Davide Baschieri

Large-scale transitions to eco-friendly vehicle fleets present complex capital budgeting challenges, requiring the integration of extensive operational data with financial modeling while balancing economic profitability and environmental sustainability. Traditional approaches often struggle to manage this complexity and quantify the inherent trade-offs. This study develops and applies an innovative integrated accounting-and-finance framework to evaluate the economic and environmental implications of green fleet transition projects, explicitly quantifying the trade-off between profitability and sustainability. Focusing on waste vehicle replacement of Iren Spa, a leading European multi-utility company, we employ the recently developed Split-Screen Approach, a unified accounting-and-finance framework grounded in the laws of motion and conservation. It automatically reconciles pro forma financial statements and generates internally consistent valuation metrics, eliminating the manual adjustments and inconsistencies of traditional models. Its built-in diagnostic checks and scalability for highly complex datasets overcome the manual adjustments and inconsistencies inherent in traditional financial models. We process 2303 inputs across multiple “green” scenarios. This methodology integrates an Engineering Model, describing fleet evolution, operating costs, and CO₂ reduction, with a HookUp Model, which serves to transform scenarios into well-defined projects. The latter model is then integrated with a Financial Model that generates pro forma financial statements, incorporates financing and payout policies, and assesses economic profitability through Net Present Value (NPV) and consistent accounting rates of return. Together, these elements form a robust framework for managing complex data integration and analysis. Our research reveals a fundamental trade-off: enhanced environmental sustainability (measured by Net Green Value, NGV), which quantifies CO₂ reduction, is achieved at the expense of economic profitability, measured by NPV. This financial sacrifice is captured by the Net Value Curve, a Pareto frontier, while the NPV-to-NGV ratio provides “shadow prices” for CO₂ reduction, revealing the financial cost per unit of sustainability gained. Based on 21 project scenarios and additional sensitivity analyses on financial inputs and energy prices, the results confirm a decreasing relationship between NGV and NPV. This study makes three main contributions: (1) it demonstrates the practical application of the Split- Screen Approach for capital budgeting under complexity, (2) it introduces the Net Value Curve framework as a useful tool for visualizing and quantifying the trade-off between profitability and sustainability, (3) it provides managers and policymakers actionable insights, supporting more informed decisions in green fleet transition planning where economic and environmental objectives may conflict. The findings provide managers and policymakers with a rigorous and transparent accounting-and-finance framework that enhances the reliability of capital budgeting decisions compared with traditional financial modeling, while offering a Paretian frontier for evaluating environmental trade-offs.

This paper employs the newly conceived accounting-and-finance engineering system (AFES) described in a previous paper (Magni 2023, “The Split-Screen Approach for Project Appraisal (Part I: The Theory)”), addressed to the analysis of capital asset investments. In this second part, we show how to implement this theoretical framework onto a spreadsheet software. We guide the analyst step by step, cell by cell, to the creation of the Split-Screen Matrices describing the project film. Because the AFES is based on two arithmetic relations (law of motion and law of conservation), we can use a minimal approach to modeling, with a frugal use of the most common spreadsheet functions (essentially INDEX and MATCH) and no use of the traditional financial functions, yet fulfilling the requisite of clearness, transparency, consistency, and ease of use. Starting from the informal description of the project, we build the model by breaking it down to 7 modules. The spreadsheet model is available online (see link provided in the paper).

Our study introduces an integrated engineering-and-finance system designed to assist business decision-makers in optimizing shareholder value and environmental sustainability. Our focus centers on addressing Iren Ambiente’s challenge of transitioning to eco-friendly fuel policies for waste vehicles. While engineering and finance approach capital budgeting projects from different angles, often overlooking each other's viewpoints, our paper aims to harmonize their traditionally separate perspectives and emphasize their interdependence. To achieve this, we construct a comprehensive engineering model encompassing 2,303 inputs across five distinct “green scenarios” for vehicle replacements. Its outputs feed into a robust financial model assessing economic profitability across the five green policies. Our findings unveil a trade-off between environmental sustainability and economic value creation, portrayed by a Pareto frontier called the Net Value curve. This curve combines Net Present Value (measuring value creation) and Net Green Value (measuring CO2 emission reduction), quantifying the value the company must forgo to increase its environmental sustainability. These insights offer valuable guidance for strategic decision-making. Furthermore, the NPV-to-NGV ratio provides "shadow" prices for CO2 reduction, indicating the precise financial sacrifice for each green scenario. To further investigate the functional relation between NGV and NPV, we expand our analysis from 5 to 21 scenarios. This analysis confirms a strictly decreasing relation between NGV and NPV, and reveals an increasing marginal rate of substitution, supported by a degree-2 polynomial regression.

We present a model of performance measurement and attribution for delegated investments that summarizes the manager effect and the client effect on value creation. In particular, we introduce an innovative two-dimensional approach that, on one hand, detects the (manager and client) decision effects, measuring the impact of manager/investor choices on the overall investment performance and, on the other hand, detects the (manager and client) period effects, measuring the impact of all the (manager and client) decisions on the investment performance in a given assessment interval. As for the decision effects, the value added of an active investment portfolio is broken down in terms of the value generated by the decisions made by the manager (manager decision effect) and the value generated by the client/investor (client decision effect). As for the period effects, we quantify the impact of all the decisions made in the assessment interval on the value creation generated in a single period by the manager (manager period effect) and the client (client period effect), so that the sum of the periods' attribution values is the investment's value added. In order to accomplish the task, we employ the Finite Change Sensitivity Index (FCSI), which enables one to quantify the impact of the most influential decisions made by manager and investor, and a truncation approach which is equivalent to the well-known residual-income approach. We then combine the two attribution dimensions into an Attribution Matrix (AM). Each element of the AM provides the amount of value added generated in a given period by the decisions made by the manager or by the investor in (the same or) another period.

The adoption of a photovoltaic system has positive environmental effects, but the main driver of the choice in the industrial and commercial sector is economic profitability. Switching from acquisition of energy to production of energy is an investment with costs (e.g. leasing annual payment, O&M costs, capital expenditure) and benefits (e.g. savings in the electric bill, sale of the energy exceeding consumptions). In this work, we use an accounting-and-finance model to calculate the Equity Net Present Value in different scenarios and a sensitivity-analysis method (Finite Change Sensitivity Index) to explain the reasons for differences in results. This technique enables identifying the contribution of any input factor in the output value variation. In this way, the investor can draw attention on the most significant critical variables in the initial estimations to ensure success in forecasting.