Environmental Decision Support Research Articles

A Trustworthy and Responsible Decision-Making Framework for Resource Management in Food-Energy-Water Nexus: A Control-Theoretical Approach

This paper introduces a hybrid framework for trustworthy and responsible natural resource management, aimed at building bottom-up trust to enhance cooperation among decision makers in the Food, Energy, and Water sectors. Cooperation is highly critical for the adoption and application of resource management alternatives (solutions), including those generated by AI-based recommender systems, in communities due to significant impact of these sectors on the environment and the economic productivity of affected communities. While algorithms can recommend solutions, effectively communicating and gaining community acceptance of these solutions is crucial. Our research stands out by emphasizing the collaboration between humans and machines, which is essential for addressing broader challenges related to climate change and the need for expert trade-off handling in the management of natural resources. To support future decision-making, we propose a successful control-theory model based on previous decision-making and actor behavior. We utilize control theory to depict how community decisions can be affected by how much individuals trust and accept proposed solutions on irrigation water rights and crop operations in an iterative and interactive decision support environment. This model interacts with stakeholders to collect their feedback on the acceptability of solutions, while also examining the influence of consensus levels, trust sensitivities, and the number of decision-making rounds on the acceptance of proposed solutions. Furthermore, we investigate a system of multiple decision-making and explore the impact of learning actors who adjust their trust sensitivities based on solution acceptance and the number of decision-making rounds. Additionally, our approach can be employed to evaluate and refine potential policy modifications. Although we assess potential outcomes using hypothetical actions by individuals, it is essential to emphasize our primary objective of developing a tool that accurately captures real human behavior and fosters improved collaboration in community decision-making. Ultimately, our aim is to enhance the harmony between AI-based recommender systems and human values, promoting a deeper understanding and integration between the two.

Enhancing Urban Data Analysis: Leveraging Graph-Based Convolutional Neural Networks for a Visual Semantic Decision Support System.

The persistent increase in the magnitude of urban data, combined with the broad range of sensors from which it derives in modern urban environments, poses issues including data integration, visualization, and optimal utilization. The successful selection of suitable locations for predetermined commercial activities and public utility services or the reuse of existing infrastructure arise as urban planning challenges to be addressed with the aid of the aforementioned data. In our previous work, we have integrated a multitude of publicly available real-world urban data in a visual semantic decision support environment, encompassing map-based data visualization with a visual query interface, while employing and comparing several classifiers for the selection of appropriate locations for establishing parking facilities. In the current work, we challenge the best representative of the previous approach, i.e., random forests, with convolutional neural networks (CNNs) in combination with a graph-based representation of the urban input data, relying on the same dataset to ensure comparability of the results. This approach has been inspired by the inherent visual nature of urban data and the increased capability of CNNs to classify image-based data. The experimental results reveal an improvement in several performance indices, implying a promising potential for this specific combination in decision support for urban planning problems.

Empowering Eco-Friendly Choices: An Environmental Impact Assessment Decision Support System for Textiles and Clothing

The textile and clothing industry is one of the industries with the highest environmental impact, with a huge amount of waste during the product life cycle. However, there are few tools available for companies to assess the environmental impact of their production process. In this way, a decision support system was developed so that producers can calculate the environmental impact associated with the textile and clothing production process, with the main objective of helping producers and, consequently, consumers to make environmentally conscious decisions given the increasingly demanding market. The methodology of this study integrated the accounting of a set of indicators, allowing producers to calculate the environmental impact associated with the textile and clothing production process. The decision support system returns, depending on the results of the calculations of the indicators, different ecolabels that allow consumers to compare different products based on their environmental performance. In short, the study in question allows us to contribute to environmentally conscious decision making, both for the producer and the consumer, to promote sustainable practices in the textile and clothing industry. The decision support system is flexible and adaptable for different companies and industries in order to meet their needs and improve their environmental performance.

Typology of intellectual environmental decision support systems

Typology of intellectual environmental decision support systems

Environmental Impact Decision Support Tools for Horticulture Farming: Evaluation of GHG Calculators

Horticulture is essential in the European agricultural sector and fundamental for many EU member states. Decision Support Systems and Tools can play an essential role in a shift to result-based agriculture and evidence-based decision making, improving productivity and environmental performance of farming practices. Investigations have been conducted on horticulture crop and farming impact on the environment and Green House Gas emissions. Despite the availability of a broad spectrum of tools, the use of Decision Support Tools in agriculture in Europe could be much higher. This research aims to analyze and recommend environmental impact Decision Support Tools for small and medium-sized companies to approach, measure, and evaluate horticulture farming. The research methodology includes a systematic review, bibliometric analysis, Multicriteria Decision analysis, and a case study analysis. During the research, multiple tools, including calculators used for an impact assessment of the agricultural sector, were selected. After applying eligibility criteria, an in-depth analysis of eight of the most suitable calculators was performed. The results of the Multicriteria Decision analysis show that the Solagro Carbon Calculator, The Farm Carbon Calculator, and the Cool Farm Tool are recommended in the first place as Green House Gas calculators for farmers.

Strategic development of environmental impact assessment decision support tool for offshore energy enables decreased costs, increased utilization, and quality

In the transition to a sustainable energy system, there is an urgent need for expansion of offshore renewable energy installations. To ensure sustainable development also with respect to the marine environment, a variety of decision support tools (DSTs) are currently under development, aiming at potentially increased quality and efficiency for environmental risk assessment (EIA) of planned offshore energy installations. However, the savings potential of a DSTs is to a large extent governed by the timing of the DST development, which in turn is directly dependent on the investment rate over time. A set of development scenarios were evaluated, simulating different degrees of strategic implementation and successful utilization of the DST for offshore energy. Using the situation in Sweden as a case study, we demonstrate that a planned investment can lead to considerably lower total costs for the EIA at a national level, at the same time allowing for improved quality of the EIA in line with the ambitions in both marine spatial planning and existing goals within marine environmental management.

Primary Energy Resources and Environmental Impacts of Various Heating Systems Based on Life Cycle Assessment

This paper utilizes a life cycle assessment (LCA) to evaluate three heating systems’ energy resources and environmental impacts. The first system uses an electric heat pump that exclusively relies on geothermal energy. The second system operates on a gas boiler system that utilizes non-renewable electricity and natural gas. Lastly, the third system incorporates an absorption heat pump utilizing geothermal energy and natural gas. In the first step, cradle-to-gate assessments were prepared for the renewable, conventional, and mixed systems. The second step involved comparing the system scenarios based on their loads and energy resources. Primary energy, material resources, emissions, and impact categories were normalized and weighted using the CML, ReCiPe, and EF 3.0 methods. Finally, models for environmental reliability and complex decision support were developed. The novelty of this research lies in analyzing the ecological burden and energy usage of a mixed energy system that incorporates both renewable and non-renewable energy sources. The results show that the gas boiler system has a higher load, primarily due to the depletion of abiotic fossil fuels. However, the acidification is higher when an electric heat pump is used. The absorption heat pump system falls between the renewable and conventional systems in terms of both fossil depletion and acidification.

Environmental decision support system development for natural distribution prediction ofFestuca ovinain restoration of degraded lands

AbstractAnthropogenic activities, species invasions, and ecological factors are driving rapid changes in rangeland ecosystems. For ensuring the richness and sustainability of plant species habitats, there is a pressing need for a reliable prediction model that can accurately forecast and map species distribution under varying ecological conditions. We aimed to compare the performance of three widely used machine learning methods: Multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) in predicting the distribution ofFestuca ovinain mountainous protected rangelands. We conducted our investigation by analyzing the distribution ofF. ovinain 305 randomly selected plant sample plots. In each plot, we recorded 10 ecological variables. Three machine learning models were developed to predict the likelihood ofF. ovinadistribution. Our results demonstrated that the RBF model had a higher number of misclassifications (11 samples) compared to MLP and SVM models (10 samples), indicating that MLP and SVM models were more accurate for distribution modeling. Additionally, MLP showed a higher R‐squared value (0.87) compared to SVM (0.85), suggesting that MLP was the most accurate model for restoring degraded lands. Hence, we developed theFestuca ovinaDistribution Model (FODM) using the MLP model. Sensitivity analyses revealed that soil texture, soil depth, electrical conductivity (EC), pH, and vegetation density significantly influencedF. ovinadistribution, with respective sensitivity coefficients of 0.48, 0.47, 0.45, 0.41, and 0.41. Based on the finalized FODM, we designed an Environmental Decision Support System (EDSS) tool to assist rangeland managers in mappingF. ovinadistribution. By applying the EDSS tool, we demonstrated its practicality in using the FODM for effective decision‐making and land management. The EDSS tool serves as a valuable resource for rangeland managers, enabling them to make informed decisions regardingF. ovinarestoration and effectively use the predictive capabilities of the FODM in real‐world applications.

Suitability evaluation of rural sewage treatment facilities in China considering lifcycle environmental impacts and regional differences

Centralized vs. decentralized sewage treatment is one of the key issues in the planning of rural sewage treatment (RST) in China due to the country's diverse regional characteristics. There are very limited comprehensive evaluation models for selecting regionally suitable schemes and facilities, particularly for national or provisional scale planning. As a scenario-based multi-attribute decision-making (MADM) issue, this paper develops a novel RST suitability evaluation model by integrating the multi-attribute analytic hierarchy process (AHP) with the technique for order preference by similarity to an ideal solution (TOPSIS). The suitability evaluation model sets up 3 small-centralized and 4 decentralized RST facilities as candidates and includes 12 evaluation indicators that cover economic cost, life cycle environmental impacts, technical features and operations management. Eight generic scenarios are classified for Chinese rural areas based on differences in three major characteristic factors, i.e., population density (PD), the economic development level (EDL), and topographic slope (TS). The universal evaluation results show that a centralized sewage treatment scheme is more suitable for areas with a high PD/high EDL/low TS, while a decentralized scheme is more suitable for areas with a low PD/low EDL/high TS. Sensitivity analysis shows that in regions with a high PD/low EDL, the indicator weight of the construction investment cost in the model has a great influence on the facility suitability ranking. However, in regions with a high PD/high EDL, the ranking is the most sensitive to the indicator weights of the global warming potential and sewage treatment effect. Furthermore, as a spatial decision issue, an RST suitability map of Hunan Province in China is produced at the county level of resolution, and the map is generally consistent with our field knowledge of several counties in Hunan Province. The presented evaluation framework can be integrated into environmental decision support systems in the future to help local and central governments, water utilities, design institutes and other stakeholders scientifically plan RST projects.

From scenario to roadmap: Design and evaluation of a web-based participatory watershed planning system for optimizing multistage implementation plans of management practices under stepwise investment

Planning multistage implementation plans, or roadmaps, based on the spatial distribution of a best management practice (BMP) scenario is essential for achieving watershed management goals under realistic conditions, such as stepwise investment plans that involve multiple stakeholders, including investors, economic and environmental beneficiaries. The state-of-the-art BMP roadmap optimization method can address this need for optimization but is over-specialized and complex to non-expert stakeholders. This study designed a user-friendly web-based participatory watershed planning system to assist a diverse group of stakeholders in reaching a consensus on optimal roadmaps. The participatory process of stakeholders includes iteratively proposing stepwise investment constraints, submitting roadmap optimization tasks, analyzing spatiotemporal results from multiple perspectives, and selecting preferred roadmaps. The proposed system design separates the participatory process of non-expert stakeholders from the specialized modeling process of constructing simulation-optimization tools for BMP roadmaps, which is done in advance by professional modelers and encapsulated as webservices on the server side. The webservices expose a small set of essential parameters to lower barriers to use. The interactive participatory process is presented to stakeholders through web browsers with an easy-to-use interface. The system design was evaluated by implementing an agricultural watershed planning system for soil erosion reduction and conducting a role-playing experiment involving three groups of stakeholders with different standpoints in proposing investment constraints. The experimental results show that the optimal roadmap sets exhibit progressive improvements across three-round optimizations started by different stakeholders, effectively capturing the varying perspectives of stakeholders and facilitating consensus-building among them. The idea of system design and example implementation can serve as a valuable reference for developing related user-friendly environmental decision support systems.

Envisioning the future by learning from the past: Arts and humanities in interdisciplinary tools for promoting a culture of risk

Disaster risk is the result of complex interactions between the drivers of vulnerability such as poverty and lack of access to resources and the impacts from multiple hazards (with differing intensities and recurrence intervals). These risks are difficult to understand, quantify or convey. Historical hazard events have important potential in generating understanding of multiple potential risks. They provide historical and near-historical records of the real-life experience of relevant hazardous events and their physical, political and social consequences in physically familiar terrain. In this paper, we explore the use of historical research, memory, and emotion in developing conversations around the complexities of multi-hazard risk in urban settings through co-produced interdisciplinary museum exhibits and an educational transmedia platform in Quito, Ecuador. We argue that the opportunity for impact in Disaster Risk Reduction (DRR) arrives by targeting DRR education from an interdisciplinary perspective, with a narrative that draws on history and memory, and that uses art to address emotions and to communicate and visualise two sometimes overlooked but essential dimensions in DRR education: 1) understanding the risk drivers that turn hazardous events into disasters, and 2) building the capacity of communities to imagine future scenarios that reduce risk and create open and participatory processes of risk-sensitive urban planning as proposed by the Tomorrow's Cities Decision Support Environment (TCDSE).

An analysis of social vulnerability in a multi-hazard urban context for improving disaster risk reduction policies: The case of Sancaktepe, İstanbul

Despite concerted calls over the past 20 years to ensure that urban development is undertaken in a manner that reduces disaster risk, urban planning often remains myopically focused on the built environment, seeing building codes, and land-use planning, as the most effective mechanisms of Disaster Risk Reduction (DRR). While these are clearly crucial elements of planning for Tomorrow's Cities, they are only a part of an effective strategy.This article makes the fundamental assertion that DRR policies addressing urban spaces must strive to redress drivers of social vulnerability. This requires an understanding of the complex interactions of forms of marginalization within the local contexts, and how these have been shaped by the broader urban planning and DRR planning environment. A qualitative research method is employed in this study to assist the development of Tomorrow's Cities Decision Support Environment (TCDSE) that facilitates co-production for risk-informed decision-making on future pro-poor urban development in the context of natural hazards. The research study involved semi-structured interviews to obtain an in-depth account of social vulnerability in Sancaktepe, İstanbul. The narratives of the people draw upon different socio-demographic, and socio-economic vulnerabilities besides vulnerabilities due to urban renewal processes which underestimate pro-poor policies in İstanbul. Drawing on the narratives of interviewees, we then highlight the added value of contextualized and (inter)subjective qualitative interpretations. In conclusion, we argue how disaster risk-informed decision-making processes can be more progressive to ensure and serve in reducing vulnerabilities through our qualitative understanding as a voice of the community. This study is completed in İstanbul, within the Tomorrow's Cities Hub.

Analysing River Systems with Time Series Data Using Path Queries in Graph Databases

Transportation networks are used in many application areas, like traffic control or river monitoring. For this purpose, sensors are placed in strategic points in the network and they send their data to a central location for storage, viewing and analysis. Recent work proposed graph databases to represent transportation networks, since these networks can change over time, a temporal graph data model is required to keep track of these changes. In this model, time-series data are represented as properties of nodes in the network, and nodes and edges are timestamped with their validity intervals. In this paper, we show that transportation networks can be represented and queried using temporal graph databases and temporal graph query languages. Many interesting situations can be captured by the temporal paths supported by this model. To achieve the above, we extend a recently introduced temporal graph data model and its high-level query language T-GQL to support time series in the nodes of the graph, redefine temporal paths and study and implement new kinds of paths, namely Flow paths and Backwards Flow paths. Further, we analyze a real-world case, using a portion of the Yser river in the Flanders’ river system in Belgium, where some nodes are equipped with sensors while other ones are not. We model this river as a temporal graph, implement it using real data provided by the sensors, and discover interesting temporal paths based on the electric conductivity parameter, that can be used in a decision support environment, by experts for analyzing water quality across time.

An integrated supervision framework to safeguard the urban river water quality supported by ICT and models

Models and information and communication technology (ICT) can assist in the effective supervision of urban receiving water bodies and drainage systems. Single model-based decision tools, e.g., water quality models and the pollution source identification (PSI) method, have been widely reported in this field. However, a systematic pathway for environmental decision support system (EDSS) construction by integrating advanced single techniques has rarely been reported, impeding engineering applications. This paper presents an integrated supervision framework (UrbanWQEWIS) involving monitoring-early warning-source identification-emergency disposal to safeguard the urban water quality, where the data, model, equipment and knowledge are smoothly and logically linked. The generic architecture, all-in-one equipment and three key model components are introduced. A pilot EDSS is developed and deployed in the Maozhou River, China, with the assistance of environmental Internet of Things (IoT) technology. These key model components are successfully validated via in situ monitoring data and dye tracing experiments. In particular, fluorescence fingerprint-based qualitative PSI and Bayesian-based quantitative PSI methods are effectively coupled, which can largely reduce system costs and enhance flexibility. The presented supervision framework delivers a state-of-the-art management tool in the digital water era. The proposed technical pathway of EDSS development provides a valuable reference for other regions.

AAPM task group report 273: Recommendations on best practices for ai and machine learning for computer-aided diagnosis in medical imaging.

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both 'traditional' machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support. This article is protected by copyright. All rights reserved.

An environmental decision support system for manufacturer-retailer within a closed-loop supply chain management using remanufacturing

<abstract> <p>Industries face many challenges when emergencies arise. In emergency, there is an increasing demand for self-administered products that are easy to use. The decay rate of these products decreases with time. Moreover, the lack of disposal of used products increases waste and carbon emissions. By observing the scenario, this study develops a closed-loop supply chain management that considers the collection and remanufacturing of used products. The manufacturing rate is linear and the demand is ramp-type and carbon emissions dependent. The model is solved by a classical optimization and calculates the optimal total cost. The results show that the retailer can handle a shortage situation when the demand becomes stable (Case 2) and the total cost increases with the production rate. A sensitivity analysis shows the changes in the total cost with respect to the parameters.</p> </abstract>

A state-of-the-art decision-support environment for risk-sensitive and pro-poor urban planning and design in Tomorrow's cities

In this Special Issue introductory paper, we present the Tomorrow's Cities Decision Support Environment (TCDSE). As the negative impacts of natural hazards continue to escalate around the world due to increasing populations, climate change, and rapid urbanisation (among other factors and processes), there is an urgent requirement to develop structured and operational approaches towards multi-hazard risk-informed decision making on urban planning and design. This is a particularly pressing issue for low-to-middle income countries, which are set to be impacted ever more disproportionately during future natural-hazard events if the “business as usual” urban-development approach continues unabated. Urban poor residents of these countries will significantly suffer under risk-insensitive development trajectories.The proposed TCDSE addresses this crucial challenge. It facilitates a participatory, people-centred approach to risk-informed decision making, using state-of-the-art procedures for physics-based hazard and engineering impact modelling, integrating physical and social vulnerability in a unified framework, and expressing the consequences of future disasters across an array of stakeholder-weighted impact metrics that facilitate democratisation of the risk concept. The purpose of this introductory paper is to provide a detailed description of each component of the TCDSE, characterising related data inflows and outflows between modules. We conclude with a short operational end-to-end demonstration of the TCDSE, using the Tomorrowville virtual urban testbed. Individual components of the TCDSE are further dealt with in detail within subsequent papers of this Special Issue.

Implementation of Life Cycle Assessment (LCA) in the Procurement Process of Buildings: A Systematic Literature Review

The construction industry adds a high share to global CO2 emissions and, thus, to the global climate crisis. Future buildings need to be planned, constructed, operated, and deconstructed in a lifecycle-oriented manner so that the building stock represents a capital asset for future generations. The greatest leverages for reducing a building’s CO2 emissions lie in the early project phase and subsequently in the tendering and awarding process, which makes early Life Cycle Assessment (LCA) indispensable. In this study, we set a sociological research framework consisting of (i) choosing a research topic, (ii) conducting a literature review, (iii) measuring variables and gathering data, (iv) analyzing data, and (v) drawing a conclusion. Since there are countless studies that apply LCA in the construction sector for environmental assessment, emission reduction, or decision support, we posed the question of whether LCA was also applied in the public building tendering and awarding process. Furthermore, we focused on identifying obstacles to LCA implementation in this early project phase. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and conducted a Systematic Literature Review (SLR). The results show that numerous articles focused on sustainable tendering or green public procurement in the construction industry; however, the LCA method is scarcely used in the procurement processes (19 articles in the final sample). Based on our findings, the main obstacles to LCA implementation in the procurement process are highlighted in the study. In the future, the mandatory integration of LCA into the procurement process will be crucial to reduce the CO2 emissions generated by the construction industry and thus contribute to the EU climate target plan to ensure carbon neutrality by 2050.

Interdisciplinarity in practice: Reflections from early-career researchers developing a risk-informed decision support environment for Tomorrow's cities

The concept of disaster risk is cross-disciplinary by nature and reducing disaster risk has become of interest for various disciplines. Yet, moving from a collection of multiple disciplinary perspectives to integrated interdisciplinary disaster risk approaches remains a fundamental challenge. This paper reflects on the experience of a group of early-career researchers spanning physical scientists, engineers and social scientists from different organisations across the global North and global South who came together to lead the refinement, operationalisation and testing of a risk-informed decision support environment for Tomorrow's Cities (TCDSE). Drawing on the notions of subjects and boundary objects, members of the group reflect on their individual and collective journey of transgressing disciplinary boundaries across three case studies between June–December 2021: operationalisation process of the TCDSE; development of a virtual urban testbed as a demonstration case for the implementation of the TCDSE; and consolidation of frequently asked questions about the TCDSE for communication purposes. The paper argues that (1) the production of boundary objects in interdisciplinary research nurtures relations of reciprocal recognition and the emergence of interdisciplinary subjects; (2) the intrinsic characteristics of boundary objects define the norms of engagement between disciplinary subjects and constrain the expression of interdisciplinary contradictions; and (3) affects and operations of power explain the contingent settlement of interdisciplinary disagreements and the emergence of new knowledge. Activating the interdisciplinary capacities of early-career researchers across disciplines and geographies is a fundamental step towards transforming siloed research practices to reduce disaster risk.

Design and assessment of pro-poor financial soft policies for expanding cities

Recent major earthquake disasters have highlighted the effectiveness of financial soft policies (e.g., earthquake insurance) in transferring seismic risk away from those directly impacted and complementing ‘hard’ disaster risk mitigation measures (e.g., structural retrofit). However, the benefits of existing financial soft policies are often not guaranteed. This may be attributed to: (1) their low penetration rate (e.g., in the case of earthquake insurance); (2) the fact that they typically neglect the explicit needs of low-income sectors in (developed and developing) modern societies, who are often disproportionately impacted by natural-hazard driven disasters; and/or (3) their failure to consider the time-dependent nature of urban exposure. We contribute towards addressing these shortcomings by proposing a flexible framework for designing and assessing bespoke, people-centred, household-level, compulsory financial soft policies (including conventional earthquake insurance, disaster relief fund schemes, income-based tax relief schemes, or a combination of these) across cities under rapid urban expansion. The proposed framework leverages the Tomorrow’s Cities Decision Support Environment, which aims to facilitate pro-poor disaster-risk-informed urban planning and design in developing country contexts. The framework specifically enables decision makers to strategically design and then assess the pro-poorness of mandatory soft policies, using financial impact metrics that discriminate losses on the basis of income. We showcase the framework using the hypothetical expanding city, “Tomorrowville”, successfully identifying pro-poor seismic-risk-related financial soft policies for different instances in the lifetime of the urban system.