D3.5 - Good practices and guidelines for avoiding damages and other direct costs in regional governance scale

Good practices and guidelines for avoiding damages and other direct costs in regional governance scale Deliverable 3.5 Accelerating and upscaling transformational adaptation in Europe: demonstration of water-related innovation packages This project has received funding from the European Union’s Horizon H2020 innovation action programme under grant agreement 101036683. TransformAR Deliverable 3.5 2 www.transformar.eu Deliverable Number and Name D3.5 - Good practices and guidelines for avoiding damages and other direct costs in the regional governance scale Work Package WP3 – Envisioning transformative pathways for the demonstrators Dissemination Level Public Author(s) NCSRD: Stelios Karozis, Ioannis Zarikos, Athanasios Sfetsos Primary Contact and Email Stelios Karozis, [email protected] Date Due 31/03/2024 Date Submitted Version 1 (31/07/2024) Version 2 (02/07/2025) - After addressing comments from the external reviewers. File Name Status Version 1 (first submittion of deliverable) Version 2 (version after addressing comments from the external reviewers) Reviewed by (if applicable) PIK: Fred Hattermann Suggested citation S. Karozis, I. Zarikos, A. Sfetsos (2025) Good practices and guidelines for avoiding damages and other direct costs in regional governance scale. TransformAr Deliverable 3.5, H2020 grant no. 101036683 © TransformAR Consortium, 2021 This deliverable contains original, unpublished work except when indicated otherwise. Acknowledgement of previously published material and of the work of others has been made through appropriate citation, quotation, or both. Reproduction is authorised if the source is acknowledged. This document has been prepared in the framework of the European project TransformAR. This project has received funding from the European Union’s Horizon 2020 innovation action programme under grant agreement no. 101036683. The sole responsibility for the content of this publication lies with the authors. It does not necessarily represent the opinion of the European Union. Neither the EASME nor the European Commission are responsible for any use that may be made of the information contained therein. TransformAR Deliverable 3.5 3 www.transformar.eu EXECUTIVE SUMMARY .............................................................................. 4 1. INTRODUCTION ................................................................................. 6 2. CURRENT BEST PRACTICES AND GUIDELINES ........................................ 7 Evolution of Best Practices and Guidelines for avoiding damages .............. 7 Frameworks and Standards ................................................................ 8 Key Principles and Components ......................................................... 10 Challenges and Opportunities ........................................................... 15 Future Directions ............................................................................ 16 3. AVOID DAMAGES FRAMEWORK IN TRANSFORMAR ................................ 17 Overall concept and method ............................................................. 17 4. AVOIDED DAMAGES ASSESSMENT: HIGH DATA AVAILABILITY – KPI BASED ................................................................................................. 18 The case study of Municipality of Egaleo (MOE) ................................... 18 The case study of Gjøvik .................................................................. 26 5. AVOIDED DAMAGES ASSESSMENT: SPARSE DATA – HIGH-LEVEL APPROACH ................................................................................................. 35 The case study of West Country region............................................... 35 The case study of City of Lappeenranta .............................................. 41 6. GOOD AND BEST PRACTICES GUIDELINES FOR AVOIDING DAMAGES ...... 46 7. CONCLUSIONS ................................................................................ 50 REFERENCES ........................................................................................ 51 ANNEX I: DEMONSTRATORS PROFILE ....................................................... 53 Municipality of Egaleo ...................................................................... 53 Lappeenranta ................................................................................. 55 Westcountry Region ........................................................................ 58 ANNEX II: CLIMATE ANALYISIS PLOT USED IN SPARSE DATA APPROACH ....... 61 West Country region ....................................................................... 61 City of Lappeenranta ....................................................................... 66 TransformAR Deliverable 3.5 4 www.transformar.eu EXECUTIVE SUMMARY The current deliverable is type “Report”, and this document summarizes the main elements of Task 3.3 towards the development of a framework for best practices and guidelines for avoiding damages and other direct costs in regional governance scale. The experience and knowledge gained from the demonstrators are used to produce a guideline of good practices, to diffuse the results and render them usable and useful in other cases. The guidelines for good practices are based on the experience and knowledge gained from D3.4 and by bringing the participatory approaches and qualitative and technical contribution at demonstrator scale. As a follow-up to the recommendations received in the context of the second project review (February 2025), the document was restructure complementary to D3.4. D3.4 is focusing on the methodologies developed to assess the avoided damages (D3.5 methodology parts was moved to D3.4 and new sparse data approach is introduced), whereas D3.5 focuses on the application and best practices. In TransformAr context, avoided damages represent the quantification of potential effectiveness of solutions implemented at demonstrator level to cope with climate change risks. More specifically, avoided damages are the benefits derived from mitigation and adaptation efforts that reduce potential adverse impacts of climate change. The framework distinguishes between climate change damages (adverse effects that cannot be adequately mitigated or adapted to) and avoided damages (benefits of effective adaptation efforts that prevent potential future impacts). The assessment compares two scenarios: the baseline situation (current conditions with or without solutions) and future climate change scenarios, with the difference between these states defining the avoided damages In order to accommodate different capacity of relevant actors to provide information and data, two methods where developed. The High Data Availability - KPI Based Approach quantifies climate hazards and their impacts using an extensive list of Key Performance Indicators (KPIs) across impact categories, whereas, the Sparse Data - High Level Approach was developed to address situations with limited data availability, following the established IPCC risk assessment framework. The High data availability approach was applied in Municipality of Egaleo demonstrator and Gjøvik replicator, whereas the Sparse data availability was applied in Lappeenranta and West Country demonstrators. Both methodologies yields similar results but with different data types and analysis, with the new Sparse data approach being more convenient to apply thus more generalised. Below the main outcomes of the assessment is presented. TransformAR Deliverable 3.5 5 www.transformar.eu Demonstrator Approach Main Hazards Short-term Avoided Damages Long-term Avoided Damages Municipality of Egaleo High Data KPI Heatwaves -11.0% -26.0% to -35.8% (RCP4.5 to RCP8.5) Gjøvik High Data KPI Flooding -23.6% 47.1% (SSP1.26) 47.6% (SSP3.7) 48.1%(SSP5.85) Lappeenranta Sparse Data Flooding 639,600.0€ 1,868,152.0€ (SSP1.26) 4,670,380.0€ (SSP5.85) West Country Region Sparse Data Drought 820,000£ 3,540,000£ (SSP1.26) 6,440,000£ (SSP5.85) Moreover, an approach on how to apply the approach of avoided damages assessment in the decision making of potential new actors, was introduced based on the experience gained in TransformAr project. TransformAR Deliverable 3.5 6 www.transformar.eu 1. Introduction Climate change damages refer to the adverse effects of climate change impacts that cannot be mitigated or adapted effectively. These damages encompass a wide range of effects, including loss of land and property, health and ecological damages, threats to human security, and economic impacts. The term "loss and damage" is often used to describe these impacts, particularly those that are beyond the capacity of mitigation and adaptation efforts. Such damages can include both sudden-onset disasters (like hurricanes and floods) and slow-onset processes (such as sea-level rise and desertification) (Geest, et al. 2019) (Voigt 2008). Avoided damages refer to the benefits derived from mitigation and adaptation efforts that reduce the potential adverse impacts of climate change. These benefits are essentially the costs that would have been incurred had no action been taken to address climate change. Effective mitigation strategies, such as reducing greenhouse gas emissions, and adaptation measures, like building flood defences, contribute to avoiding significant economic, social, and environmental damages. For instance, integrating climate science with economic models helps estimate the monetary value of avoided damages, showing that mitigation can prevent substantial costs associated with extreme weather events and other climate impacts (Hsiang et al 2017) (Kooten, et al. 2013). In ‘D3.4 - Tools on the avoided damages and benefits per demo’, a framework assessing the effectiveness of TransformAr solutions was introduced, building on the concept of avoided damages. The framework is based on quantifying effectiveness and damages qualitatively or quantitively using a list of key performance indicators (KPIs). The assessment is performed for the current situation, future scenarios, and before and after the solution implementation. By comparison of quantified values with and without the implementation of a planned solution for a set of climate scenarios, the assessment provides an estimation of the climate-avoided damages. In the current approach, the framework was expanded and further developed to define the concept of damages and avoided damages more clearly, based on discussions with the demonstrators and technical partners and the experience we gained via the interaction with the demonstrators and the barriers we had to surpass. The deliverable explores current best practices and guidelines that directly or indirectly tackle the concept of avoiding damages in climate change, presents the avoided damages assessment developed in TransformAr alongside the complete case of Municipality of Egaleo (MOE) and provides a stepped guide to replicate the process beyond the project. TransformAR Deliverable 3.5 7 www.transformar.eu 2. Current Best Practices and Guidelines Climate change adaptation strategies have led to the emergence of promising strategies for addressing pressing environmental challenges while fostering sustainable development. As the demand for adaptation approaches grows, understanding the current landscape of best practices and guidelines for avoiding damages becomes increasingly crucial. This section comprehensively reviews the state of the art in climate adaptation best practices and guidelines, drawing insights from recent literature, expert opinions, and practical experiences. Evolution of Best Practices and Guidelines for avoiding damages The evolution of best practices and recommendations, on the application of a variety of solution (NBS, digital, etc.), shows continuous learning, innovation, and adaptation to new environmental and societal concerns. These adaptation measures have evolved from a conservation and restoration idea to a complete approach considering ecological, social, and economic factors. These best practices and standards have evolved through numerous vital stages, each with a focus, techniques, and goals shift. Initially, best practices and guidelines for avoiding damages arose in reaction to environmental degradation and biodiversity loss, emphasising the significance of protecting and restoring natural ecosystems to address current conservation issues. These early guidelines emphasised themes like ecosystem integrity, connectedness, and resilience, advocating for the protection of essential habitats, the restoration of degraded landscapes, and the enhancement of biodiversity through targeted conservation efforts. These principles provided the groundwork, such as incorporating NBS into larger environmental management strategies and policies by prioritising ecological objectives. As knowledge of the connection between ecosystems and human well-being grew, best practices and guidelines for avoiding damages began incorporating social and economic factors into decisionmaking processes. This transition mirrored an awareness of the numerous benefits of adaptation interventions for both people and the environment, ranging from increasing climate resilience and minimising natural hazards to boosting public health and supporting local livelihoods. Guidelines for climate change adaptation begin to highlight ideas such as multifunctionality, participative approaches, and inclusion, advocating for incorporating multiple viewpoints and values into their design and implementation. Furthermore, developments in scientific research, technology innovation, and policy development have all contributed to establishing best practices for climate adaptation. New methodologies, tools, and approaches have been created to evaluate climate adaptation interventions' efficacy, scalability, and sustainability, allowing practitioners to make better decisions and prioritise actions that optimise positive outcomes. Guidelines for climate adaptation measures are becoming more evidence-based, using transdisciplinary knowledge and empirical evidence to inform decision-making and support adaptive management methods. More recently, the evolution of climate adaptation measures best practices has been marked by an increased emphasis on cross-sector collaboration and partnership formation. Recognising the need for integrated and collaborative solutions to complex environmental and socioeconomic concerns, Climate adaptation guidelines have aimed to stimulate collaboration across a wide range of stakeholders, including government agencies, civil society organisations, academia, and the commercial sector. This emphasis on partnership-building reflects an understanding of the TransformAR Deliverable 3.5 8 www.transformar.eu importance of collective action and shared responsibility in solving global environmental concerns such as climate change, biodiversity loss, urbanisation, and natural resource management. The evolution of climate adaptation measures best practices is likely to be influenced by continued advances in research, technology, policy, and practice. As climate adaptation measures, such as NBS among other, gain traction as a mainstream method to tackling environmental and societal concerns, its recommendations are projected to become increasingly sophisticated, context-specific, and adaptable to a wide range of geographic, cultural, and socioeconomic circumstances. By embracing innovation, cooperation, and learning-oriented approaches, the evolution of climate adaptation measures best practices hold the prospect of unleashing nature's full potential to create more sustainable, resilient, and inclusive communities for future generations. Frameworks and Standards A number of frameworks and standards have been developed to assist in the design, implementation, and assessment of climate adaptation and mitigation solutions. These frameworks were designed based on local policies, climatic conditions, and local applicability of solutions. International International frameworks play a crucial role in guiding and coordinating global efforts to adapt to climate change. One of the most prominent frameworks is the United Nations Framework Convention on Climate Change (UNFCCC) (Kuyper et al., 2018; Mantlana et al., 2024), which established the foundational legal and institutional structures for global climate policy. Under the UNFCCC, the Paris Agreement stands out as a landmark international treaty adopted in 2015, aimed at limiting global warming to well below 2°C above pre-industrial levels while pursuing efforts to limit the increase to 1.5°C. This agreement mandates countries to submit and update Nationally Determined Contributions (NDCs) that outline their mitigation and adaptation commitments. To support adaptation, the Paris Agreement encourages the development of National Adaptation Plans (NAPs), which help countries identify their mediumand long-term adaptation needs and develop strategies to address them. Solutions and interventions under NAPs often include enhancing early warning systems, climateresilient infrastructure, and sustainable water management practices. The Intergovernmental Panel on Climate Change (IPCC) also significantly influences climate adaptation strategies through its comprehensive Assessment Reports, which provide scientific evaluations of climate change impacts, adaptation, and vulnerability(Palutikof et al., 2023). These reports, including the Special Reports on global warming of 1.5°C and the impacts on land and oceans, offer essential scientific guidance for policymakers. The IPCC emphasizes the need for integrating climate adaptation into all levels of planning and policy, promoting solutions such as nature-based solutions, ecosystem-based adaptation, and the use of climate-resilient agricultural practices to secure food production and protect biodiversity. Complementing these efforts, the Sendai Framework for Disaster Risk Reduction 2015-2030 focuses on reducing disaster risks and building resilience to both natural and human-induced hazards. It emphasizes the importance of integrating disaster risk reduction and climate adaptation measures, advocating for interventions like improved building codes, land-use planning that considers future climate risks, and community-based disaster preparedness programs. These measures aim to mitigate the impact of extreme weather events and reduce vulnerability in high-risk areas. The Sustainable Development Goals (SDGs), particularly Goal 13 (Climate Action), further reinforce the need for urgent action to combat climate change and its TransformAR Deliverable 3.5 9 www.transformar.eu impacts, highlighting the interconnectedness of climate adaptation with broader sustainable development objectives. Solutions promoted under Goal 13 include transitioning to renewable energy sources, promoting energy efficiency, and fostering innovation in green technologies. Additionally, the SDGs advocate for climate education and awareness, strengthening institutional capacities to manage climate risks, and ensuring that vulnerable communities receive adequate support to adapt to changing climate conditions. Combined, these international frameworks provide a cohesive and comprehensive structure for countries to develop, implement, and monitor their climate adaptation strategies. They ensure that efforts are scientifically informed, globally coordinated, and aligned with sustainable development goals. By promoting a range of solutions and interventions, these frameworks aim to enhance resilience, protect ecosystems, and secure livelihoods against the adverse impacts of climate change. European Adaptation strategy The European Union Adaptation Strategy serves as a comprehensive framework guiding member states in enhancing resilience and reducing vulnerability to the impacts of climate change across Europe. Adopted in 2013 and updated in 2021, the strategy emphasizes the integration of climate adaptation into all relevant EU policies and funding programs, fostering a holistic and cohesive approach to adaptation. A vital strategy component is promoting nature-based solutions, such as restoring wetlands, reforesting degraded areas, and creating green urban spaces, which not only enhance biodiversity but also provide natural defences against floods, heatwaves, and other climate impacts (Remling, 2018; Rutherford et al., 2020). The strategy also focuses on climate-resilient infrastructure, advocating for including climate considerations in designing, constructing, and maintaining buildings, roads, and other critical infrastructure to withstand extreme weather events. To support the agricultural sector, the EU Adaptation Strategy encourages adopting climate-smart farming practices, including crop diversification, soil conservation techniques, and efficient water management systems, ensuring sustainable food production under changing climate conditions. Additionally, the strategy underscores the importance of early warning systems and improved risk assessment tools to better predict and respond to climate-related hazards, thereby protecting communities and economies (Rutherford et al., 2020). The strategy also aims to enhance financial support mechanisms by leveraging funds from the EU budget, such as the European Structural and Investment Funds, to invest in adaptation projects. Furthermore, the strategy promotes knowledge sharing and capacity building through platforms like the Climate-ADAPT portal, which facilitates the exchange of information and best practices among member states, regions, and cities. By prioritising these adaptation solutions, the EU Adaptation Strategy seeks to build a climate-resilient Europe that can effectively manage and mitigate the diverse and evolving challenges of climate change (European Commission, 2021). National and regional scale National Adaptation Plans (NAPs) are essential instruments developed under the United Nations Framework Convention on Climate Change (UNFCCC) framework, aimed at helping countries, particularly developing nations, identify their mediumand long-term adaptation needs and formulate comprehensive strategies to address climate change impacts. Initiated in 2010 during the UNFCCC’s Cancun Adaptation Framework, NAPs are designed to enhance nations' adaptive capacity and resilience by systematically assessing vulnerabilities and integrating climate adaptation into national policies and planning processes. NAPs emphasize various adaptation solutions tailored to each TransformAR Deliverable 3.5 16 www.transformar.eu stakeholders can overcome barriers and unlock the full potential of climate adaptation interventions to address global sustainability challenges. Future Directions Looking ahead, future research and action in the field of climate adaptation best practices and guidelines should focus on: • Advancing the evidence base through rigorous monitoring, evaluation, and knowledge exchange. • Promoting cross-sectoral and transdisciplinary approaches that integrate climate adaptation into broader policy agendas, such as climate adaptation, biodiversity conservation, and sustainable development. • Strengthening capacity building efforts to empower local communities, government agencies, and other stakeholders to implement climate adaptation solutions effectively and equitably. • Advocating for supportive policies, incentives, and regulatory frameworks that incentivize the adoption of climate adaptation solutions and remove barriers to implementation. In conclusion, the current state of best practices and guidelines for climate adaptation solutions reflects a dynamic and evolving field characterized by innovation, collaboration, and a growing recognition of the importance of nature in addressing complex sustainability challenges. By building on existing knowledge, fostering partnerships, and embracing adaptive management approaches, stakeholders can accelerate the transition towards a more resilient, equitable, and nature-positive future. TransformAR Deliverable 3.5 17 www.transformar.eu 3. Avoid damages framework in TransformAr Overall concept and method In the context of the TransformAr project, the concept of Damages and Avoid Damages from climate change is developed and defined based on the current literature. As such, damages from climate change encompass a broad range of adverse impacts that are not adequately mitigated or adapted to, while avoided damages represent the benefits of effective mitigation and adaptation efforts, preventing potential future impacts. In D3.4 the methodology for assessing damages and calculating avoided damages was presented and follows two approaches. The High Data Availability - KPI-Based Approach quantifies climate hazards and their impacts using Key Performance Indicators (KPIs) across five categories: Physical, Social, Environmental, Health, and Economic. These KPIs are scaled and weighted based on expert input to estimate direct, indirect, and people-related damages, with the effectiveness of implemented solutions assessed by comparing KPIs before and after adaptation measures. In contrast, the Sparse Data – High-Level Approach is designed for cases with limited data availability and follows the IPCC risk framework, estimating damages based on hazard exposure and vulnerability. This approach incorporates global temperature shocks and economic modeling to determine direct and indirect damages. In both approaches, the avoided damages are then calculated by comparing two scenarios: the Baseline (Bs), which represents the current situation with or without solutions, and Climate Change (CC), which represents future conditions under different climate scenarios with and without solutions. The difference in damages between these states defines avoided damages, which are categorized into Short-term, representing immediate benefits from adaptation measures, and Long-term, projecting benefits for future climate scenarios. These methodologies provide a structured approach to evaluating climate adaptation strategies and their effectiveness in mitigating risks. Table 3.1 Formulas to assess Avoided Damages from Climate Changes. Baseline (now) Future (CC) No Solution implementation 𝐷𝐵𝑠=𝑓(𝑑𝑖𝑟𝑒𝑐𝑡, 𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡, 𝑝𝑒𝑜𝑝𝑙𝑒) 𝑛𝑆 𝐷𝐶𝐶=𝑓(𝑑𝑖𝑟𝑒𝑐𝑡,𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡,𝑝𝑒𝑜𝑝𝑙𝑒) 𝑛𝑆 With Solution implementation 𝐷𝐵𝑠=𝑔(𝑑𝑖𝑟𝑒𝑐𝑡, 𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡, 𝑝𝑒𝑜𝑝𝑙𝑒) 𝑆 𝐷𝐶𝐶=𝑔(𝑑𝑖𝑟𝑒𝑐𝑡,𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡,𝑝𝑒𝑜𝑝𝑙𝑒) 𝑆 Avoided damages 𝐴𝐷𝐵𝑠 =𝐷𝐵𝑠 𝑛𝑆 − 𝐷𝐵𝑠 𝑆 𝐷𝐵𝑠 𝑛𝑆 𝐴𝐷𝐶𝐶 =𝐷𝐵𝑠 𝑛𝑆−𝐷𝐶𝐶 𝑆 𝐷𝐵𝑠 𝑛𝑆 nS: no Solution | S: Solutions | Bs: Baseline | CC: Climate Change | D: Damages | AD: Avoided Damages TransformAR Deliverable 3.5 18 www.transformar.eu 4. Avoided damages assessment: High Data Availability – KPI Based The case study of Municipality of Egaleo (MOE) Overview The municipality of Egaleo (MOE), Greece (37.9924° N, 23.6781° E) is situated at the west region of the urban planning complex of the region of Attica and has been built at both sides of the ancient road «Iera Odos». It is approximately 4km away from Athens. The borders of Egaleo can be seen in Figure 2 below. The population of the city is around 120.000. The city is exposed to heat waves (expected to increase with CC), extreme precipitations, thunderstorms, and flooding events. MOE, being part of the Western Region of Attika in Greece, belongs to the wider Mediterranean biogeographical region. As thoroughly assessed earlier on, MOE is at great risk of drought and wildfires fires of the «Baroutadiko Grove». Furthermore, heatwaves also pose a dire heat stress danger to elder citizens, vulnerable groups and to households that cannot afford adequate cooling, which also stresses the issue of energy demands of cooling. Among the major natural risks that are threatening Egaleo are fires caused by the severe temperatures and the dryness of the vegetation during summertime. Another potential natural risk for Egaleo is flooding from urban flash floods, which is due to the insufficient flood risk management systems. Egaleo is located near Kifissos river, which has a high risk of flooding during mid-autumn-winter session, causing massive damage to the area. Severe floods with peoples deaths occurred in 1934 in 1954 and in 1997 (Egaleo Book, 2023) There has been a series of wildfires roaming throughout summertime in Greece. MOE has not experienced a direct episode of fire in Baroutadiko Grove, although each summer temperatures have been reaching up to 45°C, putting the municipality in an alarming state. Τhe issues that Egaleo is facing, increasing the territory’s vulnerability from a climate perspective, including: • Slow population growth rate, high unemployment rate and the prevalence of vulnerable groups (migrants, offenders). These groups often lack the resources to protect themselves against severe climate effects and the slower economic growth of MOE makes it difficult with keeping up with their support. • Infrastructures, mostly the inefficient street planning, the lack of public spaces and parking lots. That leads to surplus people activity in public spaces and increased traffic volume that can cause amplified harm to the environment of the area. • Direct environmental issues caused by the industrial activity in Eleonas and Yula areas. • Increasing temperature in MOE during summertime is evidence for its citizens to realize the existence and impact that climate change yields. This could urge them to become more aware about their living habits and start changing their lifestyle to an eco-friendlier one (e.g. start using public transportation more, reduce in-house electricity usage, not litter the streets). The methodology was successfully applied to MOE for the climate hazard of heatwave. A series of meetings between NCSRD and MOE, alongside bilateral meetings with local stakeholders and experts of MOE’s infrastructures and services, were performed. The information compiled accomplished by following the steps below: • Define the climate problem • Decide the relevant KPIs • Quantification of KPIs (expert opinion, models, WP2 data) for 3 cases TransformAR Deliverable 3.5 19 www.transformar.eu o Current status (Baseline) o Future scenario RCP4.5 o Future scenario RCP8.5 • Assess the importance of each KPI • Present and define the added value of TransformAr solutions • Quantification of KPIs (expert opinion, models, WP2 data) having in mind the solution implementation In Table 4.1 the list of KPIs chosen and quantified for MOE are presented. Table 4.1 KPIs list used by MOE related to climate hazard and solution KPI Definition Hazard Solution People Indirect Direct Production-based CO2 intensity is calculated as CO2 emissions per capita (tonnes/person). Heat waves CIH x Ranking of cities/countries based on annual average PM2.5 concentration (µg/m³) Heatwaves SCS Variation of annual total carbon dioxide equivalent emissions from energy production, transportation and industry. Heatwaves SCS x Perceptions about threats through climate change and environmental catastrophes and how likely they are or will be prevented Heatwaves CAE / AWAR x Percentage of the labour force unemployed (working-age residents without work divided by total labour force) Heatwaves AWAR, DSI x Percentage of population with access to improved sanitation facilities/ People using safely managed sanitation services Heatwaves DSI x x Access to electricity, urban Heatwaves DSI x x TransformAR Deliverable 3.5 20 www.transformar.eu A person's actual exposure to environmental hazards, such as noise or pollution. Heatwaves CAE/SCS x x x Data on safety and risk collected in the World Risk Poll from over 125,000 people in 121 countries.The Resilience Index is and average of 4 domains: Individual, Household, Community, Society. Heatwaves CAE x x x Perceptions about threats through climate change and environmental catastrophes and how likely they are or will be prevented Heatwaves CAE / AWAR x x The Global Climate Risk Index shows the level of exposure and vulnerability to extreme weather events Heatwaves DSI x x x The mortality rate is calculated by dividing the number of total deaths by the population size for a defined population or geographical area over a specified period. An indicator that can help measure a person’s health in a community. It is a measure that affects the population Heatwaves DSI x Health impacts of air pollution: air pollutants and greenhouse gases Heatwaves DSI x x The indicator Healthy Life Years (HLY) at birth measures the number of years that a person at birth is still expected to live in a healthy condition. HLY is a health expectancy indicator which combines information on mortality and morbidity. The data required are the age-specific prevalence (proportions) of the population in healthy and unhealthy conditions and age-specific mortality information. A healthy condition is defined by the absence of limitations in functioning/disability. Heatwaves DSI x TransformAR Deliverable 3.5 21 www.transformar.eu Damage assessment Following the workflow described in the previous sections, the damages were assessed based on the chosen KPIs. The values were calculated with the help of models, WP2 data, online databases and national reports for the area of Attica. In many occasions, the information collected was needed to be downscaled for the MOE area. The latter was achieved by identifying scaling factors relevant to MOE by utilizing population density, industrial activity, local environmental conditions, and socio-economic data. In addition, interpolation methods (e.g. linear scaling) were utilized to adjust the regional data to the local context. Wherever it was possible the downscaled values were cross-checked with any available local data. The scaled (see Section 3) values are illustrated in Figure 4.1. Figure 4.1 Radar plot of the damages assessment of MOE for the case of heatwave for Current status (Baseline), the solution effectiveness and two damage assessments for future climate scenarios (RCP4.5, RCP8.5) It is found that the mortality rate is high compared to other countries 1 and the problem becomes bigger in the future for scenario RCP8.5, with the exposure to environmental hazards and contribution to climate change driver (CO2) being the most important areas of damages for MOE. In the following Tables 4.2, Table 4.3 and Table 4.4 the quantification of the damages categories (Direct, Indirect, People) for the current status (baseline) and the climate scenarios under study. In addition, the information on each solution and damage importance is presented as decided by the MOE demonstrator. 1 https://worldpopulationreview.com/country-rankings/death-rate-by-country TransformAR Deliverable 3.5 22 www.transformar.eu Table 4.2 Quantifications of Direct KPIs for MOE for the case of heatwave – no Solution (nS) KPI Definition Baseline (Bs) RCP4.5 RCP8.5 Importance(a_n) Percentage of population with access to improved sanitation facilities/ People using safely managed sanitation services2 99% 97% 94% 5 Access to electricity, urban3 100% 100% 98% 5 The Global Climate Risk Index shows the level of exposure and vulnerability to extreme weather events (Risk factor 0-4)4 1 2 2 3 Table 4.3 Quantifications of Indirect KPIs for MOE for the case of heatwave – no Solution (nS) KPI Definition Baseline (Bs) RCP4.5 RCP8.5 Importance (a_n) Production-based CO2 intensity is calculated as CO2 emissions per capita (tonnes/person).5 5.9 5.5 7.2 3 Table 4.3 Quantifications of People KPIs for MOE for the case of heatwave – no Solution (nS) KPI Definition Baseline (Bs) RCP4.5 RCP8.5 Importance( a_n) A person's actual exposure to environmental hazards, such as noise or pollution.8 37% 42% 52% 4 Perceptions about threats through climate change and environmental catastrophes and how likely they are or will be prevented12 67% 72% 82% 3 2 https://tradingeconomics.com 3 International Energy Agency (IEA) 4 World bank 5 European Environment Agency (EEA) TransformAR Deliverable 3.5 23 www.transformar.eu The mortality rate (per 1,000 people)6 12 12.3 14.5 5 Health impacts of air pollution: air pollutants and greenhouse gases13 34% 37% 42% 4 Healthy Life Years (HLY) (Measures the number of years that a person at birth is still expected to live in a healthy condition.) 7 66.6 64.5 63.3 3 Solution assessment Following the same process and data sources (models, WP2, expert opinion) the effectiveness of the solution was estimated. In the current context it is presented as the change of the KPI for the better (less damage). The latter is essential to be aligned with the previous section and used in the avoided damages assessment. The table 4.7, Table 4.8 and Table 4.9 present the changes to the KPIs after the solution applied. Table 4.7 Quantifications of Direct KPIs for MOE for the case of heatwave – Solution (S) KPI Definition Solution Solution (S) Percentage of population with access to improved sanitation facilities/ People using safely managed sanitation services DSI 99% Access to electricity, urban DSI 100% The Global Climate Risk Index shows the level of exposure and vulnerability to extreme weather events DSI 1 Table 4.8 Quantifications of Indirect KPIs for MOE for the case of heatwave – Solution (S) KPI Definition Solution Solution Baseline (S) 6 Hellenic Statistical Authority (ELSTAT) 7 Eurostat Health Statistics TransformAR Deliverable 3.5 24 www.transformar.eu Production-based CO2 intensity is calculated as CO2 emissions per capita (tonnes/person). CIH 5.1 Table 4.9 Quantifications of People KPIs for MOE for the case of heatwave – Solution (S) KPI Definition Solution Solution Baseline (S) A person's actual exposure to environmental hazards, such as noise or pollution. CAE/SCS 37% Perceptions about threats through climate change and environmental catastrophes and how likely they are or will be prevented CAE / AWAR 62% The mortality rate is calculated by dividing the number of total deaths by the population size for a defined population or geographical area over a specified period. An indicator that can help measure a person’s health in a community. It is a measure that affects the population DSI 11 Health impacts of air pollution: air pollutants and greenhouse gases DSI 32% The indicator Healthy Life Years (HLY) at birth measures the number of years that a person at birth is still expected to live in a healthy condition. HLY is a health expectancy indicator which combines information on mortality and morbidity. The data required are the population's age-specific prevalence (proportions) in healthy and unhealthy conditions and agespecific mortality information. The absence of limitations in functioning/disability defines a healthy condition. DSI 67.8 Avoided damages assessment Following the methodology presented in Section 3, the analysis of damages and solution effectiveness are combined to assess the avoided damages for the MOE case under the climate hazard of heatwave. Table 4.13 and Table 4.14 present the scaled valued (0-1) in percentage format for the baseline and the two climate scenarios. Table 4.13 refers to the total avoided damages for MOE for the case of heatwave, whereas the added value of the solutions implementation becomes higher from now to the future. The analysis yields long-term avoided damages between –35.8 to –26% with short-term values –11%. Table 4.13 Total avoided damages for MOE for the case of heatwave Total avoided damages Baseline (now) RCP4.5 RCP8.5 No Solution implementation 5.4% 6.5% 7.5% TransformAR Deliverable 3.5 25 www.transformar.eu With Solution implementation 4.8% Avoided damages -11.0% -26.0% -35.8% Figure 4.2 Bar plot of the avoided damages assessment per category (Direct, Indirect, People) for MOE for the case of heatwave Table 4.14 presents the avoided damages broken down to the three categories of Direct, Indirect and People. The direct avoided damages are higher compared to the other two categories with Indirect and People having values similar to the overall avoided damages. Based on that, MOE can benefit a lot even for short-term avoided damages by implementing TransformAr solution and more, thus, addressing the damage areas depicted in Figure 4.1. Figure 4.2 illustrates the avoided damaged categories for all cases, where the direct avoided damages importance is evident. Table 4.14 Assessment of avoided damages per category (Direct, Indirect, People) for MOE for the case of heatwave Direct Baseline (now) RCP4.5 RCP8.5 No Solution implementation 2.5% 3.9% 4.7% With Solution implementation 2.0% Avoided damages -20.2% -48.9% -57.3% Indirect No Solution implementation 6.3% 7.4% 8.7% TransformAR Deliverable 3.5 32 www.transformar.eu Figure 4.5 Bar plot of the avoided damages assessment per category (Direct, Indirect) for Gjøvik for the case of flooding. Table 4.22 shows the avoided damages divided into two categories: Direct and Indirect. The avoided damages for Indirect are higher than for Direct because citizens are willing to pay for the most effective adaptation measures. The effectiveness in preventing damages improves for the 3 SSP scenarios as risk increases. On the contrary, the effectiveness of the solutions (SWMM and URB) linked to the Direct damages increases dramatically in all near-future scenarios by at least 28.7%. Table 4.22 Assessment of avoided damages per category (Direct, Indirect) for Gjøvik for the case of flooding. Direct Baseline (now) SSP 1.26 SSP 3.7 SSP 5.85 No Solution implementation 8.0% 12.2% 12.4% 12.5% With Solution implementation 6.7% Avoided damages -16.6% -45.3% -45.8% -46.3% Indirect Baseline (now) SSP 1.26 SSP 3.7 SSP 5.85 No Solution implementation 5.0% 5.1% 5.2% 5.2% With Solution implementation 2.0% Avoided damages -59.5% -60.7% -61.1% -61.5% Far Future (FF) scenarios The methodology combines damage analysis and solution effectiveness to assess the avoided damages for the Gjøvik replicator under flooding climate hazards. Tables 4.23 and 4.24 display scaled values (0-1) as percentages for both the baseline case and three climate scenarios from 2070 to 2100. Gjøvik's total avoided damages from flooding increase over time as the benefits of the solution are realised. The analysis indicates long-term avoided damages ranging from –50.4% to –46.6%, while short-term avoided damages remain –23.6%, as in the case for the near future scenarios. TransformAR Deliverable 3.5 33 www.transformar.eu Table 4.23 Total avoided damages for Gjøvik for the case of floods. Total avoided damages Baseline (now) SSP1.26 SSP 3.7 SSP5.85 No Solution implementation 7.3% 10.4% 11.0% 11.3% With Solution implementation 5.6% Avoided damages -23.6% -46.6% -49.1% -50.4% Figure 4.6 Bar plot of the avoided damages assessment per category (Direct, Indirect) for Gjøvik for the case of flooding. Table 4.28 presents the avoided damages categorised into Direct and Indirect. The avoided damages are greater for Indirect because citizens are willing to pay more for the most effective adaptation measures. The effectiveness of damage prevention improves for the 3 SSP scenarios as risk levels rise. Conversely, the solutions associated with Direct damages (SWMM and URB) show a significant increase in effectiveness across all far-future scenarios, with improvements of at least 28.1%. Regardless of the scenarios and future periods, the damages prevented by the solutions implemented in the Gjøvik area demonstrate increased effectiveness and consequently a reduction in damages due to flooding events. Table 4.24 Assessment of avoided damages per category (Direct, Indirect) for Gjøvik for the case of flooding. Direct Short-term SSP1.26 SSP 3.7 SSP5.85 No Solution implementation 8.0% 12.1% 12.7% 13.1% With Solution implementation 6.7% Avoided damages -16.6% -44.7% -47.3% -48.8% Indirect Baseline (now) SSP1.26 SSP 3.7 SSP5.85 TransformAR Deliverable 3.5 34 www.transformar.eu No Solution implementation 5.0% 5.1% 5.3% 5.5% With Solution implementation 2.0% Avoided damages -59.5% -60.3% -62.2% -63.2% TransformAR Deliverable 3.5 35 www.transformar.eu 5. Avoided damages assessment: Sparse Data – High-Level Approach The case study of West Country region Climate change context The provided charts display projections for three key drought-related variables in the West Country, UK, derived from TransformAr outputs of WP2 8 , mean air temperature, number of hot days, and continuous dry days under two distinct climate scenarios: SSP1-2.6 (low emissions, sustainability, warming stabilizes ~1.8°C by 2100) and SSP5-8.5 (high emissions, fossil-fuel intensive, warming ~4.4°C by 2100). Mean Air Temperature for SSP1-2.6 Mean Air Temperature for SSP5-8.5 Number of Hot Days for SSP1-2.6 Number of Hot Days for SSP5-8.5 8 https://kfo.pik-potsdam.de/eur/index.html?language_id=en TransformAR Deliverable 3.5 36 www.transformar.eu Continuous Dry Days for SSP1-2.6 Continuous Dry Days for SSP5-8.5 Figure 5.1 Climate conditions for the West Country region The variables are relevant to the main extreme event under study for the region, droughts. Under higher temperatures more evapotranspiration occurs leading to soil moisture loss. The number of Hot Days is an indicator of increased drought stress and continuous dry days indicates more frequent and severe drought events. In case of Mean Air Temperature and SSP1-2.6, the temperatures rise moderately, followed by a plateau in mid-century. The increase is contained, reflecting strong mitigation. On the other hand, in SSP5-8.5, a steep, continuous rise until 2085 indicates much greater warming and drought risk. The Number of Hot Days is increase until mid-century in case of SSP1-2.6, and then stabilize or slightly decline. For the SSP5-8.5, the hot days rise sharply showing a dramatic increase that suggests frequent extreme heat events. The Continuous Dry Days in SSP1-2.6 are increasing slightly and then plateau, indicating limited additional drought risk in a low-emissions world, whereas, in SSP5-8.5 are steadily increasing, pointing to more frequent and severe droughts. Table 5.1 Summarization of climate context for West Country region The implications for Drought Risk in the UK can be summed according to each climate scenario differently. The Low-Emissions Pathway (SSP1-2.6) projects a drought-related extremes (temperature, hot days, dry spells) modest increase and then a stabilization. This potentially means that the adaptation is more manageable, and the risk of unprecedented droughts is limited. In case of HighEmissions Pathway (SSP5-8.5), all drought-related extremes rise sharply, especially after mid-century, with the frequency and severity of droughts increase substantially, with much higher uncertainty and risk of extreme events. It is noted that for the current analysis, data modelling and projection where provided from WP2 as input. Variable SSP1-2.6 (Sustainability) SSP5-8.5 (Fossil-fuel intensive) Mean Air Temperature Moderate rise, then plateau Continuous, steep rise Number of Hot Days Increases, then stabilizes Sharp, ongoing increase Continuous Dry Days Slight increase, then plateau Steady, significant increase TransformAR Deliverable 3.5 37 www.transformar.eu Historic extreme events – Drought 9 10 11 12 13 14 15 16 17 18 The West Country of the United Kingdom, encompassing South West England, has faced escalating drought-related challenges in recent years, with significant environmental, economic, and social repercussions. The available data on damages from extreme drought events in the region, drawing on hydrological records, governmental assessments, and utility company disclosures. Key findings reveal that the 2022 drought, one of the most severe in nearly a century, exposed critical vulnerabilities in water resource management, infrastructure resilience, and agricultural preparedness. The 2022 drought was characterized by a 12-month rainfall deficit ranking among the driest periods since 1891. Combined with the highest observed temperatures since 1884, evaporation rates surged, amplifying soil moisture deficits and reducing effective precipitation-the actual water available to replenish aquifers and reservoirs-by up to 40%. These conditions created a "compound drought," where heat and aridity synergistically exacerbated water scarcity. The latter had environmental damages and ecological strain to the region, alongside agricultural and economic consequences, such as crop losses and irrigation demands and water costs and utility pressures. The table below depicts the literature review results that will be used to establish a baseline for the damages in the current sparse availability analysis. Table 5.2 Quantification of impacts of historic extreme events for West Country region Description / Example Damages Quantitative Data (if available) Reference / Source Water Supply Reservoirs (e.g., Colliford, Roadford) fell to 30€ - 40% capacity; emergency drought permits issued; hosepipe bans for first time in 26 years Water bills in region: £491/year (highest in UK); 23% bill increase projected over 5 years https://www.southwestwater.c o.uk/environment/waterresources/drought-plan 9 https://www.gov.uk/government/news/all-of-england-s-south-west-region-now-in-drought 10 https://smartwatermagazine.com/news/govuk/all-englands-south-west-region-now-drought 11 https://www.southwestwater.co.uk/siteassets/documents/aboutus/wrmp/sww_draft_wrmp24_chapter_1_app_1_insights_from_2022_drought.pdf 12 https://consult.environment-agency.gov.uk/environment-and-business/drought-how-it-is-managed-inengland/supporting_documents/Drought How it is managed in England.pdf https://nhess.copernicus.org/articles/25/77/2025/ 13 https://www.southwestwater.co.uk/environment/water-resources/drought-plan 14 https://www.bbc.com/news/articles/crmkn7rjv7zo 15 https://agriculture.vic.gov.au/farm-management/dry-seasons-and-drought-support/south-west-drought-support-package 16 https://www.savemoneycutcarbon.com/learn-save/why-the-south-west-has-the-highest-water-costs-and-what-to-do-about-it/ 17 https://www.bbc.com/news/uk-england-devon-67964356 18 https://joint-research-centre.ec.europa.eu/system/files/2020-05/pesetaiv_task_7_drought_final_report.pdf TransformAR Deliverable 3.5 38 www.transformar.eu Agriculture Crop yield reductions (maize, potatoes down 15€ - 25%); early irrigation withdrawals; increased feed costs for livestock No regionspecific monetary figure; UK-wide drought cost estimated at £600 million/year average https://www.ciwem.org/assets /pdf/Policy/Policy%20Position %20Statement/Drought%20PPS %20CIWEM%20May%202025.p df Environmen tal/Ecologic al Exceptionally low river flows; fish rescues; algal blooms; habitat loss for salmon, trout, and other species No direct monetary value; multiple rivers at recordlow flows https://www.bbc.co.uk/news/a rticles/crmkn7rjv7zo Utility/Infrastructure Emergency water trucking; accelerated infrastructure spending (desalination, new sources) £36 million desalination plant https://www.southwestwater.c o.uk/environment/waterresources/drought-plan Tourism/Business Disrupted water supply to hotels, campsites, golf courses; landscaping restrictions Not quantified, but significant local economic impact reported https://www.bbc.co.uk/news/a rticles/crmkn7rjv7zo Avoided damages assessment Input data In order to implement the sparse data availability approach a compilation of different type of data was compiled from different sources. Climate information were provided via WP2, D5.2 provided the soulution effectiveness and historic damages information were gather via online resources. It is noted that climate information plots and analysis can be found in ANNEX II. Table 5.3 Input data for historic period for West Country region Historical climate data Variable Value Source Average annual extreme events 0.2 WP2 Intensity index (1-5 scale) min=6, max=70, avg=35 WP2 Area population ~5.7M (not used as no downscaled was performed) Deliverables / Online sources TransformAR Deliverable 3.5 39 www.transformar.eu Total area size [km2] ~24,386.00 (not used as no downscaled was performed) Deliverables / Online sources Climate risk index (0-1 scale) 0.2 WP2 Table 5.4 Input data for climate projections SSP126 for West Country region Low emissions future climate data Variable Value (2071-2100 SSP126) Source Projected annual events 0.6 WP2 Projected Intensity stats min=20, max=80, avg=45 WP2 Population change factor 0.15 WP2 Table 5.5 Input data for climate projections SSP585 for West Country region High emissions future climate data Variable Value (2071-2100 SSP585) Source Projected annual events 1 WP2 Projected Intensity index stats min=50, max=130, avg=80 WP2 Population change factor 0.18 WP2 Table 5.6 Input data for solution efficiency and damage assessment for West Country region Solution efficiency Damage Factor [ unit(e.g. €)/event/intensity unit ] Value Source Value Source Direct - D5.2 DB Utility/Infrastructure = £36 million desalination plant D3.5 (previous section Indirect Economic_avg(RSP)=3.05 Enviromental_ avg(RSP)=3.54 D5.2 DB Agri = £600 million/year average D3.5 (previous section People Socia_ avg(RSP)=4.52 D5.2 DB - D3.5 (previous section) Assessment To perform the analysis and assess the avoided damages, the algorithm developed and described in D3.4 and published in ZENODO https://doi.org/10.5281/zenodo.15324264 was used. The data type utilised in order to perform the assessment are shown above, and directly affect the type of results. TransformAR Deliverable 3.5 40 www.transformar.eu The solutions are not relevant with direct damages and the people category has not history damage data reference. the total damages can be shown Table 5.7 Assessment results for West Country region Total avoided damages Baseline (now) SSP2-1.6 SSP5-8.5 No Solution implementation 4,319,040£ 18,711,792£ 34,069,200£ With Solution implementation 3,502,327£ 15,175,060£ 27,631,010£ Avoided damages 820,000£ 3,540,000£ 6,440,000£ In case of West Country region, the avoided damages assessment with the sparse data approach yields 0.8M to 6.4M euros depending on the climate scenario. All damages derived from the indirect category as no data for damages were acquired for people category and no soultion relevant to direct damages was applied. It is noted that the table above is provide the damages values alongside the avoided damages, whereas, the Figure below illustrated the avoided damages only. Figure 5.2 Bar plot of avoided damages for the case of West Country region TransformAR Deliverable 3.5 41 www.transformar.eu The case study of City of Lappeenranta Climate change context The provided charts illustrate projections for two key flood-related variables in Lappeenranta: annual precipitation (top row) and climatic water balance (bottom row). Each variable is shown under two climate scenarios, SSP1-2.6 (sustainability/low emissions, left) and SSP5-8.5 (fossil-fuel intensive/high emissions, right), with the time horizon extending to 2100. The analysis focuses on how these variables that influence flood risk. The annual precipitation increase will increases flood potentially, whereas, climatic water balance if positive (precipitation > evapotranspiration) increases runoff and flood risk. Annual precipitation for SSP1-2.6 Annual precipitation for SSP5-8.5 Climatic Water Balance for SSP1-2.6 Climatic Water Balance for SSP5-8.5 Figure 5.3 Climate conditions for City of Lappeenranta In case of annual precipitation, both scenarios project a relatively stable situation across all future periods, with only minor variations between scenarios. If we take into account the uncertainty (error bars), there is some variability, but no strong upward or downward trend is observed. The stable precipitation suggests that, by itself, rainfall volume may not significantly increase flood risk, but other factors (e.g., intensity, seasonality) that are not available in TransformAr Climate Impacts in Europe database, may affect the flood risk. In case of Climatic Water Balance, the SSP1-2.6 show a moderate downward trend, but remain well above zero, indicating that precipitation continues to exceed evapotranspiration, whereas, in SSP5-8.5, the decline is much steeper approaching zero by the end of TransformAR Deliverable 3.5 48 www.transformar.eu This step will identify and characterize the region likely to be affected by climate hazards and classified by the environmental type. The types are derived from TransformAr demonstrators' characterization but can be expanded with new cases. Step 5. Identify relevant solutions Based on the region description and hazard, the team can search in the TransformAr database to identify relevant solution. This will work as an initial solution list for building the portfolio of solutions to be assessed. Step 6. Determine the most impactful solutions This step builds on the list of solutions and provides a way to identify the most effective solutions based on previous cases. The socre correlated with the solutions provide a way to order them according to their performance or information that the solutions may produce (e.g. sensors). In addition, the team based on member’s experience and expert’s opinion, performs an importance analysis towards assigning a score in each KPI (𝑎𝑛) or category expert (direct, indirect, people) depending on approach chosen. Step 7. Build your solution portfolio Combining the list of solutions coming from previous steps, the team should contact local experts and stakeholders, and analyse national or regional adaptation strategies to filter the list and add new transformative solutions. The latter introduces the local experience into the solution suggestions, thus, developing a portfolio of solutions with local characteristics, ready to be used in the avoided damages assessment. It is noted that the new additions to the solution portfolio should be linked to a set of KPIs that will quantify their effectiveness. Step 8. Assess the damages from climate change This step applies the damages assessment for the current climate conditions (Bs) and for the future climate scenario(s) (CC). The team keeps following the TransformAr’s avoided damages framework and provides values for the chosen KPIs in order to assess the direct, indirect and people damages for each hazard, without (nS) and with solution (S) implementation. Step 9. Assess the avoided damages of proposed improvements This step utilizes the information of different type of damages and applies the process to assess the avoided damages for short-term (avoided damages for current climate conditions between the no solution implementation and solution implementation, 𝐴𝐷𝐵𝑠) and long-term (avoided damages between current climate conditions with no solution implementation and the future climate scenario with solution implementation, 𝐴𝐷𝐶𝐶). Step 10. Report results and recommendations The final step of the avoided damages assessment is reporting the results in a manner usable to the decision-makers responsible. The goal of the reporting phase is to provide accurate unbiased information that clearly and accurately defines the current state and provides potential solutions for shortand long-term adaptation. TransformAR Deliverable 3.5 49 www.transformar.eu The primary aim of the good and best practices guidelines for avoided damages assessment, is to provide a common ground, whereby different data sources (e.g. models, sensors, expert opinion) and diverse transformative solution can lead to the avoided damages estimation from climate change. To achieve this, the framework developed is generalized and customizable enough and the whole process can be characterized as relatively easy to be followed based on the steps presented that summarizes the experienced gained in the demonstrator's application. TransformAR Deliverable 3.5 50 www.transformar.eu 7. Conclusions In the context of the TransformAr project, the concepts of Damages and Avoided Damages from climate change are developed and defined based on current literature. Damages from climate change include a wide range of adverse impacts that are not sufficiently mitigated or adapted to, while avoided damages represent the benefits of effective mitigation and adaptation efforts, which prevent potential future impacts. In the current deliverable, a framework was introduced to evaluate the effectiveness of TransformAr solutions by focusing on the concept of avoided damages. This framework quantifies effectiveness and damages either qualitatively or quantitatively using a set of key performance indicators (KPIs). The evaluation is conducted for both the current situation and future scenarios, comparing conditions before and after the implementation of the solutions. By comparing quantified values with and without the planned solutions across various climate scenarios, the assessment estimates the avoided climate damages. The framework was based on the work from 'D3.4 - Tools on the avoided damages and benefits per demo' and it was refined and expanded to more clearly define the concepts of damages and avoided damages. This development was based on discussions with demonstrators and technical partners and the experience gained through interactions with these stakeholders and overcoming various barriers. For validating the overall approach, in the current deliverable a review of the current best practices and guidelines related to avoided damages from climate change was performed. In addition, a detailed presentation of the implementation of the avoided damages framework with the high availability approach (KPIs) for the case of Municipality of Egaleo (MOE) and Gjøvik, alongside two cases (West Country region and city of Lappenraanta) are included. The avoided damages framework incorporates the elements of being generalized and flexible enough to fit a diverse list of solutions and being easy to replicate. Towards the replicability of the framework, good and best practices guidelines were introduced that is the result of implementing the framework during the TransformAr project. It can be considered an optimized way to implement the avoided damages framework without the barriers faced during the development. The guidelines are a series of ten steps written from the management and administration point of view, trying to avoid technical details that can been found in the methodology section. In summary, the current report is a compilation of the knowledge and experience gained from demonstrators towards the transferability of the practices used for avoiding damages at regional scale to other cases. TransformAR Deliverable 3.5 51 www.transformar.eu References Geest, K., & Warner, K. (2019). 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P., Boulter, S. L., Field, C. B., Mach, K. J., Manning, M. R., Mastrandrea, M. D., Meyer, L., Minx, J. C., Pereira, J. J., Plattner, G.-K., Ribeiro, S. K., Sokona, Y., Stadler, F., & Swart, R. (2023). Enhancing the review process in global environmental assessments: The case of the IPCC. Environmental Science & Policy, 139, 118–129. https://doi.org/https://doi.org/10.1016/j.envsci.2022.10.012 Remling, E. (2018). Depoliticizing adaptation: a critical analysis of EU climate adaptation policy. Environmental Politics, 27(3), 477–497. https://doi.org/10.1080/09644016.2018.1429207 Rutherford, V. E., Hills, J. M., & Le Tissier, M. D. A. (2020). Comparative analysis of adaptation strategies for coastal climate change in North West Europe. Marine Policy, 111, 102478. https://doi.org/https://doi.org/10.1016/j.marpol.2016.07.005 Climate change impacts are here and now. The impacts on people, prosperity and planet are already pervasive but unevenly distributed, as stated in the new EU Blueprint strategy (European Commission-EC, 2019). To reduce climate-related risks, the EC and the IPCC agree that transformational adaptation is essential. The TranformAr project aims to develop and demonstrate products and services to launch and accelerate large-scale and disruptive adaptive process for transformational adaptation in vulnerable regions and communities across Europe. The 6 TransformAr lighthouse demonstrators face a common challenge: water-related risks and impacts of climate change. Based on existing successful initiatives, the project will develop, test and demonstrate solutions and pathways, integrated in Innovation Packages, in 6 territories. Transformational pathways, including an integrated risk assessment approach are co-developed by means of 9 Transformational Adaptive Blocks. A set of 22 tested actionable adaptive solutions are tested and demonstrated, ranging from nature-based solutions, innovative technologies, financing, insurance and governance models, awareness and behavioral change solutions. TransformAR Deliverable 3.5 www.transformar.eu ANNEX I: Demonstrators profile Municipality of Egaleo The Municipality of Egaleo (MOE) will install smart climate stations (SCS) at key municipal buildings to acquire a view of the microclimatic conditions. A citizen’s app (CAE) will allow inhabitants to participate in the debate around Climate Change (CC), the changes of the microclimate and potential solutions. Awareness-raising modules (AWAR) will be designed, especially for young people and school pupils to promote climate awareness. In addition, a climate innovation hub (CIH) will be installed to promote green and climate friendly entrepreneurship. A demand analysis for social services and infrastructures (DSI) will be conducted. Sectors (KCS) impacted Three KCS where the MOE demonstrator will be implementing solutions to adapt the impacts of climate change include health, infrastructures, and urban planning, described in more detail below: Health Rapid urbanization has led to a sharp increase in emissions of CO2, NOx and other pollutants, adding to the increasing intensity of heatwaves, significantly affecting the health of citizens. Green areas are extremely limited, leading to artificial means. Even if socially vulnerable groups amount to 10% of the population of the Municipality, this remains a huge problem. Climate change is expected to increase these groups and negatively affect their living conditions. Infrastructures Building and infrastructures are not climate-proof. Thunderstorms and heatwaves cannot be dealt with the existing infrastructure degraded each year. Urban planning Climate change also reinforces the urban heat island phenomenon in Egaleo, leading to an increase in energy consumption and carbon footprint; and causing faster degradation of asphalt, tarmac, concrete and another hard surfacing with more frequent needs for maintenance works. The phenomenon thus leads to a reduction in the absorption of rainwater, which overcomes stormwater systems and increases flood risks. Climate change combined with rapid urbanization has created the need for innovative and sustainable urban planning. Description of the solutions In TransformAr project MOE will build a series of solutions that work together complementary towards climate awareness and adaptation of MOE and its citizens (see Figure 3). MOE solutions comprise of data collection from various sources, analysis and post-processing of the data and feedback the outcomes to the citizens and promote climate awareness or utilize them for evidence informed climate adaptation policy suggestions at local municipality level. Table 3.1.1 presents each solution of TransformAr for MOE. Table 3.1.1: Description of MOE solution within TransformAr project Solution Description Smart Climate Station - SCS MOE focuses on the installation of 21 Smart Climate Stations (SCS) around key areas of the city. The purpose of the SCSs is to gather real-time data regarding the micro-climate of the city, supporting the public administration office to ready the citizens in case of TransformAR Deliverable 3.5 www.transformar.eu climate emergencies. After careful consideration and design, the key areas have been identifies and the SCSs will begin their operation during the timeline of this demo. SCS will register Temperature, Relative Humidity, Atmospheric Pressure, CO2 (ppm), PM 2.5 /10 (ppm), Wind speed and Rainfall. Demand Social Analysis - DSI MOE provides a series of social/health services to the citizens. Due to climate change impact, the demand for such services, it is expected to changed. To prepare to supply the services without interruption and meet the future demand, a demand analysis for social services/infrastructures will be performed. The analysis will provide the data to plan an adaptation strategy by taking into account the supply and demand of social services of the future. Citizen App – CAE MOE will develop a mobile app for gathering and distributed information; (1) it will used to crowdsource data about climate awareness of citizens of MOE via questionnaire, (2) to disseminate TransformAr’s MOE solution to the public and (3) to provide notifications for climate related events Awareness module - AWAR MOE will develop and test a curriculum of 45 min for pupils between 16-18 years old related to climate change understanding and climate awareness Climate Innovation Hub - CIH MOE will upgrade a multi-purpose facility to Climate Innovation Hub by (a) create a permanent exhibition about climate change and TransformAr solutions of MOE, (b) demonstrate live the data streaming from SCS and the post-processing indicators and (c) organizing a series of events for promoting the climate innovation (e.g. datathons), thus, providing access to the MOE’s data and asking for innovative solutions for MOE’s climate related problems. The design and development of the MOE’s ecosystem of solutions, is using SCS, CAE and DSI as data sources for weather, environmental, demographic and modelling result and AWAR, CAE and CIH as dissemination and communication frontends for the citizens of MOE. The management and analysis of the various types of datasets is accomplished with the support of NCSRD. Overview of the expected impacts The expected impacts per solution can be found in Table 3.1.2. The impacts are either direct, thus, they are deriving directly from the solution, or indirect, thus, they support the impacts of other solutions or long-term actions beyond TransformAr lifetime and scope. Table 3.1.2: Description of expected impacts per solution Solution Expected impact SCS (1) The data will provide a detailed dataset for weather and environmental conditions at MOE that will be visualized in Citizen Application Engagement (CAE) solution and disseminate part of the result to the citizens of MOE towards climate awareness in a large (municipality) level. (2) The data will be used by the NCSRD (technical partner of MOE) to support the climate and environmental research of the organization and provide back to MOE a localized TransformAR Deliverable 3.5 www.transformar.eu and high-resolution weather forecast of 120 hours for the area of MOE. The forecast data will be used directly as an alarm system to inform the relevant Municipality Departments (Civil Protection, Municipal Police) and other First-Responders when abnormal temperature levels or precipitation levels are noted, helping to prevent casualties and loss of musicality social services. (3) Beyond the MOE TransformAr solution, the SCS data collection will be used to monitor the effectiveness of different actions and measures implemented in MOE by comparison the measurement and relevant indicators before and after the implementation, thus, helping in quantify policy suggestion and making. DSI Forecasting the changes of demand for social services due to climate change, will enable the MOE to plan how the supply of social services will be maintain by meeting the demand and adapt on the new climate conditions and extreme events, such as more intense and frequent heatwaves. CAE As CAE provides a two-way channel of communication between MOE and citizens, it is expected to impact not only the citizen understanding of climate change, but also to provide a way to MOE to monitoring the impact of other TransformAr solution either directly (questionnaires with direct reference to specific solutions) or indirectly (overall quantification of climate awareness). AWAR AWAR is expected to impacted the climate change understanding of young people and try to clear misconceptions of what is climate change, what are the impacts, how it can be tackled etc. As such, it is aspired to create a more climate awared generation that pursuit for mitigation and adaptation through the municipality scale and/or social transformation. CIH CIH consist of (i) a static climate exhibition that is expected present the climate actions of MOE to the public; (ii) a dynamic part of livestreaming and visualization of the different types of data that are gathered and processed at MOE and both it is expected to impact people visiting the exhibition by raising the climate awareness. In addition, the availability of the dataset to SMEs, groups and teams via events (e.g. datathons) is expected to promote innovation and solutions for climate related problems that MOE has or will have in the future. Lappeenranta Objectives of the Lappeenranta demonstrator are: (i) Urban surface runoff and flood mitigation, mitigation of the risk, (ii) Addressing the conveyance runoff water pipelines capacity issues, (iii) Increasing environmental awareness of emission load to recipient and groundwater, and mitigation of the load caused by the runoff, (iv) Increasing accessibility of data to all stakeholders via real-time monitoring and novel data, (v) Facilitation improvement of the choice of alternative options, e.g., green infrastructure, (vi) Increase awareness of stakeholders on climate change impacts on local level and region. Sectors (KCS) impacted For Lappeenranta, the Key Community Systems (KCS) that we focus on are water management and urban planning. Lappeenranta demonstrator will be implementing solutions to adapt to CC in these KCS described in more detail below: TransformAR Deliverable 3.5 www.transformar.eu Water management Climate change impacts on water in Lappeenranta could create new water-related challenges and exacerbate existing ones in light of changing rainfall seasonality, climate variability and extreme weather events. This is likely to have a series of ramifications on a range of economic sectors that depend and rely on water such as tourism, industry, agriculture, among others. Not to mention, water quality could be affected as heavy rainfall could lead to the dilution of polluted water, which makes treating it more challenging and requires more costly and energy-intensive technologies. Therefore, addressing water issues and ensuring a sound water management is crucial for the city of Lappeenranta. Urban planning As the city of Lappeenranta is one of the major urban centres in the Saimaa region, urban planning needs to consider and adapt to climate change. Water from melting snow and flood water bring contaminants (e.g., oils, chemicals, microplastics, as well as organic and solid matters) which are likely to decrease the quality of water in the city and the lake. An adaptive urban planning could be a key to solve these issues and improve the living conditions of Lappeenranta’s citizens. Description of the solutions Solution Description Nature based solution (URB) Supported by LUT, LAPP will use NBS as part of the urban run-off system as a flood management solution. This will allow the codesign of new integrative Nature-Based Solutions as part of new urban adaptation standards which is jointly developed with city planners, architects, and urban architecture responsible administrators. Plants and greeneries will be used for reducing urban runoff. Digital solution (SWMM) LAPP, supported by LUT, VERHAERT and its Linked Third Party Pegus Digital, will couple a first set of new sensors, measuring water quality, flow and volume within the water pipe and drainage system, to a scalable digital platform. This platform will be developed to TRL level 7 to prove monitoring scalability & impact for Lappeenranta. Analysis of available and generated data of the purification/filtration processes and control processes of the runoff-water management system will be executed by LUT with initial research by VERHAERT on applicable predictive machine learning models. Citizen app (CAF) For the city of Lappeenranta, NTNU, together with LUT, will develop and support the implementation of a citizen application for crowd sensing and real time monitoring of anomalies due to climate change events. Choice experiment (CEI) Additionally, LAPP will also conduct choice experiments for the stormwater management system (in T4.4) upscaling. The outcome will be models for integrating knowledge on stakeholder preferences for climate adaptation and related services into the design of new policy instruments and business models and the codesign and test policy tools as part of new adaptation standards jointly with stakeholders, through behavioural economic TransformAR Deliverable 3.5 www.transformar.eu experiments in the different regions. For this specific case, the Gjøvik municipality will have 2 field trips to follow the implementation of such solutions to foresee a potential replication. Overview of expected impacts The expected impacts of solutions demonstrated are described in Table 5. All four solutions can be expected to have an impact on flood vulnerability, and social acceptance or acceptance in city planning. CAF and CEI can have long-term effects on water quality. However, in CAF and CEI the impact is not measured with indicators due to the nature of the impact. URB with monitoring system, and the influent together with the groundwater will be analyzed. This empirical information can be utilized together with existing water quality of recipient lake and wetlands to evaluate the impacts. Economic assessment and impacts in this frame will be done via empirical cost estimates and comparing to alternative solutions typically applied in Lappeenranta. Tables 5. and 6. describe the expected impacts and the indicators in detail. Expected impact URB SWMM CAF CEI Flood Vulnerability Gathering runoff (stormwater) from streets and leading them to biofiltration will help free capacity from drainage and mitigate flood and drought risks. x Monitoring volume flows and surface levels provides novel information and warnings. x Distributed stormwater management mitigate flood risks x x x Realtime estimates for precipitation via cameras provide local estimates and warnings x x Water Quality Realtime monitoring of URB together with laboratory measurements provides information on runoff and groundwater quality. x Biofiltering field utilizes nutrients and filters urban emissions x Acceptance City planning gain novel information to support decision making and planning x x x x Citizen's awareness of environmental effects of runoff and choices for mitigating the risks and impacts increase x x x Economic Costs Solution costs are compared to traditional alternatives and reflected to benefits expected. x x x x TransformAR Deliverable 3.5 www.transformar.eu TransformAR Deliverable 3.5 www.transformar.eu TransformAR Deliverable 3.5 www.transformar.eu City of Lappeenranta ################################################ Historical historical_data = { under historical climate 'avg_events_year': 0.2, # this is the once in 5 years rain TransformAR Deliverable 3.5 www.transformar.eu 'min_intensity': 23 # once in 1 years rain 'avg_intensity': 30 # once in 5 years rain 'max_intensity': 68 # 30 year event 'risk_index': 0.2 # risk index (0-1 scale) # it is the once in 5 years rain } ################################################# RCP26 'low_emissions':{ under 2041-2070 RCP26 climate 'events': 0.4, # Under this climate the 5 year event would happen in 2 of 5 years 'min_intensity': 25 # in 1 years rain 'avg_intensity': 33 # once in 5 years rain 'max_intensity': 60 # 30 year event 'population_growth': 0.15 # Population change factor }, 'low_emissions': { under 2071-2100 RCP26 climate 'events': 0.4, # Under this climate the 5 year event would happen in 2 of 5 years 'min_intensity': 25 # once in 1 years rain 'avg_intensity': 34 # once in 5 years rain 'max_intensity': 70 # 30 year event 'population_growth': 0.15 # Population change factor }, ################################################# RCP85 'high_emissions': { under 2041-2070 RCP85 climate 'events': 0.5, # Under this climate the 5 year event would happen in 2.5 of 5 years 'min_intensity': 30 # once in 1 years rain 'avg_intensity': 40 # once in 5 years rain 'max_intensity': 100 # 30 year event 'population_growth': 0.18 } 'high_emissions': { under 2071-2100 RCP85 climate 'events': 1, # Under this climate the 5 year event would happen in 5 of 5 years 'min_intensity': 30 # once in 1 years rain 'avg_intensity': 40 # once in 5 years rain 'max_intensity': 90 # 30 year event TransformAR Deliverable 3.5 www.transformar.eu 'population_growth': 0.18 } TransformAR Deliverable 3.5 www.transformar.eu TransformAR Deliverable 3.5 www.transformar.eu TransformAR Deliverable 3.5 www.transformar.eu This project has received funding from the European Union’s Horizon H2020 innovation action programme under grant agreement 101036683.