Economic Input-Output Life Cycle Assessment (EIO-LCA) is a top-down approach intertwined with the environmental satellite accounts provided by the national statistical office. Through the use of economic input-output (IO) tables and industrial sector-level environmental and energy data, the EIO-LCA analysis allows for broad impact coverage of all sectors directly and indirectly involved with electricity generation.
1. Introduction
EIO-LCA is a methodology that strives to overcome some of the limitations inherent to the use of the Life Cycle Assessment (LCA) approach. It is built on an input-output (IO) table with transactions across distinct economic sectors that may be supplemented with environmental data, including extra columns and rows that depict the emissions per each activity sector. Because the emissions and flows of all activity sectors are incorporated in the assessment, the EIO-LCA approach overcomes the two key concerns created by Process LCA (P-LCA): the boundary limits are easily established since its boundaries are broad-ranging and comprehensive; and the circularity impacts are considered, because transactions within each activity sector are also contemplated [2,3,4].
The EIO-LCA approach, while not suffering from truncation like P-LCA, tends to have a higher level of sectoral aggregation [6,7]. This can make it difficult to clearly distinguish between different sectors and understand their specific contributions and interdependencies [2,3,4].
Using the EIO-LCA approach to analyze the environmental impacts of electricity generation can be challenging because published IO tables do not typically provide sufficient detail to distinguish between the impacts of increased demand for renewable energy sources (RES-E) and conventional electricity (CE). Instead, these tables only evaluate the impacts of an overall increase in demand for global electricity generation [8]. In fact, published IO tables incorporate the entire supply chain of electricity generation and use into a single electricity sector, i.e., generation, transmission, distribution, and supply-related activities [9].
2. Application of EIO-LCA to Electricity Generation
Energy analysis emerged in the aftermath of the oil crises of the 1970s as a discipline aimed at computing the total energy requirements to undertake a given activity. Originally, it considered the use of process analysis (PCA), which allows obtaining the energy required to perform the main production processes as well as a detailed assessment of its major supply chain contributors. A drawback that can be found in this approach is the choice of the system boundaries, which might lead to systematic truncation errors [23,24]. One way to overcome such errors is to combine conventional PCA with IO analysis, resulting in a hybrid method [25].
Traditional IO analysis can also be used to assess economy-wide direct, indirect, and induced employment effects [8,26,27,28,29,30,31], economic effects [10,12,20,31,32,33,34,35,36,37,38,39,40], energy requirements and pollutant emissions from electricity generation [1,9,21,41], and biodiversity [42].
2.1. Complement Some Parts of the Life Cycle Lacking Data
Usually, IO analysis is pooled with P-LCA to complement some parts of the life cycle that lack data. In this context, the IO method can either be applied for evaluating materials and non-materials-related processes, or for assessing emissions or energy use. As stressed by Mattila [
43], IO is a key tool for complementing the traditionally performed P-LCA with macroeconomic data from the background systems, and, if properly used, it may result in more accurate LCA.
For instance, Voorspools et al. [
44] combined PCA with IO analysis to compute greenhouse gas (GHG) emissions and energy use for the different economic sectors engaged in the construction of a power plant. For the operations and maintenance (O&M) of the plant, a hybrid approach is used, though the energy related to the decommissioning stage is obtained by means of a PCA. Their results are significantly different for nuclear plants but are effectively the same for wind farms.
Kumar et al. [
52] estimated the GHG emissions throughout the life cycle of wind energy farms by means of the EIO-LCA methodology in the United States. The work incorporates the installation, O&M, and decommissioning stages into the EIO-LCA framework and presents the expected life cycle GHG emissions from O&M activities, identifying uncertainty in the emissions intensity estimates and contributing to the discussion of its causes. The study concludes that, if all costs and a life cycle perspective are incorporated into the analysis, wind energy production is not completely GHG emission-free. In Muangthai and Lin [
53], the EIO-LCA approach is applied to estimate the direct and indirect impacts from the power generation sector in Thailand for the years 2005 and 2010. The domestic IO table, excluding the import values, was used to have a more accurate perception of the actual environmental impacts generated by the industrial sectors engaged with the electricity sector. In general, these studies suggest that when using IO instead of P-LCA for the corresponding life cycle phase, the corresponding energy use of that LCA stage becomes larger.
2.2. Express Some IO Sectors in More Detail
P-LCA data can also be tied to IO tables to further decompose some IO sectors [
54]. In this framework, Wiedmann et al. [
17] used the Ecoinvent database to disaggregate the wind power subsector from the electricity sector in the UK. Crawford [
55] disaggregated an IO model into 100 activity sectors, into which available process data was incorporated. By considering this approach, the overall comprehensiveness of the IO model is guaranteed, while more consistent process data can also be integrated whenever it is feasible. In addition to solving the problem of upstream truncation, this also prevents the possibility of obtaining the downstream truncation errors already mentioned.
Nagashima et al. [
56] introduced new sectors in the IO table based on data from the production processes of wind turbines, including sectors for manufacturing towers, nacelles, rotors, cables, transformers, and construction. They also used the EIO-LCA analysis method to evaluate the induced production and value-added of all sectors involved in the wind power generation system. Later, Nagashima et al. [
57] used the IO approach to study the environment, energy, and economic impacts of a wind power generation system in Japan. The study also evaluated the resulting production and value-added impacts for all sectors related to wind power generation, concluding that these overcompensate the negative effects of replacing conventionally generated electricity with electricity from wind power. In a similar vein, Wolfram et al. [
58] used the EIO-LCA approach to assess carbon footprint scenarios for RES-E in Australia. This hybrid approach combined the strengths of both methods, extending the analytical IO framework while preserving the accuracy of P-LCA for crucial processes.
One of the major limitations found regarding the use of this approach is the subjective choice of the data that requires replacement with process data in IO tables, potentially biasing the results obtained. Nevertheless, Lenzen and Munksgaard [
59] stated that the use of IO-based hybrid techniques should be preferred whenever system completeness regarding the assessment of the energy content of RES-E systems is to be attained.
This entry is adapted from the peer-reviewed paper 10.3390/en16062930
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