CIB ESG
Metrics Lab
Financed emissions, decarbonisation pathways and transaction impact simulator for a CIB-style investment-banking portfolio.
A reproducible portfolio analytics case study translating CIB exposure into financed-emissions metrics, Scope 1/2/3 attribution, data-quality scoring, simplified transition-pathway alignment and transaction-level impact analysis.
Portfolio at a glance
Ten headline metrics summarising the CIB portfolio model across exposure, financed emissions, data quality and transition alignment.
Simplified 1–5 score inspired by financial-carbon-accounting data-quality practices; 1 indicates stronger reported data, 5 indicates more modelled estimates.
Built as a disciplined ESG metrics workflow
CIB portfolio model economic and environmental data → financed-emissions attribution → portfolio carbon metrics → data-quality scoring → sector pathway alignment → transaction impact simulation → executive dashboard and methodology documentation.
Ten-phase reproducible workflow
Each phase translates a portfolio question into a structured analytical output, from scope governance to executive communication.
Scope & claim governance
Objective: Define the analytical perimeter and public claim boundaries.
Output: Case-study positioning and claim governance.
CIB portfolio model design
Objective: Build a 60-company portfolio across 11 sectors.
Output: Portfolio exposure, company-level financials and emissions fields.
Economic and environmental data schema
Objective: Structure revenue, EVIC, debt, exposure and Scope 1/2/3 variables.
Output: Reproducible data model.
Financed-emissions attribution
Objective: Apply exposure / EVIC attribution factor.
Output: Company and portfolio financed emissions.
Portfolio ESG metrics
Objective: Aggregate financed emissions, intensity, concentration and sector contribution.
Output: Executive KPI layer.
Data-quality scoring
Objective: Assess reliability of emissions estimates using a simplified 1–5 score.
Output: Weighted data-quality score and disclosure priorities.
Sector decarbonisation pathways
Objective: Compare company intensity against simplified sector pathway references.
Output: Alignment status and gap-to-target analysis.
Transaction impact simulator
Objective: Assess marginal impact of new deals on exposure, emissions, intensity and alignment.
Output: Before/after analysis and scenario recommendation logic.
Excel model and Python QA
Objective: Package calculations into a reproducible modelling workflow.
Output: Dashboard-ready assets and QA report.
Executive web dashboard
Objective: Communicate results in a concise decision-support format.
Output: Vercel-ready case-study page.
Where the carbon actually sits
The portfolio model shows a common financed-emissions pattern: climate exposure is not evenly distributed. A limited number of sectors and names drive most financed emissions, making prioritisation and data-quality governance more useful than broad portfolio averages alone.
Financed emissions by sector
ktCO₂e · coloured by alignment status
Scope 1 / 2 / 3 split
Portfolio financed emissions, %
Exposure share vs emissions share, by sector
% of portfolio total
Concentration drives priority
Oil & Gas, Power & Utilities, Steel and Cement account for the majority of financed emissions while representing a much smaller share of exposure. These four sectors define the transition-planning agenda for the portfolio model.
Top 10 financed-emissions contributors
Company universe · 59.6% of total portfolio emissions
| Company | Sector | Financed (ktCO₂e) | DQ | Alignment |
|---|---|---|---|---|
| Helios Steel Group | Steel | 690 | 3 | Misaligned |
| Aster Energy Holdings | Oil & Gas | 610 | 4 | Misaligned |
| Orion Grid Infrastructure | Power & Utilities | 580 | 2 | Partially aligned |
| Nova Cement Materials | Cement | 540 | 3 | Misaligned |
| Mariner Aviation Systems | Aviation | 475 | 4 | Misaligned |
| BlueHarbor Shipping | Shipping | 445 | 3 | Partially aligned |
| Caldera Petrochem Industries | Chemicals | 425 | 3 | Partially aligned |
| TerraFood Ingredients | Agriculture & Food | 390 | 2 | Partially aligned |
| VoltEdge Power Assets | Power & Utilities | 385 | 2 | Aligned |
| UrbanCore Properties | Real Estate | 290 | 2 | Aligned |
Decarbonisation pathway analysis
Alignment is treated as a structured analytical classification, not as an ESG label. In this case study, alignment depends on current intensity, sector pathway, data quality and transition-plan credibility.
Under the simplified pathway used in this case study, exposures are classified as aligned, partially aligned or misaligned. This does not represent official IEA alignment.
Portfolio intensity pathway
Indexed to 100 in 2024 · simplified model
Sector alignment gap
Positive = above simplified 2030 target · negative = below target
Alignment ratio and transition priority
| Sector | Alignment ratio | Gap to 2030 target | Priority |
|---|---|---|---|
| Oil & Gas | 1.72× | +72% | High |
| Cement | 1.60× | +60% | High |
| Steel | 1.53× | +53% | High |
| Aviation | 1.48× | +48% | High |
| Power & Utilities | 1.28× | +28% | Medium |
| Chemicals | 1.22× | +22% | Medium |
| Shipping | 1.16× | +16% | Medium |
| Automotive | 1.08× | +8% | Medium |
| Agriculture & Food | 1.05× | +5% | Medium |
| Real Estate | 0.84× | -16% | Low |
| Retail & Services | 0.79× | -21% | Low |
Marginal-impact analysis on a new deal
The transaction simulator illustrates how a new deal can be assessed through marginal impact: exposure, financed emissions, portfolio intensity, data quality and pathway alignment.
Scenario logic only. Not a credit decision, not investment advice and not an ESG approval framework.
Renewable utility project finance
Use of proceeds. Renewable generation capacity and grid connection assets
Expected transition effect. Adds low-carbon exposure and reduces portfolio intensity on a marginal basis.
Rationale. Lower marginal portfolio intensity, credible transition use of proceeds and stronger data-quality profile.
Reproducible workbook architecture
The analytical workflow is designed to be exportable into an Excel model with separated input, calculation, KPI, pathway, simulator, data-quality and QA sheets.
12-sheet structure
Executive KPI layer
Reproducible notebook chain
The project is structured as a reproducible Python workflow: portfolio construction, financed-emissions attribution, pathway analysis, transaction simulation, dashboard asset export and QA validation.
CIB Portfolio Construction
Output: portfolio model CSV, schema checks, exposure distribution.
Financed Emissions Model
Output: company-level financed emissions, sector aggregation, portfolio KPIs.
Decarbonisation Pathways
Output: simplified pathway indices, alignment ratios, gap-to-target analysis.
Transaction Impact Simulator
Output: before/after portfolio impact and scenario recommendation logic.
Export Dashboard Assets
Output: JSON chart assets, downloads, QA report and reproducibility checks.
Methodology-first analysis
Every chart on this page is computed through transparent formulas applied to a reproducible model dataset. Methodological references and claim boundaries are documented alongside the model.
Core calculations
Public sources informing the workflow
What this case study is and isn't
Every figure on this page is reconcilable
The reproducible workflow ships with structural, reconciliation and claim-boundary checks. Each item below is verified against the local data assets used to render this page.
Across the analytical chain
Open repository, reproducible workflow
The case study is structured for GitHub publication and Vercel deployment, with local data assets, methodology notes, QA documentation and dashboard-ready components.