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This paper presents an innovative approach to addressing critical global challenges in long-term energy planning for low- and middle-income countries (LMICs). The paper proposes and tests an international enabling environment, a delivery ecosystem, and a community of practice. These components are integrated into workflows that yield four self-sustaining capacity-development outcomes. Planning long-term energy strategies in LMICs is particularly challenging due to limited national agency and poor international coordination. While outsourcing energy planning to foreign experts may appear to be a viable solution, it can lead to a reduction in government agency (the ability of a government to make its own informed analysis and decisions). Additionally, studies commissioned by external experts may have conflicting terms of reference, and a lack of familiarity with local conditions can result in misrepresentations of on-the-ground realities. It is argued here that enhancing national agency and analytical capacity can improve coordination and lead to more robust planning across line ministries and technical assistance (TA) providers. Moreover, the prevailing consulting model hampers the release and accessibility of underlying analytics, making it difficult to retrieve, reuse, and reconstruct consultant outputs. The absence of interoperability among outputs from various consultants hinders the ability to combine and audit the insights they provide. To overcome these challenges, five strategic principles for energy planning in LMICs have been introduced and developed in collaboration with 21 international and research organizations, including the AfDB, IEA, IRENA, IAEA, UNDP, UNECA, the World Bank, and WRI. These principles prioritize national ownership, coherence and inclusivity, capacity, robustness, transparency and accessibility. In this enabling environment, a unique delivery ecosystem consisting of knowledge products and activities is established. The paper focuses on two key knowledge products as examples of this ecosystem: the open-source energy modeling system (OSeMOSYS) and the power system flexibility tool (IRENA FlexTool). These ecosystem elements are designed to meet user-friendliness, retrievability, reusability, reconstructability, repeatability, interoperability, and audibility (U4RIA) goals. To ensure the sustainability of this ecosystem, OpTIMUS is introduced—a community of practice dedicated to maintaining, supporting, expanding, and nurturing the elements within the ecosystem. Among other ecosystem elements, training and research initiatives are introduced, namely the Energy Modelling Platform for Africa, Latin America and the Caribbean, and Asia-Pacific as well as the ICTP Joint Summer School on Modelling Tools for Sustainable Development. Once deployed via workflows, the preliminary outcomes of these capacity-development learning pathways show promise. Further investigation is necessary to evaluate their long-term impacts, scalability, replication, and deployment costs. [ABSTRACT FROM AUTHOR]

While climate services for small-scale farmers are gaining recognition for contributing to adaptation and resilience to climate variability and change, their provision in developing countries remains a critical challenge. Effective climate services consider why and how farmers of varied socioeconomic background make relevant decisions avoiding the traditional prescriptive forms of transfer that merely focus on delivering climate information. Evidence from sub-Saharan Africa shows that climate services for agriculture generates transformations in how farmers access and use climate information, as well as changes in farmer decision-making. In this paper, we address the question of whether the same effect is also seen in Latin America, where farming systems, farming decisions, socioeconomic contexts and non-climate constraints are very different to those of Africa. A group of 209 farmers in the dry corridor in Honduras was studied. We find that 98% of the trained farmers did uptake and use the climate information, and some 73% expressed that the agroclimatic information was key to the success of their harvest despite the challenging 2019 season. Some 43% of the farmers made changes in farming practices. In particular, farmers changed the crop they grew, the crop and land management, the planting dates, inputs and crop varieties. These changes reportedly had positive effects on their food security, and income. These findings support the hypothesis that Participatory Integrated Climate Services for Agriculture (PICSA) plays a positive role in providing effective climate services in Central America, improving decision-making, and enabling farmers to make their own decisions based on the analysis of information and their demands regardless of their level of literacy. We pose that participatory climate services in agriculture can catalyse processes of long-term transformation in farming systems, notably through lifting farmers out of poverty and food insecurity and providing an integrated approach to make informed decisions in the face of climatic variation. [ABSTRACT FROM AUTHOR]

Seasonal tropical cyclone (TC) forecasting has evolved substantially since its commencement in the early 1980s. However, present operational seasonal TC forecasting services still do not meet the requirements of society and stakeholders: current operational products are mainly basin-scale information, while more detailed sub-basin scale information such as potential risks of TC landfall is anticipated for decision making. To fill this gap and make the TC science and services move forward, this paper reviews recent research and development in seasonal tropical cyclone (TC) forecasting. In particular, this paper features new research topics on seasonal TC predictability in neutral conditions of El Ni˜no-Southern Oscillation (ENSO), emerging forecasting techniques of seasonal TC activity including Machine Learning/Artificial Intelligence, and multi-annual TC predictions. We also review the skill of forecast systems at predicting landfalling statistics for certain regions of the North Atlantic, Western North Pacific and South Indian oceans and discuss the gap that remains between current products and potential user's expectations. New knowledge and advanced forecasting techniques are expected to further enhance the capability of seasonal TC forecasting and lead to more actionable and fit-for-purpose products. [ABSTRACT FROM AUTHOR]

Strategies to combat climate change may be based on mitigation of the phenomenon or on adaptation to its consequences. This paper aims to identify the driving factors of the choice between these two categories of strategies in the context of urban climate plans. The fight against climate change being characterized by a logic of free riding, the situation tilts the balance towards adaptation strategies in an urban context, to the detriment of mitigation. This hypothesis is tested here through a review of the existing literature on urban climate strategies. This study shows that, counterintuitively, mitigation prevails over adaptation in urban climate strategies up to now. This paper explores the explanations for this seemingly paradoxical situation. We argue that a big part of the explanation has to do with the institutional context of urban climate strategies, specifically the decision-making capacities of municipalities, or the fact that they take part in international networks promoting mitigation over adaptation. Other explanations rely on the cost/benefit impact of adopting mitigation or adaptation, like the collateral local/private benefits of urban climate strategies that are often bigger with mitigation than adaptation. Another finding is that there is no systematic planning making it compulsory to choose between mitigation and adaptation strategies, as they are in some instances complementary, providing co-benefits. [ABSTRACT FROM AUTHOR]

While many transition scenarios describe potential low-carbon systems, few link these system-level outcomes to the microlevel stakeholder decision-making needed to actualise them, resulting in a 'planning gap'. Closing this gap requires that insights from modelling-based transition scenarios on what must happen to achieve climate targets are linked to those on how to make it happen from stakeholder-focused transition scenarios. This link requires a different understanding of decision-making rationality from that of a representative agent with rational expectations, as employed in much climate-change modelling currently. Rationality conceived as 'frame-sensitive reasoning' can better account for heterogenous stakeholders' alternative preferences, the actions they take in pursuit of them, and the effect of these actions on low-carbon transitions. This paper augments the Intuitive Logics (IL) stakeholder-focused scenario approach to enable frame-sensitive reasoning and provide modelling-based transition scenarios with realistic innovation-diffusion assumptions. In so doing, the paper assists in closing the planning gap. [ABSTRACT FROM AUTHOR]

Climate change is an important driver of migration, but little research exists on whether migrant communities in the U.S. identify climate change-related factors as reasons for migrating. In 2021, we conducted a multidisciplinary, collaborative project to better understand the nexus of climate change and immigrant health in the Atlanta area. This paper presents one arm of this collaboration that explored both the role of climate change in decisions to immigrate to Georgia and the ways that climate change intersects with other possible drivers of migration. First generation migrants from Latin America were recruited primarily through CPACS Cosmo Health Center and were invited to participate in an intake survey and an in-depth interview. Results were analyzed using descriptive statistics and thematic analysis. Findings suggest that while participants may not have explicitly identified climate change as a primary reason for migration, in both surveys and in-depth interviews, participants reported multiple and intersecting social, economic, political, and environmental factors that are directly or indirectly influenced by climate change and that are involved in their decisions to migrate. The narratives that emerged from in-depth interviews further contextualised survey data and elucidated the complex nexus of climate change, migration, and health. [ABSTRACT FROM AUTHOR]

Current adaptation theory tends to consider individual buildings or the city level, which cannot address decisions related to masterplan developments on large brownfield sites. This paper investigates the drivers for building demolition or retention and adaptation decisions at the masterplan scale. Expert interviews and three case studies are used to explore how and why decisions to demolish, or to retain and adapt, are made at this scale. The research compared three different geopolitical contexts: Cambridge in the UK; Eindhoven in the Netherlands; and Sydney in Australia. Additional factors and complexities that should be considered at the masterplan scale are identified. The theoretical underpinnings of urban development processes are used to explain these complexities in relation to four existing models and demonstrate that no one model is adequate to describe the interactions. With increasing awareness of climate change impacts, it is critical that demolition decisions on masterplan developments are reviewed in the light of retaining carbon as well as heritage. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) in the building sector has become a widely used method for quantifying environmental impacts of a building over its entire life cycle. Currently, however, no standardized procedure exists for considering refurbishment measures. In addition, the LCA, according to the European standard EN 15978, is limited to building level only. This paper shows how a methodological LCA framework of refurbishment methods can be applied at three levels: single building, neighbourhood, and municipality. Initially, the proposed methodological approaches are introduced while the framework rules are defined for each of the three levels. The system boundaries of the LCA differ regarding the assessment levels within the given methodology and are adapted accordingly. In addition, the three levels of assessment are defined by the accuracy of measurement results, and data requirements, as well as by the specific value of the calculations and the ownership of the building stock. The assessment levels provide a specific quality and quantity of environmental indicator results. Thus, the complex interrelationships of the assessment levels are shown. The developed framework for the environmental assessment of refurbishment measures provides comparability at the building level. At the neighbourhood level, emissions from refurbishment measures are compared with the reductions of emissions through heating energy demand. Ultimately, the potential of refurbishment measures at the municipal level can be identified on a large scale and used as a decision-making tool. [ABSTRACT FROM AUTHOR]

Compared to other goods, buildings have a long lifespan. It is therefore the norm and not the exception that adaptation will be needed at some point in their life cycle to changing environmental conditions, technical progress and new/additional user requirements. The adaptation of existing buildings to future challenges in the form of refurbishment contributes to sustainable development. The proportion of refurbishments is increasing in both absolute and relative terms in the building sector in Europe. It is therefore surprising that the modelling particularities of refurbishment in the context of a life cycle-based environmental performance assessment are still neglected in the ENstandards. The paper deals with these particularities. Specifically, it presents a typology of approaches starting with a distinction between planned refurbishment as B5 and unforeseen refurbishment as an independent design and construction task / next life cycle, as well as the status of the discussion in selected countries. Recommendations for action for the life cycle assessment are given for specific decision-making situations. [ABSTRACT FROM AUTHOR]

One of the most important and continuously growing research areas in marine ecology is assessing the sustainability of ecosystems. The concept is traditionally approached by quantifying different anthropogenic pressures, such as top-down effects, overexploitation, invasive species, overfishing, and pollution. However, this perspective has been criticized for its inability to capture the complex interactions between natural factors (such as climate change), human factors, and sociopolitical factors that shape ecosystems. In this paper, we present a new structural model for assessing the sustainability of marine ecosystems, PCA-DEA-based model, as an extension to current models that incorporates these key interfaces into dynamic system modeling by including trade-offs on horizontal scales (e.g., decision making). [ABSTRACT FROM AUTHOR]

This article aims to introduce different views regarding the concept of adaptive capacity in water governance and unpack it in the Central Asian context, specifically. The desk study research built on the recommendations provided by the academia, policy papers and by experts on enhancing adaptive water governance. The juxtaposition of the three sets of recommendations demonstrates that the further strengthening of IWRM, as well as experimental approach to resource management, improved access to climate knowledge and disaster risk reduction mechanisms can consolidate the adaptive capacities of Central Asian water governance systems. The research was limited by the specific set of selected opinions and should be expanded in the future. As a practical outcome, the analysis aimed to provide the decision-makers in Central Asian states with better science-based avenues to planning adaptation measures ensuite the water sector. Thus, the study can be considered a stepping stone in the discussion on the adaptive capacities of water governance in the Central Asian region. [ABSTRACT FROM AUTHOR]

• First review that examines multi-actor approaches for WEFN systems. • Multi-actor approaches have been classified into four categories. • Only multi-agent based models enable operational decision making simulations. • Stakeholders must choose multi-actor approaches according to their needs. Water, energy and food are essential resources for human life; however, their security is increasingly threatened worldwide by climate change, urbanization, and population growth. The Nexus approach, conceptualized in 2011 by the Bonn Nexus Conference, addresses the interconnections and trade-offs of water-energy-food systems and analyzes the interdisciplinary and transdisciplinary dimensions of these systems with a systemic approach. This bibliographic review aims at showing how multi-actor perspectives and objectives on water-energy-food nexus systems are modeled together with socio-ecological considerations. It sheds light on the major techniques found in the literature to do so; mathematical modeling, participative approaches and multi-criteria decision-making. This paper identifies commonalities between models developed at similar spatio-temporal scales and decision levels. It also highlights literature gaps and outlines research challenges. [ABSTRACT FROM AUTHOR]

The complex interaction between city and climate crisis is converting design-based disciplines from deterministic to flexible approaches. In this regard, Decision-Making Under Deep Uncertainty (DMDU) methods and operational strategies can be valuable support mechanisms to cope with the emerging climate fragilities of urban systems. In light of recent advances in the field of adaptive approaches, this paper discusses key concepts, current limitations and the potential to introduce the DMDU in the method and practices of regenerative design. Our critical discussion aims to restore the designer’s role within the DMDU and to reduce current and future climate fragilities in European cities. [ABSTRACT FROM AUTHOR]

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix. [ABSTRACT FROM AUTHOR]

Under uncertainty about the kind, extent, or time frames of coastal threats, efficient protection requires measures that are effective in time and flexible enough to assure protection even if conditions change over time. Existing protection options are unable to offer both attributes simultaneously, creating a trade-off between short-term and long-term effectiveness in protection choice. This paper investigates the role played by differences in the temporal effectiveness of coastal protection measures in the choice of protection modes. Results from a discrete-choice experiment implemented in Papua New Guinea suggest that respondents have a strong preference for long-term over short-term effectiveness; an urgency to protect cannot be identified. Using incentivized preference measures for patience and risk-aversion as well as sociodemographic controls, we account for taste heterogeneity and validate the robustness of our results. • Adaptation to climate change requires decision making under intertemporal uncertainty. • Our DCE focuses on the role of temporal effectiveness in a developing country context. • Results suggest strong preferences for long-term over short-term effectiveness. • An urgency to protect cannot be identified. • Results are robust to including revealed preference measures for patience and risk. [ABSTRACT FROM AUTHOR]

Farmers in Europe act within guidelines set by agricultural programs, market demands and biophysical constraints. At the same time, they are social actors embedded in their respective family structures and individual lifestyles and preferences. We here present the socio-ecological land-use model SECLAND that provides an improved representation of the versatility in farmer's characteristics and subsequently its impact on land-use decision making. The model combines (1) an agent-based module (ABM) with farmers as agents whose intrinsic behavioural characteristics are represented via farming styles with (2) a probabilistic GIS-based forest regrowth module. We apply the model for the LTSER Eisenwurzen region in northern Austria to explore land-use patterns until the year 2050 under three different scenarios. Our results show the relevance of explicitly considering differences between the responses of individual farmers to changing framework conditions. In order to devise sustainable development paths for future agriculture, it is important to focus on farmers' well-being and recognize it as being equally important as economic success. • This paper provides the description of the bimodular SECLAND land-use model. • We depict the effect of intrinsic factors on farms decision-making. • We investigate the impact of socio-economic and climatic framework changes on land-use patterns. • We analyse intensity shifts, crop and forest type changes as well as forest transition. [ABSTRACT FROM AUTHOR]

Climate change triggers increasing stresses on urban settlements and coastal areas. The intensification of climate-connected impacts requires municipalities and communities to undertake adaptation measures and plans. These interventions should be capable of reducing negative climatic effects on human habitat and regional bioregions. On the one hand, the international scientific community recognized the supported adaptation planning approach as the more suitable to cope with local needs and criticalities. On the other hand, there is a lack of practical guidelines and examples that can be used to implement the theory. From a perspective of growth of global awareness and sensitivity to the climatic emergency, it is necessary to develop a practical methodology able to link together impacts perception and public decision process at the local scale. The paper aims to fill the gap between the theoretical approach and the practice, through a replicable experience of integration among climate change adaptation concepts and decision-making processes. The proposed methodology is described in a 4-step process to support decision-makers in selecting tailored adaptation policies and measures. The article is based on the experience developed within the Interreg It-Hr project "iDEAL – Decision support for Adaptation pLan" project. The research combines a quantitative and qualitative methodology in local participation processes. The approach is tested on five Mediterranean coastal cities and allowed to support the development of tailored adaptation measures. Furthermore, the interaction with local actors during the process led to an acceptance of the implemented measures, designing each measure to stakeholders' ambitions and expectations. • Local participation in climate change adaptation processes. • Climate impacts management and risk perception. • A decision support system to enhance local decision making. • Developing transboundary Adriatic coasts adaptation planning. [ABSTRACT FROM AUTHOR]

• The positional TOPSIS, combined with different optimal tail selection methods of the variable distribution, is proposed to assess the sustainable development of territorial units. • The cluster compactness measure is proposed to evaluate the classification results of the positional TOPSIS with the optimal tail selection of variable distribution. • The automated Eye-Ball method and the Guillou and Hall's method enable correct identification of extreme values of variables describing the economic, social, and environmental development of municipalities. • The positional TOPSIS with selected optimal tail selection methods enable correct classification of the municipalities and assessment of their sustainable development in terms of levels and statuses. In the face of accelerating climate change, economic instability and limited resources, it is essential to find indicators that better measure the sustainable development of territorial units. Despite ambiguous definitions of sustainable development, numerous studies describe quantitative indicators of sustainable development. Main methodological problems include data availability and their use, spatial and time scales, selection of indicators, and their aggregation. Usually, analysts and decision-makers apply well known conventional methods that have numerous limitations. These methods in many cases cause excessive simplification of the real phenomena and relations between their determinants. Moreover, variables describing the development of territorial units (especially the environmental pillar) are often characterized by strong asymmetry or extreme observations. Even a single outlier for a given territorial unit may significantly affect the analysis and the resulting conclusions. The objective of this paper is to present the application potential of a Multi-Criteria Decision Making (MCDM) approach with an optimal tail selection to assess the sustainable development of territorial units. The proposed approach is based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and different methods of optimal tail selection of variable distribution. The Oja's spatial median and the optimal tail selection methods are used to reduce the impact of strong asymmetry and extreme values of variables. Moreover, the measure of cluster compactness is proposed for the evaluation of the proposed method. The proposed approach was used to assess the sustainable development of municipalities in Poland in 2018. We found that the automated Eye-Ball method and the Guillou and Hall's method enabled correct identification of extreme values of variables describing the economic, social, and environmental development of municipalities. This made it possible to distinguish the types of sustainable development level and its pillars and enabled to provide the statuses of the sustainable development level of the examined municipalities with respect to other municipalities in Poland. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper analyzes French winemakers' decision-making process to adapt to climate change, and how the institutional and relational context of an innovation system, including a clean technological regime, affect these decisions. Our study used a mixed method research based on original face-to-face interviews with 92 winemakers in three French regional wine clusters that have been affected by climate change: Bordeaux, Champagne and Languedoc. We perform a logistic model to tests how managers' personal backgrounds, wine-producing company characteristics, and innovation system components, including cleaner technological regime, might explain the adaptation decision-making process. Our results show that economic variables have little influence on climate change adaptation decision-making. On the contrary, variables expressing the relationship built by wine producing companies within the Innovation System, their involvement in organic wine production, and the manager's personal background affect the decision-making process to adapt to climate change. Furthermore, many of the adaptation strategies rely on adopting cleaner production approach. Our findings show that the decision-making process depends on networks and clean technological regimes embedded in an innovation system, with regional and sector dimensions. • Awareness and adaptation strategies of winemaking companies differ according to regional vineyards, due to the spatial variability of climate change impacts • Decision-making process depends on networks and technological regimes embedded in an innovation system, with regional and sector dimensions • There are few links between adaptation and economic characteristics of winemaking companies [ABSTRACT FROM AUTHOR]

Strategic decision-making on long-term drought risk management can be supported by integrated assessment models to explore uncertain future conditions and potential policy actions. Such models have to meet many – sometimes conflicting – requirements posed by policy-makers, model developers, and stakeholders. This paper discusses the case of the National Water Model (NWM) that is applied for national policy-making on drought risk management in the Netherlands. The case demonstrates that the chosen assembled model set-up (in which several existing models are combined) is cost-effective and increases stakeholder acceptance, but also leads to high model complexity and computation time. To be effective for policy-making, integrated assessment models need to produce relevant model outcomes that are accepted by stakeholders, within acceptable time and cost limits. For this, the model set-up must support simulations at different aggregation levels (allowing both detailed analysis and exploratory analysis of many scenario/strategy combinations) while maintaining internal consistency. • Decision-making for drought risk management requires Integrated Assessment Models. • Integrated Assessment Models must meet many – sometimes conflicting – requirements. • There is a clear trade-off between model complexity and timeliness. • Effective models are those that allow simulations at different aggregation levels. [ABSTRACT FROM AUTHOR]