluating intervention plans based on construction cost, reliability and cascade effects shows that, under budget limitation conditions, decoupling a WDN could significantly save the cascade cost such that 1% improvement in the decoupling reliability brings about 157.42 billion Rials cascade cost saving to asset managers. On the other hand, the decoupled network is weak against hydraulic reliability, which could make it by far less resilient network than the coupled network with around 75% hydraulic reliability difference.
https://iwaponline.com/jh/article/25/5/2084/97296/Optimal-rehabilitation-planning-for-aged-waterCounterfactual explanations (CFEs) are a popular approach in explainable artificial intelligence (xAI), highlighting changes to input data necessary for altering a model’s output. A CFE can either describe a scenario that is better than the factual state (upward CFE), or a scenario that is worse than the factual state (downward CFE). However, potential benefits and drawbacks of the directionality of CFEs for user behavior in xAI remain unclear. The current user study (N = 161) compares the impact of CFE directionality on behavior and experience of participants tasked to extract new knowledge from an automated system based on model predictions and CFEs. Results suggest that upward CFEs provide a significant performance advantage over other forms of counterfactual feedback. Moreover, the study highlights potential benefits of mixed CFEs improving user performance compared to downward CFEs or no explanations. In line with the performance results, users’ explicit knowledge of the system is statistically higher after receiving upward CFEs compared to downward comparisons. These findings imply that the alignment between explanation and task at hand, the so-called regulatory fit, may play a crucial role in determining the effectiveness of model explanations, informing future research directions in (xAI). To ensure reproducible research, the entire code, underlying models and user data of this study is openly available: https://github.com/ukuhl/DirectionalAlienZoo
https://doi.org/10.1007/978-3-031-44070-0_14Concept drift refers to a change in the data distribution affecting the data stream of future samples. Consequently, learning models operating on the data stream might become obsolete, and need costly and difficult adjustments such as retraining or adaptation. Existing methods usually implement a local concept drift adaptation scheme, where either incremental learning of the models is used, or the models are completely retrained when a drift detection mechanism triggers an alarm. This paper proposes an alternative approach in which an unsupervised and model-agnostic concept drift adaptation method at the global level is introduced, based on autoencoders. Specifically, the proposed method aims to “unlearn” the concept drift without having to retrain or adapt any of the learning models operating on the data. An extensive experimental evaluation is conducted in two application domains. We consider a realistic water distribution network with more than 30 models in-place, from which we create 200 simulated data sets / scenarios. We further consider an image-related task to demonstrate the effectiveness of our method.
https://doi.org/10.1109/SSCI52147.2023.10372001Hyperspectral imaging is a suitable measurement tool across domains. However, when combined with machine learning techniques, frequently intensity and transversal shifts hinder the transfer between different sensors and settings. Established approaches focus on eliminating sensor shifts in the data or recalibrating sensors. In this contribution, we target the training procedure, propose robust training, and derive a robust feature selection strategy that can cope with multiple shift dynamics at the same time. We evaluate our approaches experimentally on artificial and real-world datasets.
https://www.esann.org/sites/default/files/proceedings/2023/ES2023-158.pdfConcept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models can become inaccurate and need adjustment. While there do exist methods to detect concept drift or to adjust models in the presence of observed drift, the question of explaining drift, i.e., describing the potentially complex and high dimensional change of distributions in a human-understandable fashion, has hardly been considered so far. This problem is of importance since it enables an inspection of the most prominent characteristics of how and where drift manifests. Hence, it allows human understanding of the change and it increases acceptance of life-long learning models. In this paper, we present a novel technology characterizing concept drift in terms of the characteristic change of spatial features based on various explanation techniques. To do so, we propose a methodology to reduce the explanation of concept drift to an explanation of models that are trained in a suitable way to extract relevant information from the drift. This way, a large variety of explanation schemes is available, and a suitable method can be selected for the problem at hand. We outline the potential of this approach and demonstrate its usefulness in several examples.
https://doi.org/10.1016/j.neucom.2023.126640This paper introduces EPyT, an open-source Python package for providing a Python-based programming interface with the open-source hydraulic and quality modeling software EPANET, created by the US Environmental Protection Agency. EPyT extends the standard capabilities of the EPANET library, through the addition of new methods for research purposes. In addition to the extensive Application Programming Interface, EPyT is accompanied by a collection of water distribution benchmarks and more than 25 code examples that researchers can use as a starting point.
https://joss.theoj.org/papers/10.21105/joss.05947#As relevant examples such as the future criminal detection software show, fairness of AI-based and social domain affecting decision support tools constitutes an important area of research. In this contribution, we investigate the applications of AI to socioeconomically relevant infrastructures such as those of water distribution networks (WDNs), where fairness issues have yet to gain a foothold. To establish the notion of fairness in this domain, we propose an appropriate definition of protected groups and group fairness in WDNs as an extension of existing definitions. We demonstrate that typical methods for the detection of leakages in WDNs are unfair in this sense. Further, we thus propose a remedy to increase the fairness which can be applied even to non-differentiable ensemble classification methods as used in this context.
https://github.com/jstrotherm/FairnessInWDNS https://doi.org/10.1007/978-3-031-43085-5_10Many Machine Learning models are vulnerable to adversarial attacks: One can specifically design inputs that cause the model to make a mistake. Our study focuses on adversarials in the security-critical domain of leakage detection in water distribution networks (WDNs). As model input in this application consists of sensor readings, standard adversarial methods face a challenge. They have to create new inputs that still comply with the underlying physics of the network. We propose a novel approach to construct adversarial attacks against Machine Learning based leakage detectors in WDNs. In contrast to existing studies, we use a hydraulic model to simulate leaks in the water network. The adversarial attacks are then constructed based on these simulations, which makes them intrinsically physics-constrained. The adversary maximizes water loss by finding the least sensitive point, that is, the point at which the largest possible undetected leak could occur. We provide a mathematical formulation of the least sensitive point problem together with a taxonomy of adversarials in WDNs, in order to relate our work to other possible approaches in the field. The problem is then solved using three different algorithmic approaches on two benchmark WDNs. Finally, we discuss the results and reflect on potentials to enhance model robustness based on knowledge about adversarial weaknesses.
https://doi.org/10.1007/978-3-031-43078-7_37We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in the domain of critical infrastructure. The underlying graphs have a comparably low node degree and high diameter, while information in the graph is globally relevant, hence graph neural networks face the challenge of long term dependencies. We propose a specific architecture based on message passing which displays excellent results for a number of benchmark tasks in the WDS domain. Further, we investigate a multi-hop variation, which requires considerably less resources and opens an avenue towards big WDS graphs.
https://doi.org/10.1007/978-3-031-30047-9_3In many real-world scenarios, data are provided as a potentially infinite stream of samples that are subject to changes in the underlying data distribution, a phenomenon often referred to as concept drift. A specific facet of concept drift is feature drift, where the relevance of a feature to the problem at hand changes over time. High-dimensionality of the data poses an additional challenge to learning algorithms operating in such environments. Common scenarios of this nature can for example be found in sensor-based maintenance operations of industrial machines or inside entire networks, such as power grids or water distribution systems. However, since most existing methods for incremental learning focus on classification tasks, efficient online learning for regression is still an underdeveloped area. In this work, we introduce an extension to the SAM-kNN Regressor that incorporates metric learning in order to improve the prediction quality on data streams, gain insights into the relevance of different input features and based on that, transform the input data into a lower dimension in order to improve computational complexity and suitability for high-dimensional data. We evaluate our proposed method on artificial data, to demonstrate its applicability in various scenarios. In addition to that, we apply the method to the real-world problem of water distribution network monitoring. Specifically, we demonstrate that sensor faults in the water distribution network can be detected by monitoring the feature relevances computed by our algorithm.
https://doi.org/10.1080/08839514.2023.2198846Introduction
To foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly, the field of explainable artificial intelligence (XAI) has resurfaced as a topic of active research, offering approaches to address the “how” and “why” of automated decision-making. Within this domain, counterfactual explanations (CFEs) have gained considerable traction as a psychologically grounded approach to generatepost-hocexplanations. To do so, CFEs highlight what changes to a model's input would have changed its prediction in a particular way. However, despite the introduction of numerous CFE approaches, their usability has yet to be thoroughly validated at the human level.
Methods
To advance the field of XAI, we introduce the Alien Zoo, an engaging, web-based and game-inspired experimental framework. The Alien Zoo provides the means to evaluate usability of CFEs for gaining new knowledge from an automated system, targeting novice users in a domain-general context. As a proof of concept, we demonstrate the practical efficacy and feasibility of this approach in a user study.
Results
Our results suggest the efficacy of the Alien Zoo framework for empirically investigating aspects of counterfactual explanations in a game-type scenario and a low-knowledge domain. The proof of concept study reveals that users benefit from receiving CFEs compared to no explanation, both in terms of objective performance in the proposed iterative learning task, and subjective usability.
We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging need for online learning methods that train predictive models on-the-fly. A series of open challenges, however, hinder their deployment in practice. These are, learning as data arrive in real-time one-by-one, learning from data with limited ground truth information, learning from nonstationary data, and learning from severely imbalanced data, while occupying a limited amount of memory for data storage. We propose the ActiSiamese algorithm, which addresses these challenges by combining online active learning, siamese networks, and a multi-queue memory. It develops a new density-based active learning strategy which considers similarity in the latent (rather than the input) space. We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels. ActiSiamese outperforms baseline and state-of-the-art algorithms, and is effective under severe imbalance, even only when a fraction of the arriving instances’ labels is available. We publicly release our code to the community.
https://www.sciencedirect.com/science/article/pii/S0925231222011481A key challenge in designing algorithms for leakage detection and isolation in drinking water distribution systems is the performance evaluation and comparison between methodologies using benchmarks. For this purpose, the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) competition was organized in 2020 with the aim to objectively compare the performance of methods for the detection and localization of leakage events, relying on supervisory control and data acquisition (SCADA) measurements of flow and pressure sensors installed within a virtual water distribution system. Several teams from academia and the industry submitted their solutions using various techniques including time series analysis, statistical methods, machine learning, mathematical programming, met-heuristics, and engineering judgment, and were evaluated using realistic economic criteria. This paper summarizes the results of the competition and conducts an analysis of the different leakage detection and isolation methods used by the teams. The competition results highlight the need for further development of methods for leakage detection and isolation, and also the need to develop additional open benchmark problems for this purpose.
https://ascelibrary.org/doi/full/10.1061/%28ASCE%29WR.1943-5452.0001601Numerical optimization is gradually finding its way into drinking water practice. For successful introduction of optimization into the sector, it is important that researchers and utility experts work together on the problem formulation with the water utility experts. Water utilities heed the solutions provided by optimization techniques only when the underlying approach and performance criteria match their specific goals. In this contribution, we demonstrate the application of numerical optimization on a real-life problem. The Belgian utility De Watergroep is looking to not only reinforce its distribution networks but to also structurally modify the network’s topology to enhance the quality of water delivered in the future. To help the utility explore the possibilities of these far-reaching changes in the most flexible way possible, an optimization problem was formulated to optimize topology and pipe sizing simultaneously for the distribution network of a Belgian city. The objective of the problem is to minimize the volume of the looped network and thereby work towards a situation where most of the customers are fed by branched extremities of the network. This objective is constrained by pressure and fire flow requirements and thresholds on the number of customers on the branched sections. The requirements for continuity of supply under failure scenarios are guaranteed by these constraints, as verified in the final solution. The results of the optimization process show that it is possible to design a network which is 18.5% cheaper than the currently existing network. Moreover, it turns out the—previously completely meshed—topology can be restructured so that 67% of the network length is turned into branched clusters, with a meshed superstructure of 33% of the length remaining.
View PublicationWater distribution networks (WDNs) evolve continuously over time. Changes in water
demands and pipe deterioration require construction upgrades to be performed on the
network during its entire lifecycle. However, strategically planning WDNs, especially for the
long term, is a challenging task. This is because parameters that are essential for the
description of WDNs in the future, such as climate, population and demand transitions, are
characterized by deep uncertainty. To cope with future uncertainty, and avoid overdesign or
costly unplanned and reactive interventions, research is moving away from the static design
of WDNs. Dynamic design approaches, aim to make water networks adaptive to changing
conditions over long planning horizons. A promising, dynamic design approach is the staged
design of WDNs, in which the planning horizon is divided into construction phases. This
approach allows short-term interventions to be made, while simultaneously considering the
expected long-term network growth outcomes. The aim of this paper is to summarize the
current state of the art in staged design of water distribution networks. To achieve that, we
critically examined relevant publications and classified them according to their shared key
characteristics, such as the nature of the design problem (new or existing network design,
expansion, strengthening, and rehabilitation), problem formulation (objective functions,
length of planning horizon), optimization method, and uncertainty considerations. In the
process, we discuss the latest findings in the literature, highlight the major contributions of
staged design on water distribution networks, and suggest future research directions.
View PublicationThe percentage of the world population living in urban settlements is expected to increase to
70% of 9.7 billion by 2050. Historically, as cities grew, the development of new water
infrastructures followed as needed. However, these developments had less to do with real
planning than with reacting to crisis situations and urgent needs, due to the inability of urban
water planners to consider long-term, deeply uncertain and ambiguous factors affecting urban
development and water demand. The “Smart Water Futures: Designing the Next Generation of
Urban Drinking Water Systems” or “Water-Futures” project, which was funded by the
European Research Council (ERC), aims to develop a new theoretical framework for the
allocation and development decisions on drinking water infrastructure systems so that they
are: (i) socially equitable, (ii) economically efficient, and (iii) environmentally resilient, as
advocated by the UN Agenda 2030, Sustainable Development Goals. The ERC Synergy grant
project tackles the “wicked problem” of transitioning water distribution systems in a holistic
manner, involving civil engineering, control engineering, machine learning, decision theory
and environmental economics expertise. Developing a theoretical foundation for designing
smart water systems that can deliver optimally robust and resilient decisions for short/long-
term planning is one of the biggest challenges that future cities will be facing. This paper
presents an overview of related past research on this topic, the knowledge gaps in terms of
investigating the problem in a holistic manner, and the key early outcomes of the project.
View PublicationVaquet V., Artelt A., Brinkrolf J. and Hammer B., "Taking Care of Our Drinking Water: Dealing with Sensor Faults in Water Distribution Networks", ICANN 2022
The water supply is part of the critical infrastructure as the accessibility of clean drinking water is essential to ensure the health of the people. To guarantee the availability of fresh water, efficient and reliable water distribution networks are crucial. Monitoring these systems is necessary to avoid deterioration in water quality, deal with leakages and prevent cyber-physical attacks. While the installation of a growing amount of sensors is increasing the possibilities to monitor the system, considering the control of the senors becomes another challenge as sensor faults negatively influence the reliability of systems dealing with leakages and monitoring water quality. In this work, we aim to overcome the negative implications induced by sensor faults by using a sensor fault monitoring system based on three steps. First, established residual based fault detection is applied. In a second step, we extend this method to a fault isolation technique and finally propose fault accommodation by standard imputation techniques and different types of virtual sensors.
View PublicationJakob J., Artelt A., Hasenjäger M. and Hammer B., "SAM-kNN Regressor for Online Learning in Water Distribution Networks", ICANN 2022
Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to consumers. In order to guarantee a working network at all times, the water supply company continuously monitors the network and takes actions when necessary – e.g. reacting to leakages, sensor faults and drops in water quality. Since real world networks are too large and complex to be monitored by a human, algorithmic monitoring systems have been developed. A popular type of such systems are residual based anomaly detection systems that can detect events such as leakages and sensor faults. For a continuous high quality monitoring, it is necessary for these systems to adapt to changed demands and presence of various anomalies.
In this work, we propose an adaption of the incremental SAM-kNN classifier for regression to build a residual based anomaly detection system for water distribution networks that is able to adapt to any kind of change.
View PublicationArtelt A., Vrachimis S., Eliades D., Polycarpou M. and Hammer B., "One Explanation to Rule them All -- Ensemble Consistent Explanations", XAI workshop at IJCAI 2022
Transparency is a major requirement of modern AI based decision making systems deployed in real world. A popular approach for achieving transparency is by means of explanations. A wide variety of different explanations have been proposed for single decision making systems. In practice it is often the case to have a set (i.e. ensemble) of decisions that are used instead of a single decision only, in particular in complex systems. Unfortunately, explanation methods for single decision making systems are not easily applicable to ensembles -- i.e. they would yield an ensemble of individual explanations which are not necessarily consistent, hence less useful and more difficult to understand than a single consistent explanation of all observed phenomena. We propose a novel concept for consistently explaining an ensemble of decisions locally with a single explanation -- we introduce a formal concept, as well as a specific implementation using counterfactual explanations.
View PublicationEvidence on the efficacy of impacts from real-world experiments in triggering transformative processes is still scarce. This study evaluates multi-actor labs to provide examples of direct impacts of real-world experiments in tackling long-standing, local, sustainability challenges.
Multi-actor labs (MALs), a form of real-world social experiments, were implemented in three coastal-rural regions in France (Charente River Basin), Spain (Mar Menor), and Greece (South-West Messinia) to better assess and tackle coastal-rural interactions that govern local sustainability challenges, such as water use conflicts and biodiversity degradation. The MALs used participative methodologies based on systems thinking and transition management. Stakeholders were continuously engaged in a series of workshops to co-produce knowledge, reach a common understanding of the sustainability challenges and issues at stake, and co-design solutions in the form of a roadmap for sustainable transitions in coastal-rural regions. This paper evaluates MALs to provide examples of successful sustainability transition experiments based on the outputs produced, outcomes achieved, and processes used in the three coastal, rural regions.
View hereThis authoritative Encyclopedia provides an innovative approach to theory, reviews, applications and examples relevant to the basic concepts of water science and water management issues in order to facilitate better interdisciplinary cooperation.
View HereThe broad economic notion of ecosystem services (ES) refers to the benefits that humans derive, directly or indirectly, from ecosystem functions. Provisioning ES refer to human-centered benefits that can be extracted from nature (e.g., food, drinking water, timber, wood fuel, natural gas, oils, etc.), whereas regulating ES include ecosystem processes that moderate natural phenomena (pollination, decomposition, flood control, carbon storage, climate regulation, etc.). Cultural ES entail nonmaterial benefits accruing to the cultural advancement of people, such as the role of ecosystems in national and supranational cultures, recreation, and the spur of knowledge and creativity (music, art, architecture). Finally, supporting ES refer to the main natural cycles that nature needs to function, such as photosynthesis, nutrient cycling, the creation of soils, and the water cycle. Most ES either depend on or provide freshwater services, so they are linked to water resources management (WRM). The concept of ES initially had a pedagogical purpose to raise awareness on the importance of reasonable WRM; later, however, it started being measured with economic methods, and having policy implications.
The valuation of ES is an important methodology aimed at achieving environmental, economic and sustainability goals. The total economic value of ecosystems includes market values (priced) as well as nonmarket values (not explicit in any market) of different services for humanity’s benefit. The valuation of ES inherently reflects human preferences and perceptions regarding the contribution of ecosystems and their functions to the economy and society. The ES concept and associated policies have been criticized on the technical weaknesses of the valuation methods, interdisciplinary conflicts (e.g., ecological vs. economic perception of value), and ethical aspects on the limits of economics, nature’s commodification, and its policy implications.
Since valuation affects the incentives and policies aimed at conserving key ES, e.g., through payment schemes, it is important to understand the way that humans decide and develop preferences under uncertainty. Behavioral economics attempts to understand human behavior and psychology and can help to identify appropriate institutions and policies under uncertainty that enhance ecosystem services that are key to WRM.
https://doi.org/10.1093/acrefore/9780199389414.013.801Ellsberg-type choices (Ellsberg's paradox) are evidence against the Bayesian theory of Subjective Expected Utility Maximization (SEUM). These choices reflect a particular attitude of the decision maker (DM), namely Ambiguity Aversion (AA). There are two competing interpretations of AA. The first recognizes AA as rational behavior, while the second views AA as a manifestation of a psychological fallacy. This paper focuses on the second interpretation of AA and specifically discusses the most important psychological explanation of AA that has been proposed in the literature, namely Fox and Tversky's (1995) Comparative Ignorance Hypothesis (CIH). CIH holds that AA is mainly a "comparative effect" that occurs when DM feels that he is epistemically inferior for some events of interest compared to others (for which she believes to be epistemically superior). As a result, DM exhibits an aversion towards betting on the epistemically inferior events. The purpose of the paper is twofold: First, to provide a survey of the literature on CIH. Second, to propose a novel "Bayesian Training" (BT) procedure based on "counterfactual thinking". A decision maker who finds BT attractive is likely to move out of the state of comparative ignorance, thereby ceasing to exhibit AA and joining the Bayesian camp.
View HereThis study addresses the estimation of water requirements across various sectors-domestic, agricultural, livestock, and industrial-within Europe (including the EU 27, UK, Norway, Switzerland, and the Balkans). Traditionally, water demand assessments have been limited to specific sectors or regions, making comprehensive data-driven assessments challenging. This research introduces a simple, data-efficient model developed in MS Excel and Python, allowing monthly estimates per sector. Urban water needs are calculated by multiplying population by daily per capita consumption rates. Livestock needs are similarly assessed using animal population and consumption data. Industrial water usage is derived from typical consumption rates per product types, and economic activity levels across key industries. Agricultural water demand is estimated based on crop mix, climatic, and irrigation factors. The model's results highlight significant variability in water needs across countries and sectors, validated by cross-referencing various data sources and literature. The study highlights the necessity to consider strategies for improving water demand management.
View HereIn recent decades, our societies increasingly face unprecedented threats (Princiotta andLoughlin 2014) . The impacts of climate change are becoming more evident, alongsidethe degradation of limited natural resources and ecosystems, unsustainable demand,production and consumption practices, biodiversity collapse, diseases, energy and foodcrises, recessions and debt crises, population crises with unequal growth anddistribution, population movements due to geopolitical and climate crises, and variousforms of inequality (Chancel 2022; Bruckner et al. 2022). Addressing theseinterconnected challenges requires urgent and coordinated efforts for globallysustainable solutions, ensuring the resilience and well- being of present and futuregenerations (Liu et al. 2015). These solutions must be based on holistic and systemicapproaches to efficiently manage the intricate interplays between environmental,economic, and social factors, while also being socially just and acceptable to ensuretheir applicability.The UN’s Sustainable Development Solutions Network (SDSN) brings together scientificand technological expertise to assess issues related to climate, energy, socio-economics, water, and biodiversity, promoting solutions for achieving the SustainableDevelopment Goals (SDGs) and the Paris Climate agreement (SDSN 2022). The SDSNviews the SDGs framework as the blueprint for addressing the aforementioned multi-crises.In this era marked by increased collaboration among scientific fields and technologicaladvances (Alamanos and Koundouri 2022), it is becoming more ‘attractive’ to transitiontoward interdisciplinary and holistic responses to complex challenges (Hernandez-Aguilera et al. 2021). Building on this interdisciplinary space and the opportunities itprovides for innovation, we established a Global Climate Hub (GCH) under the SDSN(SDSN 2023; Alamanos 2024b). This is a concerted effort to institutionalize cross-country interdisciplinary collaboration that is scientifically based and policy-oriented.
View hereIn this paper we propose a consensus group decision making scheme under model uncertainty consisting of an iterative two-stage procedure based on the concept of Fréchet barycenter. Each stage consists of two steps: the agents first update their position in the opinion metric space adopting a local barycenter characterized by the agents’ immediate interactions and then a moderator makes a proposal in terms of a global barycenter, checking for consensus at each stage. In cases of large heterogeneous groups, the procedure can be complemented by an auxiliary initial homogenization stage, consisting of a clustering procedure in opinion space, leading to large homogeneous groups for which the aforementioned procedure will be applied. The scheme is illustrated in examples motivated from environmental economics.
View PublicationThe European Green Deal (EGD) is the growth strategy for Europe, covering multiple domains, and aiming to an equitable, climate neutral European Union by 2050. The UN Agenda 2030, encompassing 17 Sustainable Development Goals (SDGs), establishes the foundation for a global sustainability transition. The integration of the SDGs into the EGD is an overlooked issue in the literature, despite Europe’s slow progress to achieve the sustainability targets. We employed a machine-learning text-mining method to evaluate the extent of SDG integration within the 74 EGD policy documents published during 2019–2023. The findings reveal a substantial alignment of EGD policies with SDGs related to clean energy (SDG7), climate action (SDG13), and sustainable consumption and production (SDG12). In contrast, there is a significant underrepresentation in areas related to social issues such as inequalities, poverty, hunger, health, education, gender equality, decent work, and peace, as indicated by lower alignment with SDGs 1, 2, 3, 4, 5, 8, 10, and 16. Temporal trends suggest a marginal increase in the attention given to environmental health (especially water and marine life) and gender equality. Furthermore, we illustrate the alignment of EGD policies with the six essential sustainability transformations proposed by the Sustainable Development Solutions Network (SDSN) in 2019 for the operationalization of the SDGs. The results indicate that besides the prevalence of “Energy Decarbonization and Sustainable Industry”, all areas have received attention, except for the “Health, Wellbeing and Demography”. The findings call for a more integrated approach to address the complete spectrum of sustainability in a balanced manner.
View PublicationChatzistamoulou, N., & Koundouri, P. (2024). Is green transition in Europe fostered by energy and environmental efficiency feedback loops? the role of eco-innovation, Renewable Energy and Green Taxation. Environmental and Resource Economics.
Green transition is in the core of the European policy agenda to achieve the ambitious goal of climate neutrality following the launch of the European Green Deal. The cornerstone of the new growth strategy of Europe is resource efciency which focuses on shifting to a more sustainable production paradigm by conserving scarce resources and by prioritizing enhanced environmental performance. Scattered eforts to investigate the drivers of resource efciency measures have shed light on the key drivers, however, those consider resource efciency measures in isolation neglecting for feedback loops infuencing green transition. Therefore, we develop a conceptual framework to study green transition as a system of resource efciency measures afected by feedback loops, path dependence, green technologies, and green policy tools. We mobilize the analysis by devising a unique balanced panel covering the EU-28 from 2010 through 2019, including policy eforts paving the way for green transition. Econometric results based on a system of fractional probit models, indicate that resource efciency measures are intertwined via feedback loops, especially in the case of environmental efciency. Green technologies afect green transition, however, rebound efects emerge in the case of energy efciency. Past performance afects current levels pushing towards divergence. Evidence suggests that green taxation fosters energy efciency whereas hinders environmental efciency. The asymmetric operation of feedback loops and green taxation on energy and environmental efciency highlights that horizontal policies hinder rather than foster green transition. This study contributes to SDGs 7, 12, 13 and 16.
View PublicationEnergy system planning has evolved from a narrow focus on engineering and supply works towards addressing more complex, multifactorial challenges. Increasingly challenged by climate change, extreme events, economic shocks, and altered supply demand patterns, the analysis of energy systems requires holistic approaches based on data-driven models, taking into account key socio-economic factors. We draw insights from reviewing the literature, indicating the need to cover the following major gaps: the shift to transdisciplinary approaches, incorporating environmental system analysis; resilient and sustainable energy designs based on flexible portfolios of renewable mixes; the integration of socio-economic aspects, economic analyses and behavioural models to ensure energy systems are not only technically sound but socially acceptable and viable; the need for stakeholder engagement considering the human angle in energy security and behavioural shifts. Responding to these pressing challenges and emerging needs, the Global Climate Hub (GCH) initiative, operating under the UN Sustainable Development Solutions Network, offers a conceptual framework, leveraging transdisciplinary approaches. In this Concept Paper, we present for the first time the idea of the GCH as a framework that we believe has the potential to address the modern holistic needs for energy system analysis and policymaking. By setting the conceptual/theoretical ground of our suggested approach, we aim to provide guidance for innovative combinations of cutting-edge models, socio-economic narratives, and inclusive interaction with relevant stakeholders for the development and the long-term implementation of sustainable pathways.
View PublicationCurrently, in the water distribution systems literature, fault detection methods are typically evaluated on benchmark water networks that do not include real-time experimental data, or on private commercial datasets, which prohibit the reproducibility of the results. Moreover, realistic modeling of faults on hydraulic system components, sensors and actuators is often unavailable. In this work, we provide a framework for the application of fault-diagnosis methodologies on WaterSafe, a water network benchmark for fault diagnosis. The WaterSafe benchmark is a small scale replica of a water transport network constructed using industrial components and devices, while the communications are implemented in a way that resemble a water utility's Supervisory Control and Data Acquisition system. A general problem formulation for fault-diagnosis on water systems is provided, in accordance to the mathematical model of the benchmark. Moreover, we provide a calibrated simulation model including system, sensor and actuator faults, based on observations from the real system. Finally, we provide open access to the datasets generated from the experiments containing the aforementioned faults.
https://www.sciencedirect.com/science/article/pii/S2405896322005870This study presents a collaborative framework developed by the Water Futures team of researchers for the “Battle of the Water Demand Forecasting” challenge at the 3rd International WDSA-CCWI Joint Conference. The framework integrates an ensemble of machine learning forecasting models into a deterministic outcome consistent with the competition formulation. The water demand trajectory over a week exhibits complex overlapping patterns and non-linear dependencies to multiple features and time-dependent events that a single model cannot accurately predict. As such, the reconciled forecast from an ensemble of models exceeds the performance of the individual ones and exhibits higher stability across the weeks of the year and district metered areas considered.
View PublicationWildfires pose a growing global danger for ecosystems and human activities. The degraded ecosystem functions of burnt sites, include, among others, shifts in hydrological processes, land cover, vegetation, and soil erosion, that make them more vulnerable to flood and extreme sediment transport risks. Several post-fire erosion and flood protection treatments (PFPs) have been developed to avoid and mitigate such consequences and risks. The Mediterranean region faces severe climate change challenges that are projected to escalate the wildfire and post-fire flood risks. However, there is limited research on the dynamics of post-fire flood risks and their mitigation through the design of the appropriate PFPs. This paper aims to cover this gap by simulating a real post-fire flash-flood event in Central Greece, and design the PFPs for this case study, considering their suitability and costs. An integrated framework was used to represent the flood under the baseline scenario: the storm conditions that caused the flood were simulated using the atmospheric model WRF-ARW; the burn extent, severity, and the flood extent were retrieved through remote sensing analyses; and a HEC-RAS hydraulic-hydrodynamic model was developed to simulate the flood event, applying the rain-on-grid technique. Several PFPs were assessed, and certain channel- and barrier-based PFPs were selected as the most suitable for the study area. The recommended PFPs were spatially represented within a geographic information system (GIS). Moreover, we present a detailed analysis of their expected costs. This study provides an interdisciplinary and transferable framework for understanding and enhancing the flood resilience of burnt sites.
https://doi.org/10.54963/ptnd.v3i2.303Koundouri, P., Papayiannis, G. I., Petracou, E. V., & Yannacopoulos, A. N. (2024). Consensus Group Decision making under model uncertainty with a view towards environmental policy making. Environmental and Resource Economics.
In this paper we propose a consensus group decision making scheme under model uncertainty consisting of an iterative two-stage procedure based on the concept of Fréchet barycenter. Each stage consists of two steps: the agents first update their position in the opinion metric space adopting a local barycenter characterized by the agents’ immediate interactions and then a moderator makes a proposal in terms of a global barycenter, checking for consensus at each stage. In cases of large heterogeneous groups, the procedure can be complemented by an auxiliary initial homogenization stage, consisting of a clustering procedure in opinion space, leading to large homogeneous groups for which the aforementioned procedure will be applied. The scheme is illustrated in examples motivated from environmental economics.
View PublicationKoundouri, P., Alamanos, A., Plataniotis, A., Stavridis, C., Perifanos, K., & Devves, S. (2024). Assessing the sustainability of the European Green Deal and its Interlin Kages with the sdgs. NATURE Climate Action, 3(1).
The European Green Deal (EGD) is the growth strategy for Europe, covering multiple domains, and aiming to an equitable, climate neutral European Union by 2050. The UN Agenda 2030, encompassing 17 Sustainable Development Goals (SDGs), establishes the foundation for a global sustainability transition. The integration of the SDGs into the EGD is an overlooked issue in the literature, despite Europe’s slow progress to achieve the sustainability targets. We employed a machine-learning text-mining method to evaluate the extent of SDG integration within the 74 EGD policy documents published during 2019–2023. The findings reveal a substantial alignment of EGD policies with SDGs related to clean energy (SDG7), climate action (SDG13), and sustainable consumption and production (SDG12). In contrast, there is a significant underrepresentation in areas related to social issues such as inequalities, poverty, hunger, health, education, gender equality, decent work, and peace, as indicated by lower alignment with SDGs 1, 2, 3, 4, 5, 8, 10, and 16. Temporal trends suggest a marginal increase in the attention given to environmental health (especially water and marine life) and gender equality. Furthermore, we illustrate the alignment of EGD policies with the six essential sustainability transformations proposed by the Sustainable Development Solutions Network (SDSN) in 2019 for the operationalization of the SDGs. The results indicate that besides the prevalence of “Energy Decarbonization and Sustainable Industry”, all areas have received attention, except for the “Health, Wellbeing and Demography”. The findings call for a more integrated approach to address the complete spectrum of sustainability in a balanced manner.
View PublicationIdentifying mechanisms of real-life human decision-making is central to inform effective, human-centric public policy. Here, we report larger trends and synthesize preliminary lessons from behavioral economic and neuro-economic investigations focusing on environmental values. We review the currently available evidence at different levels of granularity, from insights into how individuals value natural resources (individual level), evidence from work on group externalities, common pool resources, and social norms (social group level) to the study of incentives, policies, and their impact (institutional level). At each level, we identify viable directions for future scientific research and actionable items for policy-makers. Coupled with new technological and methodological advances, we suggest that behavioral economic and neuroeconomic insights may inform an effective strategy to optimize environmental resources. We conclude that the time is ripe for action to enrich policies with scientifically grounded insights, making an impact in the interest of current and future generations.
https://www.annualreviews.org/doi/full/10.1146/annurev-resource-101722-082743In recent decades, Greece has experienced devastating wildfires, particularly during the summer months. These wildfires have intensified in frequency and severity, largely attributed by experts to the impacts of climate change. Extended periods of drought, soil aridity, persistent heatwaves, and intensified winds have transformed forests into highly vulnerable areas susceptible to even the minor spark. In many cases, the fires have been uncontrollable and can be described as “megafires.” These megafires, are enormous in scale and intensity, and pose an increasingly severe threat to Greece’s landscape, necessitating a comprehensive response to protect both the environment and citizen’s safety and well-being.
This commentary highlights the need for comprehensive governmental climate action in response to Greece’s wildfires. It discusses the destruction of biodiversity and presents a holistic approach to fire management. Collaboration and the SDGs are emphasized as key elements in addressing climate change’s consequences.
View PublicationThe significance of the SDGs lies in their holistic, global and interdisciplinary nature. But this nature at the same time poses significant challenges, as it is difficult to bridge the breadth of different aspects included in the SDGs, such as the environmental and the socio-economic, both in theory, practical application and policymaking. SDG14 on “life below water” is quite a holistic concept as it refers to a natural/environmental system (seas), supporting several marine economic activities and ecosystem values, and associated with strong social and cultural characteristics of the local populations, affecting the ways they manage marine areas. The main challenges for the achievement of a sustainable life below water are analyzed, and ways forward are discussed. Holistic and well-coordinated approaches considering the complex nature of SDG14 are necessary. Moreover, we argue on the role of economic instruments that can bridge environmental and socio-economic aspects, towards more sustainable life below water. In particular, the potential of environmental valuation as a means to better inform SDG policies, is discussed, using the example of SDG14. The currently established frameworks for Country’s Sustainability Reporting, lack metrics focusing on the economic impact of the environment and the ecosystem services’ degradation or restoration rates, including ocean and marine ecosystems. Acknowledging and quantifying the costs and benefits of ocean and marine ecosystems can lead to more effective interventions (such as ocean pollution prevention, climate change mitigation, fishing exploitation, biodiversity and coral reef preservation) and a better understanding of human-environmental dynamics. This, in turn, strengthens coordinated management and cooperation.
View PublicationThis paper refers to the valuation of European, Marine and Fresh Water Ecosystem Services. Using a meta-regression approach, we estimate the Annual Willingness to Pay (WTP) for several classifications of the ecosystem services and various biogeographical and marine regions across all 27 EU markets. Moreover, we explore the correlation between WTP and the national level of achievement of the 17 SDGs, with particular focus on SDG 14—Life Below Water. Results indicate that regulating services of marine and freshwater ecosystems are ranked high and that in almost 63% of the European countries, the WTP for the improvement of the marine and freshwater ecosystem is high and exceeds estimates for terrestrial ecosystems. Valuing ecosystem services and link them to the Sustainable Development Goals, we find that marine ecosystems are mainly positively correlated to SDGs 2, 12, 13, 14, and 17, while a high MWTP value is assigned to specific SDG14 individual indicators like fish caught from overexploited or collapsed stocks and fish caught that are then discarded. Overall, results indicate that societies attributing greater value to ecosystem services mark greater progress toward the implementation of SDGs and SDG 14 in particular
https://doi.org/10.3389/frevc.2023.1160118This paper analyzes the root cause of Ellsberg-type choices. This class of problems shares the feature that at the time of the decision, t=m, the decision maker (DM) possesses partial information about the events/propositions of interest: DM knows the objective probabilities of some sub-class only, whereas she is uninformed about the probabilities of the complement of this subclass. As a result, DM may slip into the state of "comparative ignorance" (see Heath and Tversky 1991 and Fox and Tversky 1995). Under this state, DM is likely to exhibit "ambiguity aversion" (AA) for the events for which she does not have any information relative to those for which she does. AA, in turn, results in DM having non-coherent beliefs, that is, her prior probability function is not additive. A possible way to mitigate AA is to motivate DM to form her prior in a state of "uniform ignorance". This may be accomplished by inviting DM to bring herself to the hypothetical time t=0, in the context of which the information was still a contingency, and trace her "counterfactual prior back then". Under uniform ignorance, DM may adhere to the "Principle of Indifference", thus identifying the prior counterfactual probability with the uniform distribution. Once this probability is elicited, DM can embody the existing information into her current, actual set of beliefs by means of Bayesian Conditionalization. In this case, we show that this set of beliefs is additive.
https://dx.doi.org/10.2139/ssrn.4389892During its first year, the Global Climate Hub has initiated a holistic approach to climate, economic, and energy modelling to deliver a first set of results. The process described in this report consist of a thorough review and assessment of potent Integrated Assessment Models (IAMs) and the delineation of a first set of sustainable pathways on the EU energy sector, the deployment, and effects of renewable energy transition in Southeast Asia and the development of sustainable pathways for land-use and food systems in Greece.
In addition, the researchers of the GCH are examining interlinkages and complementarities across diverse IAMs which will yield horizontal and vertical synergies and elaborate on the potential pathways to net zero for 2050.
The aim of this report is to present the work of the Global Climate Hub on net zero pathways, mirroring the work undertaken in some of its distinct units. Having established the benchmark in the current state of environmental indicators and environmental policies (operating and designed), the GCH is currently collating and evaluating scientific methods embedded in modelling systems in order to integrate strands of research into coherent environmental, energy and socioeconomic pathways.
Section 2 briefly summarizes the review of Integrated Assessment Models (IAMs) which form the multi-dimension scientific arsenal in the pathways’ development, while section 3 outlines a first set of results stemming from energy and land-use models. More specifically, section 3.1 addresses the issue of decarbonization of the EU energy system as envisioned in the Fit-for-55 and RePowerEU initiatives through the projections of the BALMOREL model. Section 3.2 describes the pivotal role of renewable energy for bolstering ecosystem services in Southeast Asia and section 3.3 summarizes the key tenets of sustainable and-use and food system pathways for Greece using the FABLE Calculator. Finally, section 4 describes the potential for system integration under the auspices of the GCH and highlights the areas for future work in promoting net zero pathways
The last decades our societies are facing increasing sustainability challenges: climate change, natural disasters, biodiversity collapse, urbanization, growing population of different patterns (aging population in Europe, other continents with younger population, or exponentially growing), wars, geopolitical implications, pandemic, inequalities. The interconnectedness of the above challenges and the complex mechanisms driving them can majorly affect multiple domains, such as health, food, trade, environment, economies, etc. We present a brief overview of the existing knowledge on two main sustainability targets: food systems, and urban life. We identify the critical challenges for each one, and the main approaches followed so far to make food systems resilient to the percurrent challenges, and urban life more sustainable. Based on these examples-targets, we argue that there exist common elements indicating the emergence of overarching principles, necessary for the sustainability transition. Such principles consist a holistic approach to all the Sustainable Development Goals (SDGs), showing that they can only be achieved altogether. This kind of developing culture needs to be cultivated through systemic policy approaches aiming to equity, and interdisciplinary research and innovation exploiting new technologies. Finally, we highlight the importance of the role of humanities for this cultural transition towards a more sustainable world
https://ecsdev.org/images/conference/11thICSD2023/abstracts_11ICSD_2023.pdfEconomic rationality demands the decision maker (DM)'s degrees of beliefs to be coherent, that is to obey the rules of probability calculus. This view is usually referred to as Probabilism. Among the various justifications of Probabilism, the Dutch Book Argument (DBA) occupies a prominent place. DBA purports to show that DM's aversion to sure financial losses is sufficient to ensure that her beliefs are coherent. A tacit assumption of DBA is that DM is capable to implement a heuristic error-correction process, ECC, that yields rational beliefs. The main aim of this paper is to challenge this assumption. In order for DBA to be convincing, ECC must empower DM to detect each and every Dutch Book that may be made against her, no matter how complex this Book turns out to be. A complex Dutch book is one that requires very sophisticated calculations before its financial consequences are deduced. In the presence of complex Dutch Books, the only point that DBA makes clear is that DM has to be 'computational omnipotent' on pain of incoherence.
https://econpapers.repec.org/paper/auewpaper/2306.htmThis paper focuses on different types of ambiguity that affect climate change regulation. In particular, we analyze the effects of the interaction among three types of agents, namely, the decision-maker (DM), the climate change experts, and the society, on the probabilistic properties of green-house gas (GHG) emissions and the formation of environmental policy. These effects are analyzed under two types of ambiguity: "deferential ambiguity" and "preferential ambiguity". Deferential ambiguity refers to the uncertainty that the experts face concerning whose forecast (scenario) the DM will defer to. Preferential ambiguity stems from the potential inability of the DM to correctly discern the society's preferences about the desired change of GHG emissions. This paper shows that the existence of deferential and preferential ambiguities have significant effects on GHG emissions regulation.
https://doi.org/10.12688/openreseurope.14300.1Optimal sustainable management of natural resources has been one of the major lines of research in environmental economics at least for the last two decades. Several attempts have been made in order to describe in a quantitative fashion the notion of sustainability and distinguish management policies between sustainable and non-sustainable ones. Important aspects of this task are (a) appropriate modeling of the spatio-temporal dynamics of the state of the system, including the sources of uncertainty affecting either directly or indirectly the problem at hand (e.g. climate conditions, population growth, biological evolution), and (b) the development of appropriate criteria for evaluating the welfare of the system under study that guarantees sustainability and viability. In this chapter, we present and discuss popular and established optimization approaches for investigating policy selection problems within the sustainability framework, from the perspective of viability and optimal control theory.
https://doi.org/10.1007/978-3-030-91261-1_46-1The proactive sustainable management of scarce water across vulnerable agricultural areas of South Europe is a timely issue of major importance, especially under the recent challenges affecting complex water systems. The Basin District of Thessaly, Greece’s driest rural region, has a long history of multiple issues of an environmental, planning, economic or administrative nature, as well as a history of conflict. For the first time, the region’s key-stakeholders, including scientists and policymakers, participated in tactical meetings during the 19-month project “Water For Tomorrow”. The goal was to establish a common and holistic understanding of the problems, assess the lessons learned from the failures of the past and co-develop a list of policy recommendations, placing them in the broader context of sustainability. These refer to enhanced and transparent information, data, accountability, cooperation/communication among authorities and stakeholders, capacity building, new technologies and modernization of current practices, reasonable demand and supply management, flexible renewable energy portfolios and circular approaches, among others. This work has significant implications for the integrated water resources management of similar south-European cases, including the Third-Cycle of the River Basin Management Plans and the International Sustainability Agendas.
https://doi.org/10.3390/w14182879The theory of the modern Integrated and Sustainable Water Resources Management (ISWRM) is inherently interdisciplinary, including hydrology, hydraulics, geology, hydrogeology, meteorology, engineering, computer science, statistics, probabilistic theory, sociology, economics, political and law science, systems’ analysis, etc. The involvement of the users (stakeholders with a direct or indirect relation to the environmental management) has been proved to be necessary for the design, the evaluation and implementation of water management strategies (Alamanos et al., 2022). Multi-stakeholder platforms (MSP) are used internationally to allow stakeholders to explain their positions and objectives, give them a voice in the governance and decisionmaking process, and resolve conflicts. Conflicts are inevitable within MSP, where there are numerous alternatives, diverse backgrounds and interests, and unfortunately, most conflicts are painful or non-productive (Castro, 2019). Despite extensive research and numerous case studies in recent years on the topic of stakeholder engagement, there is no method or model that can tell any decision-maker how to evaluate the degree to which various individual (or common group) desires should be fulfilled or compromised (Scheffran and Stoll-Kleemann, 2003; Koundouri et al., 2022). This article gives a brief overview of the use of MSP, some international examples, their concerns-questions, strengths, weaknesses, analyses the sources of conflicts and ways to manage them, and summarizes points for consideration in the context of the efficient operation of MSP.
https://ic-sd.org/wp-content/uploads/2022/11/submission_79.pdfInsights from a stakeholder analysis and participation project on water resources management are presented. The case study is Thessaly, a dry agricultural Basin District in central Greece, facing multiple water management issues. A diverse multi-stakeholder platform was formed and Systems Innovation Approach (SIA), a novel framework, was used to guide the discussions (monthly meetings) and analyze the outcomes. Firstly, the group reached to a common understanding of the challenges, their technological, environmental, social, economic and political aspects. Secondly, a detailed evaluation of all existing measures, actions and initiatives followed (River Basin Management Plans, Resilience and Recovery Plan, and other initiatives). The group co-developed a common plan of action and list of recommendations, useful for the consultation of the upcoming Greek River Basin Management Plan (3rd Cycle 2022-27).
http://www.prexvi.civil.upatras.gr/PREXVI%20Analytical%20Programme%20June%2027.pdfThe Basin District of Thessaly, Greece's driest rural region, faces a number of environmental and economic issues as a result of agricultural intensification combined with poor management. For the first time, the region's key-stakeholders committed to co-design solutions during an 18-month project. The stakeholders are representatives from all governance structures, experts and experienced professionals, start-ups and technology experts, scientists, and agricultural co-operations. The first step towards a more resilient and sustainable management is to work towards the development of a geospatial interactive application. A GIS-based database is seen as a transparent and accessible means for covering existing gaps of complete and reliable data. Crop, soil, water use, pollution, water supply sources, irrigation methods, cultivation, production-yield, and basic economic data will be combined per farm level to provide necessary information to facilitate management and contribute to the implementation of results-based actions. This work presents the rationale of the stakeholder group to propose this initiative and its design. A brief review of similar tools internationally (private, commercial, public, freely accessible and academic applications-tools) is also presented and the learnings are discussed. The implementation of the proposed tool, facing data limitations, is also discussed to provide useful insights for similar initiatives. The development of the proposed tool is expected to provide multiple benefits in many levels: managerial, administrative, accountability, information sharing to enhance the work of all individual actors and bodies, informed decision-making, modernization of current tools and practices, and holistic approaches in terms of a more integrated planning.
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