Steel slag can emerge from steelmaking at temperatures as high as 1,600 °C, but recovering its heat is difficult because the material is highly viscous and difficult to handle. In the roller-crushing and hot-disintegration process examined by the researchers, molten slag is first crushed and cooled in a semi-open roller-crushing area and then transferred to a closed hot-disintegration tank. Intermittent water spraying triggers exothermic hydration of free calcium and magnesium oxides, promoting internal cracking, particle-size reduction and stabilization of the slag for later use. This treatment produces saturated steam, but the study noted that a substantial portion of the high-temperature sensible heat released during crushing was not recovered.

The proposed configuration attempts to connect two stages that operate at different heat levels. Radiative heating surfaces positioned around and above the roller-crushing region recover high-temperature heat, while hot disintegration supplies saturated steam. Part of the recovered heat preheats feedwater in a water-wall economizer, and the remainder superheats the steam before it enters the turbine. A steam drum provides steam-water separation and a location for chemical dosing and bottom blowdown. The researchers modeled a direct-steam Rankine cycle because the recovered steam can serve as the cycle working fluid; this avoided the additional heat-transfer stage and separate organic-fluid loop that would be required if an organic Rankine cycle were supplied with the steam.

To evaluate the enclosure geometry, the team built a MATLAB model of diffuse-gray surfaces exchanging radiation. The radiation network represented the slag, top surface and aggregated side walls, with slag emissivity set at 0.90 and receiver emissivity at 0.80. The top and side temperatures were prescribed at 120 °C and 25 °C in the main comparison, while convection, radiation involving participating gases or particles, and direct contact heat transfer were excluded. The rectangular enclosure measured 16 by 5 by 4 metres, whereas the cylindrical design had a radius of 5.744 metres and a height of 4 metres. Both provided about 248 m² of combined top-plus-side receiver area, but they did not have identical slag footprints: the idealized slag-emitting area increased from 80 m² in the rectangle to 103.64 m² in the cylinder.

For an idealized 30,000-kilogram slag batch with a constant heat capacity of 1.2 kJ per kilogram per kelvin, the rectangular configuration had a modeled cooling-time mean radiative heat-transfer rate of 13.83 MW, compared with 17.54 MW for the cylindrical configuration. The cylinder’s result was approximately 27% higher under the prescribed surface-radiation conditions. Because the slag-emitting area and view factors changed along with the enclosure shape, this was a comparison of the selected geometries rather than an isolated view-factor gain or a matched-footprint comparison. Sensitivity calculations also showed that lowering slag emissivity from 0.90 to 0.75 reduced the mean rates to 11.66 MW for the rectangle and 14.84 MW for the cylinder. Lowering receiver emissivity from 0.80 to 0.60 changed the modeled cylinder-to-rectangle difference from 26.85% to 23.43%. These calculations describe model sensitivity, not measured uncertainty, and the effects of evolving slag surfaces, enclosure gases, dust, deposits and fouling were not quantified.

The integrated-cycle analysis used Aspen Plus V11 with IAPWS-IF97 water properties and represented the superheater, economizer, steam drum, condenser, pump and turbine. In the selected case, 10,400 kg/h of feedwater entered the flowsheet: 2,400 kg/h passed through the economizer and 8,000 kg/h entered the hot-disintegration heat-recovery section. Their modeled heat duties were 7.247 MW for hot disintegration, 0.267 MW for feedwater preheating and 2.613 MW for steam superheating, giving a total cycle heat input of 10.127 MW. Saturated steam from the drum reached 556.8 °C at 0.2 MPa before entering the expander. The model then calculated 2.068 MW of expander shaft output and a cycle thermal efficiency of 20.4%. The reported cycle power subtracts the represented feed-pump demand from expander shaft output; it is not net exported electricity because generator losses and plant auxiliaries were outside the model boundary.

A literature benchmark tested the baseline calculation without heat supplied by the crushing enclosure. At a hot-disintegration steam pressure of 0.2 MPa and a flow of 2,600 kg/h, the model generated 291.5 kW of expander shaft output, close to the reference value of approximately 300 kW and representing a deviation of about −2.8%. Its modeled efficiency was 15.5%, however, above the literature-reported range of 10–12%, a difference attributed to calculations that did not share fully equivalent component assumptions or efficiency boundaries. The comparison therefore served as a numerical benchmark rather than experimental validation of the integrated design.

The modeled superheating increased the physical exergy carried by the steam entering the expander. Specific physical exergy rose from 586 kJ/kg at 120 °C to 1,031 kJ/kg at 557 °C, an increase of 76%. As steam flow increased from 2,600 to 10,400 kg/h, the inlet-steam physical-exergy rate rose from 0.42 to 2.98 MW and modeled cycle power increased from 291.3 to 2,067.7 kW. The ratio of modeled cycle power to inlet-steam physical exergy remained near 69% in the two cases. This ratio describes conversion within the modeled cycle, not the exergy efficiency of the complete slag-recovery system, because losses in individual recovery sections, slag exergy, generator losses and auxiliary inputs were not included.

Intermittent steam production remains unresolved. Hot disintegration depends on periodic spraying and hydration reactions, whereas the thermodynamic model assumes a steady, time-averaged supply and does not resolve batch-scale transients or water accumulation in the drum. A simplified estimate for a drum containing 20,000 kg of water at 0.2 MPa suggested a characteristic buffering time of approximately 118 seconds, or about two minutes, during an allowable transient pressure decrease to 0.15 MPa. That estimate does not demonstrate that one drum could smooth a complete batch of intermittent production. Staggered operation of multiple hot-disintegration tanks was proposed as a possible way to reduce aggregate flow fluctuations, but measured single-tank steam profiles and plant scheduling records were unavailable, so the arrangement remains an operating assumption. A preliminary cost screen estimated the added equipment at RMB 4.5 million and compared it with an indicative annual electricity value of about RMB 5.33 million at 6,000 operating hours and RMB 0.50/kWh. The resulting ratio of roughly one year is not a definitive payback because installation, civil work, generator and auxiliary consumption, maintenance, downtime, financing, taxation and electricity-price changes were excluded.

Water chemistry presents a separate constraint. Condensate collected from hot-disintegration steam at Henan Jiyuan Iron and Steel Group in China initially contained total calcium and magnesium concentrations of 1.18 and 0.80 mmol/L. In the proof-of-concept dosage series, adding sodium phosphate caused calcium and magnesium to decline sharply before varying non-monotonically. At a sodium phosphate dosage of 5.53 mmol/L, total calcium and magnesium measured 0.009 and 0.085 mmol/L, and equivalent hardness reached 0.188 ± 0.011 mmol/L, 95.2% below the untreated value of 3.944 mmol/L. Visual MINTEQ equilibrium simulations at 15 °C reproduced the general concentration trend and identified hydroxyapatite and magnesium phosphate as possible precipitating solids, but the calculations assumed chemical equilibrium and did not resolve nucleation or precipitation kinetics. Together, the radiation and cycle models and the preliminary water tests support the feasibility of the proposed integration under the studied conditions. They do not establish continuous turbine operation, industrial-scale hardness compliance or long-term chemical stability, which require dynamic pilot testing, independent treatment replicates, long-term scaling studies and validation of turbine control, blowdown, steam purity and auxiliary power.

Subject of Research: Modeled steel-slag waste-heat recovery and preliminary phosphate treatment for Rankine-cycle power generation

Article Title: An integrated waste heat recovery system for Rankine cycle power generation from the steel slag hot disintegration process

Article References: Wu, H., Zhou, T., Bai, R., Xu, Z., Li, Y., Xia, B., Zhang, M., & Yang, H. (2026). An integrated waste heat recovery system for Rankine cycle power generation from the steel slag hot disintegration process. Frontiers in Energy Research, 14, Article 1965673. https://doi.org/10.3389/fenrg.2026.1965673

Image Credits: AI Generated

DOI: 10.3389/fenrg.2026.1965673

Keywords: steel slag, waste-heat recovery, Rankine cycle, hot disintegration, radiative heat transfer, steam superheating, power generation, water hardness, phosphate treatment, steel industry, integrated, waste

The concept at the heart of the study is the cost of capital, or CoC: the rate of return that investors and lenders demand to compensate them for the risk of financing a project. This matters enormously because almost every low-carbon technology — wind turbines, heat pumps, electric arc furnaces, carbon capture systems — is capital-intensive. Most of the expense is paid up front, while the benefits, in the form of energy or materials, trickle in over decades. A technology whose costs are dominated by upfront investment is exquisitely sensitive to interest rates and return expectations. Raise the cost of capital by a few percentage points and a wind farm that looked cheap on paper can become uncompetitive against a gas plant, whose costs are spread across years of fuel purchases.

Despite this sensitivity, the integrated assessment models that inform reports by the Intergovernmental Panel on Climate Change have historically assumed a single, uniform cost of capital for all countries, sectors and technologies. The simplification was born of necessity: data were scarce and modelling complexity was already formidable. But it embeds a hidden assumption — that financing a solar project in Eastern Africa costs the same as financing one in the United States. The new research shows just how wrong that assumption is. Drawing on sovereign credit ratings and stock-market risk measures from the widely used NYU Damodaran database, the team calculated financing costs for eight climate-relevant sectors, including electricity, hydrogen production, cement, iron and steel, pulp and paper and food processing. The heterogeneity is striking: the cost of capital for electricity in high-risk regions such as Eastern Africa or Ukraine is roughly three times higher than in the United States. Even within a single low-risk country, sector differences matter — the cost of capital for American steel is about two percentage points higher than for American electricity, a gap equivalent to the difference between power projects in the United States and in India or Indonesia.

The team then fed these differentiated estimates into the IMAGE integrated assessment model, a well-established framework with high geographic and technological resolution that simulates energy systems and industry across 26 world regions through 2100. They ran two scenarios: one with the model’s typical uniform low-risk cost of capital of 5 percent, and one with their dynamic, region- and sector-specific estimates, which also capture how economic growth in developing countries gradually lowers sovereign risk premiums over time. Both scenarios were paired with a current-policies pathway, leading to about 2.8 degrees of warming, and an ambitious pathway that keeps warming well below 2 degrees, in line with the Paris Agreement.

The headline finding is that accounting for real-world financing conditions raises cumulative global mitigation costs through 2100 from 77 trillion to 84 trillion US dollars — an increase of roughly 9 percent. When the researchers decomposed this figure, they discovered that sectoral risk differences alone actually lower mitigation costs slightly, mainly by reducing electricity prices in some low-risk regions. The damage comes overwhelmingly from country risk, which adds about 6 percent to costs, and from the extra climate policy effort needed to compensate for the emissions that expensive capital leaves on the table, adding another 4 percent. In other words, the biggest distortion in decades of climate modelling has not been misjudging how risky the steel industry is — it has been pretending that a power plant in a country with a poor sovereign credit rating can be financed as cheaply as one in Zurich.

The emissions consequences are substantial. Without an endogenous policy reaction, introducing realistic financing costs raises cumulative global CO2 emissions through 2100 by roughly 60 to 70 gigatonnes — more than one additional year of current global emissions — driven overwhelmingly by high-risk regions. Expensive capital pushes electricity systems in those regions away from capital-intensive renewables and nuclear power and toward natural gas, which is less sensitive to financing costs than coal. It also suppresses investment in electricity grids in developing countries, a finding with major implications given that grid expansion needs for net zero are as large as, or larger than, those for power generation itself. And because energy prices ripple through the whole economy, sectors that were never directly modified in the model — transport and heating among them — also emit more, as higher power prices delay the switch from oil to electricity in passenger vehicles and trucking.

Perhaps the most striking results concern green hydrogen. Hydrogen production today is dominated by firms associated with the chemical sector, which carries above-average market risk, and electrolytic hydrogen is fully exposed to electricity prices, which themselves rise when the cost of capital rises. The result: hydrogen costs increase in nearly every region, with increases well above 20 percent in high-risk areas such as Western Africa — precisely the regions often envisaged as future hydrogen exporters. Global hydrogen production via electrolysis drops substantially in the model, while the incumbent technology, natural gas-based steam methane reforming, holds on longer. Cheaper-to-abate alternatives become more CO2-intensive and expensive, and hydrogen loses attractiveness for decarbonizing transport, industry and power, with end-of-century hydrogen usage falling accordingly under the ambitious climate scenario.

Heavy industry tells a similar story. In cement, responsible for about 7 percent of current global CO2 emissions, country and sector risks slow the transition from inefficient standard kilns to modern, efficient kilns equipped with carbon capture and storage, leaving a larger share of unabated production in place by 2100. In iron and steel, which accounts for roughly 8 percent of emissions, higher financing costs entrench the carbon-intensive blast furnace and basic oxygen furnace route, reduce the uptake of carbon capture on both that route and direct-reduced iron production, and squeeze out novel low-carbon processes such as hydrogen-based direct reduction and electrowinning. Industrial carbon capture by cement and steel falls substantially, overwhelmingly because of conditions in high-risk regions. The common thread is capital intensity: the very technologies needed to decarbonize industry are the ones most punished by expensive money.

The geographic redistribution is equally consequential. Mitigation costs rise not only in high-risk regions with persistently poor credit ratings, such as Ukraine or parts of South Asia, but also by 5 to 6 percent in major low-risk economies like the United States and China, as the model shifts cost-effective abatement toward lower-risk regions. In some high-risk parts of Eastern and Southern Africa, the effect is paradoxically reversed: abatement efforts shrink so much that overall abatement costs fall, even as global emissions rise. The study’s authors stress that economic growth in developing countries, while helpful, is unlikely on its own to close the financing gap, because sovereign risk also depends on institutional quality, regulatory stability and debt sustainability — factors that do not scale automatically with GDP.

The policy implications are pointed. Well-designed international climate finance can de-risk investments across all climate-relevant sectors in developing countries, helping to avoid what researchers have called investment traps, yet international mitigation finance remains heavily concentrated in low-carbon electricity generation, leaving grids, hydrogen and heavy industry underserved. The authors also caution that their estimates reflect market conditions rather than any judgment about whether financial markets price risk correctly, and that their model abstracts from sectors such as chemicals and transport finance. But the core message is hard to escape: the world’s climate models have been quietly assuming that money is cheaper than it is, especially where it is needed most. Correcting that assumption makes decarbonization slower, more expensive and more uneven — and makes the case for fixing global financing conditions itself a central piece of climate policy.

Subject of Research: The effect of country- and sector-specific cost of capital differences on global energy and industry decarbonization pathways

Article Title: The impact of financing cost differences on global energy and industry decarbonization

Article References: Waidelich, P., Crassier, C., de Boer, H. S., Tautorat, P., van Vuuren, D. P., & Steffen, B. (2026). The impact of financing cost differences on global energy and industry decarbonization. Nature Climate Change, 16(10), 1178-1186. https://doi.org/10.1038/s41558-026-02756-0

Image Credits: AI Generated

DOI: 10.1038/s41558-026-02756-0

Keywords: cost of capital, climate finance, integrated assessment models, decarbonization, renewable energy, green hydrogen, carbon capture and storage, developing countries, sovereign risk, steel industry, cement industry, IMAGE model

The study, published in the Journal of Industrial Ecology, introduces a multi-objective, multi-level cleaner production evaluation framework that does something traditional scorecards have long failed to do: it treats carbon reduction as a first-class criterion rather than an afterthought. Grounded in the principles of industrial ecology, the framework combines a full life-cycle perspective with a dedicated carbon dimension, forcing evaluators to look beyond the smokestack and consider impacts from raw material extraction all the way through to the final steel product.

Cleaner production, a concept that has guided industrial environmental policy for decades, traditionally emphasizes reducing pollution and conserving resources within factory boundaries. But as climate change has moved to the center of global concern, that narrow focus has become inadequate. The Fudan team, led by Shoubing Wang together with Yuxuan Zhang, Qi Wei and Jingru Hu, argued that evaluation systems built before the carbon era simply cannot capture the full environmental profile of a modern steel enterprise, which must now answer for greenhouse gas emissions across its entire value chain.

To construct the framework, the researchers wove together three complementary methods. A systematic policy review of China’s cleaner production regulations for the steel sector revealed how policy priorities have evolved through three distinct stages, from an initialization phase, through rapid development, and into the current era of innovation and carbon focus. A life-cycle inventory analysis then defined the system boundary and screened which indicators genuinely matter across the steel production chain. Finally, expert consultation refined and validated the resulting indicator set, ensuring the framework reflected both scientific rigor and practical industrial realities.

Scoring within the framework relies on two well-established analytical techniques. An improved analytic hierarchy process, a structured method for deriving weights from pairwise comparisons, determines how much each indicator contributes to the overall score. Fuzzy comprehensive evaluation then converts indicator data into performance grades, an approach well suited to environmental assessment where many measures are inherently imprecise or qualitative. The combination allows the framework to aggregate dozens of indicators spanning resource consumption, energy use, pollution, product management and carbon reduction into a single interpretable score.

The real test came at Baosteel, one of China’s flagship steel producers and a subsidiary of the Baowu Group, the world’s largest steelmaker. Applying the framework to real operational data, the researchers found that Baosteel’s cleaner production score climbed from 62.68 in 2014, a rating classified as Domestic Poor, to 86.39 in 2021, achieving the Domestic Advanced category. That leap of nearly 24 points over seven years represents a striking transformation, but the story behind the numbers proved even more valuable than the headline result.

The evaluation did more than certify improvement; it pinpointed precisely where the company still fell short. Two critical weaknesses emerged: carbon reduction and product life-cycle management. Even as Baosteel advanced on conventional pollution and resource metrics, the dedicated carbon dimension exposed gaps that a traditional assessment would likely have missed or underweighted. This diagnostic capability is arguably the framework’s most important contribution, because it transforms evaluation from a passive grading exercise into an actionable roadmap, generating specific recommendations for where investment and management attention should flow next.

The timing of this work could hardly be more significant. China’s steel industry, the world’s largest producer, has been formally incorporated into the national carbon emissions trading market, and the sector faces intense pressure under the country’s carbon neutrality pledges. For an industry responsible for a substantial share of global carbon dioxide emissions, the difference between a scorecard that treats carbon as peripheral and one that places it at the core could shape investment decisions worth billions. A framework that quantifies carbon performance alongside energy efficiency and pollution control gives regulators, investors and company managers a common language for assessing progress.

The researchers are careful about the limits of their findings. The full case study rests on a single Chinese enterprise, and they emphasize that the results require cautious interpretation until the framework is validated across broader industrial and geographical contexts. The framework is presented as a proof of concept, a preliminary tool that could, with further contextualization and testing, help steel companies navigate sustainable production under tightening carbon constraints. Data limitations also reflect commercial realities: the raw Baosteel data was provided under license by Baosteel Engineering & Technology Group Co., Ltd. and cannot be made public due to confidentiality agreements, although the team notes supporting data are available upon reasonable request.

Nevertheless, the implications reach well beyond one company or one country. As carbon border adjustments, emissions trading schemes and net-zero commitments spread across the global economy, heavy industries everywhere will need credible, standardized ways to measure and compare environmental performance. A life-cycle based evaluation framework with a dedicated carbon dimension offers a template that could be adapted to cement, aluminum, chemicals and other hard-to-abate sectors. If cleaner production evaluation once measured how little smoke a factory emitted, the next generation of scorecards will measure how deeply a company has rewired its entire metabolism for a carbon-constrained world, and China’s steel industry, the study suggests, has shown just how far that rewiring can go in a single decade.

Subject of Research: A life cycle based cleaner production evaluation framework with an integrated carbon dimension, applied to China's steel industry

Article Title: A life cycle based cleaner production evaluation framework integrating a dedicated carbon dimension: a case study of China’s steel industry

Article References: Wang, S., Zhang, Y., Wei, Q., & Hu, J. (2026). A life cycle based cleaner production evaluation framework integrating a dedicated carbon dimension: a case study of China’s steel industry. Journal of Industrial Ecology, 30(5), 2295-2316. https://doi.org/10.1007/s44498-026-00155-4

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00155-4

Keywords: steel industry, cleaner production, carbon reduction, life cycle assessment, industrial ecology, China, Baosteel, evaluation framework, carbon neutrality, multi-criteria decision analysis, fuzzy comprehensive evaluation, sustainable production