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

3.1 System boundary

One of distinguished features of CLCA is the expansion of system boundary, i.e. the inclusion of unit processes [16] and different products and co-products [19] to the extent of the expected changes. At the early time of CLCA development, Weidema proposed an approach, in which the expansion of system boundary was conducted under the ceteris paribus (other things being equal) assumption [20]. The author suggested an approach to identify affected products in five steps [20, 21], including:

(1) What scale and time horizon does the study apply to?

(2) Does the change only affect specific processes or a market?

(3) What is the trend in the volume of the affected market?

(4) Is there potential to provide an increase or reduction in supply and demand?

(5) Is the technology the most/least preferred?

This stepwise approach clarified the links between the product systems and unit processes through intermediate products (ALCA) as well as identified the consequences on supply and demand of products and co-products (CLCA) [21]. Due to the limitation of data availability at this time, it was suggested to assume that the scale of change was small [21]. Therefore, the suggestion of applying the ceteris paribus assumption when expanding the system boundary is reasonable.

Frischknecht and Stucki proposed a methodology framework in which depending on the changing agent, its potential effect and the size of studied product systems, different modelling techniques will be applied [22]. Specifically, if the changing agent has small potential consequences, for example individual decision of buying lamps of company X, the ceteris paribus assumption should be applied. Meanwhile, if the changing agent has large potential consequences, for example, policy to encourage the consumption of five-star energy rating lamps in country Y, the mutatis mutandis (“the necessary changes being made”) assumption should be applied [22].

In the former example, the decision of whether to buy a specific product is applicable at a small scale, and the consequence of that decision is limited in physical change, i.e. change of quantity of environmental impacts without changes in economic systems. Meanwhile, in the latter example, the decision of introducing a policy to encourage a product or technology will induce changes in other relevant economic sectors. In order to accurately quantify the impact, it is necessary to expand the system boundary. The authors concluded that CLCA, therefore, would be relevant for quantifying impacts of changes due to governmental policy or strategic international organization decision in which the investigated object has a relatively large economic size [22].

It is suggested that if the relative economic size of studied product system is small to medium, i.e. accounting for less than 0.1% or from 0.1% to 1% of the market share, respectively, the ALCA approach should be applied. In contrast, if the market share of the studied product system is larger than 1%, the CLCA approach should be applied [22]. Although the criteria are not adequately convincing, they are the initial effort of how to deal with system expansion in CLCA, based on quantitative economic value [23-27].

Among reviewed CLCA papers in power sector, some authors made it very clear on the inclusion of extended unit processes; other authors did not clearly identify the unit processes of a product system’s life cycle [28-35].

The system boundary is extended to at least two products in all reviewed studies. Moreover, it is even extended to several relevant economic sectors. The investigated products and economic sectors of some reviewed CLCA papers are specified in Table 2.

All of the reviewed studies expanded the product system boundary, by either increasing unit processes, or including relevant products and co-products [36, 37]. This approach helps to identify products or technologies being affected as consequences of changes. However, the ways how these affected products and co-products being treated were different. Some authors treated the affected product under ceteris paribus assumption. In this case, they simulated the consequences in form of physical change, or the affected products can be substituted by other similar ones. These physical changes were modelled through quantifying energy flows, for example in Jones et al.’s study which used net energy analysis [38]. The affected products were treated by substitution and cut-off, for example, marginal electricity production was replaced by power from waste incineration and material recycling as in Eriksson et al.’s study [23].

Another approach to identify affected products is considering them under the mutatis mutandis assumption. The changes in the affected products were determined by reviewing literatures, being based on stakeholders’ participations, and running economic models. In power sector, Mathiesen et al. identified marginal energy technologies by looking at publications on historical and future energy system and existing CLCAs [28]; Dandres et al. took the business-as-usual (BAU) and future renewable technology mixes from peer-reviewed publications [36]; and Gibon et al. determined the existing power generation mixes and the regional technology performance from International Energy Agency reports and the New Energy Externalities Development for Sustainability project, and identified changes in the future power system structure and fuel consumption due to the increased adoption of clean power technology based on experts’ opinions [31].

Several authors used economic models to determine marginal technologies, for example, EU Electricity Market Model (E2M2) [26], EnergyPLAN [28, 29], MARKAL [39], ETEM [30], Network Impact Assessment Model [40], TIMES (ETM-UCL) [32], Unit commitment model [33], Balmorel [24], Swiss TIMES Energy Model (STEM) [35] and JRC-EU-TIMES [27].

The system boundary of CLCA, as a result, goes beyond the physical boundary to extend to the market boundary. The physical boundary of the product system, as being widely accepted, covers a spatial, geographical dimension, for example, a region or country during four stages of a product life cycle: raw material extraction, manufacturing, using and end of life. Meanwhile, the market boundary covers a market area of several industries and economic sectors. It also considers market effects, for example, change in electricity price and production cost, and sometimes even consider rebound effect and feedback mechanism. With the expansion to market boundary, CLCA clearly covers more products compared to the traditional one. Therefore, CLCA will more accurately analyse environmental impacts, either negative or positive, of a product system in the context of policy intervention, compared to ALCA.

3.2 Socio-economic interactions

According to Weidema, Earles and Halog, Zamagni et al., being different from ALCA, which focuses on the physical inflows and outflows of environmental impacts of a product system, CLCA considers the interactions of economic sectors on the product systems and the relevant environmental impacts [13, 16, 19]. These authors agreed on the economic interactions in the CLCA through the inclusion of market mechanism or economic-based causal relationship.

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 pathway for integrating these models into CLCA is running the models to obtain scenarios with changes in affected sectors and identify affected products/ technologies. These scenarios and data on affected products/ technologies will be used for running CLCA.

Y  = Yes

Hammond and O'Grady presented three pathways to a low carbon power sector of UK by 2050, through coal phase out, and technological innovations in CCS, and combined heat and power. These pathways were developed through Stakeholders workshop; Quantitative research; and Interdisciplinary workshop. The authors mentioned of applying economic models and interdisciplinary assessment in “quantitative research” step; however, it is not clear which models have been used [45].

Among reviewed papers, the numbers of studies using PE model are much larger than those using the two latter models, at 11 studies compare to two studies applying CGE and five studies applying IO and two studies applying DM. More interestingly, it is common that the studies apply several types of economic models, for example PE in combination with CGE, PE in combination with IO. 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.

The good point of integrating economic models, e.g. IO/ PE/GE into LCA, is that they provide details of the economic causal relationship [42]. Economic models work with one or several economic sectors, therefore, they either provide a specific view of one economic sector, or a comprehensive view of the product system in relations with the economy. This will help to clearly identify the hot spot economic sectors that contribute most to the impacts.

The economic model based CLCA accurately tracks the links between environmental impacts and economic indicators. Dandres et al. applied CGE model to predict global economic perturbation potentially caused by two different European energy policies, and CLCA to quantify environmental impacts due to these policies. It was identified that, among economic sectors, the most impacted sector was coal extraction and power generation. Consequently, it contributes to most of the difference in the environmental impacts across the two scenarios. Moreover, the authors pointed out that the most sensitive causal relation lied in economic revolution or the change in the GDP, rather than the change in the demand [36]. Similarly, Igos et al. applied PE and GE models to evaluate the economic impacts of policy decisions on energy commodity demand in Luxembourg by 2030 and identify least cost technologies to meet that energy demand. The authors identified that the contribution of other economic sectors, except for energy sector, are quite similar across studied scenarios. Moreover, most of the environmental impacts originates from imported commodities [30].

Apart from economic causal relationship, CLCA also covers the social interrelations among the product system. In this case, the original changes are not limited in the decrease or increase of consumption and production which are quantitative, but also include changes in social indicators. This is mostly conducted with the application of DM such as system dynamics and agent based modelling.

CLCA based on system dynamics was applied to model sustainability impacts of three alternative vehicles including internal hybrid, plug-in hybrid and battery EV by 2050 and compared them with internal combustion vehicle [44]. The increase in the number of EVs being used caused environmental, economic and social impacts on carbon dioxide emissions, particulate matter formation, photochemical oxidant formation, vehicle ownership cost, contribution to GDP, employment generation, and the human health impacts. With CLCA approach, it was identified that EVs were expected to be the best alternative in long-term for reducing human health impacts and air pollution from transportation. Meanwhile the result based on average value indicated that plug-in hybrid vehicles had the largest potential GHG emission reductions [44].

Florent and Enrico combined agent based modelling and CLCA to model changes in vehicle private use in Luxembourg in relation with environmental impacts of battery EVs, plug-in hybrid EVs by 2020, and compared them with gasoline internal combustion vehicles and diesel internal combustion vehicles [43]. Different mobility policies cause changes on characteristics and number of travels, charging patterns and auxiliary use, consequently decrease global warming, fossil depletion, acidification, ozone depletion and photochemical ozone formation; and increase in metal depletion, ionizing radiations, marine eutrophication and particulate matter formation [43].

Recently, Frischknecht et al. [46] has reviewed papers of the 62nd LCA forum, and indicated that CLCA goes beyond the marginal mixed and avoided burdens. It involves causal modelling, which not only includes economic relationship but also social responsibility [46]. Although the social responsibility referred by Weidema concerned on the consequences of a company’s action, it could be extended to the context of governmental policy intervention. An example is the impact on social wellbeing and rate of employment/unemployment, specifically the decreased labour in coal mining industry and the increased labour in solar PV panel manufacturing, due to the policy on renewable portfolio standard. In this case, the impacts of policy intervention would be larger than those of a company decision.

Among the reviewed papers there were only two case studies covering the social aspects or social relationship of the power system. These studies either simulated the social agents and their impacts on the product system [43], or simulated the socio-economic interactions of the product system over its life cycle [44].

Although there were not many CLCA studies on the power sector considering the social interactions at present, with the call for social inclusion in LCA community and the consequential impacts of increasing the integration of renewable energy sources into the power system it is expected that there will be more need of CLCA methodology to work with socio-economic indicators in analysing and assessing impacts of power system in the context of GHG policy intervention.

3.3 Indirect environmental impacts

CLCA is successful in simulating the indirect environmental impacts. When the indirect impacts are included, the total environmental impacts when being assessed with CLCA are either larger or smaller than those being assessed with ALCA. This was observed in several case studies that reported both direct and indirect environmental impacts results such as Pehnt et al., Dandres et al., Igos et al., Frischknecht and Stucki, Raugei et al., Vandepaer et al., Blanco et al. and McDowall et al. [22, 26, 27, 30, 32, 33, 35, 36] (see Table 4).

Pehnt et al. studied the increase of off-shore wind power in Germany and its GHG emission reduction. The increased off-shore wind power substituted for thermal power from coal, lignite and gas, causing change in the power mix, operation of power system, expansion and reinforcement of the grid. The study indicated that in the low and high carbon certificate price scenarios, respectively, the specific carbon reductions per kWh offshore electricity in the year 2020 amount to 914 and 646 gCO₂e, thanks to the substitution of thermal power [26]. The inclusion of the offshore wind power into the power system also affected the operation of thermal power plants and caused the loss in its operational efficiency, as a consequence, increased the GHG emissions of wind power, up to 70 and 18 gCO₂e per kWh of off-shore wind power. The emission from wind induced grid extension is 22 gCO₂e per kWh. When the emissions from all processes, including construction, operation and disposal of the wind energy park, wind-influenced grid expansion, carbon reductions due to thermal power substitution and GHG emissions from altered power plant operation were added up, the total net carbon reduction is 822 gCO₂e per kWh and at 606 gCO₂e per kWh [26].