Search Climate Insights Directory

The document explains the use of tags in LEAP as a flexible alternative or supplement to the software's hierarchical tree structure, particularly for classifying technologies that fit multiple categories. It details how to assign, paint, and manage tags to filter results in charts and tables, and describes the specific toolbar functions for adding, deleting, and color-coding tags.

The document describes the interface and data entry fields of the TED data pages, which allow users to record technical specifications, energy demand, conversion efficiency, pollution control, costs, and environmental loadings for various technologies.

The document describes the functionality of annual emission constraints in LEAP, noting they are used to set maximum annual pollutant levels across an energy system during least cost optimization of a Transformation module.

The document outlines the requirements for using the Integrated Benefit Calculator (IBC) with LEAP, specifying that it supports 161 countries and requires national or multi-national scale areas. It details the necessary pollutant accounting—including CO2, methane, and various short-lived climate pollutants—and the specific data structure (Tree branches) required for the tool to function. Once configured, IBC allows users to analyze premature mortality, crop losses, and temperature changes based on geographic sources and pollutant contributions.

The document details four methods for entering non-fuel costs (capital, O&M, and administrative) in LEAP: Costs Per Activity, Costs Per Device Sold, Total Costs, and Cost of Saved Energy (CSE). It provides the specific calculation logic for each method and advises users to enter costs only for branches where activities or energy intensities deviate from the baseline for comparative analysis.

The document describes the functionality of the Stock variable in LEAP, which is used to model how new energy efficiency or emissions standards for new technology sales gradually improve the average values of a total device stock as older units are replaced. It details the two types of stock-turnover analysis—Transport Analysis for vehicles and standard Stock Analysis for other devices—and explains the choice between top-down and bottom-up data entry. The guide specifies that the Stock variable initializes calculations and works with the Stock Vintage Profile and First Sales Year to determine how stocks evolve through sales, retirement, and forced scrappage.

The document describes the process of using the Add Plugin wizard in LEAP to import .LEAPPlugin files, which can contain standardized methodologies, data structures, and country-specific data when used with the LEAP Cloud Data Server (LCDS). It details how users can map plugin root branches to their model's structure and manage cross-cutting data like fuels and scenarios using either simple or advanced options. After insertion, LEAP provides a summary of changes, developer instructions, and color-coded highlighting of plugin-associated branches.

The document describes the cost-benefit calculation capabilities of LEAP, detailing the types of costs it can track, the importance of defining system boundaries to avoid double counting, and the use of real and discounted currency units to determine Net Present Value (NPV) between scenarios.

The document describes the functionality of endogenous capacity in LEAP, which allows the software to automatically calculate and add capacity based on user-defined processes, addition sizes, and order to ensure a planning reserve margin is maintained. It details the user's role in balancing base and peak load plants, the use of conditional expressions for addition sizes, and the ability to create supply curves using maximum built capacity and next-generation technology markers.

Transformation calculations operate on a demand-driven basis, where modules are processed sequentially to determine the primary fossil and renewable resources needed to meet domestic and export demands. The order of modules in the system tree is critical to the accuracy of the results, as outputs from one module decrement the requirements for the next while adding their own fuel inputs.

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 document provides a comprehensive list of reserved words that cannot be used as names for LEAP branches, fuels, or regions because they are designated for use in expressions. It also outlines technical constraints for branch names, including a length limit of 2 to 50 characters, the requirement to start with an alphabetic character, and a list of permitted special characters.

The 'Renewable Target' variable defines the minimum percentage of fuel requirements that must be met by processes marked as 'Renewable Qualified'. It is used specifically for optimization-based scenarios to implement Renewable Portfolio Standards (RPS). Users must manually designate processes as renewable qualified at the process level, noting that while wind, solar, hydro, and biomass are typical candidates, certain hydro and biomass projects may not qualify for an RPS depending on the situation.

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 describes the functionality of expressions in LEAP, which are mathematical formulae used to define variable values in the Analysis View for both historical data and future scenarios. It details the various methods for editing expressions—including the Expression Builder, Time-Series Wizard, and Function Wizard—and explains the system of expression inheritance, where scenarios can inherit data from parent scenarios to reduce manual entry. The guide also covers color-coding for identifying expression sources, the ability to reference both data and results variables to create endogenous models, and the 'Expression Elaboration' feature for tracking dependencies between variables.

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 how the LEAP software resolves variable expressions in a scenario hierarchy involving multiple inheritance. Using a 'Mitigation' scenario as an example—which inherits from 'industry', 'buildings', and 'transport' scenarios—it outlines a four-step search sequence: first checking the active scenario, then the additional inherited scenarios in order, then walking up the main scenario tree to the 'Baseline' and 'Current Accounts' scenarios, and finally reverting to a default value.

The document describes the technical interface for the LCDS database, detailing the REST web service queries available to retrieve data sets, country lists, and specific time series data for energy and environment models. It provides specific URL examples for accessing UN Population Prospects, World Development Indicators, IDEES, and IPCC Shared Socioeconomic Pathway GDP projections.

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.