{"query_echo":{"q":"","filters":{"analysis_type":["technical guide"]},"warnings":[]},"total":{"value":160,"relation":"eq"},"page":6,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=technical+guide&page=6","next":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=technical+guide&page=7","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"sei-000070","url":"https://climateinsightsdirectory.org/document/sei-000070","title":"Auxiliary Fuel Use","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/Help24/Transformation/Auxiliary_Fuel_Use.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document provides technical instructions for using auxiliary fuels in LEAP's Transformation processes. It distinguishes auxiliary fuels from feedstock fuels by noting they are not converted and do not affect overall process energy efficiency. The guide details how to configure 'own use' consumption via module properties and explains how LEAP handles fuel requirements through its hierarchical module structure and iterative calculations for feedback flows.","ai_generated":true,"unit":"sei-000070#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Auxiliary fuels are defined as energy consumed per unit of energy produced or consumed in a process and differ from feedstock fuels because they are not converted, meaning their energy content is excluded from the process's overall energy efficiency calculations.","ai_generated":true,"unit":"sei-000070#insight-1","date_modified":"2026-10-23"},{"text":"Users can configure a Transformation module to meet its auxiliary fuel needs from its own outputs by enabling the 'Meet auxiliary fuel use from this module's outputs (where possible)' option in the Properties screen.","ai_generated":true,"unit":"sei-000070#insight-2","date_modified":"2026-10-23"},{"text":"When 'own use' fuel consumption is enabled, these fuels do not appear in standard energy balance reports because they are internal to the process; however, they can be tracked using the 'Outputs by Output Fuel' and 'Inputs' reports in Results View.","ai_generated":true,"unit":"sei-000070#insight-3","date_modified":"2026-10-23"},{"text":"LEAP manages fuel requirements by defaulting to supplies from lower-level transformation modules or imports, but it uses iterative calculations for 'feedback flows' when a lower module consumes fuel produced by a higher module, such as an oil refinery consuming electricity.","ai_generated":true,"unit":"sei-000070#insight-4","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000085","url":"https://climateinsightsdirectory.org/document/sei-000085","title":"Stock Analysis Calculations","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://cdn.leap.sei.org/Help24/Demand/Stock_Analysis_Calculations.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines the calculation methodology for stock analysis of demand technologies, detailing how stock turnover, energy intensity, energy consumption, and emissions are derived using variables such as vintage, survival profiles, and degradation factors.","ai_generated":true,"unit":"sei-000085#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Stock turnover is calculated based on the number of vehicles added in a year (Sales), the number of existing devices (Stock), and a lifecycle profile representing the fraction of devices surviving over time (Survival). The maximum number of vintage years (V) is automatically determined from the survival profile, capped at 30 years.","ai_generated":true,"unit":"sei-000085#insight-1","date_modified":"2026-10-23"},{"text":"Energy intensity for devices is determined by the energy use per new device purchased in a given year, adjusted by a degradation factor that represents how energy intensity changes as the technology ages; this factor equals 1 when the calendar year matches the vintage year.","ai_generated":true,"unit":"sei-000085#insight-2","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000089","url":"https://climateinsightsdirectory.org/document/sei-000089","title":"Introduction to Optimization","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/help24/Optimization/OptimizationIntroduction.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes the optimization capabilities of LEAP, which are powered by the open-source NEMO framework. It details the difference between partial cost optimization for single modules and full energy system optimization (ESO) across all transformation modules and selected demand end-uses. The guide explains that optimization aims to find the least-cost capacity expansion and dispatch by minimizing the total net present value of social costs, subject to constraints like emission caps. It also covers the implicit Reference Energy System (RES) in LEAP and the options for perfect versus limited foresight optimization.","ai_generated":true,"unit":"sei-000089#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"LEAP performs optimization through integration with NEMO, an open-source framework written in the Julia programming language. NEMO is a separate, free installation that connects to LEAP to calculate the least-cost capacity expansion and process dispatch.","ai_generated":true,"unit":"sei-000089#insight-1","date_modified":"2026-10-23"},{"text":"Optimization in LEAP is available in two primary modes: partial cost optimization, which targets a single Transformation module (such as electricity generation), and full energy system optimization (ESO). ESO allows for simultaneous least-cost calculations across all Transformation modules, optional optimization of demand end-uses with useful energy demand, and the modeling of transmission systems like pipelines and electricity networks.","ai_generated":true,"unit":"sei-000089#insight-2","date_modified":"2026-10-23"},{"text":"The optimization framework defines the 'optimal' system as the configuration with the lowest total net present value of social costs over the calculation period. This calculation considers capital costs, salvage values or decommissioning costs, fixed and variable O&M costs, fuel costs, and environmental externality values, while adhering to constraints such as emission caps, resource reserves, and technology penetration limits.","ai_generated":true,"unit":"sei-000089#insight-3","date_modified":"2026-10-23"},{"text":"LEAP utilizes an implicit Reference Energy System (RES), meaning it automatically creates connections between resources, processes, and end-use demands based on module properties and ordering, rather than requiring the user to explicitly specify the network.","ai_generated":true,"unit":"sei-000089#insight-4","date_modified":"2026-10-23"},{"text":"The software supports two types of foresight: perfect foresight, where all years are optimized simultaneously, and limited foresight, where years are divided into groups and optimized separately, with each group providing the starting conditions for the next.","ai_generated":true,"unit":"sei-000089#insight-5","date_modified":"2026-10-23"}],"climate_tags":["carbon price","Renewable portfolio standard"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000092","url":"https://climateinsightsdirectory.org/document/sei-000092","title":"Useful Energy Demand","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/Help24/Results_Categories/Useful_Energy_Demand.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document defines useful energy demand as the portion of final energy demand available for work after device losses, noting that while typically smaller than final energy demand, it can be larger for devices like heat pumps. It also describes the software settings required to calculate these demands across all branches.","ai_generated":true,"unit":"sei-000092#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Useful energy demand is defined as the portion of final energy demand that remains to perform work—such as cooking, heating, or cooling—after accounting for losses within the consuming devices.","ai_generated":true,"unit":"sei-000092#insight-1","date_modified":"2026-10-23"},{"text":"While useful energy demand is generally lower than final energy demand, certain devices like heat pumps and refrigerators can result in useful energy demand exceeding final energy demand because they utilize ambient energy in addition to electricity.","ai_generated":true,"unit":"sei-000092#insight-2","date_modified":"2026-10-23"},{"text":"By default, the software only calculates useful energy demand for branches undergoing useful energy analysis, but users can enable calculations for all demand branches via the 'Settings: Scope' screen by activating 'Calculate Useful Energy for All Demands'.","ai_generated":true,"unit":"sei-000092#insight-3","date_modified":"2026-10-23"}],"climate_tags":["energy demand","energy efficiency"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000096","url":"https://climateinsightsdirectory.org/document/sei-000096","title":"Minimum Share of Production","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/help24/Transformation/Minimum_Share_of_Production.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The 'Minimum Share of Production' variable ensures a specific process supplies at least a defined percentage of a fuel's total annual production, regardless of cost-effectiveness, to model policy targets or contractual obligations.","ai_generated":true,"unit":"sei-000096#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Minimum Share of Production variable is used in energy-system-optimization to mandate a minimum contribution from a specific technology or supply source, overriding least-cost dispatch if a cheaper alternative is available. It is typically used to model domestic-supply targets, renewable energy targets, must-run obligations, or take-or-pay contracts.","ai_generated":true,"unit":"sei-000096#insight-1","date_modified":"2026-10-23"},{"text":"The variable is expressed as a percentage from 0 to 100, with a default value of 0. It can be configured as a static value, a conditional value based on the year, or a linear interpolation between two dates.","ai_generated":true,"unit":"sei-000096#insight-2","date_modified":"2026-10-23"},{"text":"There are three primary technical constraints when using this variable: it is only visible in partial and energy-system-optimization scenarios; it cannot exceed the 'Maximum Share of Production' for the same process and year; and the sum of all minimum shares for a single fuel cannot exceed 100%.","ai_generated":true,"unit":"sei-000096#insight-3","date_modified":"2026-10-23"}],"climate_tags":["energy modeling"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000116","url":"https://climateinsightsdirectory.org/document/sei-000116","title":"Transformation Analysis","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/Help24/Transformation/Transformation.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document provides technical instructions for setting up Transformation analyses in LEAP, detailing the hierarchical structure of modules and processes used to model energy conversion sectors. It outlines the four-step setup process—identifying modules, sequencing them by energy flow, setting properties, and entering technology data—while explaining the roles of feedstock, auxiliary, and output fuels in calculating primary resource requirements.","ai_generated":true,"unit":"sei-000116#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Transformation analysis in LEAP simulates the entire energy chain from the extraction of primary resources and fuel imports to the point of final consumption, allowing for alternative scenarios based on different policy and technology assumptions.","ai_generated":true,"unit":"sei-000116#insight-1","date_modified":"2026-10-23"},{"text":"The model is structured hierarchically: modules represent energy conversion sectors (e.g., electricity generation or oil refining), and processes within those modules represent specific technologies or groups of technologies (e.g., individual power plants).","ai_generated":true,"unit":"sei-000116#insight-2","date_modified":"2026-10-23"},{"text":"Energy flows are calculated based on a specific sequence of modules; transmission and distribution are typically placed near the top (closest to demand), while primary resource extraction is placed at the bottom.","ai_generated":true,"unit":"sei-000116#insight-3","date_modified":"2026-10-23"},{"text":"The system distinguishes between three types of fuels in the transformation process: feedstock fuels (converted within the process), auxiliary fuels (used for subsidiary or own-use consumption), and output fuels (the results of the module's processes).","ai_generated":true,"unit":"sei-000116#insight-4","date_modified":"2026-10-23"},{"text":"Transformation calculations are driven by demand analysis results, where each module satisfies one set of fuel requirements while creating new input requirements, ultimately determining the total need for primary resources and imports.","ai_generated":true,"unit":"sei-000116#insight-5","date_modified":"2026-10-23"}],"climate_tags":["energy modeling","electricity generation","oil refining","district heating","energy efficiency"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000122","url":"https://climateinsightsdirectory.org/document/sei-000122","title":"Setting Up Optimization","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/help24/Optimization/Setting_Up_Optimization_in_LEAP.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines the technical setup for optimization in LEAP, including solver selection, required settings, and data input requirements for transformation modules and energy storage.","ai_generated":true,"unit":"sei-000122#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"LEAP supports several solvers for optimization; GLPK, Cbc, and HIGHS are pre-installed, while MOSEK requires a separate license key, and CPLEX and GUROBI must be purchased and installed independently.","ai_generated":true,"unit":"sei-000122#insight-1","date_modified":"2026-10-23"},{"text":"To enable optimization, users must select a Transformation module (typically for electricity generation), create or select a scenario, and set the 'Optimize' variable to 'Yes' or specify a particular solver.","ai_generated":true,"unit":"sei-000122#insight-2","date_modified":"2026-10-23"},{"text":"Optimization calculations require more extensive data than basic simulation, specifically requiring process efficiencies, availability rates, unit capital costs, fixed and variable operating & maintenance costs, and fuel costs.","ai_generated":true,"unit":"sei-000122#insight-3","date_modified":"2026-10-23"},{"text":"Energy storage modeling is exclusively supported during optimization and requires specific data including Starting Charge, Minimum Charge, and Full Load Hours.","ai_generated":true,"unit":"sei-000122#insight-4","date_modified":"2026-10-23"},{"text":"For multi-region models using full energy system optimization, users can optionally model pipelines and transmission networks, a feature not available in simulation or partial optimization scenarios.","ai_generated":true,"unit":"sei-000122#insight-5","date_modified":"2026-10-23"}],"climate_tags":["electricity generation","energy storage","electric power transmission","greenhouse gas emissions"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000146","url":"https://climateinsightsdirectory.org/document/sei-000146","title":"Environmental Results","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/help24/Results_Categories/Environmental_Results.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document provides technical instructions for displaying environmental results in LEAP, detailing the use of Global Warming Potential (GWP) factors based on IPCC reports and the four available reporting formats for GHG emissions. It specifies that LEAP employs a territorial accounting approach aligned with IPCC and UNFCCC guidelines for national inventories.","ai_generated":true,"unit":"sei-000146#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"LEAP allows users to report the combined global warming potential (GWP) of greenhouse gases measured in either Carbon (C) or Carbon Dioxide (CO2), with CO2 reports being 44/12 times larger than carbon reports. GWP factors default to IPCC 100-year integration values but can be manually edited or switched between sets from the first, second, third, and fourth IPCC assessment reports, with time horizon options of 20, 100, and 500 years.","ai_generated":true,"unit":"sei-000146#insight-1","date_modified":"2026-10-23"},{"text":"Environmental results in LEAP are available in four formats: individual effects in standard physical units; direct GHG emissions at the point of production (including sinks); direct demand plus indirect transformation GHG emissions allocated to demand branches (100-year GWP); and indirect transformation GHG emissions allocated to demand branches (100-year GWP). The latter two formats must be enabled in the 'General: Results to Save Screen'.","ai_generated":true,"unit":"sei-000146#insight-2","date_modified":"2026-10-23"},{"text":"LEAP utilizes a territorial accounting approach for environmental results, consistent with IPCC and UNFCCC National GHG Emissions Inventory Guidelines. This method includes combustion and fugitive emissions occurring within the defined LEAP area boundaries—including those from energy operations for exported final fuels—but excludes emissions from primary fuels exported and consumed elsewhere.","ai_generated":true,"unit":"sei-000146#insight-3","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","global warming potential","greenhouse gas inventory","United Nations Framework Convention on Climate Change"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000157","url":"https://climateinsightsdirectory.org/document/sei-000157","title":"Maps","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/help24/Views/Maps.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document provides technical instructions for displaying LEAP results on maps, which requires the installation of MapWinGIS. It details two primary map types: Region Maps, which use gradient, bar chart, or pie chart formats to show results for multi-region areas, and Gridded Maps, which allocate energy use and emissions to grid squares using proxy GIS data. The guide also describes map navigation tools, categorization methods for gradient maps (such as Natural Breaks and Equal Intervals), and formatting options including background image selection and transparency settings.","ai_generated":true,"unit":"sei-000157#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Visualizing results on maps in LEAP requires the separate installation of MapWinGIS software.","ai_generated":true,"unit":"sei-000157#insight-1","date_modified":"2026-10-23"},{"text":"LEAP supports Region Maps for multi-region areas, which can be displayed as gradient maps, bar chart maps, or pie chart maps. Gradient maps use color coding and can be categorized using methods such as Natural Breaks, Unique Values, Equal Intervals, Equal Count, or Standard Deviation.","ai_generated":true,"unit":"sei-000157#insight-2","date_modified":"2026-10-23"},{"text":"Gridded Maps can be used for both single and multi-region areas to display energy and environmental loading results. This requires enabling the 'Map Results to Grid' option, loading proxy GIS data sets for socioeconomic variables to allocate energy use and emissions, and using the Geography variable for allocation.","ai_generated":true,"unit":"sei-000157#insight-3","date_modified":"2026-10-23"},{"text":"The software provides a Split screen view for gridded maps that synchronizes a map with a color-coded table of values, allowing users to hover over the map to scroll the table and view time trends for specific grid squares.","ai_generated":true,"unit":"sei-000157#insight-4","date_modified":"2026-10-23"},{"text":"Map formatting options allow users to select background images from internet tile services like Bing and Open Maps, adjust the number of value divisions (linear, equal count, log, and reverse log), and set the transparency of the chart layer.","ai_generated":true,"unit":"sei-000157#insight-5","date_modified":"2026-10-23"}],"climate_tags":["environmental emission"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000171","url":"https://climateinsightsdirectory.org/document/sei-000171","title":"Extraction-Based Environmental Loadings","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://cdn.leap.sei.org/Help24/Effects/Extraction-Based_Environmental_Loadings.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes an optional methodology in LEAP for allocating emissions to the area producing the resource rather than where it is combusted. This approach helps analyze a country's dependence on fossil fuel production and its vulnerability to a global low-carbon transition. Users can specify emission factors via the Resources\\Primary branches, using manual entries, mathematical formulae, or links to the Technology and Environmental Database (TED).","ai_generated":true,"unit":"sei-000171#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"LEAP's standard environmental loading calculations follow the territorial approach used for UNFCCC reporting and IPCC guidelines, where emissions are accounted for based on the location of combustion. In contrast, extraction-based environmental loadings allocate emissions to the area producing the emissions.","ai_generated":true,"unit":"sei-000171#insight-1","date_modified":"2026-10-23"},{"text":"Comparing territorial and extraction-based loadings serves as a technique to illustrate a country's or economy's dependence on fossil fuel production and its potential vulnerability during a global transition toward low carbon economies.","ai_generated":true,"unit":"sei-000171#insight-2","date_modified":"2026-10-23"},{"text":"Extraction-based loadings are configured in the LEAP tree under Resources\\Primary branches using emission factors. These factors can be manual entries, mathematical formulae (such as those based on sulfur or carbon content), or linked from the Technology and Environmental Database (TED).","ai_generated":true,"unit":"sei-000171#insight-3","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","petroleum industry"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000172","url":"https://climateinsightsdirectory.org/document/sei-000172","title":"Geography","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://cdn.leap.sei.org/help24/Demand/Geography.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Geography variable allows users to allocate energy use and emissions data among map squares within a current area or region. This process requires the use of four specific allocation functions: Allocate, AllocateEvenly, AllocateNone, and PointSource. Geographic mappings are configured in the General: Geographic Mapping screen, and the resulting data is displayed on gridded maps in the Results View.","ai_generated":true,"unit":"sei-000172#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Geography variable is used to allocate energy use and emissions at a specific branch among map squares within a current area for one region data sets, or among map squares of the shape for the current region in multi-region areas.","ai_generated":true,"unit":"sei-000172#insight-1","date_modified":"2026-10-23"},{"text":"The Geography variable must be used with one of four specific functions to allocate values: Allocate, AllocateEvenly, AllocateNone, and PointSource.","ai_generated":true,"unit":"sei-000172#insight-2","date_modified":"2026-10-23"}],"climate_tags":["environmental emission"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000174","url":"https://climateinsightsdirectory.org/document/sei-000174","title":"Lifecycle Profiles","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://cdn.leap.sei.org/Help24/Supporting_Screens/LifeCycle_Profiles.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document explains the two types of Lifecycle Profiles in LEAP: Aging Profiles, which track how variables like fuel economy change as devices age relative to a new device, and Distribution Profiles, which describe vintage distributions within a stock. It details the software interface for creating, editing, and importing these profiles, including the option to define values via manual data points or an exponential curve formula.","ai_generated":true,"unit":"sei-000174#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Lifecycle Profiles are categorized into Aging Profiles and Distribution Profiles. Aging Profiles track how a variable changes as devices age, expressed as a percentage relative to a new device (starting at 100%), while Distribution Profiles describe the distribution of vintages within a stock of devices used in Current Accounts.","ai_generated":true,"unit":"sei-000174#insight-1","date_modified":"2026-10-23"},{"text":"Users can manage profiles through a two-pane interface that allows for the creation, renaming, and deletion of profiles, as well as the ability to import or export data using Excel spreadsheets.","ai_generated":true,"unit":"sei-000174#insight-2","date_modified":"2026-10-23"},{"text":"Profile values can be entered manually as data points or generated using an exponential curve based on the formula V[t] = V[t-1] x Exp(t * Constant).","ai_generated":true,"unit":"sei-000174#insight-3","date_modified":"2026-10-23"}],"climate_tags":["energy transition","fuel economy"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000196","url":"https://climateinsightsdirectory.org/document/sei-000196","title":"Process Efficiency, Losses and Heat Rates","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/help24/Transformation/Efficiency_Data.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines how to input process efficiency data in LEAP, detailing the use of percentage efficiency, heat rates, and energy losses. It provides typical values for steam generation and hydropower and specifies the application of these methods to storage and distribution systems.","ai_generated":true,"unit":"sei-000196#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Process efficiency data in the software can be entered using three distinct methods: percentage efficiency, heat rate, or percentage energy losses, with the specific method determined by the module properties.","ai_generated":true,"unit":"sei-000196#insight-1","date_modified":"2026-10-23"},{"text":"The default efficiency method calculates the ratio of energy outputs to feedstock energy inputs, excluding co-product energy recovery. Typical values include approximately 33% for base-load steam generation, while 100% is often used for hydropower, renewable electricity generation, and imports.","ai_generated":true,"unit":"sei-000196#insight-2","date_modified":"2026-10-23"},{"text":"Alternative data entry methods include the heat rate, which is the amount of feedstock fuel needed per unit of energy produced (common for electricity plants), and percentage energy losses, which is particularly useful for transmission and distribution systems.","ai_generated":true,"unit":"sei-000196#insight-3","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency","electricity generation","hydropower"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000206","url":"https://climateinsightsdirectory.org/document/sei-000206","title":"Capacity Credit","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/help24/Transformation/Capacity_Value.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","analysis_type"],"summary":{"text":"The document defines capacity credit as the fraction of rated capacity considered firm for calculating module reserve margins. It notes that thermal plants typically do not exceed average annual Maximum Availability, while intermittent renewable plants have lower values, and some sources, such as imported electricity, may have zero capacity credit. For endogenous capacity planning, the document specifies that at least some plants must have a capacity credit greater than zero to contribute to the reserve margin, and suggests a first-order approximation for renewable plants based on the ratio of their availability to that of a standard thermal plant.","ai_generated":true,"unit":"sei-000206#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Capacity credit is the portion of a process's rated capacity that is treated as firm when calculating the module reserve margin.","ai_generated":true,"unit":"sei-000206#insight-1","date_modified":"2026-10-23"},{"text":"Capacity credit values vary by technology: thermal plants are generally limited by average annual Maximum Availability, intermittent renewables have lower values, and some sources like imported electricity may have zero credit.","ai_generated":true,"unit":"sei-000206#insight-2","date_modified":"2026-10-23"},{"text":"In endogenous capacity specifications, plants with zero capacity credit do not contribute to the planning reserve margin; therefore, at least some plants in a module must have a credit greater than zero.","ai_generated":true,"unit":"sei-000206#insight-3","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000208","url":"https://climateinsightsdirectory.org/document/sei-000208","title":"Transport Analysis Calculations","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/Help24/Demand/Vintaging_Calculations.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document details the calculations for transport analysis, focusing on how vehicle stock is tracked by vintage and how fuel economy, mileage, and emissions degrade over a vehicle's lifecycle. It specifies that stock is determined by sales and survival profiles, while energy and pollutant emissions are calculated based on distance traveled and age-related degradation factors.","ai_generated":true,"unit":"sei-000208#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Vehicle stock is calculated based on the number of vehicles added in a specific year (sales) multiplied by a survival fraction derived from a lifecycle profile, with a maximum vintage tracking period of 30 years.","ai_generated":true,"unit":"sei-000208#insight-1","date_modified":"2026-10-23"},{"text":"Energy consumption and distance-based pollution emissions are calculated by incorporating degradation factors that represent how fuel economy, mileage, and emission rates change as a vehicle ages from its model year.","ai_generated":true,"unit":"sei-000208#insight-2","date_modified":"2026-10-23"}],"climate_tags":["fuel economy","greenhouse gas"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000224","url":"https://climateinsightsdirectory.org/document/sei-000224","title":"Scenarios","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://cdn.leap.sei.org/Help24/Concepts/Scenarios.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes how LEAP uses scenarios as self-consistent storylines to model future energy systems under various socioeconomic and policy conditions. It details the relationship between scenarios and Current Accounts data, the inclusion of sensitivities, and the use of a hierarchical expression inheritance system to streamline data management.","ai_generated":true,"unit":"sei-000224#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"In LEAP, scenarios are defined as self-consistent narratives used to evaluate how energy systems evolve based on specific socioeconomic settings and policy conditions, allowing users to conduct \"what if\" analyses on factors such as appliance efficiency, generation capacity expansion, and renewable energy adoption.","ai_generated":true,"unit":"sei-000224#insight-1","date_modified":"2026-10-23"},{"text":"All scenarios in the software rely on a shared set of Current Accounts data, which can consist of a single base year or multiple historical years; the scenario timeline begins from the First Scenario Year and continues to the End Year.","ai_generated":true,"unit":"sei-000224#insight-2","date_modified":"2026-10-23"},{"text":"LEAP incorporates sensitivities—variations in socioeconomic assumptions—within scenarios, necessitating that cost-benefit analyses only compare scenarios that share the same socioeconomic assumptions. To reduce data entry, LEAP employs a hierarchical structure where scenarios inherit expressions from parent scenarios unless specific differences are entered.","ai_generated":true,"unit":"sei-000224#insight-3","date_modified":"2026-10-23"}],"climate_tags":["energy modeling"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000240","url":"https://climateinsightsdirectory.org/document/sei-000240","title":"Cost-Benefit Analysis","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/Help24/Concepts/Social_Cost-Benefit_Analysis.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes the functionality of LEAP's Cost-Benefit View, which calculates capital, operating, and maintenance costs across energy systems, including optional environmental externalities. It emphasizes that the tool focuses on social opportunity costs rather than consumer prices or financial viability, and allows users to define costing boundaries to accommodate data limitations regarding upstream technologies.","ai_generated":true,"unit":"sei-000240#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"LEAP's cost-benefit analysis calculates costs for the energy system, including capital and operating maintenance for technologies, primary resource extraction, and fuel imports/exports, with an option to include environmental externalities by assigning costs to pollutant emissions.","ai_generated":true,"unit":"sei-000240#insight-1","date_modified":"2026-10-23"},{"text":"The analysis focuses on social opportunity costs to society rather than final consumer prices or the financial viability of a project, as prices may be distorted by subsidies or transfer payments.","ai_generated":true,"unit":"sei-000240#insight-2","date_modified":"2026-10-23"},{"text":"Users can define a costing boundary in the Transformation module to exclude costs from upstream activities, such as oil refining or coal mining, when such data is unavailable.","ai_generated":true,"unit":"sei-000240#insight-3","date_modified":"2026-10-23"}],"climate_tags":["energy modeling","energy policy"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000241","url":"https://climateinsightsdirectory.org/document/sei-000241","title":"Feedstock Fuel Shares","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://cdn.leap.sei.org/help24/Transformation/Feedstock_Fuel_Shares.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document defines feedstock fuels as those converted within a process, noting that process efficiency is the ratio of total output energy to total feedstock energy. It distinguishes feedstock fuels from auxiliary fuels, which are subsidiary energy inputs not converted in the process and therefore excluded from efficiency calculations. Most processes typically use a single feedstock fuel with a 100% share.","ai_generated":true,"unit":"sei-000241#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Process efficiency is calculated as the ratio of the total energy content of all output fuels produced to the total energy content of all feedstock fuels consumed.","ai_generated":true,"unit":"sei-000241#insight-1","date_modified":"2026-10-23"},{"text":"Feedstock fuels are distinct from auxiliary fuels; while feedstocks are converted within the process, auxiliary fuels are subsidiary energy inputs that are not converted and are excluded from the overall energy efficiency calculation.","ai_generated":true,"unit":"sei-000241#insight-2","date_modified":"2026-10-23"},{"text":"A fuel share represents the percentage of energy input a specific feedstock provides to its parent process, and in most cases, a single feedstock fuel is used, resulting in a 100% share in Current Accounts.","ai_generated":true,"unit":"sei-000241#insight-3","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency","energy modeling"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000265","url":"https://climateinsightsdirectory.org/document/sei-000265","title":"Non Energy Sector Effect Loading","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/help24/Non-Energy/Non-Energy_Sector_Analysis.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document provides technical instructions for using the LEAP software's 'Non Energy Sector Effect Loading' feature to track non-energy greenhouse gas emissions. It describes how to enable the feature via the Scope tab, build a hierarchical structure of category and effect branches, and input annual total emissions loadings, which can be entered as static values or calculated using expressions.","ai_generated":true,"unit":"sei-000265#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Non Energy Sector feature in LEAP is primarily used to complement energy sector analysis by including inventories and scenarios of non-energy sector greenhouse gas (GHG) emissions.","ai_generated":true,"unit":"sei-000265#insight-1","date_modified":"2026-10-23"},{"text":"Users set up non-energy sector analysis by enabling the 'Non Energy Sector Effect Loadings' box in the Scope tab of the Settings screen, which creates a 'Non Energy' top-level category branch.","ai_generated":true,"unit":"sei-000265#insight-2","date_modified":"2026-10-23"},{"text":"Unlike energy demand analysis, which calculates effects based on consumption and emission factors, the Non Energy Sector requires users to specify total annual loadings directly, though LEAP expressions can be used to calculate these as functions of independent variables.","ai_generated":true,"unit":"sei-000265#insight-3","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]},{"id":"sei-000278","url":"https://climateinsightsdirectory.org/document/sei-000278","title":"Sales and Sales Share","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://leap.sei.org/help24/Demand/Sales.htm","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes the application of Sales and Sales Share variables in stock-turnover analyses to model how new energy efficiency or emissions standards for new technologies impact the overall average values of a total stock as older devices are replaced. It outlines two analysis types—Transport Analysis for vehicles and standard Stock Analysis for other devices—and explains that data can be entered via bottom-up technology branches or a top-down approach using shares. It further notes that Sales variables operate from a specified First Sales Year and are paired with Survival Profiles to model device retirement.","ai_generated":true,"unit":"sei-000278#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Stock-turnover analysis is used to model the gradual improvement of average values across a total stock of devices as new technologies with updated energy efficiency or emissions standards replace older ones.","ai_generated":true,"unit":"sei-000278#insight-1","date_modified":"2026-10-23"},{"text":"Users can input Stocks and Sales data using either a bottom-up method at technology branches or a top-down approach where total values are entered at top-level branches and distributed via Stock Share and Sales Share variables.","ai_generated":true,"unit":"sei-000278#insight-2","date_modified":"2026-10-23"},{"text":"The Sales variable tracks the addition of new devices starting from the First Sales Year, while a Survival Profile is used to define the rate at which these devices are retired over time.","ai_generated":true,"unit":"sei-000278#insight-3","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency","emission standard"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical guide"]}]}