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The paper introduces the Preferable Futures (PF) Deck, a wellbeing intervention and toolkit developed through a 'research-through-design' process. Positioned at the intersection of participatory design, futures thinking, and ecological citizenship, the deck aims to inspire place-based action for an ecologically ethical transition by recognizing the link between human and planetary health. The authors propose a 'post-participatory intervention' model where designers act as catalysts for community-led practices rather than mere facilitators of predefined projects. The toolkit is intended for use by community organizers, educators, and policy-adjacent actors to facilitate democratic and ecological transformation.

The document contains technical appendices for a report on reforming environmentally harmful subsidies (EHS). It introduces a 'Fiscal Pressure' indicator to identify countries where high debt and deficits create strong incentives for subsidy reform, and provides a stakeholder analysis mapping the influence and attitudes of businesses, governments, consumers, and civil society toward EHS reform.

The document provides a technical framework for calculating the 'cost gap'—the marginal cost differential between clean energy technologies and their conventional benchmarks—for Battery Energy Storage Systems (BESS), offshore wind, solar PV, electric vehicles (EVs), and green hydrogen in India. It outlines the specific input parameters, learning rates, and benchmarks used for each technology. Furthermore, it describes the application of the Green Economy Model (GEM), a system dynamics tool, to simulate how varying levels of public funding (e.g., 30% or 100% of the cost gap) influence India's GDP, employment, and emissions through feedback loops involving energy spending, air pollution, and labor productivity.

The research introduces an interdisciplinary approach that uses 'transition narratives' as a bridge between socio-technical transition studies and integrated assessment modelling. By applying a multi-level perspective, the authors developed two contrasting narratives for the European Union's 80% greenhouse gas emission reduction target by 2050: one driven by incumbent actors and large-scale innovation, and another driven by new entrants and alternative strategies. The study concludes that while both pathways meet the 2050 objective, both require a substantial departure from current known systems.

The document outlines a two-pronged research architecture designed to analyze New Zealand-specific online extremism and international extremist activity targeting New Zealand. It defines extremism based on 'social identity' and the pursuit of in-group supremacy, focusing on far-right, far-left, and Islamist ideologies. The methodology employs a combination of qualitative ethnographic research, API-based data collection, and Natural Language Processing (NLP) for thematic classification. The authors emphasize a strict ethical framework prioritizing anonymity, the use of public data only, and the production of aggregated outputs to balance individual privacy with public safety.

The report details a System Scale Planning (SSP) framework applied to Nepal's hydropower sector, specifically the Karnali Basin. Using a Multi-Objective Evolutionary Algorithm (MOEA) and integration with the SWITCH energy model, the methodology identifies 'pareto-optimal' portfolios of projects that balance installed capacity and cost against environmental and social metrics. Key findings demonstrate that strategic project selection can significantly reduce biodiversity and livelihood impacts for a given level of energy output, and that restricting development on mainstem rivers (Scenario K03) can achieve a favorable balance of low environmental impact and low system cost.

The document proposes integrating Sovacool et al.'s "4E" typology (enclosure, exclusion, encroachment, and entrenchment) with the Mechanisms of Social Change (MOSC) mapping approach to identify causal pathways in adaptation projects. By mapping the solar electricity system in Turkana, Kenya, the authors demonstrate how specific actions—such as consultation, licensing, and facility design—create sequential pathways that determine how benefits and losses are distributed among community members.

The paper introduces a simplified cross-impact balance (CIB) approach for cross-scale scenario development, allowing global scenarios to serve as stable external conditions for local and regional analysis.

The paper presents five new consumption-based indicators for hazardous substances, utilizing a hybrid model that combines EXIOBASE with the Swedish System of Economic and Environmental Accounts. Applying these to Sweden, the study finds that the vast majority of chemicals-related pressures (76–90%) occur outside Sweden's borders, with specific pressures like pesticide use and hazardous emissions occurring primarily outside the EU.

The authors created the CACAO database, which uses a 110x110 km grid to map conflicts, climate data, and socio-economic variables across Africa. Initial descriptive analysis shows that conflicts often overlap with areas rich in natural resources and populated regions, while the relationship with temperature and precipitation changes is more complex and requires further spatial and temporal analysis.

The research introduces a method to reduce the large volume of scenarios generated by computer simulations into a manageable set for decision support. By integrating two distinct approaches—one focusing on policy vulnerabilities and the other on a diverse range of futures—the authors create a systematic selection process. This method was applied to climate resilient infrastructure planning for the Volta, Orange, and Zambezi river basins in Africa, demonstrating that a stepwise introduction of selection criteria allows for a transparent analysis of how different priorities influence scenario choice.

The document details the creation of global country-level water stress projections by aggregating sub-catchment data using a spatially weighted methodology. It utilizes IPCC AR5 data to model three scenarios—optimistic, business-as-usual, and pessimistic—across three time horizons (2020, 2030, 2040). The rankings are designed to help investors, companies, and governments benchmark national water risks and prioritize adaptation. A 2040 business-as-usual ranking identifies several Middle Eastern countries, including Bahrain, Kuwait, and Qatar, as having the highest water stress scores of 5.00.

The paper presents TAGMI, an online open-access tool using Bayesian network models to assess the likelihood of success for agricultural water management (AWM) interventions at a sub-national level. By integrating social, human, physical, financial, and natural factors, the tool evaluates the potential for outscaling three specific technologies—soil water conservation/in situ rainwater harvesting, small-scale private irrigation, and small reservoirs for communal irrigation—within the Limpopo and Volta river basins. The authors use a climate change scenario to test the robustness of these interventions and argue for the further development of Decision Support Systems and the identification of implementation best practices for poverty-constrained smallholder systems.

The document details the construction of the Aqueduct Water Risk Atlas, which uses a composite index approach to map global water risks. It describes the development of six modeled indicators (baseline water stress, inter-annual variability, seasonal variability, upstream storage, return flow ratio, and upstream protected land) and the adaptation of six externally sourced indicators (flood occurrence, drought severity, groundwater stress, media coverage, access to water, and threatened amphibians). The methodology involves using an ensemble of regression models to project water withdrawals to 2010, spatial disaggregation to the catchment scale, and a catchment-to-catchment flow accumulation approach for water supply. The final water risk scores are generated through a linear weighted aggregation process, allowing users to customize weights based on their specific sector or risk sensitivity.

The paper details the use of Knowledge elicitation tools (KnETs) in southeast Cameroon to formalize qualitative data on local strategies for community adaptive capacity and sustainable forest management. This process generates decision rules and statistical measurements that inform social simulation models by defining agent attributes and prioritizing scenarios. Findings indicate that while men and women within the same households often share common rule drivers, differences in livelihood strategies exist between poor and better-off farmers regarding the package of strategies chosen, the driver factors involved, and the prioritization of these strategies relative to individual goals.

The author challenges the consensus in international relations that the field can explain but not predict political phenomena. He argues that there is little theoretical basis for distinguishing between explanation and prediction, and that modern data and machine learning make prediction empirically possible. The paper outlines three generations of conflict prediction, from early behavioralist ambitions to current sophisticated early warning systems used by entities like the UN and EU. The author contends that focusing on prediction improves theory testing by moving beyond statistical significance to evaluate whether models can actually reproduce data-generating mechanisms. While acknowledging the challenges of 'black swan' events and non-linear complex systems, he asserts that the alternative to scientific prediction is often reliance on unreliable expert intuition.

The authors developed a method to analyze unevenly spaced paleoclimate data using the Morlet Weighted Wavelet Z-Transform (MWWZ) to avoid the spectral distortions caused by time-domain interpolation. Applying this to the GISP2 ice core oxygen isotope record from Greenland, the implementation successfully identified a known 1,470-year spectral peak between 35,000 and 31,000 years B.P. However, the results differed from previous studies in the Holocene period (last 10,000 years), where no statistically significant spectral structures were found. The authors conclude that further improvements, such as window smoothing and bias correction, are necessary before releasing the tool as an R package.

The author proposes a cognitive approach to analyze the persistent failure of global climate governance, arguing that human behavior in negotiations is driven by underlying mental representations. The paper introduces cognitive-affective mapping (CAM) as a qualitative tool to visualize these belief systems, including their emotional valences. By applying CAM to UNFCCC participants, the research aims to identify cognitive barriers to cooperation and explore how these mental structures differ between individuals and groups, potentially offering new pathways for diplomatic breakthroughs.

The authors adapt McKinsey's global abatement cost data for use in the CRED model by aggregating 21 regions into nine and 11 sectors into two (land use and industry). To resolve the theoretical conflict of 'negative-cost' abatements—where energy savings outweigh investment—the authors assign these measures a near-zero but positive cost. The resulting estimates suggest that emission reductions may be cheaper than those projected by top-down integrated assessment models like RICE or EMF-22, while remaining broadly comparable to the MIT EPPA model's positive-cost estimates.

The document describes two techniques for creating elicited probability tables (EPTs) to simplify the process of building Bayesian networks when measured data is unavailable or insufficient. Both methods utilize a user-defined "typical distribution" to represent a normal state of affairs. The first method, "Influence Weights," assumes that changes in parent nodes independently affect the child node and is best used when parent and child nodes share the same states. The second method, "Likelihood," uses a log-likelihood approach to model how parent nodes shift the child node's probability away from the typical distribution, making it suitable for cases where parent nodes have different states than the child node.