GCRI · Advanced Industry and Robotics
Industrial energy and water
Industry is a large user of energy, heat and water, and its demand shapes grids, water resources and emissions. Efficiency, electrification, heat pumps, hydrogen and water recycling can change that footprint, while energy price shocks and droughts can stop production. This page sets out how industrial energy and water use work, how they are measured, and what evidence serves the firms, regulators and planners involved.
Industrial energy and water on the record.
Industrial energy and water: how the risk develops.
Resource needs, utility interdependence. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Heat dominates industrial energy
Much industrial energy is used as heat, at temperatures from low to very high. High-temperature heat is the hardest to electrify.
Price shocks hit energy-intensive sectors
Steel, chemicals, cement, glass and paper are sensitive to energy prices. Sharp rises can curtail production and shift it elsewhere.
Water is a production input
Cooling, processing and cleaning use large volumes of water. Drought and competing demands can force cutbacks.
Efficiency has many small parts
Motors, compressed air, steam systems and heat recovery offer savings that add up but are often overlooked without energy management.
Water now in Germany.
Night lights, flood water and population density 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
Water 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
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. 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
- 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
- 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
- Estimated
- Source
- GPM IMERG (NASA GES DISC)
- Provider
- NASA GES DISC
- Licence
- NASA open data
- Vintage
- daily; Satellite estimate, near real time
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite estimate, near real time. 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
- Source
- SMAP Level 4 (NASA NSIDC DAAC)
- Provider
- NASA NSIDC DAAC
- Licence
- NASA open data
- Vintage
- daily; Model with satellite data assimilated
- Newest at build
- 2026-10-11
- Resolution
- about 2 km grid
- Uncertainty
- Model with satellite data assimilated. 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
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
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.
Water 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 SMAP · 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
- 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
- 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
- GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- SMAP Level 4 soil moisture and snow mass, NASA Global Modeling and Assimilation Office; distributed by the NASA National Snow and Ice Data Center DAAC; 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.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA SMAP. 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).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- 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 (14 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
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
Industry is a major user of energy and water.
Findings are designed for the Global Sector Risk and Resilience series. Energy management follows ISO 50001 (2018); emissions controls follow the EU Industrial Emissions Directive and its BAT references.
- Plant energy and water data.
- Efficiency programme evaluations.
Energy and circularity board.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Energy and circularity board
Industrial energy and water · Industrial park A · Year 2
Material flows
- Recycled
- Recovered
- Landfill
Targets
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Industrial final energy consumption | petajoules by sector | annual | Energy balances | Published statistics |
| Energy intensity of industry | energy per unit of value added | annual | Energy and economic statistics | ESTIMATED |
| Industrial water withdrawal | cubic metres | annual | Water statistics | Published statistics |
| Share of electricity in industrial energy | percent | annual | Energy balances | Published statistics |
| Firms with an ISO 50001 energy management certificate | count | annual | Certification statistics | REPORTED |
| Production curtailed due to energy or water constraints | events | per event | Industry and news reports | REPORTED |
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. 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
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 7%
- Assets still working
- 17%
- Amplification of the initial damage
- 11.3 times
- Steps to settle
- 3
Numbers, methods and sources
| Step | Water working (%) | Power working (%) | Telecoms working (%) |
|---|---|---|---|
| 0 | 89 | 89 | 100 |
| 1 | 44 | 44 | 89 |
| 2 | 17 | 17 | 39 |
| 3 | 17 | 0 | 33 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- 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:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Advanced Industry and Robotics in Foundry
- Tested at Advanced Industry and Robotics in Universe
- Codified in Industry, Trade and Supply Chains in Standards
- Published in Advanced Industry and Robotics in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| IEA World Energy Balances and industry tracking | Industrial energy use by sector and fuel | Licence per product (to check) |
| FAO AQUASTAT | Water withdrawal by sector | Open, FAO terms |
| Eurostat energy statistics | Final energy consumption in industry | Open, Eurostat reuse policy |
| ISO Survey of management system certificates | ISO 50001 certification counts | Public, ISO terms |
| Industrial decarbonisation roadmaps from governments | Plans and data for heavy industry | Public records, licence per government |
Model industrial energy and water 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)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)NRS-H50Hazard family: Industrial and process accidents (NHS-1 infra.industrial)NRS-H109Hazard family: Energy-transition technology failures (NHS-1 tech.energy-transition)NRS-X10Chemical, industrial, nuclear and radiological safetyNRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-K14Domain 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
Who decides, and what the evidence serves.
Advanced Industry and Robotics prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Industrial firms | Which efficiency, electrification and water measures to invest in | Option analyses; profiles |
| Energy and water regulators | How to plan supply for industrial demand | Profiles and exposure evidence |
| Industry ministries | How to support energy-intensive sectors through transition | Exposure assessments |
Outputs this topic produces
- Industrial energy and water profiles by sector
- Heat decarbonisation option analyses
- Energy price exposure assessments
- Water risk reviews for industrial clusters
The sector signature of industrial energy and water.
The sectors industrial energy and water touches and how strongly they depend on each other: what a study has to observe and model together.
Signature
Water and Energy
Water gives energy cooling water and hydropower (0.6): the strongest relations between the sectors this subject joins.
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
What experts ask.
- 01
How much does production depend on each utility?
Which processes stop, and how quickly, when power, gas, steam or water is interrupted.
- 02
How do plants affect shared resources?
How industrial demand and discharges affect grids, rivers and aquifers used by others.
- 03
What does decarbonisation change?
How electrification and new fuels shift dependence from fuels to grids.
- 04
How do droughts and heat affect industry?
Cooling limits, water rights and discharge temperature constraints.
How industrial energy and water 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 industrial energy and water: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
What drives the risk.
Energy-intensive processes such as steel, cement, chemicals, glass and paper depend on high-temperature heat and continuous operation. Interruptions can damage equipment and create unsafe conditions during shutdown and restart.
Water is used for cooling, processing, cleaning and steam. In drought, intake may be restricted; in heat, discharge temperatures may exceed permit limits. Plants and power stations on the same river compete for the same resource.
Electrification of industrial heat and the use of hydrogen shift dependence onto power grids and new infrastructure, raising questions about grid capacity and reliability.
Industrial demand also supports utility systems. Large consumers can provide flexibility to grids, and industrial symbiosis lets one plant’s waste heat or water serve another.
How evidence is gathered, and where it stops.
| Method or data | What it shows | Its limit |
|---|---|---|
| Energy and water audits | Site consumption by process | Snapshot of current operations |
| Utility dependency mapping | Which processes rely on which supplies | Backup durations often assumed |
| Catchment and grid models | Competition for shared resources | Regional data may be coarse |
| Scenario analysis | Effects of curtailment and price shocks | Depends on scenario design |
How it is studied, and what the methods cannot say.
- 01Energy generation, storage and grid technologyProcess heat options · Waste heat recovery · Supply security for energy-intensive plantsApplied here
- 02Optimisation and operations researchProduction scheduling · Energy-aware operations · Buffer and spare sizingApplied here
- 03Water and environmental engineeringEmission control selection · Industrial wastewater treatment · Community exposure monitoringApplied here
- 04Sensors, IoT and observation networksCondition monitoring · In-line quality inspection · Securing connected sensorsApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
Why is industrial heat hard to decarbonise?
What is energy intensity?
What is ISO 50001?
Why does shutdown create safety risk?
Does the platform advise on energy procurement?
What is industrial symbiosis?
Contribute evidence from this page.
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