A Conceptual Review on Using Consequential Life Cycle Assessment Methodology for the Energy Sector

Abstract

Energy is engaged in the supply chain of many economic sectors; therefore, the environmental impacts of the energy sector are indirectly linked to those of other sectors. Consequential life cycle assessment (CLCA) is an appropriate methodology to examine the direct and indirect environmental impacts of a product due to technological, economic or social changes. To date, different methodological approaches are proposed, combining economic and environmental models. This paper reviews the basic concept of CLCA and the coupling of economic and environmental models for performing CLCA in the energy sector during the period 2006–2020, with the aim to provide a description of the different tools, highlighting their strengths and limitations. From the review, it emerges that economic modelling tools are frequently used in combination with environmental data for CLCA in the energy sector, including equilibrium, input-output, and dynamic models. Out of these, the equilibrium model is the most widely used, showing some strengths in availability of data and energy system modelling tools. The input-output model allows for describing both direct and indirect effects due to changes in the energy sector, by using publicly available data. The dynamic model is less frequently applied due to its limitation in availability of data and modelling tools, but has recently attracted more attention due to the ability in modelling quantitative and qualitative indicators of sustainability.

A Review on Consequential Life Cycle Assessment in the Power Sector

“…The most common way to model the economic relationship is combining an economic modelling tool and LCA. The applicable economic models are either partial equilibrium (PE), general equilibrium (GE), input output (IO) or dynamic models (DM), which were also common in CLCA studies in the energy sector in general [46]. In power sector, two thirds of reviewed papers applied economic models to simulate the economic interactions between power sector and other sectors.…”

“…The applications of PE, GE, and IO had the advantage of available data and energy models. Meanwhile, the DM was limited in terms of availability of data and modelling tools, but worked well with socio-economic data [46]. Table 3 presents the reviewed CLCA case studies and their applicable models.…”

“…The applications of several economic models, e.g., IO/ PE/GE in CLCA provided details of the economic causal relationship [47]. PE had the advantage of conveying a specific view of one economic sector, while GE delivered a comprehensive view of the product system in relations with the economy [46]. IO can be applied to either one economic sector, e.g., EnergyPLAN for modelling power sector, or used for studying the whole economy, e.g., World Input Output Database [48].…”

CLCA for Assessing Environmental Impacts of Power Sector in the GHG Policy Context

“…The most common way to model the economic relationship is combining an economic modelling tool and LCA. The applicable economic models are either partial equilibrium (PE), computerized general equilibrium (CGE), input output (IO) or dynamic models (DM), which are also common in CLCA studies in energy sector in general [41]. Two thirds of reviewed papers applied economic models to simulate the economic interactions between power sector and other sectors.…”

“…The applications of PE, CGE and IO have the advantage of available data and (energy) economic models. Meanwhile, The DM is limited in terms of availability of data and modelling tools, but works well with socio-economic data [41]. Table 3 presents the reviewed CLCA case studies and their applicable models.…”