301
3 Key Opportunities
3.1 Opportunities: The Research Perspective
3.1.1 Integration with Other Modeling Methods
3.1.1.1 Modeling Electricity Mixes
The environmental assessment of different strategies and policies in the energy
and electricity sector, respectively, calls for integration of energy and electricity
scenario modeling and LCA. Such integration needs to take into account systemic
aspects, e.g. continuously matching power demand and supply, and should be
performed with a relatively high temporal resolution (Messagie et al. 2014 ).
Existing attempts to model electricity sector dynamics and their effect on electricity mixes have relied on standard economic models, both in the long-term (Pehnt
et al. 2008 ; Lund et al. 2010 ) and short-term (Amor et al. 2014b ). Electricity spot
markets have been modeled assuming partial equilibrium conditions (i.e. ignoring
feedback loops with markets outside the evaluated system). Electricity mixes
were calculated minimizing cost, reproducing the bid-based clearance of electricity markets.
These approaches allow the integration of short-term dynamics characteristic
of the electricity sector in long-term projections. The effect of public policies,
such as taxes or subsidies can be included in the analysis with the help of life
cycle costing. The scenarios can be, however, highly sensitive to uncertainties
(Menten et al. 2015 ), and a thoughtful scenario analysis and a clear account of the
limitations of studies is therefore needed. The integration of economic models
increases substantially the number of variables, especially if general equilibrium
models are used. In this case uncertainty assessments start to be computationally
challenging and new approaches would need to be explored (Dandres et al. 2014 ).
Initiatives such as the ENTSO-E
5 transparency platform of the pan-European
electricity market can be very helpful to reduce uncertainty and effectively integrate short-term dynamics. Finally, Frischknecht and Stucki ( 2010 ) have proposed
the so-called decisional approach and demonstrated it for electricity supply in
industry, but this approach has not received wide attention and deserves more
studies.
3.1.1.2 Filling Data Gaps
In the absence of specifi c data from facilities, statistic techniques can be used to
estimate emission factors and associated uncertainties. Using the regression
approach, emissions factors can be estimated from available data such as plant
age, fuel type and country gross domestic product (see e.g. Steinmann et al.
5 https://transparency.entsoe.eu/
21 Exploring Challenges and Opportunities of Life Cycle Management…
3 Key Opportunities
3.1 Opportunities: The Research Perspective
3.1.1 Integration with Other Modeling Methods
3.1.1.1 Modeling Electricity Mixes
The environmental assessment of different strategies and policies in the energy
and electricity sector, respectively, calls for integration of energy and electricity
scenario modeling and LCA. Such integration needs to take into account systemic
aspects, e.g. continuously matching power demand and supply, and should be
performed with a relatively high temporal resolution (Messagie et al. 2014 ).
Existing attempts to model electricity sector dynamics and their effect on electricity mixes have relied on standard economic models, both in the long-term (Pehnt
et al. 2008 ; Lund et al. 2010 ) and short-term (Amor et al. 2014b ). Electricity spot
markets have been modeled assuming partial equilibrium conditions (i.e. ignoring
feedback loops with markets outside the evaluated system). Electricity mixes
were calculated minimizing cost, reproducing the bid-based clearance of electricity markets.
These approaches allow the integration of short-term dynamics characteristic
of the electricity sector in long-term projections. The effect of public policies,
such as taxes or subsidies can be included in the analysis with the help of life
cycle costing. The scenarios can be, however, highly sensitive to uncertainties
(Menten et al. 2015 ), and a thoughtful scenario analysis and a clear account of the
limitations of studies is therefore needed. The integration of economic models
increases substantially the number of variables, especially if general equilibrium
models are used. In this case uncertainty assessments start to be computationally
challenging and new approaches would need to be explored (Dandres et al. 2014 ).
Initiatives such as the ENTSO-E
5 transparency platform of the pan-European
electricity market can be very helpful to reduce uncertainty and effectively integrate short-term dynamics. Finally, Frischknecht and Stucki ( 2010 ) have proposed
the so-called decisional approach and demonstrated it for electricity supply in
industry, but this approach has not received wide attention and deserves more
studies.
3.1.1.2 Filling Data Gaps
In the absence of specifi c data from facilities, statistic techniques can be used to
estimate emission factors and associated uncertainties. Using the regression
approach, emissions factors can be estimated from available data such as plant
age, fuel type and country gross domestic product (see e.g. Steinmann et al.
5 https://transparency.entsoe.eu/
21 Exploring Challenges and Opportunities of Life Cycle Management…