Study
ModellingHigh ImpactStrong effect

VECTOR21: Simulating Future Vehicle Market Share and CO2 Emissions

A computational model, VECTOR21, can simulate future automotive market dynamics and CO2 emissions by factoring in customer purchasing decisions, technological diffusion, production costs, and policy influences.

OPUS Publication Server of the University of Stuttgart (University of Stuttgart) · 2010

01

Key Findings

  • 01The VECTOR21 model can simulate the diffusion of vehicle technologies and fuels in the automotive market.
  • 02Relevant Cost of Ownership is a key factor in customer purchasing decisions.
  • 03Learning curves and variable margins influence production costs and selling prices.
  • 04Policy instruments and raw material prices significantly impact market outcomes and emissions.
02

Application

Design takeaway

Incorporate a lifecycle cost perspective and anticipate the impact of evolving regulations and market trends when designing new vehicles.

How to apply

Use simulation tools to test the market viability and environmental performance of design concepts under different future scenarios.

Project actions

  • 01When designing a product, think about how it will be adopted by the market over time.
  • 02Consider how external factors like cost, regulations, and new technologies might affect your design's success.
03

Method & Evidence

AimTo develop a scenario model for simulating future vehicle market shares and CO2 emissions based on various influencing factors.
MethodComputational modelling and simulation
ProcedureA computer model (VECTOR21) was developed to calculate scenarios for the future development of the automotive market. It considers market shares of different vehicle technologies and fuels, and associated CO2 emissions for passenger cars in Germany from 2009 to 2030. The model simulates customer decision-making in new vehicle purchases, reflecting the diffusion of technologies and fuels, based on 'Relevant Cost of Ownership'. It also incorporates learning curves and variable margins for producers' costs and prices, alongside the impact of tax instruments (e.g., CO2 fleet targets) and raw material prices.
ContextAutomotive market analysis, environmental impact assessment

Variables

IV["Cost of ownership","Technological advancements","Government policies (e.g., CO2 targets)","Raw material prices"]
DV["Market share of vehicle technologies/fuels","CO2 emissions"]
CV["Time period (2009-2030)","Geographic market (Germany)","Vehicle type (passenger cars)"]
04

Strengths & Limitations

Strengths

  • +Comprehensive consideration of multiple influencing factors.
  • +Simulation of complex market diffusion processes.

Limitations

The model relies on assumptions about future costs, consumer behaviour, and policy changes, which may not accurately reflect reality.

Reliability & validity

The reliability of the model depends on the consistency of its algorithms and data inputs. Validity would be assessed by comparing its predictions against actual market data over time, though this is challenging for future predictions.

Think critically

How might the assumptions within a simulation model, such as those concerning 'Relevant Cost of Ownership' or 'learning curves', introduce bias into the predicted outcomes?

05

Design Principles

"Anticipate market diffusion and environmental impact through scenario-based modelling."

Understanding the complex interplay of factors influencing vehicle adoption and environmental impact is crucial for strategic planning in the automotive sector. This model provides a framework for designers and engineers to explore the potential outcomes of design choices and policy interventions.

06

What This Means for Your Design

This study created a computer program to predict which types of cars people will buy in the future and how much CO2 they will produce, by looking at things like cost, new technology, and government rules.

How to use in your project

  • 1.Reference this study when discussing the importance of market simulation and forecasting for design projects.
  • 2.Use the concept of modelling customer decision-making to inform your own user research and product development strategy.
07

Add to My Project

08

Quick Cite

(2010). Entwicklung eines Szenariomodells zur Simulation der zukünftigen Marktanteile und CO2-Emissionen von Kraftfahrzeugen (VECTOR21). OPUS Publication Server of the University of Stuttgart (University of Stuttgart). https://doi.org/10.18419/opus-6760 Retrieved from https://designdex.org/study/fd1ede22-e3b1-42f2-93e7-d39a6dd6fdb9/vector21-simulating-future-vehicle-market-share-and-co2-emissions

Paragraph starter

The development of scenario models, such as VECTOR21, demonstrates the utility of computational tools in predicting future market dynamics and environmental impacts. This approach, which simulates customer decision-making and considers factors like cost of ownership, technological diffusion, and regulatory influences, provides valuable insights for design strategy and product development.

09

Source

OPUS Publication Server of the University of Stuttgart (University of Stuttgart)

Entwicklung eines Szenariomodells zur Simulation der zukünftigen Marktanteile und CO2-Emissionen von Kraftfahrzeugen (VECTOR21)

journal · 2010

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Questions about this research

What does the research say about vector21: simulating future vehicle market share and co2 emissions?
Incorporate a lifecycle cost perspective and anticipate the impact of evolving regulations and market trends when designing new vehicles. Evidence: OPUS Publication Server of the University of Stuttgart (University of Stuttgart) (2010).
Why does "VECTOR21: Simulating Future Vehicle Market Share and CO2 Emissions" matter for design?
Understanding the complex interplay of factors influencing vehicle adoption and environmental impact is crucial for strategic planning in the automotive sector. This model provides a framework for designers and engineers to explore the potential outcomes of design choices and policy interventions.
How can designers apply this research?
Incorporate a lifecycle cost perspective and anticipate the impact of evolving regulations and market trends when designing new vehicles.
What were the main findings?
The VECTOR21 model can simulate the diffusion of vehicle technologies and fuels in the automotive market.. Relevant Cost of Ownership is a key factor in customer purchasing decisions.. Learning curves and variable margins influence production costs and selling prices.. Policy instruments and raw material prices significantly impact market outcomes and emissions.
What research method was used?
Computational modelling and simulation.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2010 journal from OPUS Publication Server of the University of Stuttgart (University of Stuttgart).
What should I do differently in my next project?
Use simulation tools to test the market viability and environmental performance of design concepts under different future scenarios.
What are the limitations?
The model's accuracy is dependent on the quality and completeness of the vehicle and technology database, and the assumptions made about future influencing factors.
Is there evidence that co2 emissions affects design outcomes?
The study developed a simulation model that predicts how different vehicle types will be adopted in the market and their resulting CO2 emissions, by considering what influences buyers and manufacturers, including costs, policies, and learning over time. Understanding the complex interplay of factors influencing vehicle Source: OPUS Publication Server of the University of Stuttgart (University of Stuttgart) (2010).
Where does this vehicle research apply?
Automotive market analysis, environmental impact assessment It sits within modelling research on designdex.org.

Related research topics

co2 emissions design research · evidence on co2 emissions · does co2 emissions improve design outcomes · vehicle studies for designers · co2 emissions and vehicle findings · modelling research evidence