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Related Experiment Video

Updated: Mar 10, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

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Stochastic Technology Choice Model for Consequential Life Cycle Assessment.

Arne Kätelhön1, André Bardow1, Sangwon Suh2

  • 1Chair of Technical Thermodynamics, RWTH Aachen University , Schinkelstrasse 8, 52062 Aachen, Germany.

Environmental Science & Technology
|December 10, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces the Technology Choice Model (TCM) for Consequential Life Cycle Assessment (CLCA), improving upon existing equilibrium models. TCM enhances environmental impact analysis by incorporating uncertainty and market imperfections for more detailed and accurate results.

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Last Updated: Mar 10, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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Area of Science:

  • Environmental Science
  • Life Cycle Assessment
  • Economic Modeling

Background:

  • Consequential Life Cycle Assessment (CLCA) traditionally uses equilibrium models with limitations in sectoral detail and assumptions of perfect market oversight.
  • Existing models struggle to incorporate market imperfections, information asymmetry, and parameter uncertainties effectively.

Purpose of the Study:

  • To propose and validate a new modeling approach, the Technology Choice Model (TCM), for CLCA.
  • To extend the Rectangular-Choice-of-Technology (RCOT) model for CLCA applications, including stochastic elements.
  • To enhance the granularity and accuracy of environmental impact assessments in CLCA.

Main Methods:

  • Adaptation and extension of the Rectangular-Choice-of-Technology (RCOT) model to create the Technology Choice Model (TCM).
  • Incorporation of parameter uncertainties and suboptimal decisions within a stochastic framework.
  • Application of the TCM to a case study on rice production.

Main Results:

  • The TCM successfully models complex production technology mixes and their environmental outcomes under uncertainty with high detail.
  • Production constraints, uncertainty, and suboptimal decisions significantly influence technology choices and greenhouse gas (GHG) emissions.
  • The model accurately determines average and marginal environmental impacts in response to changes in final demand.

Conclusions:

  • The Technology Choice Model (TCM) offers a more robust and detailed approach to Consequential Life Cycle Assessment (CLCA).
  • Accounting for market imperfections and uncertainty is crucial for accurate environmental impact assessment.
  • The TCM provides valuable insights into the environmental consequences of production and consumption patterns.