2.1 Institutional Background

Climate change has been increasingly recognized in recent years as a high-priority issue worldwide (Munasinghe, 2010; Princiotta and Loughlin, 2014; Martens et al., 2016) caused by industrial and human activities (IPCC, 2014; Saka, et al 2014). The Intergovernmental Panel on Climate Change (IPCC) defines climate change as “a change in the state of the climate that can be identified… by changes in the mean and/or the variability of its properties and that persists for an extended period” (IPCC, 2018). Climate change leads to changes in ecosystems, desertification, rise in sea level, flooding, and drought (Hisano et al., 2018; Ouhamdouch et al., 5 Public 2019). In 2015, the Paris Agreement, the first global agreement to reduce carbon emissions, was adopted at the 21st United Nations Climate Change Conference. The ultimate objective of the regulation was limiting the increase in the global temperature to below 2°C compared to pre-industrial levels by 2030 .

The European Union has set itself the goal of achieving carbon neutrality by 2050 and promoting sustainable development through measures such as the use of renewable energy sources, improving energy efficiency and ultimately promoting the development of more efficient and low-carbon technologies (Cervantes et al, 2023). In November 2018, the European Commission presented a long-term strategic vision to reduce greenhouse gas (GHG) emissions, showing how Europe can lead the way to climate neutrality – an economy with net-zero GHG emissions. In other words, the strategy explores how this can be achieved by looking at all the key economic sectors, including energy, transport, industry and agriculture. A portfolio of options was explored to underline that it is possible to move to net-zero GHG emissions by 2050, based on existing or emerging technological solutions in key areas such as industrial policy, finance or research, while ensuring social fairness for a just transition. In such scenario, it is useful not to confuse carbon efficiency with energy efficiency.

In order to clarify corporate efforts to climate change mitigation, regulatory are increasingly mandating disclosure among firms. Overall, relevant disclosure rules are included in:

• The Sustainable Finance Disclosure Regulation (SFDR) entered into force since March 2021 and imposing disclosure obligations at both the entity and product level for financial market participants;

• The Taxonomy Regulation, providing a classification system for environmentally sustainable economic activities and directing capital flows toward green investments, aligning market practices with EU climate objectives;

• ECB Guide on Climate-Related and Environmental Risks, published in 2020 and outlining the European Central Bank’s expectations for how banks should manage and disclose their climate-related and environmental risks as part of sound risk management; 1 https://unfccc.int/process-and-meetings/the-paris-agreement 6 Public

• EBA Implementing Technical Standards (ITS) on Pillar 3 ESG Risk Disclosure, aopted in 2022, to define the required disclosures of environmental, social, and governance (ESG) risks under Pillar 3 of the Capital Requirements Regulation;

• Corporate Sustainability Reporting Directive (CSRD), replacing the Non-Financial Reporting Directive (NFRD), and broadening the scope of mandatory sustainability reporting to include more companies and introduces more detailed reporting standards aligned with EU sustainability goals.

• EBA Guidelines on the Management of ESG Risks (Currently under development), to provide financial institutions with a comprehensive framework for integrating ESG risks into their governance, risk management, and business strategies;

• Green Bonds Regulation (Still under negotiation) to establish a voluntary EU Green Bond Standard, ensuring transparency and credibility in the green bond market by aligning it with the Taxonomy Regulation.

In particular, on 21st April 2021, the European Commission adopted a proposal for a Corporate Sustainability Reporting Directive (CSRD), entered into force in 2023. The CSRD applies to all large companies and all companies listed on regulated markets (except listed microcompanies). The rules also apply to listed SMEs, taking into account their specific characteristics and to non-European companies, generating a net turnover of €150 million in the EU and which have at least one subsidiary or branch in the EU. The CSRD envisages the adoption of a defined set of mandatory EU sustainability reporting standards, adopted by the European Commission as delegated acts, based on technical advice provided by the European Financial Reporting Advisory Group (EFRAG). Listed SMEs are allowed to report according to simplified standards, while non-listed SMEs adopt them voluntarily. Reporting will need to be certified by an accredited independent auditor or certifier (for non-EU companies, either a European auditor or one established in a third country). Sustainability information should be clearly identifiable through a dedicated section of the management report and a digitalization system will be required.

Credit institutions are in scope to the CSRD to the extent that they are large companies or companies listed on regulated markets. Banks face several challenges when it comes to disclosing the environmental impact of loans to small and medium enterprises (SMEs). These challenges stem from various factors, including the nature of SMEs, the complexity of environmental impact assessment, and the lack of standardized reporting frameworks.

In order to make non-financial disclosures on the classification of financial products and the related requirements consistent and comparable across EU jurisdictions and along the investment chain, the disclosure framework is increasingly based on the EU Taxonomy, when sustainable products or those with environmental or social characteristics. Established by the Taxonomy Regulation and subsequent Delegated Acts (some still pending) from the European Commission (EC), the EU Taxonomy provides a common classification system and uniform technical screening criteria to determine which economic activities make a substantial contribution to reaching EU environmental objectives, while at the same time causing no significant harm to the environment and respecting minimum safeguards.

Several significant international initiatives are shaping the global sustainability agenda and are expected to influence the development of the EU regulatory framework by promoting harmonization and best practices across markets:

• Network of Central Banks and Supervisors for Greening the Financial System (NGFS), launched in 2017 to support the integration of climate-related risks into financial supervision and decision-making;

• G20 Task Force on Nature-related Financial Disclosures (TNFD), building on the framework of the Task Force on Climate-related Financial Disclosures (TCFD), to develop an integrated risk management and disclosure system for nature-related risks;

• Financial Stability Board (FSB) Roadmap with a coordinated global roadmap to manage climate-related financial risks;

• International Sustainability Standards Board (ISSB), established by the IFRS Foundation in 2021, developing a comprehensive set of IFRS Sustainability Disclosure Standards aimed at creating a global baseline for corporate sustainability reporting;

• Net-Zero Alliances, including the Net-Zero Banking Alliance, the Net-Zero Insurance Alliance, and the Net-Zero Asset Owner Alliance.

2.2 Carbon accounting

Bebbington and Larrinaga-González (2008) identify three key implications of climate change for corporate accounting: (1) the financial accounting of emission allowances under emissions 8 Public trading schemes, (2) the reporting of climate-related risks to corporate performance, and (3) the need to address climate uncertainty through integrated, interdisciplinary approaches. Their work supports earlier calls for normative accounting research (Gray, 2002) and greater engagement between researchers and practitioners (Adams and Larrinaga-González, 2007), particularly in developing accounting practices that contribute to climate action. In a similar vein, Hopwood (2009) highlights the growing demand for environmental information as organizational interaction with ecological systems increases. He points to evolving accounting practices, such as environmental reporting, project appraisal, and cost–benefit analysis.

Building on these perspectives, carbon accounting, also referred to as greenhouse gas (GHG) accounting, has emerged as a vital tool for corporate climate governance. It functions across institutional and geographical levels and is essential for informed decision-making aimed at reducing emissions (Nartey, 2018). In practice, carbon accounting involves two core components: the systematic collection of emissions data and the processing of this information to evaluate the effectiveness of decarbonization strategies. Robust data collection is crucial for ensuring the reliability and utility of carbon accounting systems, which ultimately support meaningful progress toward emissions reduction goals (Farbstein et al., 2023).

Several methodologies for carbon accounting have been developed and examined in the literature. One of the most widely used is the IPCC emission factor approach, which estimates carbon emissions from specific activities using standardized emission factors (Shan et al., 2016). A more comprehensive alternative is Life Cycle Assessment (LCA), which captures emissions across the entire life cycle of a product or process (Liu et al., 2023). Finally, the input–output (IO) model offers a macro-level view by quantifying inter-sectoral emissions and their distribution across the economy (Huang et al., 2019). This approach is especially relevant for studying carbon flows embedded in trade between regions and countries (Mi et al., 2019; Yuan et al., 2022; Cabernard et al., 2022).

The seminal literature on carbon accounting is both diverse and interdisciplinary, reflecting the complexity of its application and relevance. Braun (2009) and Callon (2009) examine the sociological construction of carbon markets as socio-technical systems. From a policy perspective, Boston and Lempp (2011) explore the governance challenges of climate change, describing it as a “super wicked problem” due to its urgency, complexity, and institutional fragmentation (Lazarus, 2008). On the technical front, studies such as those by Johnston, Sefcik, and Soderstrom (2008) and Freedman and Stagliano (2008) investigate sulphur dioxide 9 Public emissions accounting. Other contributions focus on national and corporate practices—for example, Hogan et al. (2011) analyze corporate carbon reporting in Australia, while KnoxHayes and Levy (2011) discuss carbon disclosure as a form of political governance. Lippert (2015) presents carbon accounting as a performative practice shaping organizational narratives.

More recently, the study of carbon emissions continues to gain traction with huge potential for meaningful change in economic activity and promote emissions reductions (Sheng et al., 2025). Mikes and Metzner (2023) examine how the effectiveness of corporate control systems is contingent upon a firm’s orientation toward decarbonization. Their findings suggest that measurement, control, and disclosure of decarbonization performance and particularly regarding Scope 1, 2, and 3 emissions, are crucial for aligning environmental strategies with broader business operations, including mergers and acquisitions. The authors argue that carbon accounting is not merely a compliance tool but can also serve as a driver of strategic transformation through the adoption of green technologies and renewable resources. Zhang et al. (2017), in their study on China’s embedded carbon emissions in international trade, highlight discrepancies in measurement techniques and reported figures across the literature. Yu et al. (2021) provide a comprehensive review of carbon leakage, identifying its primary causes, measurement approaches, and variations in outcomes across countries. Similarly, Tian et al. (2018) review the embodied environmental flows in global trade but focus more on research subjects than on methodological frameworks.

While earlier studies have demonstrated the informative value of carbon accounting in advancing corporate sustainability across economic, environmental, and social dimensions, it remains a contested and evolving field. Continued development is needed to enhance accounting tools and information systems that support effective decarbonization strategies.

2.3.Carbon Accounting for SMEs

Unlike large corporations, which may follow established sustainability reporting frameworks like the Global Reporting Initiative (GRI) or the Task Force on Climate-related Financial Disclosures (TCFD), SMEs often lack standardized reporting requirements for environmental performance. In particular, Moss et al. (2008) explore how small and medium enterprises (SMEs) can implement life cycle assessment (LCA) methods to evaluate their carbon 10 Public footprints. The study highlights the importance of using LCA to provide a comprehensive view of GHG emissions across the entire life cycle of products, from raw material extraction to endof-life disposal. However, the study emphasizes that while LCA offers detailed and scientifically robust insights into emissions, its application in SMEs faces significant challenges due to resource limitations, lack of expertise, and the complexity of data collection. To address these barriers, simplified LCA methodologies tailored to the needs and capacities of SMEs can be employed, prioritizing key stages or processes with the highest emissions (“hotspots”) to reduce the data and computational burden.

Eleftheriadis and Anagnostopoulou (2024) develop a tool tailored to SMEs for calculating their carbon footprint. This tool incorporates elements such as Scope 1 (direct emissions), Scope 2 (indirect emissions from energy use), and estimating Scope 3 (other indirect emissions) where data permits. While the tool is accessible and adaptable across industries, its simplifications may lead to trade-offs in precision, especially in complex supply chains or industries with highly variable emissions factors.

From a sector-specific standpoint, Alromaizan et al. (2023) develop a carbon accounting tool for SMEs in the agri-food sector, recognizing the unique challenges of this industry, such as seasonal fluctuations, reliance on agricultural inputs, and the significant contribution of methane and nitrous oxide. The tool is designed to identify emissions hotspots like fertilizer use and transportation while accommodating the variability inherent in agricultural processes. Focusing on the tourism sector in the UK, Berners-Lee et al. (2011) present an approach for estimating GHG footprint for small businesses using input-output (IO) data. Their study highlights the utility of IO data in estimating emissions by linking economic activity with environmental impacts across supply chains. This methodology is particularly beneficial for small firms that lack the resources for direct emissions measurement. By employing sectoral economic data, the authors demonstrate how upstream and downstream emissions can be estimated without extensive primary data collection. A potential limitation lies in the aggregation of IO tables, which may obscure sector-specific variations and lead to uncertainties in detailed emission estimates. The paper establishes a foundation for cost-effective carbon accounting, showing how IO models can fill data gaps while still offering meaningful insights into the carbon footprints of small businesses. Finally, by employing a global multi-regional input-output (MRIO) analysis, Schulte et al. (2024) provide evidence of the intrinsic uncertainties related to MRIO models, which are often used to trace emissions across international supply chains, identify error margins, and address data harmonization challenges. 11 Public This is particularly relevant for firms operating within globalized supply chains, where accurate emissions tracking depends on reconciling diverse data sources.

To Input-output models where firstly theorized by Leontief (1941) and have ever since received wide attention and recognition in the world of economics (including a Nobel Prize for Professor Leontief in 1973).

The framework is also called “inter-industry analysis” and focuses on modelling the interdependencies among industries in an economy.

The basic idea is that, as an additional unit of a product is demanded, a certain combination of other (intermediate) products is required to produce that unit, for each of these intermediate products another combination of input is demanded and so on.

The overall effect of these interdependencies is that a change in demand equal to, say, 1 unit of product A, causes a change in the overall production that is in general well above the market price of the additional unit demanded.

This is called the Leontief multiplier and has a straightforward mathematical interpretation.

The input-output model provides a framework of analysis for interdependence amongst industries within an economic area.

Since it is basically an accounting framework, it can be applied to the individual firm, provided that we identify the various components and the technological structure (in the Leontief sense) in each of their components.

The economic activities of a company can be broken up into a number of separate, but interactive business units, or individual cost centers.

The data needed are flows of products and services amongst these entities.

When constructing an input-output model for a country or a region (macroeconomic level) it is possible in theory to record all the exchanges either in physical or in monetary terms.

But since most sectors produce and sell more than one product or service, using physical units of measure poses a big challenge in aggregating the data.

Input-output models are therefore mostly built in monetary units (prices) and the same can be said for firm-level (microeconomic level) applications.

An Environmentally Extended Input-Output Model (EEIO) integrates environmental data with economic input-output analysis to assess the environmental impacts associated with economic activities.

The mathematical formulation builds upon the standard Leontief input-output model and adds environmental components.

Several studies have attempted to integrate environmental considerations within the framework of IO matrices (e.g. Duchin, 1992; Miller and Blair, 2009).

Leontief (1970) develop a seminal attempt to consider the pollution repercussions of human economic activities considered under a traditional IO framework.

Similarly, Wiedmann and Lenzen (2007) explore the environmental and social repercussions of international trade relationships, observing a spillover of negative impacts from developed to developing countries (e.g. US-China).

More recently, Lenzen et al. (2012) propose an impressive environmentally extended multi-region input–output attempt to infer about the global responsibilities for emissions embodied in internationally traded products.

Overall, EEIO represent a valuable source of useful information on the impacts associated with economic activities (Hertwich et al., 2009).

3.1 Model Development

Our model uses the multi-regional Environmentally Extended Supply-Use Table (MR-SUT) and the Input-Output Table (MR-IOT) from EXIOBASE.

In particular, the environmentally extended multi-regional input-output table (EE MRIO) includes 200 products classified in 163 industries, expressed in millions of euros at current basic prices.

It aggregates data from 44 countries, which include 28 European countries and 16 other major economies, along with five regions representing the rest of the world.

More specifically we use EXIOBASE 3.9.5 version (Stadler et al., 2025).

In the following steps, we outline the specific methodology used.

Step 1: Definition of total output per sector

We express the standard economic input-output model as:

x = A x + y

where:

• (x) is the vector of total output of all sectors (n×1);
• A is the technical coefficients matrix (n×n), where each element (a_{ij}) represents the input from sector (i) required to produce one unit of output in sector (j);
• (y) is the vector of final demand (n×1).

Step 2: Determination of technical coefficients matrix

We calculate the Leontief inverse (L), by rearranging the total output as follows:

x = (I – A)^{-1} y

where I is the identity matrix, and ((I – A)^{-1}) represents the Leontief inverse, which captures the direct and indirect input requirements to meet a unit of final demand.

Step 3: Inclusion of environmental factors in the IO setting

To include environmental impacts (Miller and Blair, 1985), we introduce a vector of environmental coefficients:

e = [e_1, e_2, \dots, e_n]^T

At this stage, we retrieve combustion originating from energy use (Stadler et al., 2018; Rasul et al., 2024) and non-combustion emissions (Stadler et al., 2018).

Specifically, we extract 418 environmental stressors combining all the different kinds of emissions attributable to each output sector per unit of final demand.

We can express the total environmental impact as:

E = e^T x

Substituting (x):

E = e^T (I – A)^{-1} y

Step 4: Vector of emissions for unit of financial output.

We first focus on CO₂ emissions derived from combustion activities. Thus, we define g as the vector of GHG emissions directly attributable to each sector and divide g by the vector of total output per sector x.

As result we obtain a vector of coefficients of CO₂ intensity for each sector per unit of output G.

G_i = g_i / x_i

Step 5: Matrix of emissions per unit of final demand.

We can express the matrix E of CO₂ emissions originating from each sector i per unit of final demand in each sector j as:

e_{ij} = l_{ij} \cdot g_i \quad i=1…163;, j=1…163

Step 6: Matrix of total emissions attributable to a company.

We now estimate the total direct and indirect CO₂ emissions attributable to each sector i originating from the final demand for each sector as:

E_{TOTALij} = y \cdot e_{ij} \quad i=1…163;, j=1…163

where y is the vector of final demand (i×1).

Step 7: Inflation-related adjustments.

To fully capture the corporate environmental footprint, we consider corporate spending.

Since the values in the final demand coefficients reflect 2022 data, we adjust the emission factor of each sector ((E_{TOTALij})) by the country-specific compounded inflation growth observed in 2023 and 2024.

Step 8: Identification of direct emission from the company.

We estimate a company’s direct emissions by multiplying its net revenues by the emission factor calculated in Step 4.

Step 9: Identification of indirect emission from the company.

The model can estimate indirect emissions attributable to a company’s supply chain, disaggregating its carbon footprint by considering contributions from the 163 industries included in the IO table.

3.2 Validation and Benchmarking

One of the main challenges for SMEs is the validity and accuracy of their carbon footprint calculations.

On one hand, the model can estimate the potential impact of management decisions on corporate emissions concerning value chain, suppliers, and expenditure levels by sector.

On the other hand, a crucial factor is the uncertainty in calculating both:

• the technical coefficients,
• the direct estimates of emissions per unit of financial output.

Sources of uncertainty

1. Standardized structure of the model → assumes homogeneity within sectors per unit of final demand.
2. Accuracy of baseline data → estimated uncertainty ~10% (Berners-Lee et al., 2011).
3. Inflation adjustments → necessary to align expenditures with current prices, but do not capture technological improvements.

It is urgent to emphasize that the historical basis of IO tables does not permit evaluating technological evolution and production efficiency within each sector.

Some studies (e.g. Berners-Lee et al., 2011) estimate uncertainty in the baseline information at ~10%, showing potential fluctuations in Leontief matrix coefficients ranging from 0% to 20%.

The boxplot analysis of CO₂ intensity in Figure 1 shows the median emission intensity across sector buckets, built on NACE classifications of the 163 activities in EXIOBASE. The figure reveals an intriguing and significant inter-sectoral variability in their emissions per unit of economic output. As expected, sectors like agriculture, transport, and construction exhibit higher median intensities, and notable standard deviations, reflecting a potential energy and resource-intensive nature. Conversely, service-oriented sectors such as education, finance, and health display lower and more concentrated intensity distributions. Notably, in sectors such as “Electricity, Gas & Water Supply” and “Waste Management & Recycling” the figure shows extremely high upper ranges, suggesting the dominance of emission-intensive sub-sectors like coal-based electricity.

Figure 2 illustrates the distribution of CO₂ intensity (kg CO₂ per million EUR of output) across the five most polluting countries, highlighting substantial inter-country variability in the carbon efficiency of economic activities. In detail, Brazil appears as the worst emitter within our sample, showing the highest median and upper-bound intensities, suggesting the presence of emission-intensive sectors within the country’s economy. Furthermore, both China and Canada show wide variation in the distribution of their economic activities intensities, reflecting diverse sectoral profiles that include both high-emission industrial processes and relatively cleaner service or hydro-based energy sectors. In contrast, the United States and Poland display more concentrated distributions, indicative of more uniform emissions across their economic sectors. This heterogeneity reinforces the need for country-based estimation of combustionbased carbon emissions, as identical financial activities can yield substantially different carbon footprints depending on the national infrastructure of belonging.

We evaluate the applicability of the tool by testing the input-output modelling principles and estimating the combustion-related carbon dioxide (CO₂) equivalent emissions of an Italian SME, according to the European Commission definition, in the textile industry.

The model leverages country- and sector-specific CO₂ intensities, expressed as kg CO₂ per million EUR, derived through input-output methodology, to estimate combustion-related emissions across Scopes 1, 2, and upstream 3 based on the enterprise’s financial flows.

Accessibility for SMEs

This approach prioritizes accessibility and adaptability, which are critical for small and medium-sized enterprises (SMEs) that often lack the data, resources, or technical capacity to conduct comprehensive environmental assessments or life cycle inventories.

By relying on readily available disclosed financial data — specifically expenditures and revenues — the tool provides an efficient and replicable way to generate initial, yet meaningful, insights into an organization’s operational emissions and environmental footprint.

Tool Functionality

The interactive tool we design aims to estimate a company’s CO₂ emissions based on its revenues and expenditures across different countries and economic sectors.

1. Revenues:
   • The user indicates the countries where the company generates revenues (or approximates with headquarters).
   • Chooses the sector(s) from a list grouped by NACE categories.
   • Provides the revenue distribution by each sector.
   • A “Not Specified” category is available, adopting the median intensity within the bucket.

   → This amount is multiplied by sector-specific CO₂ intensities (emissions per million EUR) to calculate revenue-based emissions (approx. direct emissions).

2. Expenditures:
   • The user selects the countries and sectors where the company incurs its most relevant costs.
   • The tool calculates cost-based emissions, which approximate indirect and supply chain-related upstream emissions, using expenditures and CO₂ intensities.

This enables a combined estimate of emissions associated with both income and spending activities, tailored to specific geographies and industries.
The granularity of details allows a more precise assessment of the company footprint.

Case Study: Italian Textile SME

In a practical application, we draw on an approximation of the financial data from an Italian textile sector firm:

• Revenues:
  • 19 million EUR (June 2024), allocated to the Italian “Manufacture of wearing apparel; dressing and dyeing of fur” sector.
  • Entirely attributed to operations in Italy.
• Expenditures:
  • Total: ~12 million EUR.
  • ~5.2 million EUR evenly distributed between Electricity, Gas & Water Supply and Manufacturing.
  • ~7 million EUR evenly distributed among Transport & Storage, Professional & Administrative Services, and Health & Social Work.
  • Costs assigned to Not Specified category within each bucket → median intensity used.

Results

The inputs yield an estimated total of 547,611 kg CO₂, comprising:

• 240,368 kg CO₂ → revenue-based emissions.
• 307,243 kg CO₂ → cost-based emissions.

This breakdown demonstrates both the internal consistency of the model and its ability to distinguish between:

• emissions tied to a company’s value creation,
• and those embedded in its supply chain and operational infrastructure.

Limitations

The results validate the model’s functional logic, testing its ability to produce nuanced GHG emission estimates across different sectors and geographies.

However, it is crucial to emphasize that these values:

• represent only a partial view of a firm’s carbon footprint,
• are grounded in assumptions based on publicly reported financial figures,
• lack granular data on revenue streams, production technologies, or supply chain composition.

Additionally, the model excludes non-combustion GHG emissions (e.g., from industrial processes, refrigerants, land-use changes), which are vital in a comprehensive Scope 3 inventory.

Thus, while the tool stands as a reliable and accessible entry point for GHG accounting, particularly useful in early-stage reporting or sustainability planning, it can benefit significantly from future refinement. In other words, while the model offers an accessible tool to gather first insights on a firm carbon footprint, users should avoid interpreting results as comprehensive 21 Public emission inventories without further contextual or activity-level data. Enhancements might include the integration of activity-based emissions factors, supplier-specific data, or hybrid methodologies incorporating process-based life cycle inventories. In its current configuration, the model presents a scalable, credible approach for organizations to gain actionable insight into their climate impacts using the data they already have and represents an important step toward broader environmental accountability.

Thus, while the tool stands as a reliable and accessible entry point for GHG accounting, particularly useful in early-stage reporting or sustainability planning, it can benefit significantly from future refinement. In other words, while the model offers an accessible tool to gather first insights on a firm carbon footprint, users should avoid interpreting results as comprehensive 21 Public emission inventories without further contextual or activity-level data. Enhancements might include the integration of activity-based emissions factors, supplier-specific data, or hybrid methodologies incorporating process-based life cycle inventories. In its current configuration, the model presents a scalable, credible approach for organizations to gain actionable insight into their climate impacts using the data they already have and represents an important step toward broader environmental accountability.