The ESG Lab developed a methodology to project greenhouse gas (GHG) emissions over time using geographical data from NGFS climate scenarios.
Why?
The approach links these scenarios to a defined perimeter of long-term financial assets, enabling forward-looking carbon footprint analysis. A sectoral study was integrated to capture how different industries contribute to changes in portfolio emissions.
• This required mapping between the NGFS classification and the GICS industry standard to ensure consistency in sector attribution. We combined bottom-up estimation techniques with multiple data sources, including corporate emissions databases, public ETF information, and the PCAF attribution method.
• This framework allows us to allocate projected emissions to each asset with greater accuracy. It also highlights the materiality of certain sectors in driving overall portfolio emissions. By merging spatial, sectoral, and financial data, the methodology offers a robust tool for climate scenario analysis.
The result is a granular view of potential future emissions, supporting informed decision-making on climate strategy and portfolio alignment.
We designed a simple, comprehensive Climate Taxonomy to classify economicactivities that significantly contribute to climate change mitigation andadaptation, with the aim of guiding and mobilising financial flows towards thecountry’s climate objectives.
Developed through a structured, participatory process with financial institutions,the taxonomy combines economic, social, and environmental criteria to prioritisesectors and activities.
For mitigation, a multi-criteria analysis identified key sectors based on GDPcontribution, employment, GHG reduction potential, and exposure to transitionrisks, followed by activity-level eligibility and alignment criteria.
For adaptation, another approach distinguishes activities that adapt themselvesto climate risks from those that enhance the resilience of others, supported bynational climate risk mapping.
The taxonomy is rooted in Tunisia’s policy priorities, aligned with its NDC andlow-carbon strategy, and based on the national activity classification forstatistical compatibility. It also reflects the economic reality by includingtransitional activities that can evolve towards sustainability.
Developed a financial engineering model to estimate the investment risk return profile of geoenergy (shallow geothermal) projects for heating and cooling buildings.
Why?
To facilitate the financing of shallow geothermal installations: a lowcarbon heating and cooling solution not frequently adopted cause it is CAPEX intensive.
• Studied the different existing possibilities to finance CAPEX intensive long-term low emission energy projects.
• Developed financial engineering model to estimate projects’ risk-return profile: applying project specific thermal engineering criteria (coefficient of performance, estimated energy consumption…) and financial criteria (share of debt and equity, interest rate…) generate expected return and risk metrics under different scenarios.
• Analyzed how environmental gains (emission reduction, through an LCA approach) may benefit financing (regulation compliance and carbon credits).