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The document describes how LEAP handles process dispatching during simulation, detailing five specific rules—PercentShare, ProportionalToCapacity, FullCapacity, MeritOrderDispatch, and RunningCost—and the data requirements necessary to enable each.

The 'Fraction of Scrapped Replaced (%)' variable in LEAP determines what proportion of scrapped devices are immediately replaced by new sales, which then affect the stock's energy intensities and emission factors. The default setting is 100%, meaning all scrapped vehicles are replaced, and these replacements are counted separately from the primary 'sales' variable.

The document outlines four approaches to capacity data in LEAP: ignoring capacity limits, using exogenous capacity for manual control of additions, using endogenous capacity for automatic timing based on reserve margins, and using least-cost optimization for automated selection and timing of capacity. It notes that optimization requires detailed cost data, whereas the other methods do not.

The document describes the various branch types used in LEAP to organize data, including Key Assumption, Category, Technology, Fuel, Environmental Loading, and Indicator branches, detailing their specific roles in demand, transformation, and resource analysis.

The document provides a step-by-step procedure for activating land-based resource calculations in LEAP, starting with enabling the 'Land-Use Change and Land-Based Resources' option in the Scope & Scale settings. It details the necessity of defining land types and regions, as well as configuring resource properties in the General: Fuels screen. The guide notes that enabling these settings reveals new branches in the main tree for specifying land areas and conversions, and that the available variables under the Resources branch vary depending on whether the user is analyzing wood resources or other renewable energy forms like solar, wind, and biomass.

The document serves as a technical support guide for the LEAP software, offering instructions on optimizing calculation speeds, troubleshooting electricity generation modules, and clarifying the software's capabilities regarding GHG inventories and life cycle analysis. It also explains specific data entry functions, such as the distinction between exogenous and endogenous capacity and the use of share versus saturation units in demand analysis.

The document describes the View Bar in LEAP, which allows users to navigate between different system views. It details the Analysis View for data and model creation, the Results View for customized charts and maps, the Energy Balance View for standard tables, and Sankey diagrams for visualizing energy flows. Additionally, it explains specialized tools for Cost-Benefit Summaries, Affordability Analyses, Marginal Abatement Cost Curves (MACCs), Decomposition Reports, and the Technology and Environmental Database (TED).

The document provides a comprehensive checklist of data requirements for users of the LEAP software, emphasizing that requirements vary based on whether a user chooses an aggregate top-down or disaggregated bottom-up approach. It details necessary inputs across demographic, macroeconomic, and energy sectors, including demand forecasting metrics and supply-side technical data. The guide notes that national energy balances are the most critical data source and suggests supplementing missing domestic data with international sources or estimates.

The report details the creation of the LEAP Ireland 2050 model to simulate GHG emissions from Ireland's energy and agriculture sectors. It evaluates the impact of the 2019 Climate Action Plan (CAP) targets, specifically electric vehicle (EV) penetration and residential retrofitting, while introducing the Application Script Editing Tool (ASET) to improve model topology and sensitivity analysis.

The document examines the application of the Low Emissions Analysis Platform (LEAP) in Tamil Nadu to help state planners design low-carbon energy pathways. It highlights Tamil Nadu's significant renewable energy potential and current capacity, while warning that continuing current trends without intervention will lead to a sharp increase in emissions due to thermal energy generation. The study argues for an integrated planning approach that includes phasing down inefficient thermal plants, managing capacity additions to avoid surpluses, and implementing demand-side energy-efficiency measures.

The EV Grid Simulator is a tool developed by the World Resources Institute and the UPS Foundation to help stakeholders like city planners and utility operators analyze how EV charging affects distribution transformers and substations. By inputting data on EV characteristics, charging infrastructure, and grid capacity, users can estimate unmanaged energy demand, identify grid vulnerabilities, and calculate optimized charging curves to defer infrastructure upgrades. The tool also explores vehicle-grid integration (VGI) potential for grid services and helps determine optimal charging locations based on grid resilience and parking habits. However, the model is limited by the accuracy of user inputs, does not provide financial cost-benefit analyses, and cannot accommodate vehicles that charge more than once per day.