GCRI · Advanced Industry and Robotics

Energy technology for industry

Energy technologies shape how industry is powered and heated. In this field they include high-temperature heat pumps, electrified heating, hydrogen, heat recovery and on-site renewables, assessed for cost, emissions and reliability of supply.

01Register

Energy technology for industry on the record.

Method recordSTEM method
  1. MethodEnergy generation, storage and grid technology
  2. Applied inAdvanced Industry and Robotics
  3. Canonical pageSTEM Commons
02Application

What energy generation, storage and grid technology does here.

Energy generation, storage and grid technology cover the devices and systems that produce, store and deliver energy. They shape the reliability, cost and emissions of energy supply on which all other sectors rely. In industry and robotics, work and skills and trade, resources and supply chains, energy technology assessment informs transition pathways that keep supply reliable during shocks. It links climate, materials and financial risks in the energy system. The canonical description is on the STEM Commons.

Process heat options

Heat pumps, electric boilers, biomass and hydrogen are compared for the temperature ranges and duty cycles of specific industrial processes.

Waste heat recovery

Heat streams within a plant are mapped and matched with heat demands, inside the plant or in nearby networks.

Supply security for energy-intensive plants

Exposure to energy price spikes and supply interruptions is assessed, along with options such as contracts, storage and flexibility.

03Geography

Energy now in Germany.

Night lights, population density and sunlight over Germany, from NASA GIBS. Each layer carries its own date.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Energy now, for this page

About this layer
Evidence
Observed
Source
VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
Provider
NASA LAADS DAAC
Licence
NASA open data
Vintage
daily; Satellite radiance, corrected for moonlight and atmosphere
Newest at build
2026-10-12
Resolution
about 500 m grid
Uncertainty
Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Estimated
Source
Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
Provider
NASA SEDAC (CIESIN, Columbia University)
Licence
CC BY 4.0
Vintage
null; Census-based estimate, static (2020)
Resolution
about 1 km grid
Uncertainty
Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Modelled (latest month GIBS publishes)
Source
MERRA-2 (NASA GMAO / GES DISC)
Provider
NASA GMAO / GES DISC
Licence
NASA open data
Vintage
monthly; Reanalysis model; latest month GIBS publishes
Newest at build
2026-07-01
Resolution
about 2 km grid
Uncertainty
Reanalysis model estimate of monthly mean incoming sunlight over land; an indicator of solar resource, not a site yield assessment.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed
Source
VIIRS NOAA-20 corrected reflectance (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
daily; Satellite image
Newest at build
2026-10-14
Resolution
about 250 m grid
Uncertainty
Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Modelled (latest year GIBS publishes)
Source
MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
Provider
NASA LP DAAC
Licence
NASA open data
Vintage
yearly; Satellite classification; latest year GIBS publishes
Newest at build
2024-01-01
Resolution
about 500 m grid
Uncertainty
Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed (clouds leave gaps)
Source
VIIRS NOAA-20 (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
daily; Satellite retrieval; clouds leave gaps
Newest at build
2026-10-14
Resolution
about 1 km grid
Uncertainty
Satellite skin temperature of the ground on the day shown, an indicator of cooling demand; not air temperature or a heat warning.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Energy now, framed on Germany.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA/NOAA VIIRS · NASA JPL OPERA DIST-ALERTSources and licences

  • We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • MERRA-2 reanalysis, NASA Global Modeling and Assimilation Office; distributed by the NASA GES DISC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • Observed (EO): NASA/NOAA VIIRS (Suomi NPP). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
  • Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
  • Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
  • Optional layers (10 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Incoming sunlight at the surface over land, monthly mean: MERRA-2, NASA GMAO / GES DISC · July 2026
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
  • Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
  • Land surface temperature, day: VIIRS NOAA-20, NASA LANCE · 13 Oct 2026

04Data

What the method consumes.

Datasets and records used by energy generation, storage and grid technology on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

DataRoleLabel
Plant energy auditsDemand profilesOBSERVED
Technology cost dataOptionsESTIMATED
Energy price seriesExposureOBSERVED
05Simulator

A shock through an input-output economy.

A loss of output in one sector propagates through the technical coefficients to its suppliers and customers, and recovers at the rate you set; the coefficients are teaching values, not national accounts. Teaching model with stated illustrative values, not a prediction. The map on this page shows Germany; the model itself is not tied to a place.

Controls

Sector hit first (position in the list)
0
Size of the shock
30% of its output
Duration of the shock
30 days
Recovery rate
0.08 per day
Days simulated
180 days

Readouts

Output lost over the run
0.87% of a year’s output
Total over direct (multiplier)
1.85 times
Most affected other sector
Agriculture
Its peak inoperability
3.2%
Numbers, methods and sources
SectorPeak inoperability (%)Output weight (%)
Power30.9520.4
Fuels2.5119.8
Heavy industry2.3617.7
Transport3.1711.7
Households and services0.3712.6
Agriculture3.2317.8

Methods

  1. Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
  2. Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
  3. Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
  4. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  5. One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Data

The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:

  • Seaports Natural Earth
  • Maritime chokepoints Nexus (named straits and canals at their published coordinates)
  • Railways Natural Earth
  • Major roads Natural Earth
A shock through an input-output economy. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: Input-output analysis (Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press); Inoperability input-output model (Haimes and Jiang (2001), Journal of Infrastructure Systems 7); Dynamic inoperability and recovery (Lian and Haimes (2006), Systems Engineering 9).
06Validation

How results are checked.

Technologies are evaluated through pilot deployments, standard testing and operational monitoring. Model results are compared with metered performance.

CheckHow it is done
Temperature matchOptions are checked against required process temperatures
Metered baselinesSavings are measured against metered baselines
Cost sensitivityOption rankings are tested against price scenarios

Performance under extreme weather and at large scale may differ from tests. Supply chains for components and minerals are concentrated.

07Twin and cascade simulator

Model energy technology for industry as a system. Run the cascade.

Assembled from the Nexus Standards area profile Industry and manufacturing: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.

Hazards

  • Natech event
  • Dam failure
  • River flood (set off downstream)
  • Flash flood (set off downstream)
  • Mass displacement (set off downstream)
  • Chemical spill or leak (set off downstream)
  • Emergency-response overload (set off downstream)
  • Widespread power outage (set off downstream)
  • Water supply shortfall (set off downstream)
  • Toxic gas release (set off downstream)
  • Industrial fire (set off downstream)

Controls

  • NS-0 Constitution and principles
  • NS-9 Contextual UNIs (deployments, instances, programme instances)
  • NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)
  • NS-12 Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)
  • NRS-H60 Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)
  • NRS-H50 Hazard family: Industrial and process accidents (NHS-1 infra.industrial)
  • NRS-H109 Hazard family: Energy-transition technology failures (NHS-1 tech.energy-transition)
  • NRS-X10 Chemical, industrial, nuclear and radiological safety
  • NRS-H54 Hazard family: Signal and platform disruption (NHS-1 cyber.platform)
  • NRS-K14 Domain pack profile: Manufacturing industrial (DOMAIN-014)

Observation

  • eMARS Major Accident Reporting System (listed for reference)
  • World Development Indicators (listed for reference)
  • ILOSTAT (listed for reference)
  • Manufacturing value added as a proportion of GDP and per capita
  • CO2 emission per unit of value added
  • Manufacturing, value added (% of GDP)
  • Direct economic loss to all other damaged or destroyed productive assets attributed to disasters

Example geography

  • The Strait of Malacca: 227 hexagonal cells
  • Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
  • Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
  • Rain rate, last half hour (IMERG), NASA GIBS
  • Surface soil moisture (SMAP), NASA GIBS
08Connections

Topics that rely on this method.

09Sector signature

The sector signature of energy technology for industry.

The sectors energy technology for industry touches and how strongly they depend on each other: what a study has to observe and model together.

Select corners · hover lines6 / 15 relations

Signature

Energy and its partners

Energy gives power for transport and telecoms to infrastructure (0.8), pumping and treatment to water (0.5) and fertiliser, machinery and cold chains to food (0.5).

W · E · I · F · H · B

Sectors
4 of 6
Relations
6 of 15
Coupling
0.46 share of total

Strongest relations in play

  • Energy → Infrastructure · power for transport and telecoms0.8
  • Water → Energy · cooling water and hydropower0.6
  • Water → Food · irrigation0.8
  • Infrastructure → Water · pipes, dams and treatment works0.6
Teaching values for a typical city-region, not measurements for any place. Coupling is the summed strength of the relations in play, as a share of all fifteen.
10Stewardship

Who maintains the method.

Energy Nexus stewards energy generation, storage and grid technology for the Consortium and reviews how it is applied across platforms.

11Methods and evidence

How energy technology for industry is modelled, measured and tested.

The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.

System model

The couplings between the systems in energy technology for industry: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.

++−++IndustryWorkTradeCommunitiesEconomyR reinforcingB balancing
Causal loop diagram from the page’s systems; signs show the direction of influence. Illustrative structure for a system dynamics or network model, not estimated couplings.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
12Connected

Where this page leads across the Consortium.

13Questions

Questions and answers.

What are the limits of energy generation, storage and grid technology in this field?
Performance under extreme weather and at large scale may differ from tests. Supply chains for components and minerals are concentrated.
How are results checked?
Technologies are evaluated through pilot deployments, standard testing and operational monitoring. Model results are compared with metered performance.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

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