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The document outlines three main sets of proposals to align Brazil's agricultural credit and insurance systems with climate goals. It advocates for increasing funding for the ABC+ and PRONAF ABC+ programs, restricting credit to properties with illegal deforestation or environmental embargoes, and improving rural insurance through the Rural Insurance Premium Subsidies Program (PSR) and the implementation of ZARC Stewardship Levels. Additionally, it suggests redirecting 10% of Constitutional Financing Funds (FCFs) toward sustainable practices and harmonizing these funds with the National Rural Credit System.

The document argues that Brazil's Plano Safra must align with climate goals by prioritizing low-carbon credit lines, improving rural insurance for climate resilience, and redirecting constitutional funds toward small-scale sustainable producers.

The document analyzes the current lack of consensus and transparency in how Brazilian government agencies and environmental assessments delimit the 'area of influence' for infrastructure projects. It highlights the risks of relying solely on distance-based radii, which may overlook indirect economic impacts, and proposes a 'market access' methodology to more accurately identify affected regions. The authors recommend separating the conceptual definition of the area of influence from its physical delimitation and integrating environmental parameters earlier in the project cycle, specifically within Feasibility Studies (EVTEA).

The World Resources Institute developed Global Forest Watch Water (GFW Water) to provide a public database and mapping tool for 230 major global watersheds. The tool assesses watershed health by scoring four primary risks—recent forest loss, historical forest loss, erosion, and fire—within the context of baseline water stress. It aims to support downstream beneficiaries, financing institutions, and researchers in implementing natural infrastructure, such as forest protection and restoration, to enhance water security and quality. The document details the underlying hydrologic science, the specific spatial data sets used (including Landsat imagery and the RUSLE erosion model), and the normalization process used to generate risk scores from 1 to 5.

The India Energy Policy Simulator (EPS-India), developed by Energy Innovation LLC and the World Resources Institute, is a System Dynamics computer model used to provide quantitative analysis of policy packages. It aims to support India in achieving its Nationally Determined Contribution (NDC) to reduce emissions intensity per unit of GDP by 33 to 35 percent below 2005 levels by 2030.

The World Resources Institute introduces a framework for planning passive cooling infrastructure in cities using OpenUrban, a 1-meter resolution urban land-use and land-cover dataset. By combining OpenUrban with remotely sensed data on albedo, tree canopy, and temperature, the framework generates three implementation scenarios—technical, achievable, and program—for trees, cool roofs, reflective pavements, and shade structures. The tool is designed to be city-agnostic and low-burden, requiring no local data inputs and providing both area-wide potential indicators and spatial simulation maps to support policy and planning.

The document introduces the Indonesia Energy Policy Simulator (EPS), a System Dynamics computer model developed by a consortium including the World Resources Institute Indonesia and Energy Innovation LLC. The tool is intended to provide quantitative analysis to help Indonesia achieve its 2030 climate targets of reducing emissions by 29 percent unconditionally or up to 41 percent with international assistance, while balancing the needs of a growing population.

The document introduces an open-source method to model the Universal Thermal Climate Index (UTCI) at a 1-meter resolution using the SOLWEIG model and open-access data. This framework allows cities to assess baseline heat risks and simulate the cooling effects of interventions such as tree planting, shade structures, and cool surfaces. The method's accuracy was validated against LiDAR-supported models, showing a mean absolute error of 0.39°C.