Creazione e implementazione di un modello quantitativo per le metriche d'impatto

• 02.07.2026

Tramite il progetto Positive Impact Research Market (PIRM), nel febbraio 2026 Ti-Riuso ha proposto di creare un modello che consentisse di quantificare la CO2 contenuta grazie agli interventi della nostra associazione. Riportiamo di seguito la parte iniziale della tesi di Bachelor che è stata redatta in tale ambito.
Ringraziamo sentitamente Adrian Cazar per l'interesse dimostrato verso Ti-Riuso e per aver sviluppato un modello che ci consentirà di comprendere meglio l'impatto della nostra iniziativa e quindi di migliorarla.

INTRODUCTION

1. Context and Motivation
The construction sector is highly material intensive and generates significant environmental impact through extraction, manufacturing, transport, demolition, and waste generation. According to UNEP, the construction industry accounts for a substantial share of global energy use and CO2 emissions, with materials such as cement and steel representing an important part of building-related emissions. In a linear life-cycle model, materials are extracted, used, and then discarded or downgraded, even when they may still retain functional value.

Reuse and recovery offer a practical alternative to this linear model. Instead of treating removed materials and objects as waste, reuse keeps them in circulation and can reduce demand for newly produced equivalents. This is particularly relevant in construction, where many materials and components may remain technically usable after renovation, demolition, or house-clearing activities.

However, the environmental value of reuse is not directly visible in company records. Operational files usually describe quantities, prices, revenues, and transactions rather than avoided emissions, avoided production impacts, or energy savings. Therefore, the value of reuse must be translated from business data into environmental indicators through a transparent and reproducible model.

This project addresses that translation problem in the specific case of Ti-Riuso. The goal is not to produce a generic sustainability statement, but to develop a computational model that converts Ti-Riuso’s available operational data into clear impact metrics.

2. Problem Statement

Ti-Riuso carries out material recovery and reuse activities, but its available records were created primarily for operational and commercial purposes rather than environmental impact evaluation. The company has useful information on materials, sales, quantities, masses, prices, and revenues, but it did not yet have a structured computational model that converts this information into environmental impact metrics.

The problem addressed in this project is the absence of a reproducible and transparent impact model adapted to Ti-Riuso’s actual data. From an informatics perspective, this required transforming Excel workbooks that were not designed as databases into normalized transaction data, implementing calculation logic, managing assumptions explicitly, and producing outputs that can be inspected both as tables and through a dashboard.

The core challenge is therefore twofold:

• Environmental challenge: estimate avoided CO2e emissions and energy savings generated by recovery and reuse activities.

• Computational challenge: design a reproducible pipeline that transforms semi-structured company data into structured environmental indicators.

3. Project Objectives

The objective of the project is to create and implement a quantitative model that estimates the environmental benefit of Ti-Riuso’s recovery and reuse activities. The model is designed to be practical and reproducible rather than a complete product-level LCA for every recovered item.

The project aims to:

• parse and normalize Ti-Riuso’s operational Excel workbooks;

• construct a transaction-level dataset suitable for environmental calculation;

• connect recovered materials and reusable objects to external impact factors;

• calculate gross avoided CO2e using an avoided-production logic;

• calculate gross avoided energy using energy-intensity factors where available;

• estimate transport-related operational emissions and energy use through a documented scenario;

• calculate net avoided CO2e and net energy savings by subtracting allocated transport burden;

• preserve data gaps explicitly instead of hiding non-calculable rows;

• expose the results through CSV outputs, summary tables, figures, and a dashboard.

The implementation combines:

• a Python-based data processing pipeline;

• CSV-based intermediate datasets;

• a local API layer;

• a React/Vite dashboard for interactive inspection.

CO2 is the primary impact indicator because it is widely understood and consistently supported by the available impact factors. Energy savings are included as a complementary environmental indicator because avoided production also implies avoided energy demand. Reporting both indicators makes the model more informative for sustainability communication.

4. Scope and Delimitations

The scope of the project is defined by the available Ti-Riuso data and by the need to maintain methodological transparency. The current model focuses on recovered materials and reusable objects for which mass, category, or impact-factor information can be derived.

The model includes:

• raw material transactions;

• stone material transactions;

• intervention-related recovered materials;

• reusable-object sales where mass or proxy information is available;

• external CO2e and energy impact factors;

• replacement-rate assumptions for reusable objects;

• transport-emission and transport-energy estimates based on available operational assumptions.

The model excludes:

• a complete cradle-to-grave LCA for every product;

• detailed use-phase impacts after resale;

• detailed product composition for all reusable objects;

• verified trip-level transport logs;

• storage and building energy emissions where measured data is not available;

• avoided landfill impacts unless supported by reliable data.

These exclusions are methodological boundaries rather than failures of the model. They prevent the analysis from presenting uncertain estimates as measured results.