Identifying the Issues: Major Methodological Challenges

Data Issues: Generating Electricity Life Cycle Inventory Datasets

According to ISO 14044, electricity inventories shall take into account electricity mixes, fuel efficiencies, as well as transmission and distribution losses. Given the heterogeneity of electricity LCI data, representativeness is an important aspect when conducting an LCA. Referring to ISO 14044 (ISO 14044: 2006), data representativeness is the qualitative assessment of the degree to which the data set reflects the true population of interest for a specific application: geographical coverage, timerelated coverage, and technology coverage. Other quality indicators are such as completeness, consistency and uncertainty are also addressed. Aspects covering special challenges in the electricity sector are highlighted in the following sections.

2.1.1 Geographic Coverage

The geographical coverage is the geographical area from which data for unit processes should be collected to satisfy the goal of the study. In the context of electricity process, LCA practitioner can face two main challenges: the first one refers to the situation where no regionalized electricity data is available (Sect. and the second one refers to the grid delimitation (Sect. Extending the Geographical Coverage: Improving Production Data Accuracy

Regionalization of inventory data is recognized as an important need to increase the accuracy of LCA results, even if it is disputed down to which level the regionalization should go. Recent efforts have been undertaken to increase the geographical scope of inventory data and using country-specific statistics. The life cycle inventory database ecoinvent version 3 covers nearly 85 % of global electricity production in 2008 (Treyer and Bauer 2013) with country (or even region-specific) LCI data showing substantial differences in LCI data between specific countries and regions.

Despite the geographical coverage increase, gaps in LCI data keep existing and are in general more pronounced in non-OECD countries, where often extrapolations are unavoidable, increasing uncertainty (Treyer and Bauer 2013; Schmidt et al. 2011; Laurent and Espinosa 2015). Grid Mix Boundaries: From Production Mixes to Supply Mixes

Once available in the grid, it is not possible to know where the electricity is coming from (Dones et al. 1998; Itten et al. 2014; Weber et al. 2010). This tracking issue becomes even more challenging as electricity grids are increasingly getting interconnected, and hence makes selecting a grid mix boundary a complicated task for the practitioner.

The common approach is to use national electricity mixes and accounting for imports from the neighboring jurisdictions. The underlying justification is that neighboring countries have either physical connections or administrative contracts to trade (Treyer and Bauer 2014). However, the boundaries selection is to some extent arbitrary and raises equity issues. As an example, if we take the NorthAmerican electricity grid, different resolutions are available: national, interconnect, Jurisdiction-average production and consumption mixes (US countries, Canadian provinces, etc.), ISO/RTO, EPA's eGrid subregions, and EIA region (Weber et al. 2010).

On top of that, congestion can effectively limit electricity transmission within a national boundary (an administrative barrier), which even makes the common approach selection (i.e. using the national energy mixes) unrealistic. A recent study developed an approach creating clusters of data according to the congestion status and its location within the Ontario (Canada) grid-mix. As an example, the avoided greenhouse gas emissions varied, for uncongested (i.e. using as a common approach selection the production energy mix: Ontario mix) and congested hours, between 280 and 390 kg/MWh. Even if these empirical estimates cannot be generalized to other contexts, the study underscored the importance of congestion in defining the grid mix boundary (Amor et al. 2014a).

2.1.2 Temporal Aspects of Electricity

Our capacity to store electricity is very limited, and in practice demand is dynamically (hour by hour) matched with a range of production technologies. Obtaining past yearly-averaged country supply mixes to be used in attributional LCA (ALCA) is relatively straightforward by using national statics. Typically organizations such as International Energy Agency (IEA) provide these data, even if the often-rough categorization of fuel and power plant categories in the statistics calls for assumptions and extrapolations increasing uncertainties. However, predicting and capturing changes in time of the electricity sector – being relevant in consequential LCA (CLCA) – is a challenging task, for both temporal scopes: short-term and long-term horizon. Short-Term

In countries with de-regulated energy sectors, an independent system operator coordinates most of the markets by using price based dispatch systems. Price bids from generators, defining the supply curve would be ideal for analyzing the short-term variation of power plant production following different resolution: from hourly to annually and then be able to consider intermittency of renewable energy, intertemporal arbitrage, spinning and non-spinning reserves or ramp-rate limitations of producers.

However, price bids are not always publicly available. In the absence of such data, a procedure for integrating the short-term time variations of technologies is missing. Such a procedure could play an important role in increasing the robustness of LCA studies and refining their environmental impact estimates. Additionally, not all electricity markets have the same extent of de-regulation. As an example, leading players like China, the world largest consumer of electricity, still relies on a considerably more complex multi-level dispatch hierarchy partially based on generator output planning (Kahrl and Wang 2014). Long-Term

In the long-term, additional capacity would need to be installed to cover increases in demand. Changes in the electricity sector depend on political, environmental and economic considerations that are substantially uncertain and country specific.

Different techniques are available to estimate prospective electricity mix. These techniques are useful from the average ALCA perspective and also from the CLCA perspective.

Future supply mixes can be estimated from national forecast, such as IEA annual energy outlook (e.g. Hertwich et al. 2015). In the absence of such available data, specific models (e.g. partial equilibrium models) can be used to estimate future average supply mixes: LEAP, TIMES, (see Pfenninger et al. 2014 for a review). CLCA often follows the step-wise procedure presented by Ekvall and Weidema (2004) and updated in Weidema et al. (2009) to identify marginal technologies but its application to the electricity sector is not yet satisfactory (Treyer and Bauer 2014). Marginal changes in the electricity sector are likely to affect a range of technologies (Pehnt et al. 2008; Mathiesen et al. 2009) and it is not straightforward task to consistently identify them with a heuristic approach (Zamagni et al. 2012; Earles and Halog 2011; Menten et al. 2015). Energy system models such as TIMES or LEAP can help to overcome such difficulties.

2.1.3 Technology Coverage

The main challenges in technology data coverage concerns currently used technologies and those, which will be installed in the future and are not yet commercially available (e.g. on a pilot plant level). Actual Technologies

There is a wide variation among generation stations in terms of emissions and inputs per unit generation across and even within fuel types. Such variation becomes even more challenging with the differences among statistics sources (e.g. Eurostat and EIA) treating the same technology using a given fuel type at a given geographic location (e.g. jurisdiction). Specification of the time frame of LCI data can also be challenging: statistic sources often refer to different years and the availability of up-to-date data is not always given, depending on the type of environmental exchanges. In addition to that, there is considerable uncertainty over certain emission factors, even for mature technologies, such as hydropower or coal (Hertwich 2013; Henriksson et al. 2014). Moreover, not only LCI data for power plants as such can substantially vary, also specific fuel supply as well as infrastructure manufacturing chains can have important effects on LCA results (Bouman et al. 2015; Yue et al. 2014). Prospective Technologies

Prospective LCA studies often rely on LCI data of current electricity generation, even if technology performance of current power generation chains is likely to improve in the future and new technologies will emerge (IEA 2014). Modeling how technology performance will change over time is particularly difficult for nascent technologies (Curran et al. 2005) such as organic photovoltaic panels or carbon capture and storage (Volkart et al. 2013). Moreover, disruptive technologies can bring improvements in efficiency, but also have implied changes in infrastructure and user behavior, which are more difficult to predict (Miller and Keoleian 2015).

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