Paper: arXiv 2611.09213

Authors: Charles Minier, Stéphane Loisel, Renata Alcoforado

Abstract

Motor insurance claims management increasingly faces a dual objective: controlling claim costs while reducing greenhouse-gas emissions associated with vehicle repair decisions. This paper proposes a bivariate, discrete-time modelling framework that links the dynamics of financial claims and repair-related carbon footprint inspired by a BINAR(1) dependence structure, allowing for temporal persistence, cross-dependence and a positive/negative correlation between the two dimensions. Using externally sourced emission factors and cost information for replacement parts, we operationalise the carbon footprint of claims under two repair pathways: new parts versus circular-economy reused parts (PIEC). We then conduct scenario analyses in which the acceptance rate of reused parts varies, and we quantify the joint implications for expected monetary outlays and CO 2 -equivalent emissions. Results show that higher PIEC acceptance can simultaneously reduce expected emissions and average claim cost, and the magnitude of the joint benefit depends on the dependence structure and persistence captured by the BINAR(1) process. The proposed framework provides a transparent, reproducible tool for insurers to evaluate repair-policy levers and to align claims operations with decarbonisation targets while retaining an explicit stochastic risk perspective.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a highly mathematical framework (BINAR(1) processes, ruin theory) applied to a novel problem in sustainable finance. It demonstrates strong empirical rigor through scenario simulations using real-world data, making it a ‘Holy Grail’ paper.

Research Flowchart

  flowchart TD
    A[Research Goal: Jointly model financial cost & carbon footprint for circular auto parts adoption in motor insurance] --> B{Methodology: Bivariate, discrete-time modelling framework (BINAR(1) structure)};
    B --> C[Data/Inputs: Financial claim costs, externally sourced emission factors, new vs. circular-economy reused parts (PIEC) info];
    C --> D{Computational Process: Operationalize carbon footprint, scenario analysis (varying PIEC acceptance rate)};
    D --> E[Outcome 1: Higher PIEC acceptance can simultaneously reduce expected emissions and average claim cost];
    E --> F[Outcome 2: Magnitude of joint benefit depends on BINAR(1) dependence structure and persistence];
    F --> G[Outcome 3: Provides a transparent tool for insurers to evaluate repair policies and align with decarbonization targets];