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The project introduces the Local Knowledge Scenarios (LKS) method, which integrates fieldwork and Agent-Based Models to document local perceptions of environmental change and their impact on decision-making. Testing across five diverse ecological zones in the Americas revealed that while local awareness of issues like deforestation and hydrological change is increasing, the effectiveness of environmental governance is often hindered by a lack of local acceptance of laws or the absence of flexible national policies for adaptation.

The Agenda 2030 Compass is a decision-support system consisting of a Context Mapper and a Strategy Analyser. It enables organisations to quantify how a planned action affects the 17 SDGs, accounting for context-specific synergies and trade-offs. The tool was developed through a collaboration between the Stockholm Environment Institute, Jernkontoret, and the MIT Center for Collective Intelligence, and has been pilot-tested in Swedish case studies involving e-scooters, housing, urban mobility, and regional innovation.

The NewClimate Institute developed a methodology to assess the design of NDCs based on four criteria: increased mitigation ambition, comprehensiveness, implementation plans, and transparency. Applying this to 20 diverse NDCs, the analysis found that while most countries meet requirements for comprehensiveness and transparency, less than half demonstrated higher mitigation ambition compared to their first NDC, and many lack clear implementation plans.

FSD Africa, in collaboration with The Good Economy and Tandem, has developed a four-step framework to measure how financial sector deepening impacts job creation. The framework emphasizes the use of full-time equivalent (FTE) metrics to account for underemployment and employs a sectoral approach to assess job quality and 'decent work' multipliers. The process involves a theory-based assessment of materiality, estimation of direct and indirect jobs, a decent jobs assessment using sector proxies, and optional bespoke research for high-impact projects.

The World Resources Institute developed a three-step method to estimate thermal power plant water demand: identifying cooling and fuel types via satellite images, assigning water-use factors from empirical data, and calculating total use based on net generation. Tested on 200 U.S. plants, the method achieved 90% precision in cooling and 89% in fuel identification, though water demand estimation precision was lower at 69%. The authors note limitations regarding image resolution, the inability to distinguish specific generation technologies within a fuel type, and the reliance on U.S.-based water-use factors which may vary globally.

The Stockholm Environment Institute and the Swedish Steel Producers Association (Jernkontoret) are creating a methodology and toolbox—a 'societal value compass'—to guide the Swedish steel industry toward its 2050 vision of creating positive societal value. The tool integrates the 17 Sustainable Development Goals (SDGs) with capital theory (natural, real, human, and social capital) to evaluate how industry activities affect broader societal interests. The project involves cross-impact balance analysis of SDG targets, focus group consultations, and the development of a prototype to be tested through topical case studies. This framework aims to enhance the industry's global competitiveness and facilitate communication with investors, employees, and policymakers regarding its contribution to sustainability.

The research paper develops a methodology for scenario analysis of consumption-based GHG emissions, moving beyond traditional production-based accounting. It introduces a framework using a modified Kaya Identity to forecast emissions by analyzing population, consumption activity, and GHG intensity. The authors provide a catalogue of low-GHG behavioral measures across food, goods, services, construction, transport, and home energy. Piloting the method in Seattle and the U.S., the study finds that while aggressive behavioral shifts and production improvements can reduce consumption-based emissions by 47% by 2030, the resulting levels remain significantly above the global average required to limit warming to 2°C.

The Uniform World Model (UWM) is a simplified environmental impact assessment tool designed to calculate health impacts and damage costs from air pollution. It uses a few key input parameters and assumes a uniform distribution of receptors to provide a transparent alternative to complex 'black-box' software. The author validates the UWM by comparing its results with established models like CALPUFF, ISC3, EMEP, and GAINS across various scales and regions, including Europe, China, India, and the USA. Findings indicate that the UWM is robust, typically falling within a ±50% deviation range, and is particularly accurate for regional or continental estimates and tall stack emissions.

The project developed the Local Knowledge Scenarios (LKS) method, which integrates field work and Agent-Based Models to document local perceptions of environmental change and their impact on decision-making. Testing across five diverse American regions revealed that local awareness of issues like deforestation in the Amazon does not always translate to the acceptance of environmental laws, and that fishing communities in Canada require national public policies to successfully diversify their income sources.