Using quantitative dynamic adaptive policy pathways to manage climate change-induced coastal erosion

ABSTRACT: Adaptation requires planning strategies that consider the combined effect of climatic and non-climatic drivers, which are deeply uncertain. This uncertainty arises from many sources, cascades and accumulates in risk estimates. A prominent trend to incorporate this uncertainty in adaptation planning is through adaptive approaches such as the dynamic adaptive policy pathways (DAPP). We present a quantitative DAPP application for coastal erosion management to increase its utilisation in this field. We adopt an approach in which adaptation objectives and actions have continuous quantitative metrics that evolve over time as conditions change. The approach hinges on an adaptation information system that comprises hazard and impact modelling and systematic monitoring to assess changing risks and adaptation signals in the light of adaptation pathway choices. Using an elaborated case study, we force a shoreline evolution model with waves and storm surges generated by means of stochastic modelling from 2010 to 2100, considering uncertainty in extreme weather events, climate variability and mean sea-level rise. We produce a new type of adaptation pathways map showing a set of 90-year probabilistic trajectories that link changing objectives (e.g., no adaptation, limit risk increase, avoid risk increase) and nourishment placement over time. This DAPP approach could be applied to other domains of climate change adaptation bringing a new perspective in adaptive planning under deep uncertainty.Alexandra Toimil acknowledges the financial support from the FENIX Project funded by the Government of Cantabria. This research was also funded by the Spanish Government through the grant RISKCOADAPT (BIA2017-89401-R)

기후변화 적응계획 의사결정지원을 위한 적응경로 탐색 모델 개발

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2021.8. 현정희.기후변화 적응은 미래에 가속화될 기상변화에 대응하기 위해 다양한 시나리오에 따른 기후 영향을 현재 시점에서 분석하고, 잠재적인 적응 옵션을 확인하며, 정책 결정 과정에서 제기될 수 있는 의문들을 식별할 수 있어야 한다. 그러나 기후적응의 중요성이 부각됨에도 불구하고, 실제로 이행된 적응정책은 상대적으로 적다. 이행 부족의 원인으로는 미래에 대한 예측이 어렵고, 이에 대비할 수 있는 최적의 대응책이 무엇인지를 판단하기 위한 정보가 부족하며, 판단할 수 있는 명확한 방법이 없다는 점을 들 수 있다. 기후변화 적응정책의 특성상 다양한 이해관계자가 관여되어 있고 막대한 비용이 소요된다. 그럼에도 적응계획을 세울 때 다양한 이해관계자들을 설득하고 지지를 얻기 위해서는 합리적으로 판단을 내릴 수 있는 정책평가 자료가 필요하다. 따라서 기후변화 적응정책을 효율적으로 이행하기 위해서는 정책에 대한 명확한 이해와 객관적인 평가가 바탕이 되어야 하지만 정성적인 판단으로 정책이 수립되고 있을 뿐, 정책 효과의 정량적인 판단이 이루어지지 않고 있다. 따라서 본 연구는 미래 피해에 따른 적응대책별 효과를 대입하는 탐색적 모델을 개발하여 적응계획에서 활용할 수 있도록 하는 것을 목표로 한다. 본 연구의 주요 목표는 상대적 비용 효율성과 효과적인 기후 영향 감소를 달성하는 최적의 적응경로의 파레토를 식별할 수 있는 탐색적 계획 모델을 개발하는 것이다. 적응경로는 16개의 연속된 5년단위의 계획 기간으로 구성되어 있으며 각 계획 기간별로 각 적응기술의 규모가 조정될수 있다. 우수한 적응경로는 미래의 기후영향을 더 적응하거나 비용을 낮추면 선택되도록 모델을 설계 하였다. 다양한 적응경로를 탐색하기 위해 다목적 최적화 방법으로 머신러닝 기반 진화 알고리즘인 비지배적 정렬 유전 알고리즘(NSGA-II)을 사용하였다. 먼저, 적응경로 모델을 소개하고 이를 두 가지 의사결정 문제에 적용한다. 두 가지 의사결정 문제는 1) 전략적 목표 설정과 2) 이행과제 우선순위 선정이다. 첫 번째 모델 적용 사례에서는 제약 조건 및 적응 목표를 사전 설정된 값으로 고정하는 대신 의사결정자의 선호도를 매개화 하여 다양한 시나리오를 탐구한다. 본 사례에서는 도시에서의 폭염 관련 상병자를 줄이기 위해 직접적인 적응대책(상병자 수 저감)과 간접적인 적응대책(옥외 열환경 개선)을 적용했다. 목표 설정 옵션을 탐구하기 위해, 다양한 예산 및 영향 감축 방식을 평가하였다. 두 번째 사례의 경우, 적응경로 모델을 수정하여 다른 미래 완화 정책 목표 시나리오(RCP 2.6은 1.5°C 증가 한계를 나타내고, RCP 4.5는 2°C 증가 한계를 나타내고, RCP 8.5는 가장 높은 배출 시나리오)에 따라 다부문 위험을 평가하였다. 도시 열과 홍수 영향을 줄이기 위해 그린인프라 기반 적응 기술의 효과를 확인하였다. 첫 번째 모델 적용 사례에서는 우수한 적응경로는 미래의 기후 영향에 적응할 수 있거나, 적응 비용이 적은 경우에 선택되도록 모델을 설계하였다. 특히, 의사결정자의 선호도(제약 조건 및 적응목표)에 따라 도시에서의 폭염 관련 상병자를 줄이기 위해 직접적인 적응 효과(상병자 수 저감)와 간접적인 적응대책(그린인프라를 통한 옥외 열 환경 개선)을 적용하여 RCP 4.5와 8.5 시나리오에서의 최적의 적응계획을 살펴보았다. 먼저, 예산이 높다고 해서 반드시 최적의 적응계획으로 이어지지 않았으며, 오히려 낮은 예산 조건에서 적응목표 설정에 따라 비교적 더 효율적인 적응계획이 도출되었다. RCP 8.5 시나리오에서는 현재의 적응 옵션 포트폴리오로는 2065년 이후의 영향에는 완전히 적응하기에 불충분한 것으로 분석되었다. 또한, 10년 이상 적응 행동을 지체하게 되면 이후에는 적응이 불가능하다. 마지막으로 그린인프라는 시간에 따라 적응 효과가 증폭되어, 증가하는 미래 영향을 저감하는 데에 효과적인 적응기술임이 검증되었다. 도시의 폭염과 홍수의 미래 영향을 줄이기 위해 그린인프라 기반 적응 기술의 효과와 효율성은 복합적인 것으로 확인되었다. 부문 우선순위에 따라 비지배적 최적화된 적응 경로를 정렬할 때 가장 비용 효율적인 경로가 최적으로 식별되었다. 비용 효율성은 미래의 영향 수준과 그린인프라 기술의 비용 절충에 민감했다. RCP 2.6의 영향은 RCP 4.5에 비해 적기 때문에 규모의 경제로 인해 현재 적응 기술이 비용효율적이지 않아 적응을 덜 하게 되는 적응경로들이 최적화되는 결과가 나왔다. 그린인프라 기반 기술의 증가하는 효과는 적응 경로 모델이 RCP 2.6 시나리오에서 고려하기 어려웠으며 2050년 이전에는 과소적응, 2050년 이후에는 과잉 부적응을 초래했습니다. 그린인프라 기반 적응에 대한 사회적 할인율의 영향은 다음과 같습니다. 비용 보조금이 물 부문의 기술에 투자를 늘리기 위한 추가 자원을 제공하는 경우 간접적인 효과가 있었다. 이러한 결과는 적응을 위한 계획에서 다차원을 고려할 필요가 있음을 시사하고 불확실성 하에서 보다 명확한 의사 결정을 위한 기반으로 탐색적 모델링을 사용하는 이점을 보여한다. 이 연구의 전반적인 결과는 적응 경로 모델링에 대한 연구와 의사 결정 기반 적응 계획 지원 도구 간의 격차를 메웁니다. 두 경우의 결과는 적응계획을 위한 의사결정 방법을 적용할 때 참고할 수 있다.Adaptation to climate change should be able to analyze the climate impact of future scenarios, identify potential adaptation options, and identify questions that may be raised in the policy-making process. Despite the growing importance of climate adaptation, there are relatively fewer and smaller scale adaptation policies implemented in response to climate change. The reasons for the lack of implementation are that it is difficult to predict the future, there is not enough information to determine the optimal adaptation measure, and there is no clear evaluation method to make final decisions. Furthermore, various stakeholders are involved in climate change adaptation policy-making and substantial costs with immeasurable benefits are common for many adaptation policies. Nevertheless, to persuade and gain support from various stakeholders when setting up an adaptation plan, policy evaluation data that can make rational decisions are needed. Therefore, to effectively implement climate change adaptation policies, a clear understanding of the policy and objective evaluation must be the basis, but policies are established based on qualitative judgments, and quantitative judgments of policy effects are not made. The main goal of this study is to develop an exploratory planning model that can identify optimal adaptation pathways that achieve relative cost-effectiveness and effective climate impact reduction. Optimal adaptation pathways are selected if greater future damages are adapted and costs are lowered. Adaptation pathways for reducing impacts from 2020~2100 were generated as 16 consecutive 5-year plans referencing Koreas current adaptation planning period. At each 5-year planning time frame the scale for each adaptation measure was altered according to future impact level. To search for the optimal adaptation pathways, a machine-learning based evolutionary algorithm, the non-dominant alignment genetic algorithm (NSGA-II) was selected as the optimization method. This thesis first introduces the developed adaptation pathway model, which is then applied to two decision-making problems. The two decision-making issues are 1) setting strategic goals and 2) prioritizing implementation tasks. In the first model application case, various scenarios are explored by mediating the preference of decision makers instead of fixing constraints and adaptation goals to preset values. In this case, direct adaptation measures (reducing the number of mortalities from heat risk) and indirect adaptation measures (improving the outdoor heat environment) were applied to reduce the number of projected mortality from heat stress. To explore goal setting options, various budgets and impact mitigation approaches were evaluated. In the second case, the adaptation pathway model is modified to accommodate different future mitigation policy target scenarios (RCP 2.6 represents the 1.5°C temperature increase limit scenario, RCP 4.5 represents the 2°C temperature increase limit scenario, while RCP 8.5 is the highest emission scenario). The first application study found that after 2065, current adaptation strategies cannot reduce the impacts of heat mortality even with high budgets. A low budget limits adaptation for both ambitious and conservative goal settings while a higher budget did lead to greater adaptation but was not necessary for the conservative goal setting suggesting that efficient pairing of budget level based on the adaptation goal can be beneficial. Further, the longer the delay in investment toward adaptation results in irrecoverable reduction in adaptation. For the second application case, the effectiveness and efficiency of green infrastructure-based adaptation technology was varied for reducing the impact of urban heat and flooding. When sorting the non-dominated optimized adaptation pathways according to sector prioritization, the most cost-efficient pathways were identified as optimal. The cost-efficiency was sensitive to future impact level and the cost trade-off of green infrastructure technologies. RCP 2.6 impacts were too little for current adaptation technologies to be cost-efficient relative to RCP 4.5 due to economies of scale. The increasing effects of green infrastructure-based technologies was difficult for the adaptation pathway model to consider under the RCP 2.6 scenario and resulted in under maladaptation before 2050 and over maladaptation after 2050. The effect of a social discount rate to green infrastructure-based adaptation was indirectly realized, where the cost subsidy provided additional resource to increase investment in non-green infrastructure technologies for the water sector. The overall results of this study suggest the need to consider multiple dimensions in planning for adaptation and proves the benefits of using exploratory modeling as a base for clearer decision-making under uncertainty. The overall findings in this study fills the gap between research on adaptation pathway modeling and decision-based adaptation planning support tools. The results of both cases can be referred to when applying the decision-making method for adaptation planning.I. Introduction 1 II. Literature Review 7 1. Challenges in decision-making for adaptation planning 7 2. Valuation of adaptation benefits 11 3. Exploratory modeling for decision-support 13 4. Decision-making under deep uncertainty 17 III. Scope of study 26 1. Setting adaptation goals according to decision-making preferences 29 2. Prioritizing adaptation options considering multi-sector impacts 30 IV. Methods 34 1. Adaptation pathway model architecture 35 1.1. Model algorithm 35 1.2. Objective functions 37 1.3. Model parameters 38 2. Model inputs 41 2.1. Future impacts by sector 41 2.2. Selected adaptation options 43 2.3. Decision problem settings 49 V . Results 54 1. Cost-benefit of adaptation based on goal settings 54 1.1. Optimization results according to the adaptation goal setting 54 1.2. Decision-maker preference effects on adaptation over time 58 1.3. Selecting adaptation pathways from modeled results 60 2. Prioritizing adaptation options considering multi-sector impacts 61 2.1. Optimization results according to RCP scenarios and multi-sector objectives 62 2.2. Green infrastructure effects on adaptation over time 65 2.3. Optimized adaptation options according to sector prioritization 68 VI. Discussion 73 1. Dependency and limitations on adaptation according to future scenarios 73 2. Avoiding maladaptation through economical decision-making 76 3. Developing the adaptation pathway model as a climate service 79 VII. Conclusion 83 VIII. Bibliography 85 Abstract in Korean 93박

An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together?

A highly uncertain future due to changes in climate, technology and socio-economics has led to the realisation that identification of “best-guess” future conditions might no longer be appropriate. Instead, multiple plausible futures need to be considered, which requires (i) uncertainties to be described with the aid of scenarios that represent coherent future pathways based on different sets of assumptions, (ii) system performance to be represented by metrics that measure insensitivity (i.e. robustness) to changes in future conditions, and (iii) adaptive strategies to be considered alongside their more commonly used static counterparts. However, while these factors have been considered in isolation previously, there has been a lack of discussion of the way they are connected. In order to address this shortcoming, this paper presents a multidisciplinary perspective on how the above factors fit together to facilitate the devel- opment of strategies that are best suited to dealing with a deeply uncertain future

An exploratory research on the impact of IoT and 5G technology in the climate policymaking process

For decades, climate change and climate-related issues have exponentially increased, causing a global multi-sectoral and multi-stakeholder negative impact. In response to this global issue, policymakers and decision-makers have begun to scheme climate policies and responses to avoid further harm. However, the policy process and its currents policy infrastructure, instruments, and tools seem to not be up to the task to tackle a complex and irreversible systemic problem engulfed in uncertainties that expands on a broad temporal and spatial scale. The climate policy cycle is a challenging task requiring enormous data, planning, Evaluation, and monitoring. However, these procedures are often ignored due to their complexity, the lack of climate information, and climate portfolios available to the different stakeholders. In this exploratory research, we delve and explore the challenges and difficulties of the climate policymaking process and how can the research and development of Information and Communication Technologies (ICTs) that enable the collection of real-time climate data, specifically the Internet of Things (IoT) and 5th Generation of Mobile Communication Systems (5G), can become a potential climate policymaking instrument and tool.Durante décadas, as mudanças climáticas e as questões relacionadas ao clima aumentaram exponencialmente, causando um impacto negativo global multissetorial e de múltiplas partes interessadas. Em resposta a esta questão global, os formuladores de políticas e tomadores de decisão começaram a traçar políticas e respostas climáticas para evitar mais danos. No entanto, o processo político e sua atual infraestrutura, instrumentos e ferramentas parecem não estar à altura da tarefa de lidar com um problema sistêmico complexo e irreversível envolto em incertezas que se expande em uma ampla escala temporal e espacial. O ciclo da política climática é uma tarefa desafiadora que exige enormes dados, planejamento, avaliação e monitoramento. No entanto, esses procedimentos são muitas vezes ignorados devido à sua complexidade, à falta de informações sobre o clima e aos portfólios climáticos disponíveis para as diferentes partes interessadas. Nesta pesquisa exploratória, nós aprofundamos e exploramos os desafios e dificuldades do processo de formulação de políticas climáticas e como a pesquisa e o desenvolvimento de Tecnologias de Informação e Comunicação (TICs) que permitem a coleta de dados climáticos em tempo real, especificamente a Internet das Coisas ( IoT) e 5ª Geração de Sistemas de Comunicação Móvel (5G), pode se tornar um instrumento e ferramenta potencial de formulação de políticas climáticas

The SIPHER consortium : introducing the new UK hub for systems science in public health and health economic research

The conditions in which we are born, grow, live, work and age are key drivers of health and inequalities in life chances. To maximise health and wellbeing across the whole population, we need well-coordinated action across government sectors, in areas including economic, education, welfare, labour market and housing policy. Current research struggles to offer effective decision support on the cross-sector strategic alignment of policies, and to generate evidence that gives budget holders the confidence to change the way major investment decisions are made. This open letter introduces a new research initiative in this space. The SIPHER (Systems Science in Public Health and Health Economics Research) Consortium brings together a multi-disciplinary group of scientists from across six universities, three government partners at local, regional and national level, and ten practice partner organisations. The Consortium’s vision is a shift from health policy to healthy public policy, where the wellbeing impacts of policies are a core consideration across government sectors. Researchers and policy makers will jointly tackle fundamental questions about: a) the complex causal relationships between upstream policies and wellbeing, economic and equality outcomes; b) the multi-sectoral appraisal of costs and benefits of alternative investment options; c) public values and preferences for different outcomes, and how necessary trade-offs can be negotiated; and d) creating the conditions for intelligence-led adaptive policy design that maximises progress against economic, social and health goals. Whilst our methods will be adaptable across policy topics and jurisdictions, we will initially focus on four policy areas: Inclusive Economic Growth, Adverse Childhood Experiences, Mental Wellbeing and Housing

Screening robust water infrastructure investments and their trade-offs under global change: A London example

We propose an approach for screening future infrastructure and demand management investments for large water supply systems subject to uncertain future conditions. The approach is demonstrated using the London water supply system. Promising portfolios of interventions (e.g., new supplies, water conservation schemes, etc.) that meet London’s estimated water supply demands in 2035 are shown to face significant trade-offs between financial, engineering and environmental measures of performance. Robust portfolios are identified by contrasting the multi-objective results attained for (1) historically observed baseline conditions versus (2) future global change scenarios. An ensemble of global change scenarios is computed using climate change impacted hydrological flows, plausible water demands, environmentally motivated abstraction reductions, and future energy prices. The proposed multi-scenario trade-off analysis screens for robust investments that provide benefits over a wide range of futures, including those with little change. Our results suggest that 60 percent of intervention portfolios identified as Pareto optimal under historical conditions would fail under future scenarios considered relevant by stakeholders. Those that are able to maintain good performance under historical conditions can no longer be considered to perform optimally under future scenarios. The individual investment options differ significantly in their ability to cope with varying conditions. Visualizing the individual infrastructure and demand management interventions implemented in the Pareto optimal portfolios in multi-dimensional space aids the exploration of how the interventions affect the robustness and performance of the system

Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world

Climate change adaptation is unavoidable, particularly in developing countries where the adaptation deficit is often larger than in developed countries. Robust Decision Making (RDM) approaches are considered useful for supporting adaptation decision making, yet case study applications in developing countries are rare. This review paper examines the potential to expand the geographical and sectoral foci of RDM as part of the repertoire of approaches to support adaptation. We review adaptation decision problems hitherto relatively unexplored, for which RDM approaches may have value. We discuss the strengths and weaknesses of different approaches, suggest potential sectors for application and comment on future directions. We identify that data requirements, lack of examples of RDM in actual decision-making, limited applicability for surprise events, and resource constraints are likely to constrain successful application of RDM approaches in developing countries. We discuss opportunities for RDM approaches to address decision problems associated with urban socio-environmental and water-energy-food nexus issues, forest resources management, disaster risk management and conservation management issues. We examine potential entry points for RDM approaches through Environmental Impact Assessments and Strategic Environmental Assessments, which are relatively well established in decision making processes in many developing countries. We conclude that despite some barriers, and with modification, RDM approaches show potential for wider application in developing country contexts

Knowledge co-production for decision-making in human-natural systems under uncertainty

Decision-making under uncertainty is important for managing human-natural systems in a changing world. A major source of uncertainty is linked to the multi-actor settings of decisions with poorly understood values, complex relationships, and conflicting management approaches. Despite general agreement across disciplines on co-producing knowledge for viable and inclusive outcomes in a multi-actor context, there is still limited conceptual clarity and no systematic understanding on what co-production means in decision-making under uncertainty and how it can be approached. Here, we use content analysis and clustering to systematically analyse 50 decision-making cases with multiple time and spatial scales across 26 countries and in 9 different sectors in the last decade to serve two aims. The first is to synthesise the key recurring strategies that underpin high quality decision co-production across many cases of diverse features. The second is to identify important deficits and opportunities to leverage existing strategies towards flourishing co-production in support of decision-making. We find that four general strategies emerge centred around: promoting innovation for robust and equitable decisions; broadening the span of co-production across interacting systems; fostering social learning and inclusive participation; and improving pathways to impact. Additionally, five key areas that should be addressed to improve decision co-production are identified in relation to: participation diversity; collaborative action; power relationships; governance inclusivity; and transformative change. Characterising the emergent strategies and their key areas for improvement can help guide future works towards more pluralistic and integrated science and practice