Report on uncertainty methods

The issue of uncertainty is critical for climate change science and policy. A great deal of research analysis has gone into identifying the scope and character of uncertainty in climate change itself, in how analysts and assessment teams can and should communicate that uncertainty to policy- and decision-makers, and how policy- and decision-makers can then incorporate nowledge about the sources and magnitude of uncertainty in their choices. The primary purpose of this deliverable is to summarize that literature, and to synthesize it in a manner that is useful for the Mediation project, namely in improving the practice of assessing adaptaion needs and options, and in building a useful decision-support platform or system. The report starts with a user-driven focus, summarizing the literatures on both descriptive and normative models of decision-making under uncertainty, in order to identify the most effective and esential information inputs for each of these models. The report then summarizes some of the main guidance documents on communicating uncertanty, prepared for or in use by the Intergovernmental Panel on Climate Change, the United States government, and the Dutch government. Fially, the report synthesizes these previous studies for use in the Mediation project and its users by focusing on three essential characteristics of uncertainty communication: parsimony, personalization, and practicality. It identifies specific strategies for using these three criteria to ensure that assessments for climate adaptation are salient, credible, and legitimate, and thus ultimately construtive inputs into policy- and decision-making

Learning and climate change

Learning – i.e. the acquisition of new information that leads to changes in our assessment of uncertainty – plays a prominent role in the international climate policy debate. For example, the view that we should postpone actions until we know more continues to be influential. The latest work on learning and climate change includes new theoretical models, better informed simulations of how learning affects the optimal timing of emissions reductions, analyses of how new information could affect the prospects for reaching and maintaining political agreements and for adapting to climate change, and explorations of how learning could lead us astray rather than closer to the truth. Despite the diversity of this new work, a clear consensus on a central point is that the prospect of learning does not support the postponement of emissions reductions today.Learning; Uncertainty; Climate change; Decision analysis

Decision-making and integrated assessment models of the water-energy-food nexus

Studying trade-offs in the long-term development of water-energy-food systems requires a new family of hydroeconomic optimization models. This article reviews the central con- siderations behind these models, highlighting the importance of water infrastructure, the foundations of a theory of decision-making, and the handling of uncertainty. Integrated as- sessment models (IAMs), used in climate change policy research, provide insights that can support this development. In particular, IAM approaches to intertemporal decision-making and economic valuation can improve existing models. At the same time, IAMs have weak- nesses identified elsewhere and can benefit from the development of hydroeconomic models, which have complementary strengths

Appropriate Accuracy of Models for Decision-Support Systems: Case Example for the Elbe River Basin

Given the growing complexity of water-resources management there will be an increasing need\ud for integrated tools to support policy analysis, communication, and research. A key aspect of the design is the\ud combination of process models from different scientific disciplines in an integrated system. In general these\ud models differ in sensitivity and accuracy, while non-linear and qualitative models can be present. The current\ud practice is that the preferences of the designers of a decision-support system, and practical considerations\ud such as data availability guide the selection of models and data. Due to a lack of clear scientific guidelines the\ud design becomes an ad-hoc process, depending on the case study at hand, while selected models can be overly\ud complex or too coarse for their purpose. Ideally, the design should allow for the ranking of selected\ud management measures according to the objectives set by end users, without being more complex than\ud necessary. De Kok and Wind [2003] refer to this approach as appropriate modeling. A good case example is\ud the ongoing pilot project aiming at the design of a decision-support system for the Elbe river basin. Four\ud functions are accounted for: navigability, floodplain ecology, flooding safety, and water quality. This paper\ud concerns the response of floodplain biotope types to river engineering works and changes in the flooding\ud frequency of the floodplains. The HBV-D conceptual rainfall-runoff model is used to simulate the impact of\ud climate and land use change on the discharge statistics. The question was raised how well this rainfall-runoff\ud model should be calibrated as compared to the observed discharge data. Sensitivity analyses indicate that a\ud value of R2 = 0.87 should be sufficient

Modelling net-zero emissions energy systems requires a change in approach

Energy modelling can assist national decision makers in determining strategies that achieve net-zero greenhouse gas (GHG) emissions. However, three key challenges for the modelling community are emerging under this radical climate target that needs to be recognized and addressed. A first challenge is the need to represent new mitigation options not currently represented in many energy models. We emphasize here the under representation of end-use sector demand-side options due to the traditional supply side focus of many energy models, along with issues surrounding robustness in deploying carbon dioxide removal (CDR) options. A second challenge concerns the types of models used. We highlight doubts about whether current models provide sufficient relevant insights on system feasibility, actor behaviour, and policy effectiveness. A third challenge concerns how models are applied for policy analyses. Priorities include the need for expanding scenario thinking to incorporate a wider range of uncertainty factors, providing insights on target setting, alignment with broader policy objectives, and improving engagement and transparency of approaches. There is a significant risk that without reconsidering energy modelling approaches, the role that the modelling community can play in providing effective decision support may be reduced. Such support is critical, as countries seek to develop new Nationally Determined Contributions and longer-term strategies over the next few years

Linking Science and Management in a Geospatial, Multi- Criteria Decision Support Tool

Land managers are often faced with balancing management activities to accomplish a diversity of objectives in complex, dynamic ecosystems. In this chapter, we present a multi-criteria decision support tool (the Future Forests Geo-Visualization Decision Support (FForGeoVDS)) designed to inform management decisions by capturing information on how climate change may impact the structure and function of forested ecosystems and how that impact varies across the landscape. This interactive tool integrates spatial outputs from various empirical models in a structured decision framework that allows users to customize weights for multiple management objectives and visualize suitability outcomes across the landscape. As a proof of concept, we demonstrate customized objective weightings designed to: (1) identify key parcels for sugarbush (Acer saccharum) conservation, (2) target state lands that may serve as hemlock (Tsuga canadensis) refugia, and (3) examine how climate change may impact forests under current and future climate scenarios. These case studies exemplify the value of considering multiple objectives in a flexible structure to best match stakeholder needs and demonstrate an important step toward using science to inform management and policy decisions

Modelling net-zero emissions energy systems requires a change in approach

Energy modelling can assist national decision makers in determining strategies that achieve net-zero greenhouse gas (GHG) emissions. However, three key challenges for the modelling community are emerging under this radical climate target that needs to be recognized and addressed. A first challenge is the need to represent new mitigation options not currently represented in many energy models. We emphasize here the under representation of end-use sector demand-side options due to the traditional supply side focus of many energy models, along with issues surrounding robustness in deploying carbon dioxide removal (CDR) options. A second challenge concerns the types of models used. We highlight doubts about whether current models provide sufficient relevant insights on system feasibility, actor behaviour, and policy effectiveness. A third challenge concerns how models are applied for policy analyses. Priorities include the need for expanding scenario thinking to incorporate a wider range of uncertainty factors, providing insights on target setting, alignment with broader policy objectives, and improving engagement and transparency of approaches. There is a significant risk that without reconsidering energy modelling approaches, the role that the modelling community can play in providing effective decision support may be reduced. Such support is critical, as countries seek to develop new Nationally Determined Contributions and longer-term strategies over the next few years

Managing stakeholder knowledge for the evaluation of innovation systems in the face of climate change

Purpose – The aim of this paper is to frame the stakeholder-driven system mapping approach in the context of climate change, building on stakeholder knowledge of system boundaries, key elements and interactions within a system and to introduce a decision support tool for managing and visualising this knowledge into insightful system maps with policy implications. Design/methodology/approach – This methodological framework is based on the concepts of market maps. The process of eliciting and visualising expert knowledge is facilitated by means of a reference implementation in MATLAB, which allows for designing technological innovation systems models in either a structured or a visual format. Findings – System mapping can contribute to evaluating systems for climate change by capturing knowledge of expert groups with regard to the dynamic interrelations between climate policy strategies and other system components, which may promote or hinder the desired transition to low carbon societies. Research limitations/implications – This study explores how system mapping addresses gaps in analytical tools and complements the systems of innovation framework. Knowledge elicitation, however, must be facilitated and build upon a structured framework such as technological innovation systems. Practical implications – This approach can provide policymakers with significant insight into the strengths and weaknesses of current policy frameworks based on tacit knowledge embedded in stakeholders. Social implications – The developed methodological framework aims to include societal groups in the climate policy-making process by acknowledging stakeholders’ role in developing transition pathways. The system map codifies stakeholder input in a structured and transparent manner. Originality/value – This is the first study that clearly defines the system mapping approach in the frame of climate policy and introduces the first dedicated software option for researchers and decision makers to use for implementing this methodology

Prediction of CO2 Emissions Using Machine Learning

Carbon dioxide (CO2) is one of the important issues concerning human evolution that drives global climate change. It is emitted from the combustion of fuels causing global warming. The global community has gradually turned to pay more attention to environmental issues. This paper implements four prediction models using Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) and Convolutional Neural Network (CNN, or ConvNet) to predict CO2 trapping efficiency among CO2 emissions, energy use, and GDP. The Machine Learning (ML) approaches used in this study have shown good performance with SVM and CNN models with MAPE. The result can be a significant model for the decision support system to improve a suitable policy for global CO2 emission reduction

From climate change to pandemics: decision science can help scientists have impact

Scientific knowledge and advances are a cornerstone of modern society. They improve our understanding of the world we live in and help us navigate global challenges including emerging infectious diseases, climate change and the biodiversity crisis. For any scientist, whether they work primarily in fundamental knowledge generation or in the applied sciences, it is important to understand how science fits into a decision-making framework. Decision science is a field that aims to pinpoint evidence-based management strategies. It provides a framework for scientists to directly impact decisions or to understand how their work will fit into a decision process. Decision science is more than undertaking targeted and relevant scientific research or providing tools to assist policy makers; it is an approach to problem formulation, bringing together mathematical modelling, stakeholder values and logistical constraints to support decision making. In this paper we describe decision science, its use in different contexts, and highlight current gaps in methodology and application. The COVID-19 pandemic has thrust mathematical models into the public spotlight, but it is one of innumerable examples in which modelling informs decision making. Other examples include models of storm systems (eg. cyclones, hurricanes) and climate change. Although the decision timescale in these examples differs enormously (from hours to decades), the underlying decision science approach is common across all problems. Bridging communication gaps between different groups is one of the greatest challenges for scientists. However, by better understanding and engaging with the decision-making processes, scientists will have greater impact and make stronger contributions to important societal problems