database. According to the authors the use of IMAGE results as a source of future
projections greatly simplifies the data collection stage and allows the systematic and
consistent modification of ecoinvent background processes and market mixes. The
authors pointed out the importance of a standard format of the results to allow to a
large degree the automatization of future background databases to reflect different
time horizons or scenarios.
2.2 Lack of Data and Difficulties in the Integration
of the Results
The integration of energy system models and life cycle methods is an approach
widely used to facilitate the modelling of prospective scenarios including environmental and economic aspects as well as political constraints, enabling sound
decision making and reducing the risk of burden-shifting. Two of the most overwhelming challenges are the lack of data (e.g. power plant operation and emission
factors) and the coupling of the results of the energy system models with life cycle
inventories. Astudillo et al. [7] identified data gaps related to the energy supply and
demand by analysing time-series and literature on emission factors and developed a
screening algorithm enabling database integration.
2.3 Capturing the Interplay of Changes in the Heat Sector
and the Electricity Sector in LCM Models
Bertrand et al. [8] developed a mixed integer linear programming model for the
regional valorisation of industrial waste heat from manufacturing, energy production and waste incineration industries. Using waste heat would save resources and
reduce emissions, as highlighted by the European Union in 2012 with the Energy
Efficiency Directive [9]. By applying their modelling to a case study involving steel
plants as heat sources and industries/towns as sinks, they demonstrated the economic profitability of using waste heat for all stakeholders involved.
2.4 Management and Integration of Stochastic Renewables
in LCM Models
A growing share of renewable intermittent electricity such as wind and solar power
leads to a fluctuating feed-in of electricity which might not correspond to electricity
demand in a temporal dimension, leading to regional electricity surpluses. Seier
et al. [10] proposed “temporal electricity purchase shifting” (TEPS) as one possible
Life Cycle Management of Energy and Energy Transitions …
245
projections greatly simplifies the data collection stage and allows the systematic and
consistent modification of ecoinvent background processes and market mixes. The
authors pointed out the importance of a standard format of the results to allow to a
large degree the automatization of future background databases to reflect different
time horizons or scenarios.
2.2 Lack of Data and Difficulties in the Integration
of the Results
The integration of energy system models and life cycle methods is an approach
widely used to facilitate the modelling of prospective scenarios including environmental and economic aspects as well as political constraints, enabling sound
decision making and reducing the risk of burden-shifting. Two of the most overwhelming challenges are the lack of data (e.g. power plant operation and emission
factors) and the coupling of the results of the energy system models with life cycle
inventories. Astudillo et al. [7] identified data gaps related to the energy supply and
demand by analysing time-series and literature on emission factors and developed a
screening algorithm enabling database integration.
2.3 Capturing the Interplay of Changes in the Heat Sector
and the Electricity Sector in LCM Models
Bertrand et al. [8] developed a mixed integer linear programming model for the
regional valorisation of industrial waste heat from manufacturing, energy production and waste incineration industries. Using waste heat would save resources and
reduce emissions, as highlighted by the European Union in 2012 with the Energy
Efficiency Directive [9]. By applying their modelling to a case study involving steel
plants as heat sources and industries/towns as sinks, they demonstrated the economic profitability of using waste heat for all stakeholders involved.
2.4 Management and Integration of Stochastic Renewables
in LCM Models
A growing share of renewable intermittent electricity such as wind and solar power
leads to a fluctuating feed-in of electricity which might not correspond to electricity
demand in a temporal dimension, leading to regional electricity surpluses. Seier
et al. [10] proposed “temporal electricity purchase shifting” (TEPS) as one possible
Life Cycle Management of Energy and Energy Transitions …
245