# Prospective Characterization Factors for Assessing Climate Change Impacts in Life Cycle Assessments

## TL;DR

This paper introduces new climate impact factors for future-oriented life cycle assessments, showing how emissions will affect climate change under different scenarios.

## Contribution

The paper introduces consistent prospective characterization factors (pCFs) for climate change impact assessment in pLCA.

## Key findings

- Methane and nitrous oxide pGWP100 values vary significantly compared to current factors.
- Climate impacts for rice production increase by 8% using pGWP100.
- Using pGTP100, impacts in sectors like steel and cement decrease substantially.

## Abstract

Prospective life
cycle assessment (pLCA) is a future-oriented approach
that estimates the environmental impacts of products and systems under
future technological changes, market dynamics, and policy shifts.
However, pLCA lacks consistent prospective characterization factors
(pCFs) to assess the climate impacts of future emissions and align
the inventory and impact assessment phases. This work produces pCFs
by integrating gas-specific climate parameters with future emission
scenarios from the Integrated Assessment Models (IAM). Prospective
Global Warming Potential (pGWP20, pGWP100) and
Global Temperature change Potential (pGTP50, pGTP100) are computed for emission years until 2050. Relative to present-day
CFs, methane pGWP100 varies from −8% to +23%, and
nitrous oxide varies from −17% to +7%. CH4 pGTP100 shifts from −24% to +22%, while N2O pGTP100 shifts from −27% to +8%. For non-CO2-dominated
activities such as rice production, climate impacts increase by 8%
in terms of pGWP100. With pGTP100, impacts of
ammonium nitrate decrease by 9%. When pCFs are combined with prospective
background inventories, impacts are substantially lower in sectors
such as steel (−44%), road transport (−58%), and cement
(−31%) under pGTP100. Overall, the availability
of pCFs for multiple climate metrics and IAM scenarios enables a consistent
coupling of impact assessment with future-oriented inventory data,
improving the robustness and coherence of pLCA.