The scale of the problem is considerable. Process heat dominates the environmental footprint of many chemical products, particularly those sitting far downstream in a value chain. Previous analyses have found that heat accounts for between 14 and 54 percent of the total climate change impact of the examined chemical products, while other assessments based on greenhouse gas inventories attribute roughly 48 percent of direct industrial carbon dioxide emissions to energy generation. Because steam generation today relies overwhelmingly on burning fossil fuels or organic residues, it represents a substantial share of Scope 2 emissions across the chemical sector. Any tool that sharpens the accuracy of steam accounting therefore has an outsized influence on how green a product appears.

The difficulty lies in the sheer complexity of industrial steam networks. A typical chemical site operates several steam mains at different pressure levels, commonly very high pressure at 40 bar absolute, high pressure at 16 bar, medium pressure at 8 bar and low pressure at 4 bar. High-pressure steam is relaxed through back-pressure turbines and valves, generating electricity as a by-product on its way to the consumers. Every heat exchanger that condenses steam adds another functional unit to the system, creating a web of co-products whose burdens are notoriously difficult to disentangle. Conventional life cycle assessment practice simply plugs in static background datasets, such as those from ecoinvent, which average away all of this site-specific detail and cannot distinguish the distribution losses that vary with pressure level, pipe length, insulation and leakage.

The Braunschweig team’s answer is a generic simulation model built in the open-source Python framework TESPy, with thermophysical properties supplied by CoolProp and life cycle data handled through brightway2.5. The prospective scenario adjustments draw on premise, which links the databases to integrated assessment model projections, while the SiModIn package glues the simulation and the life cycle machinery together. The model represents a complete distribution network: steam generation linked to a background dataset, expansion through a back-pressure turbine with electricity production, pipelines with convective heat losses calculated from established correlations, physical leakage losses assumed at a default 7.5 percent by mass, frictional pressure drops solved via the Darcy-Weissbach equation, optional condensate injection for desuperheating, and make-up water to compensate for losses and purge accumulated impurities.

Crucially, the functional unit is not the steam itself but the delivery of one megajoule of heat at a defined temperature level, which allows distribution losses to be counted explicitly. The model also confronts the thorny question of multifunctionality head-on. Because the network simultaneously delivers heat to other consumers and electricity from the turbine, the researchers compare two treatments sanctioned by ISO 14044: substitution, in which the generated electricity is credited against the average market mix, and allocation, in which burdens are divided according to the exergy destroyed in the turbine and the condensers. Their analysis shows these methodological choices can flip the ranking of which steam pressure level is environmentally preferable, meaning that process design decisions guided by ecological criteria may differ depending on the accounting convention chosen.

The headline finding concerns losses. Across the parameter space explored in a sensitivity analysis of more than 20,000 samples generated with Saltelli’s extension of the Sobol sequence, overall distribution losses ranged from 4.8 to 14 percent, with a mean distribution efficiency of 90.2 percent. That is roughly three times higher than the 2 to 5 percent assumed in a widely used Ecoprofiles report, though below the 13.9 percent figure cited in steam system engineering literature. The practical consequence is stark: ignoring distribution losses, as benchmark datasets effectively do, leads to an underestimation of the environmental impact of steam supply by approximately 5 to 15 percent. For products where steam dominates the footprint, that gap can materially distort comparative claims about competing technologies.

The model’s precision is notable in another respect. When the researchers compared the variance produced by their parameter uncertainty analysis against the background uncertainty of the ecoinvent benchmark dataset, running 17,047 simulations for each case, the model’s standard deviation of 0.004 kilograms of carbon dioxide equivalent per megajoule was about 3.5 times smaller than the benchmark’s 0.014. The model mean sat only about 5 percent above the benchmark mean. The authors are careful to stress that this is a plausibility check rather than a validation; the model has not yet been tested against primary data from real plants and should be treated as exploratory, generating scenario-based estimates rather than verified inventory data.

The prospective dimension of the study adds a twist with real strategic implications. Using the REMIND integrated assessment model under the SSP2-NDC scenario, adjusted through premise for the decades from 2020 to 2040, the researchers show that as electricity grids decarbonize, the substitution credit for turbine-generated electricity shrinks, raising the apparent impact of steam supply, especially at low pressure levels where more electricity is produced. They introduce an energy impact ratio, the ratio of the specific impact of electricity generation to that of steam generation, as a simple diagnostic: when the ratio falls below one, the impact of distributed steam increases as temperature decreases, and vice versa. Under exergy allocation, by contrast, low-pressure steam always remains preferable because more of the burden is assigned to electricity. Notably, the human toxicity category behaves differently from climate change, underscoring the authors’ warning that methodological choices can shift environmental problems between impact categories.

One of the most consequential conclusions concerns electrification. Because the analysis indicates that high-pressure steam from fossil combustion carries a lower allocated burden than low-pressure steam under substitution, replacing low-temperature steam demand with heat pumps or mechanical vapor recompression powered entirely by renewable electricity emerges as a technically and ecologically sound strategy, conveniently aligned with the temperature ranges where those technologies operate most efficiently. Such a shift would require redesigned distribution systems that generate steam directly at the required pressure level. The authors also flag what their scenarios leave out: carbon capture, renewable hydrogen and heat-pump-based steam raising were outside the scope of this study and belong in future work.

What makes the contribution potentially transformative is its accessibility. The code is published openly on GitHub and archived on Zenodo, designed for reuse through SiModIn, with documentation, unit-aware parameters via the Pint library, built-in validity checks and the ability to export results as brightway2.5-compatible datasets. ISO 14044 explicitly recognizes process simulation as a valid data source when it matches a study’s goal and scope, and the authors lay out transparent criteria for when a generic model beats a generic dataset and when it does not. For an industry racing toward carbon neutrality under mounting regulatory scrutiny, a free tool that captures the real thermodynamics of steam networks, from pipe friction to turbine exergy, could change how the next generation of chemical processes is designed, assessed and ultimately decarbonized.

Subject of Research: A generic thermodynamic simulation model for quantifying the environmental impact of industrial process steam supply in life cycle assessment

Article Title: Generic model for determining the environmental impact of process steam supply

Article References: Generic model for determining the environmental impact of process steam supply. (n.d.). https://doi.org/10.1007/s44498-026-00180-3

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00180-3

Keywords: life cycle assessment, process steam, chemical industry, steam distribution, carbon emissions, thermodynamic simulation, open-source model, combined heat and power, ecoinvent, process heat, industrial decarbonization, exergy allocation