ANÁLISIS PESTA

The results obtained from the research developed from chapters 2 to 6 still leave room for future research challenges, which are discussed below. The challenges left by the methodological issues of this PhD Dissertation (chapters 2 and 3) can be also visualized in a schematic representation in Figure 7.1.

Figure 7.1: Representation of the future challenges left by chapters 2 and 3 of this PhD Dissertation

It is possible to couple the results from chapter 2 into certain RAM. This could first be done with the CEENE method (as it was already performed in section 2.2.3), creating an updated version on that method, so-called CEENE v2.0. In this case, the CEENE v2.0 would differ from the previous version solely regarding the land resources, and would have the following categories: (1) Abiotic renewable resources, (2) Fossil fuels, (3) Nuclear energy, (4) Metal ores, (5) Minerals, (6) Water resources, (7) Land resources, and (8) Atmospheric resources. The characterization factors from chapter 2 would then be used in the category (7).

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This could also be done for energy-based RAM, for instance the CED, creating a new version of this method, or even another method (e.g. ―CED-land‖). Although, this is not as straightforward as in the case of the CEENE method, since the characterization factors from chapter 2 are in exergy values, while the CED method is in energy. Therefore, the first step would be to convert all characterization factors from chapter 2 into energy terms. The simplest way would be to divide them by 1.06, which is considered to be a typical exergy/energy ratio for biomass (as stated in section 2.3.1.1). Further on, the categories ―renewable, biomass‖ and ―non-renewable, biomass‖ (Hischier et al., 2009) would be swapped by a category named ―land resources‖ that would have the results from chapter 2 (but divided by 1.06).

The approach used in chapter 2 is focused on terrestrial land resources, but it could also be applied to ‗marine and freshwater resources‘. Therefore, a future challenge is to create spatial- differentiated characterization factors in exergy terms for marine and freshwater resources, i.e., occupation of marine/freshwater areas (in the case of man-made systems, as aquaculture in natural waters) and extraction of marine/freshwater natural biomass (in the case of natural systems, as fishery). It is important to clarify that the method proposed in chapter 2 is already able to account for aquaculture in artificial waters, built on areas that used to be terrestrial land (with the terrestrial natural potential NPP).

The method proposed in chapter 2 can be coupled with certain RAM, as CEENE and CED, as previously mentioned. These RAM are often considered to be a midpoint LCIA method in the resource depletion impact pathway. Although, further research could be done in order to implement the ΔNPPLC indicator from Haberl et al. (2007) as an endpoint LCIA method,

which is the change in NPP in a certain area due to land use. The proposed endpoint LCIA method would evaluate the impact from land use on biotic resources in the AoP ‗Natural resources‘. In cases where less (or none) biomass is currently produced in a certain land in comparison to its natural potential value (ΔNPPLC < 0), there would be an environmental

impact at that AoP (e.g. built-up land use with no biotic production), but when more biomass is produced in comparison to its natural potential value (ΔNPPLC > 0), there would be an

environmental gain at the AoP Natural Resources (e.g. sugarcane cultivation in Brazil – as shown in chapter 3). One advantage of this endpoint LCIA method is that it could be spatial- differentiated: Since potential NPP values are already available in that way, the additional work would be to produce site-specific characterization factors for the actual biomass

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production, which need to be plant-specific as well (e.g. productivity of maize in northern France).

The indicator proposed in chapter 3 (ΔEP) sums the exergy value of different resources in a simple way, regardless the renewability degree of the resource (e.g. metals and biomass). One future challenge left from chapter 3 is to generate weighting factors for different types of resources (e.g. metals), based on their renewability degree. After that, another indicator could be added to the ΔEP (for instance named as ΔEPw), where the sum of the exergy values would be performed after taking into account this weighting factor. For instance, considering a situation where 10 MJex of biomass is produced, a NPPpot of 4 MJex, and a consumption of 2

MJex of fossil fuels and 1 MJex of metals, as non-local resources; the result for ΔEP would be

+3MJex. Although, if there would be certain weighting factors based on renewability degree,

the results could be different. Just for illustration, considering arbitrary renewability degree factors of 1, 3, and 2, for biomass, fossils, and metals, respectively, this additional indicator ΔEPw would result in -2MJex. This would support a discussion over the use of non-renewable

resources for biomass production.

The ΔEP indicator appeared to be more holistic than traditional indicators. Therefore, a future challenge could be the valorization of this indicator to be used in agricultural and forestry studies, promoted by the scientific community and governmental agencies, as an additional indicator to the net energy value. Eventually it could be used, with or without an additional indicator (ΔEPw), as another option for resource efficiency indicator for policy-making (BIO Intelligence Service, 2012), a hot topic at European policy (European Commission, 2011a). One final perspective regarding the methodological issue of this PhD Dissertation is to elaborate correlation studies between the new CEENE method (‗CEENE v2.0‘) and other endpoint LCIA methods. This type of study has been done by Huijbregts et al. (2010) and Huijbregts et al. (2006), but solely for the fossil fraction of the CED. In this way, this study could be used as scientific basis to link the results of the CEENE v2.0 with specific environmental impacts categories (e.g. toxicity and biodiversity loss).

Regarding the applied issue of this PhD, a similar study from chapters 4 and 5 could be done considering social and economic aspects (social-LCA and life cycle costing, respectively), i.e., making a life cycle sustainability assessment (LCSA) (Kloepffer, 2008). In this way, the three pillars of sustainability could be covered. However, LCSA is not yet well-established, mainly due to the social-LCA, which is still on early stages of development, contrary to life

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cycle costing and (environmental) LCA (Zamagni, 2012). Nevertheless, the social hotspot database (http://socialhotspot.org/) appears to be a good source of data for social-LCA studies, where data for different social indicators (e.g. social equity) can be obtained for different countries and/or different economic sectors (e.g. chemical industry).

Another perspective regarding chapter 5 is to evaluate the consequential LCA of the bioethanol-based PVC considering other iLUC scenarios, for instance: (a) deforestation occurring outside Brazil, induced by the extra demand of Brazilian bioethanol; (b) importation of bioethanol from USA to fulfill the extra demand of bioethanol from Brazil (leading to different direct and indirect environmental impacts); (c) increase in the use of gasoline in Brazil, due to increase in bioethanol prices (causing higher emission of fossil CO and CO2 and higher use of non-renewable energy, but less emissions of hidrocarbonates, total

aldehydes, and NOx (CETESB, 2004)); (d) considering the increase in food prices in addition to LUC impacts, e.g., considering that an extra demand of ethanol in Brazil will cause the importation of ethanol from USA (item b); leading to less available corn for food/feed in USA; leading to the importation of corn from Brazil to the USA; leading to an increase in prices of corn for the internal market in Brazil.

Currently, LCA is mainly used to compare two (or more) products that have the same final use, based on the same functional unit (e.g. transportation of 1 ton of feed by truck or by train). The work done in chapter 6 did not follow this approach, but it compared two different final uses for the same feedstock (bioethanol), through LCA. Bioethanol is not the only biobased intermediate product that might have more than one possible final use, though. In this sense and taking into account the expected increase in biobased products, the creation of a standardized framework for this type of approach should be considered, since it is an unconventional one. Considering a future environmentally sustainable biobased economy, different final uses for biobased (intermediate) products should be analyzed in order to promote those with the best environmental gains.

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