Estimating firms' emissions from asset level data helps revealing (mis)alignment to net zero targets

Domain: chemistry
Published: 2026-03-07
Editorial outcome: major_revision

Abstract
We develop a bottom-up methodology to estimate companies’ (mis)alignment to net-zero scenarios. The approach relies on asset-level data for individual production units, enabling a detailed estimation of corporate emissions trajectories. We apply the methodology to the steel sector globally and find that companies’ projected emissions for 2030 exceed those implied by the International Energy Agency’s (IEA) Net Zero Emissions (NZE) scenario by between 10% and 22%, depending on the assumptions about the future evolution of emission factors of steel production. Further, we find that projected emissions for 2030 exceed companies’ aggregate stated targets, even under the optimistic assumption of electricity supply decarbonization rate following the net-zero scenario, with the gap primarily driven by the largest steel companies. Our results show that a bottom-up asset-level approach allows for a reality check of companies’ contributions to national decarbonization plans. This, in turn, is crucial to inform more targeted industrial policies for decarbonization, and regulatory disclosure.

Introduction
Avoiding the most adverse impacts of climate change requires to achieve net-zero greenhouse gas (GHG) emissions at the global level before 2050 [1]. In this context, the question of reliably estimating companies’ alignment to net-zero GHG emission scenarios (hereafter, net-zero alignment) is crucial both to inform climate policies and regulations and to enable global financial institutions’ to evaluate and manage climate-related risks.

On the one hand, earlier works have introduced methodologies to set net-zero targets and assess firms’ net-zero alignment [2–4]. These also include the Science Based Targets Initiative [5]. On the other hand, other works have focused on assessing the alignment of financial portfolios to decarbonization targets, such as 2 degrees C or net-zero 2050 [6–8], using as input estimates of companies’ projected emissions. However, these estimates are most often based on firms’ self-reported GHG emissions, and are obtained by extrapolating historical GHG emissions using a linear regression.

Linear extrapolation from historical emissions suffers from three main shortcomings. First, often only few (less than 5) years of data on emissions are available: for example, 20,202 companies disclosed their climate performance to CDP in 2023, but only 9,526 did so in 2020 (see cdp.net/en/companies/cdp-2023-disclosure-data-factsheet). The emission trajectories can be volatile due to exogenous shocks (e.g. the COVID-19 pandemic and the war in Ukraine). In this context, the use of machine learning techniques to fill the data gaps or to reduce noise is of limited help. Indeed, machine learning methods for estimating past emissions for non-reporting companies [9–11] are trained on emission data from reporting companies, which may be subject to biases and inconsistencies [12, 13].

Second, even if longer timeseries are available (e.g., 10 years), linear extrapolation can lead to either overestimate or underestimate future emissions. For instance, if a company has been on an emission growth trajectory in the last 5 years but it is now in the course of a transformation to low-carbon technologies, projections for 2030 will exceed the actual emissions levels. Conversely, if the company has been decreasing emissions due to exogenous shocks, but it has not invested in low-carbon technologies, the projected emissions for 2030 are lower than today, while the actual ones can be much higher.

Third, the above problems are exacerbated by the use of metrics of emissions’ financial intensity, such as the ratio of emissions to revenues or the ratio of emissions to market capitalization, which are more volatile than emission measures expressed in physical units.

Thus, while using companies’ self-reported emissions to estimate future emission trends has been an important development for disclosure in recent years, from a policy and investment perspective there is a need to independently validate such self-reported estimates. In particular, the misestimation of emissions is problematic because decision makers within the companies themselves or in public authorities may find out too late that companies’ stated targets are not credible, or will not be met. When this occurs systematically across companies and sectors, it can jeopardize the achievement of the 2 degrees C objective. If, instead, discrepancies can be detected early on, countermeasures can be put in place, including a revision of disclosure requirements and of the reporting regulation.

To address these problems here, we develop a methodology to estimate companies’ alignment to net-zero scenarios. Our approach makes use of asset-level data, i.e., data on companies’ emissions at the level of their production units. Instead of relying on linear extrapolations of emissions, we take into account the opening/closing dates of each production unit, as well as its technology and its emission physical intensity (i.e. tonnes of CO2e per tonnes of physical output). Further, for technologies that use electricity as an input, we consider alternative scenarios for the decarbonization rate of the electricity supply. By aggregating emissions of production units at the level of individual companies (hereafter, bottom-up approach), the methodology yields an independent estimate of future emission trajectories which reflect the objective capabilities of companies in terms of technologies and investments on the ground. These estimates can then be compared to those provided, at sectoral level, by the reference scenarios of the International Energy Agency (IEA), or to the decarbonization targets stated by companies themselves, when available.

In this work, we apply the methodology to the steel sector worldwide. Steel is a key sector for decarbonization scenarios [14–17], because it will remain an essential input material for many economic activities, and it is one of the metals required for expanding electrification infrastructures (e.g., railways, power transmission, renewable energy facilities).

We find that our bottom-up projected emissions in 2030 exceed the emissions trajectories in the net-zero scenario of the IEA to an extent varying between 10% and 22%, depending on the assumptions on the rate of decarbonization of the electricity supply to the steel sector and on the future evolution of the emission factors of steel production. Further, the sum of projected emissions in 2030 across companies exceeds the sum of emissions foreseen by companies’ stated targets, even in the optimistic case that the decarbonization rate of electricity supply follows the net-zero scenario. Notably, this discrepancy is driven by the largest companies.

Our methodology could be applied to other sectors as well, in particular those for which production is carried out by means of a small set of alternative technologies for which the emission physical intensity is known. In Supplementary Section 3 (Cross-sector applicability) and Supplementary Table 3 (Plant-level and technology data availability for various sectors) we discuss in detail the challenges for applying it to cement, aluminum, pulp & paper and power sectors, and point the reader to relevant data sources.

While ongoing developments in Artificial Intelligence can further facilitate the automatic extraction of emission data from firms’ reports, the need will remain for complementary and independent estimates of companies’ future emission trajectories. Our methodology can contribute to improve the quality of emissions disclosure, and standards for firms’ emissions on alignment plans. This information, in turn, is crucial to support credible climate policies and effective international supervision.

Results
Methodology for projecting future company emissions from asset-level data.

Our first contribution is a bottom-up methodology for estimating future carbon emissions of companies based on their assets (i.e., production units) and based on climate policy scenarios. The workflow of the methodology is illustrated in Figure 1 and is described in detail in section Methodology.

The methodology is based on four types of data sources:

i. Scenario data. This includes projections of production volume in relevant climate scenarios and projections of carbon intensity of electricity generation in the same scenarios (for estimating Scope 2 emissions). In our application to iron and steel sector , we use the IEA World Energy Outlook 2023 scenarios, hereafter referred to as IEA scenarios.

ii. Asset-level data. This is the main source of data for the methodology, which includes, for each company, capacity, technology, and past production volume of each their assets (production units), as well as their planned opening/closing dates (when available).

iii. Technology specific data. This includes past and present carbon emission factors per physical unit of production for each relevant production technology at the available granularity (world or country-level) as well as the information about potential future reductions of these emission factors (best available technologies to reduce emissions, their abatement potential and present state of deployment).

iv. Reported emission data. This includes company-level reported emissions from CDP (formerly Carbon Disclosure Project) and Thomson Reuters (Refinitiv).

Scenario, asset-level and technology data are then combined to estimate the future raw bottom-up emissions. This step involves: a) the estimation of the future utilization rates based on past production and scenario-based future production; b) the estimation of the future emission factors based on technology data and scenario-based electricity generation carbon intensity; c) the identification of which assets will be in operation at a given future date, based on information on opening/closing dates.

The estimation of future raw bottom-up emissions is then aggregated at the firm level and translated into future emissions by means of a statistical model (linear regression), trained on past bottom-up emissions aggregated at firm level, and past firm’s reported scope 1 and 2 emissions. This step enables us to account for emission sources not directly related to the production process such as electricity consumption for other uses than electric arc furnace steel production.

To quantify the future utilization rate of steel plants, we consider two alternative plausible assumptions about its evolution. In the first method (the country-level utilization rate), all steel plants of a given country are assumed to have the same utilization rate, determined by taking the past utilization rate and adjusting it with a common global multiplier to match the scenario-based production. In the second method (constant market share), all companies are assumed to have constant market share, and all plants of a given company are assumed to have the same utilization rate. Despite being based on different assumptions, the two methods give very similar results. For this reason, in the discussion of the main results of the paper we only use the constant market share method. In the Methods section and in Supplementary Figure 5, we present the results for the country-level utilization rate method, and also, to explore the uncertainty associated with projecting future plant utilization rates, we compute upper and lower bounds on future emissions by affecting all scenario-based production to, respectively, the most polluting and the least polluting plants of each country.

The future firm-level emissions based on asset-level data are then used to answer two questions related to net zero alignment of the iron and steel sector:

1. To what extent is the iron and steel sector as a whole aligned with net zero scenario?
To answer this question, we aggregate our estimated firm-level future emission trajectories at the sector level, and compare them with the IEA Net Zero 2050 (NZE) scenario for the steel sector. In Supplementary Figures 1 and 2 we also compare the estimated firm-level future emission trajectories with IEA Announced Pledges (APS) and Stated Policies (STEPS) scenarios.

2. Are the future decarbonization trajectories of the companies in the iron and steel sector, as estimated from asset-level data, consistent with the stated decarbonization objectives of the same companies? To answer this question, we aggregate our estimated firm-level future emission trajectories only for companies for which decarbonization targets are available. We then aggregate the published targets for these companies and compare the target decarbonization rates with the actual decarbonization rates from asset-level data.

Evaluating the net zero alignment of the iron and steel sector

We find that the iron and steel sector as a whole is not aligned with the IEA Net Zero 2050 scenario.

Figure 2 compares the bottom-up (asset-level) emission trajectories for the entire iron and steel sector with the target emission trajectories for the net zero scenario. To ensure coherence between the global steel production trajectory and the reference emission trajectory, we assume that steel production also follows the IEA Net Zero 2050 scenario trajectory.

The three graphs and the projected emission curves within each graph differ in the assumptions made to determine the future emission factors of the main steel production routes, Electric Arc Furnace (hereafter EAF) and Blast Furnace–Basic Oxygen Furnace (hereafter BF-BOF or simply BOF). Namely, in the left graph, the carbon intensity of electricity for EAF emission factors is taken from the NZE scenario, in the right graph it is taken from the APS scenario and in the bottom graph from the STEPS scenario. Note that the three IEA scenarios are not directly comparable with each other: while the APS and STEPS datasets include regional disaggregation, the NZE scenario is published only at the global level.

In each graph, the red curve assumes a constant emission factor for the BF–BOF technology, the orange curve uses an emission factor extrapolated by linear regression from the past 20 years, while the blue curve represents the emission factor under full implementation of best available technologies for decarbonizing the BF–BOF process (see Methods section for details).

Even in the most optimistic case where the electricity sector achieves the net zero target, and all available abatement technologies are fully implemented (left chart, orange curve) the iron and steel sector overshoots its net zero target by 10%. In the least optimistic case, where the BF-BOF emission factor stays constant and the electricity carbon intensity follows the STEPS scenario rate, the overshoot increases to 22%.

Comparing bottom-up emission projections with stated targets

Based on estimates from asset-level data, we find that the decarbonization pathways of companies from the iron and steel sector, which publish decarbonization targets, are, on average, not aligned with those targets.

Figure 3, for the subset of companies with reported stated targets, compares the future emissions computed from stated targets with future emissions estimated from asset level data. Since in this case the reference emission trajectory is given by company stated target rather than taken from a scenario, we ensure coherence between future carbon intensity of electricity intensity and future global steel production, by taking them from the same scenario. In the left graph, the future global steel production and the carbon intensity of electricity production are given by NZE scenario, in the middle graph by the APS scenario, and in the right graph by the STEPS scenario.

Similarly to Figure 2, in each graph, the red curve assumes a constant emission factor for the BF–BOF technology, the orange curve uses an emission factor extrapolated from the past 20 years, while the blue curve represents the emission factor under full implementation of best available technologies.

We find that even under the most optimistic assumption about the future evolution of the BF-BOF emission factor, the companies substantially overshoot their stated targets with respective differences of 15%, 21% and 28% for the NZE, APS and STEPS scenarios.

Note that the targets examined in this paper, as well as the IEA emissions scenarios used for comparison, are formulated in terms of annual emissions, whereas IPCC carbon budgets are defined cumulatively. The use of annual emission targets is standard in the scenario literature, as it allows one to track the time profile of sectoral emission pathways and to analyze the dynamics of transition in the iron and steel sector.

Methods
Data sources

Our framework relies on four types of data sources (see Figure 1): global and sector-specific scenario data; asset-level data; technology data; reported data on GHG emissions and company-level decarbonization targets.

Scenario data

For global scenario data, we use the World Energy Outlook 2023 scenarios from the IEA (see iea.org/reports/world-energy-outlook-2023), including NZE, APS and STEPS. They include values for global production, emissions, electricity intensity and carbon prices. The IEA provides country level scenario data only for STEPS and APS. For the NZE scenario, we use global-level values.

For sector-specific scenario data, we use the Energy and Technology Perspectives report on Iron & Steel (see iea.org/reports/energy-technology-perspectives-2023) from the IEA, in order to derive sector-specific values (such as the historical level of emissions, which serves as a starting point for describing emissions trajectories).

Asset-level data

The Global Iron and Steel Tracker (previously known as Global Steel Plant Tracker, see globalenergymonitor.org/projects/global-iron-and-steel-tracker/) database tracks over 950 plants owned by over 600 companies and representing and annual crude steel capacity of 2.9 billion tons (94% of the total). For each plant, it includes plant capacity, production, development status (operating, construction...), main production method (BF-BOF, EAF) and ownership fraction. Start year for projected plants is also available, allowing for asset-level emission projection. Figure 4 plots the location of plants on a map.

Opening dates of future plants are well represented in the database, however closure dates of some plants may not be fully represented. For this reason, we add closure dates for the oldest blast furnace plants to the database, separately for each scenario to meet a global scenario-dependent capacity constraint, coherently with the following approach:

• Global capacity is projected based on the latest historical value and the annualized growth rate of global production from a given scenario.
• At each future date, we compare bottom-up global capacity with the above projection, and shut down the oldest blast-furnace plants whenever the constraint is not satisfied. We apply this procedure at the global level for all plants, disregarding specific geographic or firm-level capacity constraints.

Technology data: GHG emission factors

The main technologies for crude steel production are Blast Furnace (for converting iron ore into pig iron) combined with Basic Oxygen Furnace (for converting pig iron into steel), abbreviated as BF-BOF, and Electric Arc Furnace (EAF).

We use country-level emission factors from [26] (see ourenergypolicy.org/resources/steel-climate-impact-an-international-benchmarking-of-energy-and-co2-intensities/), calculated with 2019 data. This study covers 16 major steel producing countries / regions, accounting for 87% of global steel production and 91% of global steel production through the BF-BOF route, which is responsible for most carbon emissions.

For the remaining countries, we use global emission factors from the World Steel Association (see worldsteel.org/steel-topics/sustainability/sustainability-indicators/), calculated from 2021 data and reported in Table 1. It should be noted that steel production emission factors are highly uncertain as they depend on the age and other features of the specific plant, electricity generation energy mix, etc. For plants with mixed or unknown technology, we use the highest emission factor available for the sector at the country or global level.

Future emission factors of various steel making technologies are likely to evolve due to decarbonization efforts of companies. For the BF-BOF route, future emission reductions depend strongly on the progress and adoption of energy-efficiency and decarbonization technologies. Historical evidence shows that carbon emissions from BF–BOF steel production have declined steadily in recent decades owing to continuous process improvements [27], and this downward trend is likely to continue. In the short term (up to 2030), emission reductions are likely to come from upgrades to Best Available Technology (BAT) and improved process control rather than from emerging new technologies with low TRL.

Table 2 below lists the principal BAT options for BF-BOF steel production route together with their estimated abatement potentials, following [28] (see also [29] for an earlier study).

The cumulative abatement potential of all technologies amounts to approximately 493 kg CO2 per tonne of crude steel. However, plant-specific and country-specific information on the current deployment of these technologies—and on future deployment plans—is often incomplete. We therefore adopt a hybrid approach that combines technology-based and statistical estimates.

First, we estimate near-term emission-reduction trends using historical country-level data for scope-1 emissions from steel production over 2000–2019, provided to us by the authors of [27]. For each country, we compute the trend in the BF–BOF emission factor and its statistical confidence bounds. The results (Supplementary Figure 3) show consistently declining emission factors across most producing countries, with faster reductions in developing regions and slower ones in developed economies, as expected.

We next estimate the technology-based emission reduction potential for each country in our database. Where available, we use data on the 2020 deployment levels of the BAT technologies listed in Table 2. Supplementary Table 4 lists the deployment rates of these technologies for major steel producing countries. If no information is available for a specific technology or country, the deployment level is set to zero. The maximum remaining abatement potential for each technology is then computed as

remaining potential=(100%−2020 deployment)×max potential,

where the maximum potential corresponds to the values in Table 2. Summing across technologies yields the remaining abatement potential for each country.

We finally update the statistical confidence bounds of the first step assuming that the emission factor cannot decrease below the maximum cumulative abatement potential achievable by full BAT deployment over the same period. Accordingly, if the statistical lower bound falls below the value implied by complete BAT deployment, it is set to that lower limit.

The principal figures in the paper include uncertainty ranges based on these updated confidence bounds, which incorporate both observed historical trends and the quantified technological abatement potentials. More specifically, in each graph of Figures 2 and 3, the red curve assumes a constant emission factor for the BF–BOF technology, the orange curve uses an emission factor extrapolated using the trend estimated on 2000-2019 data, while the blue curve represents the hybrid lower bound computed by combining the statistical lower bound and the lower bound assuming maximum BAT deployment as described above.

For the EAF production route, we assume that the emission factors follow the global decarbonization trend of electricity production, which is available in IEA scenarios, at the global level for the NZE scenario and at the regional-level for the APS and STEPS scenarios.

Reported emissions data and stated targets

We use reported scope 1 and (location-based) scope 2 emissions from the CDP questionnaires, and the reported scope 1 and scope 2 emissions from Refinitiv Eikon. For companies, which are present in both data sources, we adopt a conservative approach by defining the top-down (reported) emissions as the maximum value between CDP and Refinitiv scope 1 and 2 emissions, for each company and year. Reported emissions are used to compare them to bottom-up emissions, at company level, and train the statistical model used for estimating adjusted bottom-up emissions.

To this end, based on the corporate tree structure data from Refinitiv and the publicly available information, we reconstruct corporate ownership and attribute bottom-up emissions from all identified subsidiaries to the ultimate parent. We match companies from top-down and bottom-up datasets based on PermID or fuzzy matching algorithms.

We compute standardized company-level target emission reduction rates using stated emission-reduction targets from Refinitiv, the Green Steel Tracker, the Net Zero Tracker, and the Transition Pathway Initiative. Stated targets are available for 36 companies in our database, representing approximately 24% of global steelmaking capacity in 2022.

Target-setting practices vary considerably across firms, leading to potential inconsistencies in the data. Companies differ in their choice of base years, the use of absolute versus intensity-based metrics, and the extent of disclosure. Of the 36 companies we analyze, five have had their targets validated by the Science Based Targets initiative (SBTi), providing a degree of methodological assurance and comparability.

Asset-level emission projection and alignment assessment

We develop a two-step methodology for estimating future company-level CO2 emissions from asset-level data (see Figure 1). In the first step, we estimate the raw bottom-up emissions, as follows

E(cid:101) BU = \sum Πp∗EFp (1)
f,t t t
p

= \sum Cp∗URp∗EFp (2)
t t t
p

where E(cid:101)BU are raw bottom-up emissions of firm f for a period t, Πp is the production value of the plant p for the period t, Cc is the capacity of the plant p for the period t, URc is the utilization rate for the plant p for the period t and EFp is the emission factor for the plant p and period t. The sum is computed over all plants owned by firm f in the period t.

For estimating historical bottom-up emissions, we use formula (1), with production values computed as explained below. For estimating future bottom-up emissions, formula (2) is used instead. This method requires information regarding:

• The list of assets of each company with their capacities and production technologies for the period t. This is computed from the GIST database, which provides closure dates for existing plants and opening dates for planned production sites.
• The emission factors for each technology. The emission factors are projected separately for the BF-BOF and for the electric technology, as explained above.
• The utilization rates for each plant. The projected future utilization rate are computed to match the global production from a given reference scenario according to one of the two alternative plausible assumptions: (i) country-level utilization rate; and (ii) constant market share. These two approaches are explained in detail below.

In the second step, to account for emission sources that are not directly related to the production process such as electricity consumption for other uses than electric arc furnace steel production, we use a statistical model to correct the raw bottom-up emissions, see Equation (3).

Historical asset-level production estimation

The Global Iron and Steel Tracker provides excellent coverage for capacity—over 95 % globally and for most major producing countries. In contrast, production coverage is substantially lower. Only about 54% of capacity has production data for the United States, the number is even lower for China, and below 75 % for most other producers (See Supplementary Figures 4 and 5).

Because of this limitation, estimating plant-level capacity utilization directly from the GIST dataset could introduce substantial uncertainty and bias. To ensure consistency and representativeness, we therefore rely on country and technology-level utilization rates derived from World Steel Association (WSA) production data combined with GIST capacity data throughout the analysis. Figure 5 compares the country-level utilization rates for the BF-BOF steel production route computed with WSA production data with those estimated at the plant level with GIST production data. This graph shows important bias in plant level utilization rates of developing countries, potentially due to the better production data availability for more modern plants, which are also more likely to operate at higher utilization rates.

We thus replace the actual utilization rate of the plant URp with the average value for the plant’s category U(cid:103)R , and compute the estimated production Π(cid:101)p = CpU(cid:103)R , where Cp is the plant’s capacity. Since bottom-up global production does not exactly match the true global production (e.g., because the GIST database does not contain all plants), in the last step we adjust the estimated bottom-up production values by multiplying them by the ratio of true global production for the given year to estimated bottom-up production.

Projecting asset-level production

We use two alternative methods to project future utilization rates and production values at plant level, based on global production from IEA scenarios. Both methods lead to very similar results.

• Method 1: country-level utilization rate: We calculate historical country-level utilization rates based on 2022 production data and normalize them with a common global factor to match the global steel production of the given scenario Πs:

U(cid:103)R c = U(cid:103)R c * (Πs / Πc) (c is country index)

where Cc denotes the total steel-making capacity of country c at time t. For this method, there is a small number of plants for which historical country level utilization rate is zero due to low data quality. We assume all plants will be operating in the future, so we impute the median global utilization rate for these plants.

• Method 2: constant market share: For each firm f, we first compute the market share, based on 2022 production data, which either comes from the World Steel Association Reports, when such information is available, or is estimated from the Global Iron and Steel Tracker Database. Then, assuming that the market share stays constant, we estimate future production Πf from global production Πs in the given reference scenario. Finally, we assume each plant of this firm to have the same utilization rate defined as:

U(cid:103)R f = (Πf / Cf) (f is firm index)

where Cf denotes the capacity of the firm f.

• Upper and lower bounds on emissions: In addition to the two reference methods, we compute the upper and lower bounds on future emissions. To this end, we first determine the future production of each country based on global projected production from scenarios, assuming a constant market share for each country (based on 2022 values). Then, for the upper bound, we assume that in each country, the steel is produced by the most polluting plants (with the highest carbon intensity), and for the lower bound, we consider that the most carbon efficient plants are used to match the scenario-based production estimate. In the rare occurrence of future plants opening in countries with no pre-existing market share, we assume all plants operate at full capacity.

Figure 6 shows the impact of utilization rate uncertainty on the projected emissions of the iron and steel sector. This graph can be directly compared to the left graph in Figure 2: the orange curve (central estimate) is the same in both graphs; the pink shaded area in Figure 2 shows the uncertainty due to possible evolution of the BF-BOF emission factor, while the blue shaded area in Figure 6 highlights the uncertainty due to possible evolution of plant utilization rates. We see that the utilization rate uncertainty can modify the 2030 emission estimate by at most 5% which does not qualitatively change the conclusions of our study.

Estimating adjusted bottom-up emissions

Out of over 600 companies represented in GIST database, there are 50 which report their emissions, representing around 25% of the global steelmaking capacity. This number is relatively low because many smaller steel companies, especially those located in countries with low reporting standards, do not report their emissions. The self-reported emissions from these 50 companies can be used to (1) validate the methodology and (2) correct some biases in bottom-up emissions. Figure 7 plots the self-reported firm-level emissions ETD (also referred to as top-down emissions) for these companies for the year 2021 as function of the raw bottom-up emissions E(cid:101)BU. This graph shows that (1) the points are close to the diagonal and (2) top-down emissions may nevertheless deviate from bottom-up estimates due to uncertainty of emissions factors and other emission sources, not directly explained by the basic production process, which are reflected in self-reported emissions but not in bottom-up estimates.

To improve our emission estimates we build a statistical model for the reported company-level emissions ETD, which uses the raw bottom-up emissions E(cid:101)BU as explanatory variable. The model is specified as follows:

logE f,t = β0 + β1 logE(cid:101) f,t + ϵf,t, (3)

where ETD denotes the top-down emissions for firm f and period (year) t, E(cid:101)BU f,t denotes the corresponding raw bottom-up estimate, β0 and β1 are coefficients and ε is the noise term. The model is fitted on historical data spanning the 2019-2021 period, for which both bottom-up and top-down data are available after removing outliers as described in the following paragraph. Fitting the model (3) on the remaining ones we obtain estimated intercept value βˆ0 = −0.0826, estimated slope value βˆ1 = 0.9964 and coefficient of determination R2 = 0.964. On the one hand, these results, in particular, slope and R2 values, which are both quite close to one, validate our bottom-up estimates. On the other hand, we consider that after removing outliers, the self-reported historical emissions faithfully reflect the actual carbon emissions of each company. Assuming that the relationship between bottom-up and top-down emissions remains stable in time, this linear model can be therefore used to improve our estimates of future emissions. We therefore define adjusted bottom-up emissions for firm f and future period t using

E f,t = βˆ0 + βˆ1 E(cid:101) f,t,

where E(cid:101) f,t is the raw bottom-up emissions estimate given by formula (2) and βˆ0 and βˆ1 are estimated values of, respectively, intercept and slope of regression (3).

Treatment of outliers

Since for some companies, reported emissions are not available for the entire 3-year period, 50 companies with self-reported emissions over 3 years of data yield a total of 137 data points. Some of these data points are considered to be outliers. On the one hand, when top-down emissions are substantially larger than bottom-up emissions, it may indicate that a company’s main operation is not steelmaking. On the other hand, when bottom-up emissions are substantially larger than top-down emissions, it may indicate misreported top-down emissions (especially for smaller companies), or company mismatch (e.g., a firm actually belongs to a higher-level company and its emissions should be attributed to the latter one). We remove outliers using the IQR method [30], that is, we consider outliers to be all data points for which the inequalities

Q1 − 1.5 IQR < log(ETD f,t / E(cid:101)BU f,t) < Q3 + 1.5 IQR

are not satisfied where Q1 and Q3 are first and third quartiles of data and IQR := Q3 − Q1 is the interquantile range. This method removes 18 data points out of 137.

Estimating stated emissions trajectories

For companies with reported reduction rates related to given baseline and target years, we derive the stated emissions in 2030 as follows:

• We calculate 2030 emissions based on announced reduction rates and top-down (company-reported) emissions from Refinitiv, and from company sustainability reports for the reported baseline year. When top-down emissions are unavailable for that baseline year, we use values from the closest year (e.g. 2018 emissions for a 2017 reported baseline). When reported baseline years are unavailable, we replace them with the year 2019, being a pre-COVID year with a good coverage of top-down emissions.
• We calculate the annualized emissions growth rate between the baseline year and 2030 for each company.
• We then apply annualized growth rates to 2022 bottom-up emissions onwards, in order to deduce the stated target trajectory, and to make historical and projected trajectories comparable.

Data availability

Input data

Forward-looking data (growth rates of steel production, decarbonization slopes of electricity generation) are taken from the World Energy Outlook 2023 dataset (iea.org/data-and-statistics/data-product/world-energy-outlook-2023-free-dataset-2). The free version provides aggregated values and the extended version provides more granular values at the regional level. Our results are based on the latter. Plant-specific data (capacity, technology, opening and closing dates...) used in this study are available from Global Iron and Steel Tracker (globalenergymonitor.org/projects/global-iron-and-steel-tracker/) free of charge. We use country-level emission factors from [31] and global emission factors from World Steel Association 2023 Sustainability Indicators report of (worldsteel.org/steel-topics/sustainability/sustainability-indicators-2023-report/). For estimating plant-level production we use country-specific utilization rates, derived for each country from production data from the World Steel in Figures report of the World Steel Association (worldsteel.org/publications/bookshop/world-steel-in-figures-2023/) and capacity data from the OECD (oecd.org/sti/ind/steelcapacity.htm). Lastly, we use reported emissions from both the Carbon Disclosure Project (cdp.net/en/investor/ghg-emissions-dataset) and Refinitiv as top-down emissions for calibrating our model. We use corporate stated targets from Refinitiv, the Net Zero Steel Tracker (zerotracker.net/) and the Green Steel Tracker (industrytransition.org/green-steel-tracker/).

Output data

The data generated in this study (historical and projected company-level emissions) are available free of charge and without access restrictions from the Pladifes database (pladifes.institutlouisbachelier.org/bottom-up/).

Code availability

The code to reproduce our results and plot the main graphs is publicly available from Zenodo [32] with the permanent DOI: doi.org/10.5281/zenodo.18508837.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper presents a bottom-up estimation of future emissions in the steel industry, utilizing factory-level operational\nforecasts derived from asset conditions. This methodology, which is based on estimates derived from broad classifications of\nplants, their operational status, and operational feasibility(closure etc.), is primarily employed at the company, limited\nregional, or national level. Consequently, it may be perceived as lacking novelty for academic publications in this regard.\nHowever, the strength of this work lies in its innovative expansion of this bottom-up approach to a global scale. To elucidate\nfuture emissions in a specific sector (steel) on a global level, the selection and processing of data, integration of diverse data\nsources, and addressing challenges unique to a broad scope are commendable aspects of this study.\nTo further enhance the value of this paper, which strengths lie in the expansion of evaluation to a global scale, I encourage\nthe authors to connect this global assessment to a fair aggregation of each company's performance within the global context.\nAddressing the following concerns will help strengthen this connection.\nThe primary concern pertains to the apparent contradiction between the assertion that the methodology can predict future\nemissions and the insufficient consideration of future technological advancements.\nThe emissions are derived from production volume and emission factors; however, the setting of these crucial emission\nfactors appears imprecise. The calculations that utilize varying values for each company's plant primarily account for on-off\noperations, operating rates, and major technological types, such as between blast furnaces (BF) and electric furnaces, while\nassuming constant emission factors for each technology. However, the perspectives regarding energy-saving and\ndecarbonization technologies, as well as the levels of technological advancement and future developments for each country\nand company, are not sufficiently addressed. In reality, these factors reflect the differences in corporate efforts and regional\ncharacteristics (both natural and social conditions). If technological advancements are not incorporated, there is a risk of\noverestimating emissions for the entire steel sector, which may weaken the paper's argument regarding discrepancies with\nthe IEA Net Zero 2050 scenario. Furthermore, the title \" Estimating firms’ emissions from asset level data \" may be\nmisleading if it fails to account for the differences in technological advancements among companies as previously\nmentioned.\nBelow is a summary of opinions, acknowledging that some points may overlap with the aforementioned concerns:\n  The paper states that national-level emission factors are employed, with the World Steel Association's factors utilized in\ntheir absence. Although this study endeavors to enhance prediction accuracy by leveraging the Global Steel Plant Tracker\ndatabase to gather detailed information on over 950 plants, there appears to be a lack of alignment between these emission\nfactors and actual conditions, which warrants to be improved. The progress in emission reduction varies significantly based\non the advancement and adoption of energy efficiency and decarbonization technologies, leading to differing emission\nfactors. It is crucial to systematically organize the factors that significantly impact emission factors and to quantitatively\ndemonstrate how these differences influence the results. Particularly for future emission factors of technologies such as BF-\nBOF, where various energy efficiency and decarbonization technologies can be applied, it is not reasonable to rely solely on\nhistorical values or fixed numbers. As previously mentioned, in addition to technological advancements, the degree of\ntechnology adoption and diffusion also influences emission factors. If stick to the methodology of historical trends, both in\ntechnological progress and in technology adoption and diffusion need to be thoroughly examined, and each requires careful\nscrutiny. Given that detailed data for each company is utilized, incorporating these aspects could enhance the novelty of the\nstudy. Furthermore, there are increasing opportunities for energy-saving technologies and decarbonisation initiatives to be\ndisclosed in corporate reports, such as sustainability reports and websites with their investment plans, and it is believed that\nrecent advancements in AI and text mining can facilitate the verification of this content. It would also be beneficial to mention\nthe scope of impact related to changes in policies, raw material and fuel procurement, and recent movements in energy\nsecurity, even if a comprehensive analysis cannot be conducted within this paper.\n  The paper employs global-level values from the NZE scenarios while also incorporating regional-level values from the\nAPS and STEPS scenarios. However, the lack of consistency in the application of global and regional scenario data has not\nbeen discussed, and it would be prudent to address how this inconsistency impacts the results.\n  The statement \"We always use the most granular geographical level for which data is available\" is acknowledged;\nhowever, while this may be the best available option, it is a matter that requires careful consideration throughout the entire\ncontext. For instance, companies and plants lacking data are often situated in developing regions where data organization is\ndelayed or among companies with limited financial resources. In such instances, the figures from a select few companies\nwith available data may not accurately represent the region or country. If such figures are applied to the aforementioned\ntypes of companies, it could lead to either over- or underestimation. Therefore, evaluating the impact of this issue is\nessential. If these impacts are significant, it would be advantageous to incorporate the differences in capabilities among\nsuch companies into the evaluation. The same consideration applies to the discussion on utilization rates.\n  Regarding cross-sector applicability, the methodology utilized in the steel sector is suggested to be applicable to other\nsectors. While it is evident that the broad framework can be applied, the authors should demonstrate how the challenges in\nobtaining data for other sectors (such as cement, aluminum, pulp and paper, and power) differ from those encountered in the\nsteel sector and how these differences may affect the results. In addition to the unique challenges of applying this\nmethodology across various sectors, summarizing the availability of plant-level data (e.g., plant closures and utilization\nrates) in a table would be beneficial. This summary could include the degree of technological progress and the\nadoption/diffusion of various energy efficiency and decarbonization technologies, which the authors have not fully\nconsidered thus far. Furthermore, providing concrete examples and data to support the claim of applicability to other sectors\ncould lead to significant enhancements. It would also be helpful to indicate instances where evaluation processes distinct\nfrom those used in this steel study are required for specific sectors.\n  It is recommended to thoroughly evaluate the impact of uncertainty. As previously noted, future technological\nadvancements and their rates of adoption/diffusion are critical factors. If it is challenging to examine these within the scope of\nthis paper, it is essential to demonstrate that their impact on the results has been adequately assessed. Clearly defining the\nrange of uncertainty and evaluating the sensitivity of the results to these uncertainties is recommended.\n  Additionally, it is crucial to assess whether the assumptions regarding the carbon intensity of future power generation are\nrealistic, as these assumptions significantly impact the results. A careful examination of the basis for the scenarios and their\nconsistency with historical data is necessary, along with sensitivity analyses on representative factors to validate the\nscenarios. Recent advancements in information technology have raised the possibility of a significant increase in future\nelectricity demand, particularly drawing attention to the energy procurement strategies of IT companies. While there are\nindications that this demand may be met through decarbonized energy sources and nuclear power, there remains a\ndivergence of opinions regarding how the balance of supply and demand will be forecasted across various scenarios. It\nwould be beneficial to present these perspectives and incorporate sensitivity analyses for several scenarios to provide a\nmore comprehensive understanding of the potential outcomes.\n  Discrepancies between the targets published by companies and the actual emission pathways may stem from data\ninconsistencies or methodological issues. To address this concern, verifying the transparency of the companies' target-\nsetting processes and reporting requirements is essential. For instance, in my experience, discrepancies have sometimes\nbeen noted between CDP data and the data published by individual companies. There may also be instances where the\ncompilation year is outdated. Acknowledging these issues would be prudent.", "reviewer_2": "The authors develop a way to calculate the trajectories of firm-level emissions and benchmark\nthis with net-zero targets. The authors combine four data sources, namely the IEA scenario data\n(for the targets), asset-level firm data, technology data (from the World Steel Association), and\nemissions data (from CDP and Refinitiv). The authors use projections of assets, its projected\nutilization and projected emissions factors to derive at the trajectory of (bottom-up) emissions\npaths. They then compare these trajectories with that of the EIA to determine whether firms\nare on-track or not in meeting net-zero targets. The authors focus on the iron and steel industry.\nThey conclude that the iron and steel industry is projected to overshoot by 8-20% from the\nlevels of the corresponding net-zero scenario.\nStrengths of the paper\nI commend the authors! They address a very important topic. The paper is well-written, well-\ndocumented, and very straightforward. I enjoyed reading the paper. My quick takeaway is\nactually a very optimistic one. The Iron and Steel industry is only about 20% away from their\nnet-zero targets. This is actually very impressive. I would not have been surprised if it were\nabout 50-60% overshoot from net-zero targets. Their calculation is already somewhat takes a\nconservative approach (applying highest emissions factor when there is ambiguity, picking the\nmaximum reported scope between CDP and Refinitiv, etc.). I also applaud the author for using\nfirm-level data from CDP and Refinitiv. These datasets are sometimes messy and frustrating to\nwork with, so utilizing these data sources is an incredible task of its own.\nThe paper creates an important discussion not just for the iron and steel industry, but for all\nother industries and what the different challenges and opportunities are.\nOpportunities for improvement\nMy only recommendation is to clarify the nature of net-zero targets (the difference and\nsimilarities between stated and IEA’s targets). Are the net-zero targets/scenarios in IEA\ncumulative? To meet these goals then – should the emissions trajectories be cumulative?\nCurrently, from my understanding of the presentation of the methods, it is not cumulative. It is\nonly based on annual projections and that it was matched in terms of % reduction but not at the\ncumulative level. Could this be the reason why the gap is only around 20% rather than perhaps\nsomething like 50-70%? I could be wrong here, but maybe the authors can shed light on the\nnature of cumulative emissions net-zero targets in the IEA scenarios. Moreover, are the firm-\nlevel stated targets also cumulative or simply on a year-to-year annual production basis and\nhow does that map to a cumulative emissions calculation vs non-cumulative.\nLimitations\nI have worked with the CDP data extensively. The CDP data is very challenging to work with. For\nScope 1 location based data, sometimes you get missing data or a facility missing. But I don’t\nthink this completely negates the value of the study. It may just help to mention this nuance\nthat sometimes firms’ disclosure is not as comprehensive.\nRecommendation\nOverall, I am positive about the paper. I hope the authors take my comments/questions\nconstructively as an innocent inquiry/clarification. I think shedding light on these variations on\ncumulative vs non-cumulative/annual production trajectories may strengthen the paper and\nmake it more precise. It is important to compare apples to apples between IEA scenarios and\nthese trajectories."}, "author_response": "Estimating firms’ emissions from asset level data\nhelps revealing (mis)alignment to net zero targets\nHamada Saleh ∗ Stefano Battiston † Irene Monasterolo ‡\nThibaud Barreau § Peter Tankov ¶\nResponse to reviewers\nComments from reviewer 1\nThe primary concern pertains to the apparent contradiction between the asser-\ntion that the methodology can predict future emissions and the insu!cient\n∗InstitutLouisBachelier\n†UniversityofZurich&UniversityofVenice\n‡UniversityofUtrecht,CEPR,WUWien\n§InstitutLouisBachelier\n¶ENSAE,InstitutPolytechniquedeParisandInstitutLouisBachelier\n1\nconsideration of future technological advancements.\nThe emissions are derived from production volume and emission factors;\nhowever, the setting of these crucial emission factors appears imprecise. The\ncalculations that utilize varying values for each company’s plant primarily ac-\ncountforon-o!operations, operatingrates, andmajortechnologicaltypes, such\nas between blast furnaces (BF) and electric furnaces, while assuming constant\nemission factors for each technology. However, the perspectives regarding\nenergy-saving and decarbonization technologies, as well as the lev-\nels of technological advancement and future developments for each\ncountry and company, are not su!ciently addressed. In reality, these\nfactors reflect the di!erences in corporate e!orts and regional characteristics\n(both natural and social conditions). If technological advancements are not in-\ncorporated,thereisariskofoverestimatingemissionsfortheentiresteelsector,\nwhich may weaken the paper’s argument regarding discrepancies with the IEA\nNet Zero 2050 scenario. Furthermore, the title ” Estimating firms’ emissions\nfromassetleveldata”maybemisleadingifitfailstoaccountforthedi!erences\nin technological advancements among companies as previously mentioned.\nWe thank the reviewer for raising this important issue. In response to it,\nwenowaddresstheimpactofthefuturedevelopmentsinenergy-savingandde-\ncarbonization technologies through both statistical and descriptive technology-\nbased analysis. Please see the answer to the 2nd comment below.\n1. Thepaperstatesthatnational-levelemissionfactorsareemployed,withthe\nWorld Steel Association’s factors utilized in their absence. Although this\nstudy endeavors to enhance prediction accuracy by leveraging the Global\nIron and Steel Tracker database to gather detailed information on over\n950 plants, there appears to be a lack of alignment between these\nemission factors and actual conditions, which warrants to be im-\nproved.\nWe acknowledge this important point. The issue of alignment between\nemissionfactorsusedinbottom-upanalysisandactualconditionsisexplic-\nitlyaddressedinourpaperbyadjustingthebottomupemissionsthrough\na statistical model, see paragraph ”Estimating adjusted bottom-up emis-\nsion” in the Methods section. In this paragraph, using company-level\ndata from 50 companies in our database which report their carbon emis-\nsions (either through sustainability reports on their web sites or through\nCDP), we (a) test the validity of our bottom-up estimates by comparing\nthereportedemissionswithbottom-upestimatesand(b)trainastatisti-\ncalmodeltoimproveemissionestimatesforfutureprojectionsusingthese\ntwodatasources(reportedandbottom-upemissions). Wefindthatboth\ntheslopevalueandthecoe!cientofdeterminationofthelinearregression\nof log reported emissions on log bottom-up emissions is very close to one\n(ωˆ =0.9964 and R2=0.964) which validates our bottom-up estimates.\n1\n2. The progress in emission reduction varies significantly based on the ad-\nvancement and adoption of energy e”ciency and decarbonization tech-\n2\nnologies, leading to di!ering emission factors. It is crucial to system-\natically organize the factors that significantly impact emission\nfactors and to quantitatively demonstrate how these di!erences influence\nthe results. Particularly for future emission factors of technologies such\nasBF-BOF,wherevariousenergye”ciencyanddecarbonizationtechnolo-\ngies can be applied, it is not reasonable to rely solely on historical values\nor fixed numbers. As previously mentioned, in addition to technological\nadvancements, the degree of technology adoption and di!usion also influ-\nences emission factors. If stick to the methodology of historical trends,\nboth in technological progress and in technology adoption and di!usion\nneed to be thoroughly examined, and each requires careful scrutiny. Given\nthatdetaileddataforeachcompanyisutilized, incorporatingtheseaspects\ncould enhance the novelty of the study.\nWeappreciatethereviewer’sthoughtfulanddetailedcomment. Weagree\nthatemissionreductionsinBF–BOFsteelmakingdependstronglyonthe\nprogress and adoption of energy-e!ciency and decarbonization technolo-\ngies,andthatasystematicrepresentationofthesefactorsisessential. In-\ndeed,historicalevidenceshowsthatcarbonemissionsfromBF–BOFsteel\nproduction have declined steadily in recent decades owing to continuous\nprocess improvements [3], and this downward trend is likely to continue.\nIn the short term (up to 2030), emission reductions are likely to come\nfromupgradestoBestAvailableTechnology(BAT)andimprovedprocess\ncontrol rather than from emerging new technologies with low TRL.\nToaddressthereviewer’srequesttosystematically organize emission\nreduction factors, we have added Table 1 below (now included in the\nrevised manuscript), which lists the principal BAT options for BF-BOF\nsteelproductionroutetogetherwiththeirestimatedabatementpotentials,\nfollowing [2].\nThecumulativeabatementpotentialofalltechnologiesamountstoapprox-\nimately493kgCO pertonneofcrudesteel. However,plant-specificand\n2\ncountry-specificinformationonthecurrentdeploymentofthesetechnolo-\ngies—andonfuturedeploymentplans—isoftenincomplete. Wetherefore\nadoptahybridapproachthatcombinestechnology-basedandstatistical\nestimates.\nFirst, we estimate near-term emission-reduction trends using historical\ncountry-leveldataforscope-1emissionsfromsteelproductionover2000–2019,\nprovided to us by the authors of [3]. For each country, we compute the\ntrendintheBF–BOFemissionfactoranditsstatisticalconfidencebounds.\nTheresults(Figure1,nowincludedintheSupplementaryMaterial)show\nconsistently declining emission factors across most producing countries,\nwithfaster reductionsin developingregions andslower onesin developed\neconomies, as expected.\nWe next estimate the technology-based emission reduction potential for\neach country in our database. Where available, we use data on the 2020\n3\ndeployment levels of the BAT technologies listed in Table 1. Table 4 in\nSupplementarymaterialliststhedeploymentratesofthesetechnologiesfor\nmajorsteelproducingcountries. Ifnoinformationisavailableforaspecific\ntechnologyorcountry,thedeploymentlevelissettozero. Themaximum\nremaining abatement potential for each technology is then computed as\nremaining potential=(100% 2020 deployment) max potential,\n→ ↑\nwherethemaximumpotentialcorrespondstothevaluesinTable1. Sum\nmingacrosstechnologiesyieldstheremainingabatementpotentialforeach\ncountry.\nWe finally update the statistical confidence bounds of the first step as\nsuming that the emission factor cannot decrease below the maximum cu\nmulativeabatementpotentialachievablebyfullBATdeploymentoverthe\nsame period. Accordingly, if the statistical lower bound falls below the\nvalue implied by complete BAT deployment, it is set to that lower limit.\nTheprincipalfiguresintherevisedpapernowincludeuncertaintyranges\nbased on these updated confidence bounds, which incorporate both\nobservedhistoricaltrendsandthequantifiedtechnologicalabatementpo\ntentials.\nTomainchangesmadetoaddressthispointcanbefoundonpages10-11\n(descriptionoftheemissionfactorprojectionmethodology)andinfigures\n2 and 3 (new uncertainty bounds).\n3. Furthermore, there are increasing opportunities for energy-saving tech\nnologiesanddecarbonizationinitiativestobedisclosedincorporatereports,\nsuchassustainabilityreportsandwebsiteswiththeirinvestmentplans,and\nitisbelievedthatrecentadvancementsinAIandtextminingcanfacilitate\nthe verification of this content.\nWe acknowledge that ongoing developments in AI could facilitate the ex\ntractionofinformationrelatedtofirms’emissionsprojectionsfromannual\nreports. However, it remains unclear to what extent AI could address\nthe verification of physical measures of emissions on the ground, given\nthat there are also agency and incentive issues. In any case, it is impor\ntant to have a method to estimate firms’ emissions that is independent\nof the firms’ disclosure and it is instead grounded on the data of produc\ntion plants, their technology and capacity. We expect that having such a\nmethod will remain important, notwithstanding the AI developments.\nTo address this comment we have mentioned this point in the last para\ngraph of the Introduction to better clarify the relevance of our contribu\ntion.\n4. Itwouldalsobebeneficialtomentionthescopeofimpactrelatedtochanges\nin policies, raw material and fuel procurement, and recent movements\nin energy security, even if a comprehensive analysis cannot be conducted\nwithin this paper.\naround the future levels of decarbonization of the electricity sector, in\nparticular because of evolving fuel procurement policies. Moreover, it in\ncreases also the uncertainty around the cost of alternative technologies\nfor production of steel based on the BOF pathway, as listed in Table 2\nof the Section Methods of the main paper, because of access to raw ma\nterials. However, our goal in the paper is to assess the potential discrep\nancy between current firms’ stated emissions and independent estimates\nofemissionsunderreferencescenariosdevelopedbyinternationalagencies\n(i.e. IEA). The elaboration of alternative scenarios that would take into\naccountgeopoliticalfactorsisbeyondthescopeofthiswork. Inourview,\neven in the current context of uncertainty regarding climate policies, our\nmethodcaninforminvestors’decisionstoallocatetheirlimitedcapitalin\nfirms with more credible decarbonization plans. We have addressed this\ncomment by adding a sentence in the discussion section.\n5. The paper employs global-level values from the NZE scenarios while also\nincorporating regional-level values from the APS and STEPS scenarios.\nHowever, the lack of consistency in the application of global and\nregional scenario data has not been discussed, and it would be pru\ndent to address how this inconsistency impacts the results.\nWeappreciatethereviewer’sobservation. Indeed,aninconsistencyexists\namong the three IEA scenarios: while the APS and STEPS datasets in\nclude regional disaggregation, the NZE scenario is published only at the\nglobal level. Because these scenarios are widely used and form an es\ntablishedreferenceinbothindustryandacademicstudies,weretainedall\nthreeforconsistencywithstandardpractice,despitethislimitation. Inthe\nrevised version of the paper, we explicitly acknowledge this inconsistency\nand clarify that, for this reason, NZE results are not directly comparable\nwith those from the APS and STEPS scenarios (see page 6, lines 4–7).\n6. The statement ”We always use the most granular geographical level for\nwhich data is available” is acknowledged; however, while this may be the\nbest available option, it is a matter that requires careful consideration\nthroughout the entire context. For instance, companies and plants lack\ning data are often situated in developing regions where data organization\nis delayed or among companies with limited financial resources. In such\ninstances, the figures from a select few companies with available data may\nnotaccuratelyrepresenttheregionorcountry. Ifsuchfiguresareappliedto\ntheaforementionedtypesofcompanies,itcouldleadtoeitherover-orun\nderestimation. Therefore, evaluating the impact of this issue is essential.\nIf these impacts are significant, it would be advantageous to incorporate\nthedi!erencesincapabilitiesamongsuchcompaniesintotheevaluation.\nThe same consideration applies to the discussion on utilization rates.\nWe appreciate the reviewer’s valuable observation. To address this con\ncern, we have carefully re-examined our data sources and revised the\n5\nmanuscript accordingly. Two distinct issues arise: the availability of ca\npacity data and of production data.\nAs shown in Figure 2 below (included in the Supplementary Material),\nthe Global Iron and Steel Tracker provides excellent coverage for capac\nity—over 95 % globally and for most major producing countries, both\ndeveloped and developing. In contrast, production coverage is substan\ntially lower: Figure 3 shows that production data are incomplete, with\ncoverageonlyabout54%fortheUnitedStates,evenlowerforChina,and\nbelow 75 % for many other producers.\nBecause of this limitation, estimating plant-level capacity utilization di\nrectly from the GIST1 dataset could introduce significant uncertainty.\nMoreover,asthereviewerrightlynotes,extrapolatingutilizationrates\nfrom plants for which production data is available to all plants\nin a given country may introduce biassincedataisusuallyavailable\nfor large and more modern installations, whose utilization rates may be\nabovethenationalaverage. Toensureconsistencyandrepresentativeness,\nwe therefore rely on country-level utilization rates for each technology,\nderived from World Steel Association (WSA) production data and GIST\ncapacity data throughout the analysis. Figure 4 compares the country\nlevel utilization rates for the BF-BOF steel production route computed\nwith WSA production data with those estimated at the plant level with\nGIST production data. This graph shows significant bias in plant level\nutilization rates of developing countries.\nA detailed discussion of these data issues has been added to the revised\nmanuscript(seebottomofpage13andtopofpage14),Figure4hasbeen\naddedtothemainpaperandallotherfiguresmentionedabovehavebeen\naddedtotheSupplementaryMaterial. Inaddition,uncertaintyassociated\nwith the projection of future utilization rates is illustrated in Figure 6 in\nthe main paper and Figure 5 in Supplementary Material.\n7. Regarding cross-sector applicability, the methodology utilized in the steel\nsector is suggested to be applicable to other sectors. While it is evident\nthat the broad framework can be applied, the authors should demonstrate\nhow the challenges in obtaining data for other sectors\n(such as cement, aluminum, pulp and paper, and power) di”er\nfrom those encountered in the steel sector and how these di”er\nences may a”ect the results. In addition to the unique challenges of\napplying this methodology across various sectors, summarizing the avail\nability of plant-level data (e.g., plant closures and utilization rates) in a\ntable would be beneficial. This summary could include the degree of tech\nnological progress and the adoption/di!usion of various energy e”ciency\nand decarbonization technologies, which the authors have not fully\nconsidered thus far. Furthermore, providing concrete examples and data to\n1GIST(GlobalIronandSteelTracker)isthenewname(since2025)oftheGlobalSteel\nPlantTrackerdatabase\n6\nsupport the claim of applicability to other sectors could lead to significant\nenhancements. It would also be helpful to indicate instances where evalu\nation processes distinct from those used in this steel study are required for\nspecific sectors.\nWe thank the reviewer for this valuable comment. In response, we have\nintroducedanewsection(Section2,Cross-sectorapplicability)intheSup\nplementaryMaterial,whichprovidesadetaileddiscussionofdecarboniza\ntion pathways and the specific challenges of applying our methodology\nto four sectors: cement, aluminum, pulp and paper, and power. To ad\ndresstherequestforadatasummary,thissectionincludesatable(Table\n3, Plant-level and technology data availability for various sectors), which\nlists data sources and provides references to the relevant databases.\n8. It is recommended to thoroughly evaluate the impact of uncertainty. As\npreviously noted, future technological advancements and their rates of\nadoption/di”usionarecriticalfactors. Ifitischallengingtoexaminethese\nwithin the scope of this paper, it is essential to demonstrate that their\nimpact on the results has been adequately assessed. Clearly defining the\nrange of uncertainty and evaluating the sensitivity of the results to these\nuncertainties is recommended.\nThe di”erent sources of uncertainty in our methodology are discussed in\ndetailinSupplementarymaterial,section“Sourcesofuncertaintyintheas\nsetlevelemissionprojectionmethodology”. Thisdiscussionwasextended\nto include the uncertainty related to the future evolution of emission fac\ntors, as suggested by the reviewer.\nIntherevisedversionofthemainpaper,weincludetheestimatesofuncer\ntainty from two sources, considered to be most material: (i) uncertainty\nrelated to future utilization rates of plants, which was already evaluated\nin the initial submission; (ii) uncertainty related to future evolution of\nemission factors, following the reviewer’s suggestion.\nTo evaluate the latter uncertainty, we conduct the estimation procedure\nusing the higher and lower bounds on future emission factors computed\nusing the projection method discussed above in the answer to the second\ncomment. Inaddition,byconsideringalternativedecarbonizationscenar\nios for the electricity sector, we capture the influence of this factor on\noverall uncertainty.\nThemainchangesinthepapermadetoaddressthiscommentincludethe\ndiscussionofemissionfactorprojections(pages10and11intheMethods\nsection), new figures 2 and 3 in the main paper (emission factor uncer\ntainty) as well as their discussion on pages 5 and 6, new figure 6 in the\nmain paper and figure 5 in Supplementary material (utilization rate un\ncertainty).\n9. Additionally, it is crucial to assess whether the assumptions re\ngarding the carbon intensity of future power generation are re\n7\nalistic, as these assumptions significantly impact the results. A careful\nexamination of the basis for the scenarios and their consistency with his\ntorical data is necessary, along with sensitivity analyses on representative\nfactors to validate the scenarios. Recent advancements in information\ntechnologyhaveraisedthepossibilityofasignificantincreaseinfutureelec\ntricity demand, particularly drawing attention to the energy procurement\nstrategies of IT companies. While there are indications that this demand\nmay be met through decarbonized energy sources and nuclear power, there\nremains a divergence of opinions regarding how the balance of supply and\ndemand will be forecasted across various scenarios. It would be benefi\ncial to present these perspectives and incorporate sensitivity analyses for\nseveral scenarios to provide a more comprehensive understanding of the\npotential outcomes.\nWe fully agree with the reviewer that the future carbon intensity of elec\ntricity generation—and thus the emissions associated with steel produc\ntion—is subject to considerable uncertainty. However, a detailed exam\nination of the assumptions and internal consistency of power-sector de\ncarbonization scenarios lies beyond the scope of this study. Our primary\nobjectiveistoassessthealignmentofthesteelsectorwithspecific,widely\nusedprospectivenet-zeropathways—inparticular,thosedevelopedbythe\nInternational Energy Agency (IEA), which are the standard reference for\nalignment analyses [1].\nSectoralalignmentanalysisevaluateswhetheragivenindustry’sprojected\nemissions trajectory is consistent with a predefined transition pathway,\nunder the macroeconomic and energy-system assumptions of that sce\nnario. In this sense, our claim is that steel-sector companies are not\naligned with the IEA NZE scenario, meaning that even if the power sec\ntor decarbonizes at the rate assumed in this scenario, the steel indus\ntry would still fall short of its prescribed target. Evaluating consistency\nwith other scenario frameworks, such as the Enerdata EnerOutlook sce\nnarios (eneroutlook.enerdata.net/energy-scenarios-description.\nhtml) or the scenarios from the NGFS scenario database (ngfs.net/\nngfs-scenarios-portal/), could be informative but would dilute the\ncentral message of our paper.\nThis being said, in the Supplementary Material (Section 2 and Figure\n3), we compare the IEA scenarios for the decarbonization of the elec\ntricity sector with the corresponding scenarios from the NGFS scenario\ndatabase, and find that for NZE and STEPS scenarios, the IEA decar\nbonization rates are more conservative than the NGFS ones and for the\nSTEPS scenario, the IEA rate is within the range of rates obtained with\ndi”erent models for the corresponding NGFS scenario.\nWe also acknowledge that the evolving electricity consumption and pro\ncurement strategies of IT companies, particularly data centers could po\ntentiallyaltertheIEAprojectionsandmakethesescenarioslessrelevant.\ntential impact of this increase of consumption on the carbon intensity of\nelectricity.\nTo this end, we refer to the IEA’s Energy and AI report (April 2025),\nwhichprovidesdetailedprojectionsofenergydemandandsupplyfordata\ncenters through 2035. In its baseline scenario, global electricity use for\ndata centers is expected to rise from 460 TWh in 2024 to around 1,000\nTWhby2030(about3%ofglobalelectricitygeneration). Thelargestin\ncreasesareprojectedinmainlandChina( 175TWh,+170%),theUnited\n↓\nStates( 240TWh,+130%),theEuropeanUnion( 45TWh,+70%),and\n↓ ↓\nJapan( 15TWh,+80%). Accordingtothereport,abouthalfofthisad\n↓\nditional demand will be met by renewable sources, with the remainder\nsupplied by natural gas (20%), coal (20%), and nuclear power (10%). A\nregional breakdown of the supply mix is not provided.\nToevaluatetheimplications,weestimatedtheupdatedprojectedemission\nfactors for electricity in 2030 in the IEA STEPS and APS scenarios (be\ncauseinNZEscenariothereisnoregionalbreakdownofelectricitygener\nation),applyingtheaboveassumptionstogetherwithdefaulttechnology\nspecific life cycle emission factors from the IPCC Fifth Assessment Re\nport.2 Table 2 below compares these updated factors with the original\nbaseline scenario values. The results indicate that the impact of addi\ntional generation linked to AI is relatively small and does not materially\nalter the main estimates of our paper.\nThis sensitivity analysis is now included in the Supplementary Material\nof the paper (Section 6 and Table 5).\n10. Discrepancies between the targets published by companies and the actual\nemission pathways may stem from data inconsistencies or methodological\nissues. To address this concern, verifying the transparency of the compa\nnies’ target-setting processes and reporting requirements is essential.\nFor instance, in my experience, discrepancies have sometimes been noted be\ntween CDP data and the data published by individual companies. There\nmay also be instances where the compilation year is outdated. Acknowl\nedging these issues would be prudent.\nWeappreciatethereviewer’scommentregardingthetransparencyofcom\npany target-setting processes. In the revised manuscript, we explicitly\nacknowledge that these processes may vary significantly across firms and\nthat data inconsistencies can arise (see the paragraph on stated targets\nonpage12). Companiesadoptdi”erenttarget-settingapproaches—using\ndi”erentbaseyears,absoluteversusintensitytargets,andvaryingdegrees\nof disclosure.\nAmong the 36 companies in our database that have announced emission\nreduction targets, only 5 have had their targets validated by the Science\n2Coal: 820gCO2eq/KWh;Gas: 490gCO2eq/KWh;Renewable(Solar): 45gCO2eq/KWh;\nNuclear: 12gCO2eq/KWh.\nologicalconsistency. Whilewerecognizethattargetsfromothercompanies\nmay be less transparent or standardized, we believe that the substantial\ndivergence we observe between the aggregate corporate target trajectory\nandourmodeleddecarbonizationpathwayscannotbeexplainedsolelyby\nsuch data inconsistencies or methodological di”erences.\nComments from reviewer 2\nMy only recommendation is to clarify the nature of net-zero targets (the\ndi!erence and similarities between stated and IEA’s targets). Are the net-zero\ntargets/scenarios in IEA cumulative? To meet these goals then – should the\nemissions trajectories be cumulative? Currently, from my understanding of the\npresentation of the methods, it is not cumulative. It is only based on annual\nprojections and that it was matched in terms of % reduction but not at the\ncumulative level. Could this be the reason why the gap is only around 20% rather than perhaps\nsomething like 50-70%? I could be wrong here, but maybe theauthorscanshedlightonthenatureofcumulativeemissionsnet-zerotargets\nintheIEAscenarios. Moreover,arethefirm-levelstatedtargetsalsocumulative\nor simply on a year-to-year annual production basis and how does that map to\na cumulative emissions calculation vs non-cumulative.\nWe appreciate the reviewer’s comment and the opportunity to clarify this\npoint. In both the IEA scenarios and our analysis, the net-zero targets are\ndefinedonanannualbasis,notcumulatively. Accordingly,ourcomparisonsare\nmadebetweenannualemissiontrajectories—thatis,annualcompany-orsector-\nlevel targets—and the corresponding annual values from the IEA scenarios."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I would like to express my sincere gratitude for your detailed and thoughtful responses to my review comments. I greatly\nappreciate the effort you have taken to address each point thoroughly. Your revisions have significantly enhanced both the\nclarity and robustness of the manuscript.\nAdditionally, I would like to offer one further comment for your consideration. On page 11, in the section discussing \"the\ntechnology-based emission reduction potential,\" I would like to suggest referencing previous works that are closely related\nto the methodology employed in this study. Specifically, there has been a study utilizing technology deployment rates (see\nbelow) that initiated similar approaches to estimating technology-based emission reduction potentials. Including such\nreferences could provide valuable context and further strengthen the scientific foundation of this section.\nThank you once again for your meticulous responses and for the opportunity to review this high-quality study. I look forward\nto seeing the final version of your manuscript.\nReference:\nIPCC Fourth Assessment Report, Mitigation, Chapter 7, Figure 7.1 refers to:\nTanaka, K., R. Matsuhashi, N. Masahiro, and H. Kudo, 2006: CO2 reduction potential by energy efficient technology in\nenergy intensive industry. [Available at: http://eneken.ieej.or.jp/en/data/pdf/324.pdf]", "reviewer_2": "I am the second reviewer.\nThank you for the response. My only recommendation and suggestion is that the authors be more upfront and clear about\nthis key difference. The IPCC emissions budget, which is typically the ground reference for many carbon targets, is\ncumulative, but that the targets examined in this paper are not, and whether or not these annual targets translate to the\nappropriate cumulative carbon emissions targets we need to meet to remain below the 1.5/2 degree thresholds.\nOverall, I commend the authors for this paper. I wish them the best."}, "author_response": "Estimating firms’ emissions from asset level data\nhelps revealing (mis)alignment to net zero targets\nResponse to the reviews of the revised version\nHamada Saleh1, Stefano Battiston2,3, Irene Monasterolo4,5,6,\nThibaud Barreau1, Peter Tankov7,1*\n1Institut Louis Bachelier.\n2University of Zurich.\n3University of Venice.\n4University of Utrecht.\n5CEPR.\n6WU Wien.\n7CREST, ENSAE, Institut Polytechnique de Paris.\n*Corresponding author(s). E-mail(s): peter.tankov@ensae.fr;\nWethankbothreviewersandtheeditorfortheircarefulreadingoftherevisedversion\nofourpaper.Thisdocumentprovidesdetailedanswerstothereviewers’comments.A\ncompleted copy of the checklist with editorial revisions is uploaded separately.\nReviewer 1\nI would like to suggest referencing previous works that are closely related to the\nmethodology employed in this study. Specifically, there has been a study utilizing\ntechnology deployment rates (see below) that initiated similar approaches to estimat\ning technology-based emission reduction potentials. Including such references could\nprovide valuable context and further strengthen the scientific foundation of this\nsection.\nOur answer\nThe suggested reference was added to the reference list and quoted in the paper (see\ntop of p.12)\nMyonlyrecommendationandsuggestionisthattheauthorsbemoreupfrontandclear\nabout this key difference. The IPCC emissions budget, which is typically the ground\nreferenceformanycarbontargets,iscumulative,butthatthetargetsexaminedinthis\npaper are not, and whether or not these annual targets translate to the appropriate\ncumulativecarbonemissionstargetsweneedtomeettoremainbelowthe1.5/2degree\nthresholds.\nOur answer\nTo address this comment, the following paragraph was added in the results section\n(see bottom of p.7):\nNotethatthetargetsexaminedinthispaper,aswellastheIEAemissionscenarios\nused for comparison, are formulated in terms of annual emissions, whereas IPCC car\nbon budgets are defined cumulatively. The use of annual emission targets is standard\ninthescenarioliterature,asitallowsonetotrackthetimeprofileofsectoralemission\npathways and to analyze the dynamics of transition in the iron and steel sector.\n2"}]

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