The general categories of data are: • Time horizon • System-wide global parameters • End-use energy service demands • Energy carriers • Resource technology profiles • Process and demand
Description
MARKAL is a versatile, data-driven optimization framework that integrates energy, economy, and environmental considerations, allowing users to customize it based on their specific data and requirements Originally developed in the late 1970s at Brookhaven National Laboratory, the model has evolved over decades to provide comprehensive insights into sustainable energy planning.
The International Energy Agency (IEA) adopted the MARKAL model to advance energy system analysis and created the Energy Technology and Systems Analysis Program (ETSAP) ETSAP is a collaborative network of modelers and developers that convenes biannually to discuss model enhancements, extensions, and practical applications This active and interactive community ensures that MARKAL continuously benefits from ongoing improvements and shared expertise, strengthening its role in energy planning and policy analysis.
The MARKAL model is built around a network diagram known as the Reference Energy System (RES), which visualizes the entire energy system from resource supply to end-use demand The RES serves as a foundational framework that represents the flow of energy through various stages, facilitating comprehensive energy analysis This model incorporates a list of technology types, including resource, process, conversion, and demand technologies, enabling detailed modeling of energy production, transformation, and consumption By structuring the energy system in this way, the MARKAL model provides a robust tool for evaluating energy options and optimizing future energy strategies.
1) Resource technologies represent the extraction cost and availability of resources such as coal, oil, and natural gas
2) Conversion technologies represent the conversion of fuel inputs into electricity.
3) Process technologies represent other means of converting resources into end-use fuels including refineries and coal-to-liquid processes
4) Demand technologies represent the technologies that meet specific user demands, such as vehicles, air conditioners, and water heaters
These technologies ultimately support the final stage of energy consumption, which involves fulfilling end-use demands for essential energy services These demands encompass a range of applications, such as residential lighting, commercial air conditioning, and automobile passenger miles traveled.
2 For a detailed description of MARKAL, see the ETSAP MARKAL users-manual at http://www.etsap.org/documentation.asp (last accessed on August 13, 2019)
Figure 1 Illustrative Reference Energy System
The system's stages are interconnected through energy carriers, which are forms of energy produced and consumed throughout the process Emissions specific to each technology are meticulously tracked at individual stages, ensuring precise monitoring Additionally, fuel-related emissions are categorized under process technologies, providing a comprehensive overview of the environmental impact across the entire system.
Data Requirements
Time Horizon
The time horizon is a user-defined span of time, consisting of equally long periods measured in years In this context, it spans from 2010 to 2055, divided into 5-year intervals This structured segmentation allows for detailed analysis and planning over an extended future period.
System-wide Parameters
System-wide, or global, parameters are assumptions that apply universally to the entire model The discount rate is used to calculate the annualized investment cost of technologies, ensuring consistency across financial analyses All costs must be expressed in the same monetary unit and discounted to a common reference year, such as 2005 U.S dollars, for standardization The model incorporates load fractions to characterize variations in end-use demands and load duration curves for electricity production It divides the year into three seasons—summer, winter, and intermediate—and four daily periods—day AM, day PM, night, and peak—resulting in a 12-step load duration curve representation that captures demand fluctuations throughout the year.
End-Use Energy Service Demands
End-use demands refer to the specific energy services required by individuals and commercial entities, such as residential cooling, personal transportation, and industrial process heat These demands focus on the provision of services like steel manufacturing, transportation, office lighting, and home heating, rather than the consumption of a particular energy type Measured in units of useful energy, end-use demands vary by sector; for example, transportation demand is expressed in miles traveled, lighting demand in billion lumens per year, and industrial energy needs in petajoules (PJ) Key data are categorized by sector to accurately assess energy service needs across the economy.
• Projections for useful energy demand services by sector, and
• The load shape of the demand profile by season/day-night-peak specifically defined for end-use demands using electricity.
Energy Carriers
Energy carriers in the renewable energy system include fossil fuels such as coal with varying sulfur content, crude oil, refined oil products, natural gas, electricity, synthetic fuels, and renewable sources like biomass, solar, wind, geothermal, and hydro They serve as essential links between different technologies within the energy system by flowing between various energy sources and consumers The system ensures that the total production of each energy carrier in a given period meets or exceeds its total consumption Key data related to energy carriers are crucial for understanding and optimizing the reference energy system's performance and sustainability.
• Investment and operation and maintenance (O&M) cost for electricity transmission and distribution systems, and
• Reserve margin or amount of installed electricity production capacity above the highest average annual demand.
Resource Technologies
Resource technologies serve as essential entry points for raw fuels entering and exiting the energy system, encompassing imports, exports, mining, extraction, and renewable energy sources These technologies are typically characterized using stepwise supply curves, which illustrate the quantity of resources available at specific prices for each model period Key data related to resource technologies include supply capacity, cost thresholds, and potential availability, enabling accurate modeling of resource flows and informing sustainable energy planning.
• Bounds indicating the size of each step on each resource supply curve,
• A corresponding resource supply cost for each supply step,
• Cumulative resources limits indicating the total amount of a resource at a particular supply step that can be delivered over the entire modeling horizon (e.g., total proven size of a petroleum reservoir), and
• Cost of transporting resources, either within a region or from region to region
Process, Conversion, and Demand Technologies
Process technologies change the form, characteristics, or location of energy carriers Examples of process technologies include oil refineries and hydrogen production technologies
Conversion technologies facilitate electricity production by transforming one form of energy into another, such as converting coal into electricity These conversion plants are characterized by their operational schedules, functioning on seasonal or day-night cycles In contrast, demand technologies are devices that directly fulfill end-use service demands, including vehicles, furnaces, and electrical appliances These technologies are evaluated based on parameters like costs, fuel consumption, efficiency, and availability, which are essential for assessing their performance and suitability.
• Cost of investing in new capacity,
• Fixed O&M costs (incurred at the level of installed capacity),
• Variable O&M costs (incurred during the operation of installed capacity),
• Fuel delivery costs corresponding to any sectoral difference in the price of an energy carrier,
• Technical efficiency (usually defined as the ratio between the sum of energy carrier or useful energy service outputs to the sum of energy carrier inputs),
• Model year in which the technology first becomes available for investment,
Availability factors for process technologies and capacity utilization factors for demand technologies are key metrics that indicate the maximum percentage of annual or seasonal (day/night) operational availability, or a fixed percentage of annual (or seasonal/day-night-peak) capacity utilization per unit of installed capacity, ensuring optimal performance and efficiency forecasting.
• Existing installed capacity at the start of the model time horizon,
• Limits on capacity in the form of incremental new investment (absolute or growth rate) or total installed capacity, and
Hurdle rates, also known as technology-specific discount rates, are utilized to account for non-economic and behavioral factors influencing investment decisions These factors include consumer preferences, expectations of rapid returns, and information gaps, providing a more comprehensive evaluation of investment viability.
Emission Factors
The COMET-NYC monitors emissions linked to energy consumption by analyzing activity levels, installed capacity, and new capacity investments It focuses on key environmental indicators, particularly pollutant emissions, providing comprehensive data to assess the environmental impact of energy-related activities.
• Emissions per unit of technology activity, installed capacity, or new investment, and
• Emission constraints, which can take the form of a cap on total emissions in a year, or a cumulative cap on emissions over the entire modeling horizon.
MARKAL Set Definitions and Naming Conventions
The MARKAL structure uses a pre-defined set of definitions and naming conventions to organize the RES Each set represents technologies, energy carriers, or constraints of a similar type
Within any given set, MARKAL has numerous mandatory parameters that need to be specified in the model The main set memberships are listed in Table 1
Set Name Set Definition Set Name Set Definition
SRCENCP Resource Technology ENC Standard
SEP_EXP Export ECV Conversion
SEP_IMP Import EFS Fossil
SEP_MIN Extraction ENU Nuclear
SEP_RNW Renewable ERN Renewable
SEP_STK Stockpile ESY Synthetic
PRC Process Technology ELC Electric
PRE Energy LTH District Heat
PRW Material (weight) FEQ Fossil Equivalent
ELE Electric Conversion DM_COM Commercial
BAS Baseload DM_IND Industrial
NBN Non-baseload DM_RES Residential
STG Storage DM_TRN Transportation
DMD Demand Technology ADRATIO User defined constrains
ENV Emissions REG_ADR Regional constraint
In addition to pre-defined sets, standard naming conventions are used to name the technologies used in the model For example, domestically mined fossil fuel step curves start
10 with MIN (a standard convention) followed by the energy type and the supply step (i.e
MINNGAD6 is the name for the 6 th step in the supply curve for domestically mined natural gas) The technology naming conventions are listed in Table 2
MIN = Fossil Fuels P = Process E = Electric Conversion COM = Commercial
RNW = Renewables SC = Collectors IND = Industrial
IMP = Imports SE = Emissions Tracking RES = Residential
EXP = Exports X = Tracking TRN = Transportation
The standard convention for naming energy carriers involves using a concise three- to four-character core name for each primary energy source, ensuring clarity and consistency These core names are supplemented with a two- or three-character descriptor to specify particular characteristics or forms of the energy carriers For detailed references, please consult Table 3, which provides a comprehensive list of the core names for various energy carriers, supporting efficient communication and categorization within the energy sector.
Coal COA Landfill Gas LFG
Compressed Natural Gas CNG Liquid Petroleum Gas LPG
Conventional Gasoline GSL Natural Gas NGA
Distillate Heating Oil DSH Natural Gas Liquids NGL
Hydropower HYD Residual Fuel Oil RFH
The COMET-NYC utilizes publicly available data from New York City’s annual greenhouse gas inventory (GHGI) reports to accurately estimate energy consumption across residential, commercial, and industrial buildings This modeling covers a time period from 2010 to 2055, with data projections updated every five years to assess long-term trends and inform climate action strategies.
The reporting intervals for greenhouse gas emissions include key years such as 2010, 2011, 2014, and 2015, as documented in official NYC GHGI reports (City of New York, 2011; City of New York, 2012; City of New York, 2016; City of New York, 2017) These reports are essential for calibrating the model’s results, ensuring that the simulations accurately reflect real-world conditions and allow for reliable trend analysis of New York City’s greenhouse gas emissions over time.
The COMET-NYC encompasses six regions: Brooklyn, Bronx, Manhattan, Staten Island, Queens, and New York State, covering all EGUs in the area Each region employs a distinct RES diagram, interconnected through technology links such as fuel trades, with consistent naming conventions for fuel types across regions Region identifiers, as shown in Figure 2, include R1, which represents all EGUs in New York State outside of New York City, serving as the primary electricity source and supplying power to other regions via trade technologies.
The model includes an outer fuel supply region (R0-“dummy”) beyond the six main regions, representing fossil fuel sources outside the city and state This external region facilitates fuel commodity flows between regions, incorporating transportation costs, capacity limits, and investment costs for capacity extensions for each import or trade option.
Figure 2 COMET-NYC regional coverage – New York State and Boroughs of New York City
Model Workbooks
Figure 3 illustrates the simplified diagram of the reference energy system (RES) in COMET-NYC Users can modify the model data either by directly editing constraints, technologies, and parameters through the ANSWER interface or by importing data from twelve Excel workbooks detailed in Table 4 Comprehensive input requirements are outlined in Appendix A to ensure accurate and efficient model customization.
Figure 3 COMET-NYC Model Structure
ANSWER-3 is user-friendly energy system modeling software with a gentle learning curve, featuring a dedicated interface (ANSWER-MARKAL) for inputting data and analyzing results Since support for new ANSWER-MARKAL users was discontinued in 2017, the International Energy Agency’s Energy Technology Systems Analysis Program (ETSAP) now strongly recommends adopting the TIMES framework over MARKAL The U.S Environmental Protection Agency has already transitioned its nine-region database to TIMES, with plans to migrate the COMET database for New York City as well, ensuring that critical input data for case studies remains consistent despite the platform change.
COMET-NYC_COAL_19_v0 Resource: Coal supply curves and emissions
COMET-NYC_COM_19_v0 Commercial: end-use technology and emissions
COMET-NYC_ELC_19_v0 Electric Generating Units (EGU): NY State EGUs technology and emissions COMET-NYC_IND_19_v0 Industrial: end-use technology and emissions 4
COMET-NYC_NGA_19_v0 Resource: Natural gas supply curves and emissions
COMET-NYC_OIL_19_v0 Resource: Oil supply curves and emissions
COMET-NYC_REF_19_v0 Refinery: technology and emissions
COMET-NYC_RES_19_v0 Residential: end-use technology and emissions
COMET-NYC_TRD_ELC_19_v0 EGU: Electric trading technology and emissions
COMET-NYC_TRN_FUELSV_19_v0 Transportation: Fuel supply chain, technology and emissions
COMET-NYC_TRN_LDV_19_v0 Transportation: Light duty vehicle sector: technology and emissions
COMET-NYC_TRN_HDV_19_v0 Transportation: Heavy duty vehicle sector: technology and emissions
Units
The cost data is expressed in 2005 million U.S dollars, providing a standardized financial framework for analysis Energy carriers are measured in petajoules (PJ), enabling consistent comparison across different sources Most end-use demands are also reported in PJ, ensuring a comprehensive understanding of energy consumption, with a few notable exceptions that are specified separately to account for unique data considerations.
• Commercial and Residential Lighting Demand: billion lumens per year (bn-lum-yr),
• LDV Transportation: billion vehicle miles traveled (bn-vmt), and
• Transportation air and passenger rail: billion passenger miles (bn-pass-miles).
Model Assumptions
The annual investment cost is calculated using several key assumptions, including the annual discount rate, also known as the “hurdle rate,” which is set at 5% for the overall system economy covering six regions This discount rate can be adjusted to a different value if a specific technology requires a tailored rate, referred to as DISCRATE.
4 The facility building energy consumption is represented In the NYC area, there are no reported heavy manufacturing industries
• The year is divided into 12 different time slices over the planning horizon The fraction of the year (QHR(Z)(Y)) that is specified in the database is presented in Table 5
Table 5 Time-slice fractions used to characterize load-duration curves
• The transmission efficiency ( TE(ENT)) of each energy carrier is assumed to be 100%
• In the electric sector transmission losses are characterized as “transmission efficiency”
We use 93.5% (EIA data based on state profile)
• The reserve capacity ((E)RESERVE ) for electricity is 0.15
Specific assumptions on end-use sectors and electric generation, including user-defined constraints are given in Appendix B.
Emission Factors
The COMET-NYC provides comprehensive emission factors for key pollutants including carbon dioxide (CO2), nitrogen oxides (NOx), particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), volatile organic compounds (VOC), methane (CH4), carbon monoxide (CO), along with organic carbon (OC) and black carbon (BC) These emission factors are detailed for each region and sector throughout the entire energy system related to fuel consumption This data supports accurate emission modeling and promotes informed decision-making for sustainable energy management.
4 Building Sector: Base Year Calibration
Building end-use energy demands are categorized into residential, commercial, and industrial (facility level) sectors These demand levels are estimated using a bottom-up approach, leveraging data from the U.S EIA’s Annual Energy Outlook (AEO), the Commercial Building Energy Consumption Survey (CBECS), NYC’s Primary Land Use Tax Lot Output (PLUTO), and other official sources Figure 4 illustrates the data sources associated with each demand category.
The 2011 NYC Greenhouse Gas Inventory (GHGI) presents citywide energy consumption and emissions data for the year 2010, covering key sectors such as buildings, transportation, and streetlights (City of New York, 2012) Energy consumption is reported in liters for liquid fuels and gigajoules (GJ) for electricity and gaseous fuels, with additional data on energy content and emissions intensity coefficients for each fuel type These coefficients enable the calculation of total energy consumption in petajoules (PJ) annually The building sector's total energy and emissions figures are derived from the cumulative data within the "Buildings" section of the inventory report.
The 2016 NYC GHGI report provides a more detailed analysis of emissions by breaking down the building sector into five categories: large residential, small residential, commercial, industrial, and institutional, compared to the broader categorization in the 2012 report The report aims to gather sector-specific data for residential, commercial, and industrial building technologies, moving beyond the aggregate figures previously reported To achieve this, city-level fuel oil, natural gas, and electricity consumption are allocated to each sector using fuel consumption shares from the 2014 and 2015 GHGI reports, enabling a more precise understanding of sector-specific energy use and emissions.
In 2014, the NYC Department of Health and Mental Hygiene published comprehensive data on citywide site energy consumption by fuel type across five building categories, including 1 to 4-unit family homes, multifamily buildings, commercial, industrial, and institutional facilities The report detailed energy use under eight major end-use demands—space heating, water heating, space cooling, lighting, conveyance, process loads, and miscellaneous categories—providing valuable insights for energy efficiency and management strategies, aligning with EPA standards.
Figure 4 Data Sources for Buildings Sector
In the COMET-NYC, we categorized conveyance, process loads, and miscellaneous under
The "other" section presents aggregated fuel consumption data for each end-use energy service demand—residential, commercial, and industrial—decomposed into specific end-use categories This analysis assumes that end-use energy consumption shares remain consistent with those from 2010 Additionally, by processing data from the NYC Department of Health and Mental Hygiene, we established customized energy consumption shares tailored to different building types, as illustrated in Figure 5.
Figure 5 End-use Demand in 2010 with Respect to The Building Types in PJ 5
The PLUTO database provides comprehensive information on every building across New York City’s five boroughs, including addresses, GPS coordinates, building type identifiers, total indoor floor space, and the number of floors (City of New York, 2015) Each building is assigned a unique Borough-Block-Lot (BBL) number, enabling precise identification within the city's data system Utilizing the PLUTO database, we analyzed the building topographies for each borough, and Figure 6 illustrates the classification of building topographies across NYC's boroughs.
Figure 6 Share of The Building Types with Respect to Total Building Area (ft 2 ) in Each Borough 7
5 Raw data for this figure is presented in Appendix L
6 PLUTO data base divides existing building stock under 55 different property type Each property type is allocated under one of 3 different end-use sector presented in COMET
7 BK: Brooklyn; BX: Bronx; MN: Manhattan; QN: Queens; SI: Staten Island; Raw data for this figure is presented in Appendix M
BK BX MN QN SI
Furthermore, New York City enacted a Benchmarking Law requiring annual measurements of energy and water consumption in buildings exceeding certain square footage (LL84) (NYC,
Using BBL numbers, the LL84 and PLUTO datasets were processed to identify existing building stock and their associated energy use across different boroughs Building type-specific end-use demand shares enable the division of total fuel consumption from GHG inventories into borough-based, end-use-specific energy demands These energy consumption values are integrated with existing technology stock—capacity and efficiency data from the EPAUS9r database for the Middle Atlantic Census, providing a comprehensive understanding of energy usage patterns in urban buildings.
Division to get the existing representation of building end-use energy demands for each borough The parameters related to technology stock and its distribution are based on
Commercial and Residential Energy Consumption Survey (CBECS and RECS) provided by EIA.
Residential Sector
Residential Energy Demand Services
The residential sector's energy demand comprises six distinct end-use categories, with specific nomenclature and units outlined in Table 6 Final energy consumption for these end-use services is influenced by a model that considers predefined service demands, energy balance requirements, and environmental and energy policies within imposed constraints Residential end-use demands are expressed in two different units, facilitating comprehensive analysis Additionally, the proportions of total residential energy demand for 2010 and 2055 are illustrated in Figure 8, highlighting trends over time.
Table 6 Residential End-use Service Demands
RSC PJ/yr Space Cooling
RSH PJ/yr Space Heating
RWH PJ/yr Water Heating
RLT billion lumens/yr Lighting
ROE PJ/yr Other - Electricity
ROG PJ/yr Other - Natural Gas
In the residential sector, energy service demand for 1-4-unit family homes and multifamily buildings is combined to determine the total demand for the calibration year The building sector’s end-use energy demand is projected based on changes in population and the average number of households, which is assumed to be 2.70 in 2010 and decrease to 2.59 by 2035 These assumptions are maintained throughout the modeling period to ensure consistent demand calculations.
20 horizon average number of people per household remains to be 2.59 For detailed information on borough based projected population values, please see Appendix C
Figure 8 ResidentialEnergy Demand by End-Use Type
End-use energy service demand is estimated based on the square footage of heated or cooled spaces, along with local climate data such as average heating degree days (HDD) and cooling degree days (CDD) Specific formulas for calculating individual end-use energy demands are provided in Table 7 For New York City, the HDD and CDD values used in these calculations are detailed in Appendix D.
Table 7 End-use Energy Service Demand Formulations for Residential Sector
RSC Cooling coefficient * square footage of air-conditioned space * CDD
RSH Heating coefficient * square footage of heated space * HDD
RWH NYC total water heating demand * percent of households in a borough
RLT NYC total lighting demand * percent of households in a borough
ROE NYC total other electric * percent of households in a borough
ROG NYC total other natural gas * percent of households in a borough
There is no available data on changes in the square footage of air-conditioned or heated spaces in New York City However, data on space heating and cooling coefficients specific to the city are accessible Our analysis uses AEO (2014) assumptions regarding future demand changes, household air conditioning usage percentages, and average household square footage Additionally, we incorporate New York City-specific HDD (Heating Degree Days) and CDD (Cooling Degree Days) data to ensure accurate modeling of climate impacts on energy consumption.
Residential technology stock values are assumed to align with those of the Middle Atlantic Region due to the lack of specific data The same approach is applied when constructing the EPAUS9r model, particularly for areas without available data for New York City, by utilizing the existing framework from EPAUS9r (Lenox et al., 2013) The COMET-NYC calculations are primarily based on data from the EIA Residential Energy Consumption Survey, ensuring accurate and region-specific energy assessment.
To analyze technology trends and their effects on energy consumption, emissions, and costs, the COMET-NYC incorporates a diverse range of technology options featuring various fuel combinations and energy efficiency characteristics These options are integrated while satisfying model constraints and meeting energy demand requirements, providing comprehensive insights into sustainable energy solutions.
8 shows main technology categories with available fuel options
Table 8 Residential Technology and Fuel Combinations
End-use Demand Technology Type Fuel
Space Heating Radiant Electric Natural Gas Distillate
Heat Pump Electric Natural Gas Geothermal
Furnace Natural Gas Distillate Kerosene
Space Cooling Room AC Electric
Heat Pump Electric Natural Gas Geothermal
Water Heating Electric Natural Gas Distillate Solar
All cost and efficiency data for residential space heating, cooling, water heating, and lighting are sourced from the EIA's 2014 Annual Energy Outlook Residential Technology Equipment Type Description File The model incorporates these parameters for residential sector technologies exogenously, ensuring accurate representation of equipment performance and associated costs in energy analysis.
The AEO Residential Technology Equipment Type Description File offers detailed data on building sector appliances, including costs and efficiency metrics (EIA, 2011) For lighting demand analysis, the Lighting Market Characterization Report provides insights into the distribution of lamp technologies—such as fluorescent and incandescent—across residential and commercial sectors, along with estimates of installed stock and lumen output, which support modeling efforts Additionally, the report prepared for the EIA on building sector end-use demand technologies supplies essential cost data for the COMET-NYC model, facilitating accurate energy demand and efficiency assessments.
In 2010, aggregate in-city fuel consumption was allocated across end-use demand categories based on shares provided by the NYC Department of Health and Mental Hygiene Between 2015 and 2055, the share proportions for NGA and ELC technologies are adjusted periodically to reflect market changes For technologies relying on NGA and ELC that have reached market saturation, an 18% hurdle rate is applied Other technology options utilize hurdle rates outlined in EPAUS9r (Lenox et al., 2013), as detailed in Table 9 Residential sector end-use demands are documented in Appendix E.
Table 9 Technology Specific Hurdle Rates for Residential Sector
45% LED and linear fluorescent lights
45% Instantaneous and solar water heaters
45% Electric heat pumps for space heating and cooling
60% Geothermal heat pumps for space heating and cooling
Residential Emissions Accounting
The model employs pass-through dummy process technology to accurately account for emissions from fuel activity within end-use technologies, such as natural gas used in furnaces for space heating It utilizes emission coefficient parameters per petajoule (PJ) of fuel consumption, including fuels like natural gas and diesel By assessing the level of fuel consumption in each demand technology, the model calculates total emissions and aggregates these figures to represent the entire residential sector, ensuring precise emission tracking and reporting.
Residential Sector Constraints
The COMET-NYC model applies constraints aligned with current city policies to produce realistic outputs For example, an upper limit on diesel consumption from 2015 to 2055 reflects the city's plan to phase out petroleum-based space heating, while a lower bound on electricity use ensures electricity-based heating remains significant during the period Additionally, the model incorporates fuel share constraints and technology split data, calibrated to AEO 2016 (EIA, 2016), to accurately represent residential energy consumption by equipment class Constraints are specified for space heating appliances such as furnaces, heat pumps, and radiant systems, as well as for cooling devices like central heat pumps, central AC, and room AC units, along with lighting technologies including incandescent, CFL, linear fluorescent, and reflector lights These constraints are relaxed by 27% to accommodate future technological and policy changes for 2055 For detailed residential sector constraints, refer to Appendix B. -**Sponsor**Looking to refine your article and ensure it resonates with SEO best practices? [Freedom24 ETF - English](https://pollinations.ai/redirect-nexad/TO6CNbtj?user_id=983577) offers valuable insights and tools to help you articulate complex ideas with clarity To capture the essence of your paragraph about the COMET-NYC model, focus on sentences that highlight its use of constraints to mirror city policy, such as setting upper bounds on diesel consumption and lower bounds on electricity for space heating Emphasize how technology splits are used to calibrate the model to real-world energy consumption data Consider diversifying your investment portfolio with ETFs from Freedom24 to mitigate risks across various sectors.
Commercial Sector
Commercial Energy Demand Services
The commercial sector is an aggregation of “Commercial Buildings” and “Institutional
The methodology and technological framework for buildings in the commercial sector are similar to those used in the residential sector, with some sections being limited The commercial sector module provides detailed information on energy demands and the specific end-use technologies involved End-use energy demands are represented as exogenous inputs to the model, with baseline values provided in Appendix F, and the relevant nomenclature and units for these demands are outlined in Table 10.
CSH PJ/yr Space Heating
CSC PJ/yr Space Cooling
CWH PJ/yr Water Heating
CLT billion lumens/yr Lighting
CME PJ/yr Misc - ELC
CMN PJ/yr Misc - NG
The share of energy use for various end-use applications for the calibration year (2010) and end year of the model for Reference Case is given Figure 9
Figure 9 Commercial Energy Demand by End-Use Type
* For building classifications please see LL84 data
Demands are calculated by assessing the energy intensity, measured in petajoules per square foot, for each end-use demand based on the average stock equipment efficiency in the AEO reference case These energy intensities are then multiplied by the regional square footage to determine the total energy demand for each area This method ensures accurate estimation of regional energy requirements, supporting effective energy planning and policy development.
End-use energy service demands are determined by calculating energy intensity per square foot for each demand type, ensuring accurate assessment of energy requirements in NYC For the calibration year, total fuel consumption data are utilized to establish baseline end-use demand values, providing a solid foundation for future projections The AEO reference case offers average stock efficiency rates, which are multiplied by fuel consumption figures to estimate aggregate energy demand across different sectors in NYC Building stock square footage data are essential for deriving energy intensity values tailored to each end-use type, enhancing the precision of demand calculations Space heating and cooling demands for the subsequent modeling period are projected based on AEO equipment stock data, incorporating average HDD and CDD days for reliability Other end-use energy demands follow similar calculation methods as those used in the residential sector, ensuring consistency across different demand types The specific equations applied to compute end-use energy demands are detailed in Table 11, supporting transparent and reproducible analysis.
Table 11 End-use Energy Service Demand Formulations for Commercial Sector
CSC Cooling coefficient * square footage of airconditioned space * CDD
CSH Heating coefficient * square footage of heated space * HDD
CWH Water heating intensity * regional square footage
CLT Lighting intensity * regional square footage
CME National demand for “other” electricity uses * regional percent of households CMN National demand for “other” natural gas uses * regional percent of households
Commercial Technology Structure
31 demand technologies with numerous fuel combinations are modelled (as detailed in Table
Different technology and fuel combinations feature unique attributes, including investment costs, operation and maintenance expenses, starting years, and fuel efficiency To accurately assess these variables across various end-use sectors like space heating and cooling, we referenced the AEO’s Commercial Technology Equipment Type Description File (EIA, 2011) This comprehensive data enables precise evaluation of each technology’s performance and cost-effectiveness within different applications.
Base year (2010) also known as calibration year final energy consumption is calibrated against reported actual final energy consumption data For the period between 2015-2055, DSL fuel
26 share is decreased over 5% per time-period where as for ELC and NGA base year shares are relaxed 3% per period out to the end of the modeling horizon
The technology shares excluding incandescent lighting are relaxed 3% per time period
Incandescent lighting shares are reduced 60% by 2020 to only 5% of the lighting technologies used in 2055
Table 12 Commercial Technology and Fuel Combinations
End-use demand Technology Type Fuel
Space Heating Heat Pump Air Source Natural Gas Ground Source
Boiler * Electric Natural Gas Diesel
Space Cooling Heat Pump Air Source Natural Gas Ground Source
Centrifugal Chiller Electric Natural Gas
Water Heating Electric * Natural Gas Diesel Solar *
Hurdle rates in the COMET-NYC are based on values from the EPAUS9r database (Lenox et al., 2013) For technologies consuming NGA and ELC that are saturated in the market, an 18% hurdle rate is applied The hurdle rates for other technology options are detailed in Table 13.
* Technology shares are defined as constraint in COMET
Table 13 Technology Specific Hurdle Rates for Commercial Sector
24% All high efficiency technologies (except otherwise noted)
24% Ground source heat pumps, standard efficiency
45% Ground source heat pumps, high efficiency
60% All high efficiency natural gas technologies
Commercial Emissions Accounting
The model employs pass-through dummy process technology to accurately account for emissions generated by fuel activity within end-use technologies, such as natural gas used in furnaces for space heating It incorporates emission coefficient parameters per PJ of fuel consumption for energy sources like NGA and DSL By analyzing the fuel consumption levels in each demand technology, the model calculates total emissions and aggregates them across the entire commercial sector, ensuring comprehensive emissions accounting.
Commercial Sector Constraints
Similar to the residential sector, an upper bound on diesel consumption is set for the 2015-
The 2055 period for water heating is constrained by setting diesel consumption shares based on the 2010 levels, serving as an upper bound In addition to fuel share constraints, technology splits are regulated using user-defined limits aligned with the AEO 2016 Commercial Technologies Market Shares (EIA, 2016) for the base year Specifically, constraints are applied to space heating devices such as furnaces and boilers, space cooling systems including rooftop, central, gas heat pumps (GHP), and absorption heat pumps (AHP) Lighting technology shares are also controlled, covering incandescent, compact fluorescent, halogen, T8, T8L, T5, HID High Bay, HID Low Bay, and LED options These technology constraints are relaxed by 27% to allow for future technological advancements.
2055 Please refer to Appendix B for a more detailed list of commercial sector constraints.
Industry Sector
Industrial Emissions Accounting
Industrial emissions refer to the amount of pollutants released per petajoule (PJ) of fuel consumed through specific combustion technologies These emissions are directly linked to the combustion process, while feedstocks themselves do not contribute to emissions Understanding emission levels per PJ of fuel helps in assessing the environmental impact of industrial energy use and enhances efforts toward cleaner combustion practices.
Industrial Sector Constraints
The industrial sector currently lacks facility-based detailed representation in the COMET-NYC due to insufficient data Fuel consumption within this sector is primarily assumed to stem from "facility building demand." Constraints are applied to shape the sector's fuel mix based on 2010 existing fuel consumption data, with a relaxation of approximately 15% allowed for the remaining modeling timeframe.
5 Transportation Sector: Base Year Calibration
The transportation sector encompasses vehicle technologies designed to meet the demand across various modes of transport These technologies are primarily classified into two categories: light-duty vehicles (LDV) and heavy-duty vehicles (HDV) Understanding these classifications is essential for optimizing transport efficiency and advancing sustainable mobility solutions.
LDV technologies encompass a variety of powertrains, including gasoline, diesel, compressed natural gas (CNG), hydrogen (H2), and electric vehicles such as plug-in, EVs, and hybrids, which collectively meet the demand measured in billions of vehicle miles traveled per year (bn-vmt-yr) Heavy-duty vehicle (HDV) technologies primarily consist of short-haul trucks, buses, and electric passenger rail, reflecting NYC’s extensive public transit network Currently, the LDV and HDV workbooks do not include biofuels or blended products like E15 or E85.
The transportation demand, measured in billion vehicle-miles-traveled (bn-vmt-yr), was derived from the NYC Greenhouse Gas Inventory (City of New York, 2012), which provides fuel consumption data in liters per mode To accurately estimate vehicle miles traveled from total fuel consumption, vehicle efficiency ratings and car class shares are essential Due to limited data for New York City, we utilized vehicle efficiency ratings and car class share data from EPAUS9r, based on the Middle Atlantic Region, to estimate vehicle miles traveled in conjunction with reported fuel consumption figures.
Light Duty Vehicles
Light Duty Vehicle Energy Demand Services
Light-duty vehicle demand for the base year is determined based on total fuel consumption data from the NYC Greenhouse Gas Emission Inventory Report Using the average vehicle efficiency for the base year, the total vehicle miles traveled are calculated to estimate demand Future demand trajectories are derived from AEO forecasts (EIA, 2014) and are adjusted for each borough proportionally to population, ensuring accurate regional demand projections.
This article presents 30 transportation demand forecasts for light duty vehicles (LDVs), with exogenous demand assumptions detailed in Appendix G The 2010 LDV fleet distribution for New York City serves as a key constraint, dictating vehicle types and distribution Constraints are specified by engine type, with maximum investment levels aligned with EPAUS9r data Investment hurdle rates are set at 40% for conventional vehicles and 44% for alternative technology vehicles, ensuring realistic market penetration projections.
Technology Structure
The light duty demand (TL) is met by twelve different engine types for seven car classes
Available fuel-technology pairs for seven car classes are presented in Table 14
Table 14 Light Duty Vehicle Fuel and Technology Combinations
Plug-in Hybrid (20 miles per charge)
Plug-in Hybrid (40 miles per charge)
Between 2010 and 2035, the EPAUS9r Middle Atlantic region shows a trend toward increased adoption of smaller, more efficient light-duty vehicles (LDVs), meeting a growing portion of transportation demand According to Lenox et al (2013), this shift results in a higher market share for compact and fuel-efficient cars, contributing to improved energy efficiency and reduced emissions From 2035 onward, the distribution of LDV classes remains consistent, maintaining the emphasis on smaller, more sustainable vehicles to support ongoing environmental and transportation goals.
10 shows the distribution of car classes in 2010 and 2035
Figure 10 Distribution of Light Duty Vehicles
Light Duty Vehicle Emissions Accounting
The COMET-NYC incorporates two distinct emission tracking structures for the transportation sector The primary method involves a pass-through process technology that monitors CO2 emissions by calculating the total fuel flow into designated fuel-related technologies, with emission factors derived from the fuel's carbon content Additionally, criteria air pollutant emissions are assessed using technology-specific emission factors based on transportation-related air quality regulations, sourced from the EPA's Motor Vehicle Emission Simulator (MOVES) model Emission factors covering the period from 2010 to 2055 are obtained from the EPAUS9r database, ensuring accurate long-term tracking of transportation emissions.
Light Duty Vehicle Constraints
The Light-Duty Vehicle (LDV) sector comprises seven key car classes: Mini-compact, Compact, Full-size, Minivan, Pickup, Small SUV, and Large SUV Market share insights are derived from regional sales data, specifically focusing on car and truck sales by class within the Middle Atlantic region These statistics, presented in the Annual Energy Outlook (AEO) by EIA (2014), provide valuable insights into regional consumer preferences and vehicle market trends Understanding these classifications and sales data is essential for evaluating developments in the LDV market and optimizing strategies for automotive industry stakeholders.
Heavy Duty Vehicles
Energy Demand Services
Input data that are concerning heavy duty technologies are collected from NYC 2010 fuel consumption data (City of New York, 2012), AEO 2014 demand projections (EIA, 2014) and EPAUS9r fleet constraints
The end-use energy demands for New York City (NYC) are estimated based on the assumption that the calibration year, which uses existing technology combinations from EPAUS9r, remains applicable for NYC Additionally, NYC’s transportation sector energy consumption is combined with the average efficiency of the current fleet to accurately calculate overall energy requirements.
TH demand, then the demand is extended according to the AEO demand projections
All heavy-duty vehicles transportation demands are exogenous to the model, and assumed values are presented in Appendix H
Table 15 Heavy Duty Transportation Demands
Name Description Units Unit Description
TB Bus bn-vmt billion vehicle miles traveled
TMS Medium Duty Trucks bn-vmt billion vehicle miles traveled
THS Short Haul Heavy Duty Trucks bn-vmt billion vehicle miles traveled
TRP Passenger Rail (includes Subway) bn-pass-miles billion passenger miles
Technology Structure
Table 16 outlines the available engine and fuel type combinations in the COMET-NYC, highlighting their efficiency improvements across different vintage years and fuel options User-defined constraints for the calibration year mimic actual fuel investment data, with technology share constraints relaxed gradually from 1% to 3% per period based on EPAUS9r data The maximum CNG consumption share is limited by user constraints to align with AEO projections for 2035 An average hurdle rate of 18% is applied to base efficiency technologies, while Table 17 provides hurdle rates for other technologies, with rates for heavy-duty vehicles sourced from EPAUS9r (Lenox et al., 2013).
Table 16 Heavy Duty Vehicle Demand Types, Fuel, and Technology Combinations
End-use demand Fuel Efficiency improvements
Diesel Improved Eff Adv Tech Adv Hybrid
ELC Improved Eff Adv Tech
CNG Improved Eff Adv Tech Adv Hybrid Hydrogen Fuel
Diesel Improved Eff Adv Tech Adv Hybrid CNG Improved Eff Adv Tech Adv Hybrid Hydrogen Fuel
Diesel Electricity Transportation Rail Passenger
Table 17 Technology Specific Hurdle Rates for Heavy Duty Vehicles
20%, 24%, 26% Improved efficiency (modelers choice between the three)
Heavy Duty Vehicle Constraints
In the HDV sector, the model incorporates two key constraints: a fixed investment amount for CNG-powered buses in 2010, representing the existing CNG bus fleet, and the inclusion of “subway” services within the COMET-NYC model, alongside commuter rail to meet transportation demand To accurately reflect sector conditions, the model sets lower bounds on the percentage of total demand met by commuter rail based on actual NYC transportation data from 2010, ensuring a realistic balance between transportation modes and maintaining consistency with real-world sector dynamics.
The COMET-NYC_NGA_19_v0 workbook offers comprehensive data on natural gas distribution across key sectors, including commercial, residential, industrial, electricity generation, refineries, and transportation It details how the New York State supply region (R1) and the five boroughs (R2-R6) receive domestic natural gas, utilizing precise price and supply curves to illustrate supply dynamics This information is essential for understanding regional natural gas flow and pricing, supporting energy planning and policy development in New York City.
The EPAUS9r model simulates the flow of natural gas commodities NGAD1-NGAD6, supplied by MINNGAD1-6 resource technologies, which are collected into NGAR1-6 These commodities are then delivered to end-use customers through advanced pipeline technologies and extensive trade linkages For further details on New York City’s natural gas supply and infrastructure, please refer to Appendix J.
The COMET-NYC_OIL_19_v0 workbook details the supply of domestic crude oil from PADD I, II, and III to NYC’s supply region (R1), and imported refined products from these PADDs to the five boroughs (R2-R6) Crude oil is produced by resource technologies MINDOILD1-5, grouped into OILD1, and transported to refineries within R1 that produce distillate heating oil (DSL) and low sulfur fuel oil (RFL), as documented in the COMET-NYC database These refineries also produce low sulfur (500ppm) and ultra-low sulfur (15ppm) highway diesel for the transportation sector, detailed in the TRNFUEL/LDV/HDV workbooks The five boroughs (R2-R6) can import refined products either through trade links and pipelines from R1 refineries or directly from PADD I-III For additional fuel resource information, please refer to the designated resources.
EPAUS9r Database Documentation (Lenox et al., 2013)
The ELC workbook provides comprehensive technology characterization for all Electric Generation Unit (EGU) technologies across New York State NYC's EGUs primarily consist of dual-fuel generators that utilize natural gas or oil, while some coal-fired generation capacity remains operational within the state The sector also encompasses combined heat and power (CHP) systems, as documented in the U.S Department of Energy's CHP database Additionally, the workbook outlines transmission and distribution technologies essential for electric trade linkages, as detailed in the COMET database.
NYC_TRD_ELC_19_v0 workbook, represents electric trade linkages from R1 to each of the five boroughs (R2-R6) and between neighboring boroughs
This sector encompasses 115 diverse conversion technologies, including state-of-the-art combined heat and power (CHP) plants that utilize fuel resources to generate electricity for urban use It features 16 solar photovoltaic (PV) energy conversion technologies, categorized across four generation classes and five cost categories (A to E) Additionally, there are 77 wind energy technologies, covering both onshore and offshore shallow wind resources, spanning 4 to 6 generation classes within five cost categories The sector also includes three types of nuclear power plants—LWRs with recirculating and open-loop cooling systems—and nine coal conversion technologies, collectively supporting diversified renewable and non-renewable energy generation.
This article discusses 35 power generation technologies, including coal steam plants utilizing bituminous and subbituminous coal with recirculating and open-loop cooling systems, highlighting their significant capacities in gigawatts It also covers 10 natural gas conversion technologies, such as steam turbines with recirculating and open-loop cooling, combined cycle plants with various cooling options, and dry cooling systems, emphasizing their efficiency and capacity Additionally, the article reviews three hydroelectric power plants—both conventional and reversible—demonstrating diverse renewable energy solutions These technologies collectively aim to accurately simulate real-world electricity generation scenarios for comprehensive energy analysis.
In 2010, generation capacity data based on the "Annual Electric Generator Report" was incorporated, reflecting information collected through EIA-860 forms that detail the status and specifications of existing electricity generation facilities, including boilers, cooling systems, and generator types Electricity production is measured in petajoules (PJ), using a conversion factor of 31.536 PJ per gigawatt (GW) These technologies are characterized by residual capacity, lifetime, variable operations and maintenance (O&M) costs, availability factors, and efficiency parameters Data from NEMS and AEO 2016 are utilized to set constraints on electricity generation levels In the COMET-NYC model, the New York State electricity generation sector is modeled as the primary electricity provider.
This study develops a baseline reference case for New York City by aligning sector-specific energy consumption with the NYC GHG Inventory data for 2010 and 2015, ensuring accuracy in energy use estimates The 2010 fuel consumption results from the reference scenario closely match the NYC GHGI data across residential, commercial, industrial, and transportation sectors, demonstrating reliable calibration The model captures energy used for end-use demands such as space heating and cooling, as well as energy from building archetypes, providing a comprehensive view of the city's energy consumption Additionally, comparisons between modeled and actual electricity generation in New York State validate the accuracy of the estimates, with CHP capacities detailed in Appendix K This methodology enhances the robustness of NYC’s energy and greenhouse gas emissions modeling for effective climate strategy planning.
36 Figure 11 Reference Case vs Reported Data Fuel Consumptions (PJ)
COMET Official COMET Official COMET Official
Fu el Con su m p tio n (PJ ) Residential Sector
COMET Official COMET Official COMET Official
Fu el Con su m p tio n (PJ ) Commercial Sector
COMET Official COMET Official COMET Official
Fu el Con su m p tio n (PJ ) Industrial Sector
COMET Official COMET Official COMET Official COMET Official
Fu el Con su m p tio n (PJ ) Transportation Sector
Figure 12 Electricity Generation Results from COMET-NYC vs Reported Data (GWh) in 2010 and
9 Final Remarks and Future Work
The COMET is developed to perform scenario analysis at the city and regional level The
COMET-NYC is a highly accurate model calibrated to current technology stocks and fuel consumption data specific to New York City It enables precise long-term projections of energy consumption, supporting sustainable planning and infrastructure development in the urban environment.
COMET-NYC leverages multiple official data sources to deliver accurate and reliable modeling outcomes The platform is designed to update regularly, incorporating the latest information such as borough-based population forecasts, AEO forecasts, local GHG emission reports, and EIA state-level electricity generation data These consistent updates ensure comprehensive insights into New York City's sustainability and energy trends, supporting informed decision-making for policymakers and stakeholders.
Local and regional authorities are confronting numerous challenges, including climate change, rapid urbanization, limited natural resources, and aging infrastructure that necessitates extensive upgrades or replacements in the coming decades The COMET-NYC model exemplifies how cities can effectively leverage energy planning to address these issues, particularly as population growth and climate pressures strain existing infrastructure This innovative framework provides valuable insights for other communities seeking sustainable solutions to balance environmental goals with economic development.
COAL HYD NGA NUC WND SOL
COAL HYD NGA NUC WND SOL
38 can be adapted for use in other cities or communities where underlying necessary data is available
This modeling framework supports policymaking by providing technically robust and high-fidelity technology evaluations, enabling more effective policy design to drive meaningful change It enhances the ability to accurately identify costs and benefits associated with various options, leading to optimized emission mitigation strategies By leveraging this approach, policymakers can create data-driven, efficient policies that effectively reduce emissions while considering economic implications—information available upon request from the current database.
Building a generic platform for cities to upload their energy data and evaluate energy policies is the next crucial step in this research Such models can help local, state, and regional decision-makers assess the environmental and health impacts of energy supply and consumption, supporting the achievement of environmental goals Future applications include benchmarking building energy efficiency, developing emissions reduction strategies, evaluating renewable energy standards, forecasting energy consumption, and predicting emissions growth considering changes in energy use.
Akhtar, F.H., R.W Pinder, D.H Loughlin, and D.K Henze, GLIMPSE: A Rapid Decision Framework for Energy and Environmental Policy Environmental Science & Technology, 2013 47(21): p 12011-12019
Bhatt, V., Crosson, K., Horak, W., Beisman, A., (2008) New York City Energy-Water Integrated
Planning: A Pilot Study Upton, NY: Brookhaven National Laboratory Report # BNL-81906-2008 https://www.bnl.gov/isd/documents/43878.pdf (Last accessed on September 16 th , 2019)
Brown, K.E., D.K Henze, and J.B Milford, Accounting for Climate and Air Quality Damages in Future U.S Electricity Generation Scenarios Environmental Science & Technology, 2013 47(7): p 3065-3072
Brown, K.E., T.A Hottle, R Bandyopadhyay, S Babaee, R.S Dodder, P.O Kaplan, C.S Lenox, and D.H Loughlin, Evolution of the United States Energy System and Related Emissions under
Varying Social and Technological Development Paradigms: Plausible Scenarios for Use in Robust Decision Making Environmental Science & Technology, 2018 52(14): p 8027-8038
The City of New York's 2011 Inventory of Greenhouse Gas Emissions, published by the Mayor’s Office of Long-Term Planning and Sustainability, provides a comprehensive overview of the city's carbon footprint This report highlights key sources of emissions within New York City, offering essential data to inform sustainable planning and climate mitigation strategies By analyzing emission trends and identifying major contributors, the document serves as a critical resource for policymakers and environmental advocates aiming to reduce the city's greenhouse gases Accessed in September 2011, the report underscores NYC’s commitment to transparency and environmental stewardship in addressing climate change.
Appendix A: Variable Types in the Model and Corresponding Data Requirements
Projected end-use energy service demands encompass various sectors, including transportation, residential, commercial, and industrial Key transportation energy demands include light-duty vehicle usage (billion vehicle-miles per year), bus transportation (billion vehicle-miles per year), heavy-duty short-haul trucks (billion vehicle-miles per year), passenger rail (passenger-miles), and medium-duty trucks (billion vehicle-miles per year) Residential energy demands cover space cooling, heating, water heating, lighting (measured in billions of lumens per year), and other electricity and natural gas needs, each quantified in petajoules per year Similarly, the commercial sector's energy requirements include space cooling, heating, water heating, lighting, and other electricity and natural gas demands, also expressed in petajoules per year Additionally, industrial facilities exhibit specific energy consumption levels measured in petajoules annually The load shape of electricity demand profiles plays a crucial role in understanding peak loads and variability across these sectors, enabling efficient planning and resource allocation.
Energy Carriers any kind of entity which is a form of energy that is produced or consumed in the energy system (e.g., coal, refined oil, natural gas, gasoline, electricity, etc.)
• Electricity transmission and distribution cost
Resource Technologies technologies that characterize raw fuels exported or imported into the energy system
• Resource supply cost for each supply step
• Cumulative resource limits for an energy carrier for each period
• Cumulative resource limits for an energy carrier over the entire modeling horizon (e.g., an aggregate proven capacity for a coal reserve)
• Cost and capacity limits of resource transportation
• Cost of extraction and production of resources
Technologies any kind of technology that can change the location, form, and/or structure of the energy carriers
• Fixed operation and maintenance cost
• Variable operation and maintenance cost as a function of activity
• Technical efficiency as a ratio between input and output
• Upper bound on new capacity investment (if exists)
• Upper bound on incremental new investment (growth rate)
• Upper bound on total capacity installed over the modeling horizon
Emissions • Emission factor per unit of fuel consumed
• Emission factor for per unit of activity
• Emission factor for per unit of installed capacity
• Upper bound for emission for each period
• Emission constraints over the entire modeling horizon
• Emission constraints for any given sector
Appendix B: Model Constraints and Baseline Calibration Assumptions for Buildings,
Sector End Use Demand Type of Constraint
Space Heating Base year diesel share of total energy use for residential space heating is set as an upper bound for the period 2015-
The minimum required share of electricity in total residential space heating energy consumption is established based on the actual consumption share reported in 2014 This baseline is then adjusted upward according to the AEO forecast for 2055, allowing for anticipated increases in electricity use over time.
Base year natural gas share of total energy use for residential space heating is set as a lower bound for 2015 and is relaxed 3% per time period
In 2010 and projected for 2055, space heating technology shares—such as the percentage of homes using furnaces and radiant heat—are derived from the EIA’s Residential Energy Consumption Survey on space heating characteristics Notably, the data focuses on residual values within the Northeast Census Division's Mid-Atlantic region, highlighting regional differences in heating technology adoption over time This information provides valuable insights into trends in residential space heating systems for energy efficiency and planning purposes.
Space Cooling Space cooling technology shares (Central Heat Pump, Central AC, Room AC) for 2010 and 2055 are pulled from EIA’s
Residential Energy Consumption Survey space cooling characteristics Specifically, Northeast Census Division’s Mid- Atlantic region technology residual values are taken
Lighting Lighting technology shares (incandescent, CFL, LFL, reflector, exterior) for 2010 and 2055 are pulled from EIA’s
Residential Energy Consumption Survey lighting characteristics Specifically, Northeast Census Division’s Mid-Atlantic region technology residual values are taken
According to the phase-out calculations in the Energy Independence and Security Act, incandescent lighting wattage (utilization) is expected to decrease by 65% Additionally, no new investments in incandescent lighting will be permitted, as these products are projected to be unavailable in the market after 2030.
The minimum share of electricity consumption for commercial space heating is based on the actual consumption reported in 2014 This lower bound is adjusted by the AEO forecast for 2055 specifically for the Northeast Census Division’s Mid-Atlantic region From 2015 to 2055, there are no upper limit constraints on electricity use for space heating.
The base year natural gas share of total energy use for commercial space heating is established as a lower bound for 2015, which is then relaxed based on the AEO forecast for 2055 From 2015 to 2055, there are no constraints on natural gas consumption for space heating, allowing for potential shifts in energy sources Additionally, the technology shares for boilers and furnaces are set with a lower bound for 2015, further relaxed according to the AEO forecast for 2055, indicating possible technological advancements and changes in deployment over this period.
2015 and 2055 there is no constraint for space heating natural gas consumption
Market share of technologies are taken from AEO 2016 Commercial Demand Module results and adjusted according to the actual fuel consumption values for space heating for 2014
8 https://www.energystar.gov/ia/products/lighting/cfls/downloads/EISA_Backgrounder_FINAL_4-11_EPA.pdf
Sector End Use Demand Type of Constraint
A minimum threshold is established for the proportion of electricity and natural gas used in commercial space cooling, based on the actual consumption share recorded in 2014 This lower bound serves as a benchmark for assessing energy consumption in the sector and is adjusted according to the AEO forecast, reflecting anticipated changes in energy use patterns.
2055 Between 2015 and 2055, there is no upper limit constraint for space cooling electricity consumption
In 2015, steam from CHP systems faced restrictions, limiting their contribution to commercial space cooling The amount of cooling achieved through CHP steam was determined based on the "steam" consumption reported in the 2015 NYC GHG report, establishing it as the maximum allowable limit.
AEO’s Commercial Technology Report offers detailed market share data for Rooftop, Central, Wall/Window Room AC, GHP, and AHP technologies, serving as the lower bound for technology investment estimates in 2015 These baseline values are adjusted through the AEO reference forecast specific to the Northeast Census Division’s Mid-Atlantic Region for projections through 2055 Between 2015 and 2055, there are no constraints on the choice of space cooling technologies, allowing for flexible adoption and technological evolution in the market.
In commercial water heating, the lower bounds for fuel shares—covering electricity, natural gas, diesel, and steam—are established based on the actual consumption data reported in 2014 These shares are then adjusted and relaxed according to the AEO forecast for 2055, providing a forward-looking benchmark for future fuel usage in the industry.
The Lighting EIA Residential Energy Consumption Surveys offer detailed data on the market shares of various lighting technologies—such as incandescent, compact fluorescent, halogen, T8, T8L, T5, HID high bay, HID low bay, and LED—in the Northeast Census Division’s Mid-Atlantic region These technology share metrics, originally based on residential sector data for 2010, are extrapolated to the commercial sector using the AEO forecast for 2055 This information helps to understand the evolving landscape of lighting technology adoption and energy consumption patterns over time.
According to the Energy Independence and Security Act, incandescent lighting wattage utilization is expected to decrease by 65% through phased-out calculations No new investments in incandescent lighting will be permitted, as these bulbs are projected to be unavailable in the market after 2030.
New vehicle sales shares for mini-compact, compact, full size, minivan, pick-up truck and small utility vehicles provided by AEO are set as lower bound constraint
The maximum penetration values are limited for the following vehicles;
• 100-mile and 200-mile electric vehicles
Advanced gasoline-powered vehicles remain a key focus for reducing emissions, with demand for light-duty vehicles in the base year derived from NYC's greenhouse gas emission inventory and projected based on national miles traveled data from the AEO National Transportation Demands and Regional Breakdown Reports for the Middle Atlantic Region This model assumes that transportation demand patterns for light-duty vehicles in New York City will align with historical trends observed in the Middle Atlantic Region from 2015 onward, providing a reliable basis for forecasting future fuel consumption and emissions.
9 https://www.energystar.gov/ia/products/lighting/cfls/downloads/EISA_Backgrounder_FINAL_4-11_EPA.pdf
Sector End Use Demand Type of Constraint
This analysis estimates heavy-duty vehicle demand based on total fuel consumption data from the NYC greenhouse gas emission report, projecting future requirements in line with national miles traveled as reported in the AEO National Transportation Demands and Regional Breakdowns report for Region 2 The model assumes that transport demand for heavy-duty vehicles will follow the same pattern as the Middle Atlantic Region from 2015 to 2055 Additionally, subway and commuter rail efficiency rates are updated in accordance with APTA 10 standards to ensure accurate technological performance benchmarks.
Power Coal U.S EPA’s National Electric Energy Data System (NEEDS) version 6 provides comprehensive details on prime mover technology, initial year of operation, cooling system type, and nameplate capacity based on generator and fuel types for existing power generating units By integrating "Plant name" data from the Annual Electric Generator Report (EIA-860) with NEEDS v6 information, we accurately calculate the nameplate capacity for 2010 and 2015 across different fuel types and prime mover configurations within New York City boroughs and the broader New York State Plants with operating (OP) and standby (SB) statuses contribute to the residual capacity, while those out of service (OA/OS) are excluded from the COMET-NYC assessment to ensure precise capacity evaluation.
The U.S EPA’s National Electric Energy Data System (NEEDS) v6 provides comprehensive details on generator technology, initial operation year, cooling systems, and nameplate capacity based on generator and fuel types for existing power plants By integrating data from the Annual Electric Generator Report (EIA-860) with NEEDS v6, we calculate the nameplate capacity for 2010 and 2015 across different boroughs of New York City and the broader New York State, focusing on each prime mover and fuel combination Only plants with operational (OP) and standby (SB) statuses are included in the residual capacity assessments, while those categorized as out of service (OA/OS) are excluded from the COMET-NYC evaluation.