Abstract
Purpose
Palm biodiesel life cycle studies have been mainly performed for Asia and focused on greenhouse gas (GHG) intensity. The purpose of this article is to present an environmental life cycle assessment (LCA) of biodiesel produced in Portugal from palm oil (PO) imported from Colombia, addressing the direct effects of land-use change (LUC), different fertilization schemes, and biogas management options at the extraction mill.
Methods
An LC inventory and model of PO biodiesel was implemented based on data collected in five Portuguese biodiesel plants and in a palm plantation and extraction mill in the Orinoquía Region of Colombia. The emissions due to carbon stock changes associated with LUC were calculated based on the Colombian oil palm area expansion from 1990 to 2010 and on historical data of vegetation cleared for planting new palm trees. Five impact categories were assessed based on ReCiPe and CML-IA methods: GHG intensity, freshwater and marine eutrophication, photochemical oxidant formation, terrestrial acidification. A sensitivity analysis of alternative allocation approaches was performed.
Results and discussion
Palm plantation was the LC phase which contributed the most to eutrophication and acidification impacts, whereas transportation and oil extraction contributed the most to photochemical oxidation. An increase in carbon stock due to LUC associated with the expansion of Colombian oil palm was calculated (palm is a perennial crop with higher carbon stock than most previous land-uses). The choice of the fertilization scheme that leads to the lowest environmental impacts is contradictory among various categories. The use of calcium ammonium nitrate (followed by ammonium sulfate) leads to the lowest acidification and eutrophication impacts. The highest GHG intensity was calculated for calcium ammonium nitrate, while the lowest was for ammonium sulfate and poultry manure. Biogas captured and flared at the oil extraction mill instead of being released into the atmosphere had the lowest impacts in all categories (GHG intensity reduced by more than 60 % when biogas is flared instead of released).
Conclusions
Recommendation on the selection of the fertilization scheme depends on the environmental priority. ReCiPe and CML showed contradictory results for eutrophication and photochemical oxidation; however, uncertainty may impair strong recommendations. GHG intensity and photochemical oxidation impacts can be significantly reduced if biogas is flared instead of being released. However, more efficient biogas management should be implemented in order to reduce the impacts further.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
1 Introduction
Palm oil (PO) is a major feedstock accounting for 34 % of the world vegetable oil production (OECD-FAO 2013). The significant increase of PO production, mainly due to its use as food and feedstock in the production of biodiesel, has been a focus of discussion and controversy due to the potentially high environmental impacts associated with land-use change (LUC) (Reijnders and Huijbregts 2008; Reinhard and Zah 2009; Castanheira et al. 2014). The environmental impacts of PO biodiesel also depend on the land-use practices, palm oil mill effluent (POME) treatment, biogas management options, and residue disposal practices (Choo et al. 2011; Hansen et al. 2012; Harsono et al. 2012; Lam et al. 2009; Patthanaissaranukool et al. 2013; Achten et al. 2010; Lam and Lee 2011; Stichnothe and Schuchardt 2010).
The life cycle studies that accounted for carbon emissions from LUC (e.g., Reijnders and Huijbregts 2008; Harsono et al. 2012; Hassan et al. 2011; Siangjaeo et al. 2011; Yee et al. 2009; Wicke et al. 2008; Schmidt 2010; Rodrigues et al. 2014; Souza et al. 2010, 2012) showed that it has an important influence on the greenhouse gas (GHG) intensity of PO biodiesel; however, a wide range of results was reported since the estimation of the impacts of oil palm area expansion has high uncertainty associated (Lechon et al. 2011; Castanheira et al. 2014). The calculation of nitrogen (nitrous oxide N2O, nitrates NO3 −, ammonia NH3, nitrogen oxides NOx), and phosphorus field emissions from palm plantation is also a critical aspect of a life cycle assessment (LCA) of PO biodiesel, since it influences the results of several environmental impacts, such as eutrophication, acidification, and GHG intensity (Choo et al. 2011; Harsono et al. 2012; Souza et al. 2010, Achten et al. 2010; Reijnders and Huijbregts 2011).
There are substantial disagreements in current LCA studies due to the use of different multifunctionality approaches (Castanheira et al. 2015, 2014; Manik and Halog 2012; Malça and Freire 2011, 2010; van der Voet et al. 2010). There are several possible multifunctionality procedures to deal with the production of co-products in the PO biodiesel chain, and a sensitivity analysis of alternative multifunctionality procedures should be conducted to evaluate the influence on the results for the various impact categories. However, in the majority of the LCA studies for PO biodiesel, only a single approach was adopted, while only few studies performed a sensitivity analysis for alternative approaches (Reinhard and Zah 2009; Castanheira et al. 2015, 2014; Papong et al. 2010; Schmidt 2010).
Several life cycle studies of PO have been published in article journals; however, the majority have focused on GHG emissions and energy requirements (e.g., Angarita and Lora 2009; Castanheira et al. 2014; Kaewmai et al. 2012; Choo et al. 2011; Papong et al. 2010; Hassan et al. 2011; Patthanaissaranukool et al. 2013; Hansen et al. 2012; Harsono et al. 2012; Lam et al. 2009; Pleanjai and Gheewala 2009; Thamsiriroj and Murphy 2009; Souza et al. 2010; Siangjaeo et al. 2011; Yee et al. 2009; Wicke et al. 2008; Queiroz et al. 2012; Rodrigues et al. 2014) and only a few LCAs addressed a wider set of environmental impacts (e.g. Achten et al. 2010; Stichnothe and Schuchardt 2011; Schmidt 2010; Silalertruksa and Gheewala 2012; Reinhard and Zah 2009). In addition, most of the mentioned life cycle studies were performed for PO produced in South-East Asia and Brazil and no LCA articles (with a set environmental impacts) were published for Colombia (the first producer of PO in Latin America and the fourth largest producer worldwide). This article builds on Castanheira et al. (2014), a life-cycle GHG assessment of LUC scenarios, fertilization schemes and biogas management options, and Castanheira et al. (2011), a preliminary LCA of PO in Colombia presented in a conference.
The main goal of this article is to present an environmental LCA of biodiesel produced in Portugal based on PO imported from Colombia, addressing four fertilization schemes and two biogas management options at the oil extraction mill. A comprehensive assessment of carbon stock changes due to LUC was performed based on historical data of Colombian palm area expansion. The ReCiPe 1.10 (Goedkoop et al. 2012) and CML-IA 3.01 (Guinée et al. 2002) methods were adopted to calculate various environmental impact categories and to determine the extent to which the results are influenced by the method applied. Different LCIA methods can lead to different results (Schmidt 2007), which jeopardizes the consistency across these methods and the comparison between studies (Cavalett et al. 2013, Dreyer et al. 2003). The influence of different allocation approaches (mass, energy, and price based allocation) is assessed for the various impact categories. This article is organized in four sections, including this introduction. Section 2 briefly describes the life cycle model and inventory. Section 3 presents and discusses the results, whereas section 4 draws the conclusions together.
2 Life cycle model and inventory
2.1 Goal and scope
A life cycle (LC) model and inventory of PO biodiesel was implemented based on data collected in five Portuguese biodiesel plants and in a representative palm plantation and oil extraction mill in Colombia (Castanheira and Freire 2011; Castanheira et al. 2014). The farm is located in the Orinoquía Region and has 14000 ha with an average productivity similar to the national average (Fedepalma 2009); however, other plantations in Colombia may have different practices, which were not addressed in this article. In 2014, Colombia was the fourth largest producer of palm oil worldwide, and it is expected that Colombia will become an exporter of biodiesel in the medium term (Pinzon 2012).
A flowchart of PO biodiesel production is presented in Fig. 1, in which the main system inputs, products, and yields are also shown. The functional unit adopted was 1 MJ of biodiesel energy content (measured in terms of the lower heating value, LHV, 37 MJ kg−1).
The system is multifunctional, with palm kernel oil (PKO), palm kernel meal (PKM), and glycerin being also produced (Fig. 1). Three allocation procedures were adopted based on physical properties (mass and energy content) and price of products. Table 1 presents the physical properties and prices of products, as well as the allocation factors. Energy allocation factors were calculated based on the lower heating value (LHV) of products, and price allocation factors were obtained based on the world average annual prices (US$) of oil and meal (2009–2013 period) (FAO 2013; World Bank 2013). The average annual price of biodiesel (2009–2013 period) was based on the price paid to biodiesel producers, according to the Portuguese regulation. The price of glycerin was based on market information provided by Portuguese biodiesel companies. To account for price variability, two scenarios were implemented based on the ratio of oil and meal prices; however, it was concluded that using another price allocation procedure will not influence the results (Castanheira et al. 2014).
2.2 Land-use change emissions
Information on LUC in Colombia as a result of oil palm expansion is sparse (Henson et al. 2012). The emissions due to carbon stock changes (∆CS) associated with LUC were calculated based on the Colombian oil palm area expansion from 1990 to 2010 and on historical data of vegetation cleared for planting new oil palm. Colombian oil palm area expanded by 84 % from 1990 to 2010 (FAO 2013), mainly from shrubland (50.7 % of the total LUC area), savanna/grassland (41.5 %), cropland (6.8 %), and forest (1 %) (Fedepalma 2009). This is in agreement with other sources, including Romero-Ruiz et al. (2012), Rincón (2009) and Cenipalma (2010). Romero-Ruiz et al. (2012) identified oil palm cultivation as one of the main drivers for the alteration of savannas in the Orinoquia region (1987 to 2007); Rincón (2009) reported that the majority of land converted to palm in this region (1972 to 2009) was either pasture and savanna, herbaceous vegetation, or annual crops, while land with high biomass (such as forests) almost not being used. Cenipalma (2010) showed that most LUC from other uses to oil palm has involved pastures and other crops.
The ∆CS were calculated based on the difference between the average carbon stock associated with previous land uses and the carbon stock of oil palm plantation, following IPCC Tier 1 methodology (IPCC 2006), the European Directive 2009/28/EC, and the guidelines for the calculation of land carbon stocks (European Commission 2009, 2010b). Figure 2 presents the ∆CS due to palm area expansion in Colombia from 1990 to 2010.
Indirect land-use change (ILUC) was left out of the scope of this LCA since the aim was to calculate local LUC based on specific carbon stocks in Colombia, and there is no consensus on how to account for ILUC (Schmidt et al. 2015; European Commission 2010a). Several methods have been developed to address ILUC (Audsley et al. 2009; Cederberg et al. 2011; Schmidt et al. 2015) but significant discrepancies on the results were obtained among different approaches (Vazquez-Rowe et al. 2013). Furthermore, the ILUC methods are based on the assumption that markets are global and for this reason the differentiation between direct and indirect LUC is complex (Munoz et al. 2014; Finkbeiner 2013).
2.3 Palm plantation, oil extraction, and transport
The palm requirements for nutrients were met by the application of fertilizers and residues from the oil mill (pruned fronds and empty fruit bunches). To assess the influence of applying different types of N-fertilizers on the results, four N-fertilization schemes were considered: ammonium sulfate (#AS), calcium ammonium nitrate (#CAN), urea (#U), and poultry manure (#Poultry). Ammonium sulfate and urea are the preferred nitrogen source for palm (von Uexküll and Fairhurst 1991), whereas urea, AS, and CAN are the main N-fertilizers consumed in Colombia (IFA 2013). Poultry manure was considered to compare the impacts of applying an organic fertilizer (with a lower availability of mineral N) and a mineral fertilizer.
We considered that to obtain an average annual yield of 19.5 tons of fresh fruit bunches (FFB) per hectare, the same quantity of nutrients (N, P and K) was applied in the different fertilization schemes, corresponding to different quantities of fertilizers, since the nutrient concentrations vary. Regarding N balance, an application of 140 kg N ha−1 requires 400 kg ha−1 of ammonium nitrate (N concentration = 35 %), 528 kg ha−1 of calcium ammonium nitrate (27 %), 300 kg ha−1 of urea (46 %), and 3000 kg ha−1 of poultry manure (5 %). In the #AS, #CAN, and #U fertilization schemes, the same quantities of single superphosphate (as P2O5) and potassium chloride (as K2O) were applied as mineral fertilizers. In the #Poultry scheme, palm phosphorus (P) needs were fulfilled by manure, while potassium (K) needs were fulfilled by the poultry manure together with the application of potassium chloride (174 kg K2O ha−1).
Table 2 presents the field emissions from fertilizers and residues application for the four fertilization schemes. The IPCC tier 1 methodology (IPCC 2006) was adopted to calculate direct and indirect N2O emissions. Indirect N2O emissions were calculated considering the nitrate (NO3 −), ammonia (NH3), and nitrogen oxides (NOx) emissions. The NO3 − emissions were calculated based on Faist Emmenegger et al. (2009), NH3 emissions were estimated based on the rate of N-volatilization for the different N-fertilizer types (Erisman et al. 2009; Asman 1992), and NOx emissions were calculated based on the emission factors for each group of fertilizer (FAO and IFA 2001). The calculation of phosphate (PO4) and phosphorus (P) emissions was based on the models provided in Prasuhn (2006). Regarding the NO3 − and P emissions, it was considered an annual precipitation of 2500 mm year−1 and a clay content in the soil of 54 % (USDA 1999). It was also considered a nitrogen uptake of 6 kg N per ton of FFB harvested (Corley and Tinker 2003). The CO2 fixed in the urea production process that is released when urea is applied as fertilizer was also calculated (IPCC 2006).
Direct N2O emission factor from fertilizer application from IPCC (2006) is largely based on Bouwman et al. (2002a, b) that found significant differences in emission factors depending on fertilizer type. If direct N2O emission factors specific for the fertilizer type (based on Bouwman et al. 2002a, b) were used in our calculations, the direct N2O emissions would be lower for #CAN (−16 %), #Poultry (−11 %), and #AS (−1 %) but slightly higher for #U (+4 %) relatively to emissions calculated based on IPCC tier 1 (2006).
PO extraction emissions arise from POME treatment and from the production of energy. POME (4.2 kg kg−1 PO) was treated in anaerobic and stabilization lagoons. Biogas produced from POME treatment (22 m3 t−1 POME) is captured and flared; however, before the year 2005, biogas was released into the atmosphere (nowadays also occurring in some other mills). Thus, both situations were assessed. Methane (CH4) emissions from POME treatment were calculated considering: (i) a chemical oxygen demand (COD) of untreated POME of 60 g L−1, (ii) a COD removal efficiency of 97 %, (iii) a flare efficiency of 90 % (i.e., 10 % of CH4 emissions in biogas flared), and (iv) a CH4 emission rate of 0.22 L CH4 g−1 COD removed. The variability of the CH4 emission rate (from 0.15 to 0.42 L CH4 g−1 COD removed, Lam and Lee 2011) in the results was also assessed. Hydrogen sulfide, N2O, and ammonia emissions from biogas released into the atmosphere, as well as carbon monoxide, sulfur dioxide, nitrogen oxides, and particulates emissions from biogas captured and flared were calculated based on Schmidt (2007).
Fibers and shells (14 and 6 % of FFB processed) were used as a fuel in a cogeneration plant to produce electricity and steam. Total energy use at the extraction mill was 1875 MJ t−1 FFB processed: steam (1731 MJ t−1 FFB) was totally produced onsite from the combustion of fibers and shells, whereas the electricity consumed (144 MJ t−1 FFB) was supplied by the cogeneration plant (54 %) and the grid (46 %).
The emissions from fertilizer production, fossil fuel production, and combustion from agricultural operations, as well as the emissions from fibers and shells combustion at the cogeneration plant (some adjustments were implemented according to the dry matter content and low heating value of fibers and shells) were adopted from Bauer (2007); Spielmann et al. (2007) and Nemecek and Kägi (2007). The emissions of electricity from grid were calculated based on the Colombian electricity mix (IEA 2009).
The palm oil mill is surrounded by the palm plantation and for this reason it was considered that there is no emissions associated with the transport of fresh fruit bunches from plantation to the mill. It was assumed that palm oil is transported from the mill to the port of Santa Marta by lorry (1300 km) and by transoceanic freighter to the port of Lisbon, in Portugal (7077 km). Regarding transport of palm oil from port of Lisbon to biodiesel production plant, a distance of 100 km by lorry was adopted. Emissions have been calculated based on factors of Spielmann et al. (2007).
2.4 Biodiesel production
Biodiesel production consists on the transesterification reaction of the triglyceride of the fatty acid in the oil with methanol, catalyzed by a base or acid to produce methyl ester (biodiesel) as main product and glycerin as co-product. The life cycle inventory of biodiesel production was implemented based on a data collected in five Portuguese plants for 2009 and 2010 (Castanheira and Freire 2011). Emission factors for chemicals and process energy were adopted from Jungbluth et al. (2007), Althaus et al. (2007), Sutter (2007), Faist Emmenegger et al. (2007) and Jungbluth (2007). The emissions of electricity from grid were calculated based on the Portuguese electricity mix (Garcia et al. 2014).
3 Results and discussion
3.1 Life cycle impact assessment
This section presents the life cycle impact assessment (LCIA) for PO biodiesel, calculated with two LCIA methods (ReCiPe (version 1.10) and CML-IA (version 3.01)) to determine the extent to which the results are influenced by the method applied and focusing on the contribution of each LC phase for four environmental impact categories: GHG intensity, freshwater and marine eutrophication/eutrophication, photochemical oxidant formation/photochemical oxidation, terrestrial acidification/acidification. The impact categories were selected to address a comprehensive set of environmental issues related to cultivation and biofuels, following state-of-art LCA on these topics. Despite land use and land-use change being pointed out as the main drivers of biodiversity loss and degradation (Frischknecht et al. 2016), these were not assessed since there is no clear consensus on how to quantify land-use impacts on biodiversity. The ReCiPe and CML methods were adopted because they are widely used and accepted as reliable among LCA practitioners.
The results presented in this section were calculated (adopting energy allocation approach) for the different fertilization schemes (ammonium sulfate #AS, calcium ammonium nitrate #CAN, urea #U, and poultry manure #Poultry) and biogas management options (biogas is captured and flared or is released into the atmosphere). The software Simapro 7.1 (www.pre.nl) was used to compute the LCA.
3.1.1 Greenhouse gas intensity
Figure 3 shows the GHG intensity of PO biodiesel. ReCiPe and CML results were not compared since the same characterization model (IPCC 2007) was adopted in both methods. GHG intensity of PO biodiesel ranged from 4 g CO2eq MJ−1 (#AS and biogas flared) to 25 g CO2eq MJ−1 (#CAN and biogas released), showing the significant influence of the fertilization scheme and biogas management option. A huge variation in the GHG intensity of PO biodiesel can be observed for the two biogas management options: for biogas released, the GHG intensity was three to five times higher than for biogas flared. The GHG intensity of PO extraction for biogas flared (2.3 g CO2eq MJ−1) was about eight times lower than for biogas released into the atmosphere (19.0 g CO2eq MJ−1). For both biogas management options, methane emissions from POME treatment are those that contributed most to GHG intensity of oil extraction (more than 80 %). GHG intensity of PO biodiesel can be reduced 65–80 % for biogas flared instead of being released. The GHG intensity ranges obtained for the variability of the CH4 emission rate are presented in the chart as error (range) bars. A significant variation in the total GHG intensity can be observed, particularly for biogas released into the atmosphere.
The results greatly depend on the LUC emissions, which represent 33 to 46 % of the GHG intensity of PO biodiesel. An increase in the carbon stock due to LUC associated with the expansion of Colombian oil palm area was calculated (−24 g CO2eq MJ−1). Palm is a perennial crop with a higher carbon stock (mainly in the vegetation, Cveg) than in most previous land-uses (mainly shrubland and savanna). These findings are consistent with the previous research from the authors (Castanheira et al. 2014) that showed that the lowest GHG intensity of palm oil was obtained for conversion of savannas, shrublands, and croplands.
The GHG intensity of palm plantation varies from 13 to 17 g CO2eq MJ−1. Field N2O emissions from fertilization contributed the most to the GHG intensity of plantation but the variation on the GHG intensity of the different fertilization schemes is mainly caused by the emissions from fertilizer production: the GHG intensity of calcium ammonium nitrate production is about 6 g CO2eq MJ−1 whereas the GHG intensity of ammonium sulfate or poultry manure production is less than 2 g CO2eq MJ−1 (considering the application of 140 kg N ha−1). A reduction of about 4 g CO2eq MJ−1 on the GHG intensity of PO biodiesel can be achieved by replacing the nitrogen fertilizer. The GHG intensity of palm plantation addressing direct N2O emissions from fertilizer application based on Bouwman et al. (2002a, b) instead of IPCC (2006) would be slightly higher for #U (+2 %) but lower for #CAN and #poultry (both around −6 %). No differences were found for #AS.
3.1.2 Freshwater and marine eutrophication (ReCiPe) versus eutrophication (CML)
Figure 4 presents freshwater and marine eutrophication (FE and ME) versus eutrophication impacts of PO biodiesel. The majority (more than 80 %) of FE, ME, and eutrophication impacts were caused by the emissions from palm plantation in all fertilization schemes. No variation on results occurs among biogas management options.
Comparing the fertilization schemes using ReCiPe method, contradictory results were obtained for FE and ME impacts: #CAN presented the highest FE impact (10.3 mg Peq MJ−1) and the lowest ME (0.28 g Neq MJ−1), whereas #Poultry was the scheme with the highest ME (0.30 g Neq MJ−1) and the lowest FE (9.5 mg Peq MJ−1). Regarding FE impact, #CAN (and remaining schemes of mineral fertilization) had the highest impact due the phosphate emissions from production and application of single superphosphate. Even though the phosphate emissions from poultry manure application were higher than those from the application of single superphosphate, mineral P-fertilizer was not applied in #Poultry scheme and therefore there were no emissions of its production. Concerning ME, nitrate (NO3 −) emissions were the most important. Despite ammonia emissions slightly contribute to ME, #Poultry scheme had the highest ME impact due to higher ammonia emissions from poultry application compared to the other fertilization schemes.
Eutrophication impact calculated with CML method varies from 0.19 g PO4 3−eq. MJ−1(#CAN) to 0.25 g PO4 3−eq. MJ−1 (#Poultry). Nitrate emissions contributed the most to this impact; however, the difference on results among the fertilization schemes was mainly related to the ammonia emissions from fertilization, which depends on the type of fertilizer: a rate of N-volatilization (as NH3) of 25 % for poultry manure and of 2 % for calcium ammonium nitrate were adopted from Erisman et al. (2009) and Asman (1992).
3.1.3 Photochemical oxidant formation (ReCiPe) versus photochemical oxidation (CML)
The photochemical oxidant impacts (ReCiPe and CML) of PO biodiesel are presented in Fig. 5. CML results vary from 4.6 g C2H4eq MJ−1 (biogas flared, #Poultry) to 9.6 (biogas released, #CAN), whereas for ReCiPe, there is no significant differences for the biogas management options. Although the main substances contributing to photochemical oxidation are the same in both methods (SO2, NOx and biogenic CH4), the characterization factors are different in ReCiPe and CML (e.g., in CML, biogenic CH4 has a much higher characterization factor relatively to SO2 and NOx), which leads to different results. In ReCiPe, nitrogen oxides emissions contributed the most to photochemical oxidant formation, whereas with CML is sulfur dioxide and biogenic CH4. Transportation emissions are those that contributed most to ReCiPe photochemical oxidant formation impact (51–56 %). For CML, PO extraction emissions contributed the most to this environmental impact when biogas was released (54–58 %), whereas for biogas flared transportation emissions are those that contributed the most (46–51 %). There is no significant variation in the impacts for the various fertilization schemes, but the life cycle phase which contributed the most to this impact depends on the LCIA method.
The sensitivity analysis performed for methane emissions from POME treatment (presented in the chart as error range bars) shows that photochemical oxidation impact of PO biodiesel varies widely depending on the CH4 emission rate. This variation is more evident for the scenario in which biogas was released into the atmosphere and when CML method was adopted.
3.1.4 Terrestrial acidification (ReCiPe) versus acidification (CML)
Acidification impact of PO biodiesel ranged from 0.2 to 0.7 g SO2eq MJ−1 (Fig. 6a) for ReCiPe and from 0.2 to 0.5 g SO2eq MJ−1 (Fig. 6b) for CML. There is a huge variation in the results for the different fertilization schemes: the highest impact was obtained for #Poultry scheme and the lowest for #CAN (with both methods). The main reason for #Poultry scheme presents a terrestrial acidification/acidification impact two to three times higher than #CAN is that emissions from palm plantation (mainly NH3 emissions from fertilization) contributed more than 42–85 % to this impact, and the NH3 emissions were calculated on the basis of a rate of N-volatilization of 2 % for #CAN and 25 % for #Poultry.
Terrestrial acidification results are 10–40 % higher in ReCiPe than in CML due to a higher ReCiPe characterization factor for NH3 (more 50 %). Ammonia (NH3), NOx, and SO2 emissions contributed the most to terrestrial acidification/acidification impact. The emissions from transportation (73–75 mg SO2eq MJ−1) contribute 12 to 44 % to acidification. Terrestrial acidification/acidification impact of oil extraction is about 50 % higher for biogas released than for biogas flared. However, the biogas management option had a low effect in the total acidification impact of palm biodiesel since oil extraction contributes to less than 12 % of the total impact.
3.1.5 Relative comparison of ReCiPe and CML results
Figure 7 presents a relative comparison of ReCiPe and CML impacts (normalized relatively to the scenario with highest impact) for each set of similar categories. Eutrophication and photochemical oxidation calculated with ReCiPe and CML are contradictory: eutrophication calculated with CML varies among the various fertilization schemes, while for ReCiPe, there is no variation; photochemical oxidation with CML is 50 % lower for biogas captured and flared than for biogas released, whereas for ReCiPe, there is no significant difference between the two biogas management options. An analogous ranking of fertilization schemes is observed for marine eutrophication (ReCiPe) and eutrophication (CML) impacts (#CAN is the preferable fertilization scheme), while different ranking was obtained for freshwater eutrophication (the lowest impact was calculated for #Poultry).
The differences in eutrophication and photochemical oxidation can be explained by different impact models in CML and ReCiPe. CML photochemical oxidation model uses a simplified description of the atmospheric transport, whereas ReCiPe employs an atmospheric fate model combined with a dynamic model (Van Zelm et al. 2008). Eutrophication in Recipe is treated as two categories (freshwater and marine eutrophication) using the CARMEN model (Klepper et al. 1995) to calculate the changes in nutrient loads (European conditions) and assuming that aquatic ecosystems are saturated by either nitrogen or phosphorus but only the non-saturated element (the limiting nutrient) will cause eutrophication (N in marine waters, phosphorus in freshwaters) (Goedkoop et al. 2012). The eutrophication model in CML addresses together terrestrial and aquatic systems. Thus, eutrophication calculated with CML and ReCiPe cannot be fully compared. No significant differences were obtained for acidification calculated with ReCiPe and CML, despite the different characterization models (both address acidifying chemicals at the European scale, but ReCiPe only for terrestrial ecosystems).
To sum up, the results indicate that the LCIA method may influence PO biodiesel impacts and that further research is needed to harmonize LCIA methods and provide recommendations, in spite of previous relevant work (EC‐JRC 2011). This is the focus of ongoing work at the UNEP/SETAC Life Cycle Initiative (Jolliet et al. 2014).
3.2 Sensitivity analysis to allocation approaches
The effect of multifunctionality approach on the environmental impacts of PO biodiesel (calculated with ReCiPe 1.10 method) is presented Fig. 8. It can be seen that the allocation approach adopted has a low influence on the results. This is due to the relatively high mass share of palm oil (72 %) compared with the palm kernel meal and oil (20 and 8 %). The environmental impacts calculated with energy and price allocation are, in general, similar and higher than those obtained with mass allocation. The extent of the influence of the allocation approach on the results is different for the various impact categories due to the contribution of each LC phase to the environmental impacts of PO biodiesel: the highest results were calculated with price allocation for all impact categories, except for GHG intensity in which the highest results were calculated with energy allocation.
4 Conclusions
This article presents a life cycle assessment of biodiesel produced in Portugal with palm oil imported from Colombia. A comprehensive evaluation was performed of the implications of LUC, different fertilization schemes, and biogas management options on the environmental impacts. GHG intensity, acidification, eutrophication, and photochemical oxidant formation were calculated based on two LCIA methods (ReCiPe and CML). The GHG intensity of PO biodiesel greatly depends on LUC emissions. We calculated an increase in carbon stock due to LUC associated with the expansion of Colombian oil palm, since palm is a perennial crop with higher carbon stock than most previous land-uses (shrubland, savanna/grassland and cropland). Palm plantation contributed the most to eutrophication and acidification impacts, whereas transportation and oil extraction contributed the most to photochemical oxidation. ILUC was left out of the scope of this LCA. Thus, the GHG intensity of PO biodiesel does not include potential emissions from ILUC, which is a controversial issue with no consensus on how to account for it.
The choice of the fertilization scheme that leads to the lowest environmental impacts of PO biodiesel is contradictory among various categories: on the one hand, the use of calcium ammonium nitrate (followed by ammonium sulfate) is the fertilization scheme that leads to the lowest acidification (both methods) and eutrophication (CML) impacts. On the other hand, the highest GHG intensity was calculated for calcium ammonium nitrate, while the lowest was for the use of ammonium sulfate and poultry manure as fertilizers. Recommendation on the selection of the fertilization scheme depends on the environmental priority. A choice focused on climate change will promote the use of ammonium sulfate or poultry manure, whereas focusing on local and regional impacts of agriculture will support calcium ammonium nitrate or ammonium sulfate. A trade-off choice addressing both priorities could be ammonium sulfate. However, it should be noted that the differences in the results are not very significant, and the uncertainty and variability of results (inherent to the LCA of agricultural products) may impair robust conclusions and recommendations.
Regarding biogas management options, biogas captured and flared at the oil extraction mill instead of being released into the atmosphere had the lowest impacts in all categories, in particular GHG intensity can be reduced by more than 60 % when biogas is flared instead of released. However, more efficient biogas management, namely, recovery for energy generation instead of flaring, should be implemented in order to reduce the impacts further.
The comparison of results calculated with ReCiPe and CML showed contradictory results for photochemical oxidation and eutrophication: photochemical oxidation calculated with CML is 50 % lower for biogas captured and flared than for biogas released, while for ReCiPe, there is no significant difference between the two biogas management options; eutrophication impacts calculated with CML vary among the various fertilization schemes, whereas for ReCiPe, there is no variation. A sensitivity analysis of alternative allocation approaches showed that price and energy allocation leads to similar impacts, slightly higher than those calculated with mass allocation.
References
Achten WMJ, Van den Bempt P, Almeida J, Mathis E, Muys B (2010) Life cycle assessment of a palm oil system with simultaneous production of biodiesel and cooking oil in Cameroon. Environ Sci Technol 44(12):4809–4815
Althaus HJ, Chudacoff M, Hischier R, Jungbluth N, Osses M, Primas A (2007) Life Cycle Inventories of Chemicals. Final report ecoinvent data v2.0. Volume: 8. Swiss Centre for LCI, Empa - TSL. Dübendorf, Switzerland
Angarita EY, Lora ES (2009) The energy balance in the palm oil-derived methyl ester (PME) life cycle for the cases in Brazil and Colombia. Renew Energy 34:2905–2913
Asman WAH (1992) Ammonia emission in Europe: updated emission and emission variations. Rep. 228471008. Bilthoven, the Netherlands: National Inst. of Public Health and Environmental Protection
Audsley E, Brander M, Chatterton J, Murphy-Bokern D, Webster C, Williams A (2009) How Low Can We Go? An Assessment of Greenhouse Gas Emissions from the UK Food System and the Scope to Reduce Them by 2050. WWF-UK
Bauer C (2007). Holzenergie. Sachbilanzen von Energiesystemen. Final report No. 6 ecoinvent data v2.0. Editors: Dones R. Volume: 6. Swiss Centre for LCI, PSI. Dübendorf and Villigen, Switzerland
Bouwman AF, Boumans LJM, Batjes NH (2002a) Emissions of N2O and NO from fertilised fields: summary of available measurement data. Glob Biogeochem Cycles 16:1058. doi:10.1029/2001GB001811
Bouwman AF, Boumans LJM, Batjes NH (2002b) Modeling global annual N2O and NO emissions from fertilised fields. Glob Biogeochem Cycles 16:1080. doi:10.1029/2001GB001812
Castanheira ÉG, Freire F (2011) Environmental performance of palm oil biodiesel in a life-cycle perspective. IEEE International Symposium on Sustainable Systems and Technology (ISSST), Chicago, 16–18 May 2011. doi:10.1109/ISSST.2011.5936843
Castanheira ÉG, Acevedo H, Freire F (2014) Greenhouse gas intensity of palm oil produced in Colombia addressing alternative land use change and fertilization scenarios. Appl Energy 114:958–967
Castanheira ÉG, Grisoli R, Coelho S, Silva GA, Freire F (2015) Life-cycle assessment of soybean-based biodiesel in Europe: comparing grain, oil and biodiesel import from Brazil. J Clean Prod 102:188–201
Cavalett O, Chagas MF, Seabra JEA, Bonomi A (2013) Comparative LCA of ethanol versus gasoline in Brazil using different LCIA methods. Int J Life Cycle Assess 18:647–658
Cederberg C, Martin Persson U, Neovius K, Molander S, Clift R (2011) Including carbon emissions from deforestation in the carbon footprint of Brazilian beef. Environ Sci Technol 45(5):1773–1779
Cenipalma (2010) Analisis multitemporal de coberturas en areas de interes para el cultivo de la palma. (Multi-temporal analysis of coverage in areas of interest for oil palm cultivation.) 2000–2002; 2005–2007. Cenipalma - Centro de Investigación en Palma de Aceite. 2010. Internal report. Bogota.
Choo YM, Muhamad H, Hashim Z, Subramaniam V, Puah CW, Tan YA (2011) Determination of GHG contributions by subsystems in the oil palm supply chain using the LCA approach. Int J Life Cycle Assess 16:669–681
Corley RHV, Tinker PB (2003) The Oil Palm. World Agriculture Series, 4th edn. Oxford, UK, Blackwell Publishing
Dreyer LC, Niemann AL, Hauschild MZ (2003) Comparison of three different LCIA methods: EDIP97, CML2001 and eco-indicator 99. Int J Life Cycle Assess 8:191–200
EC‐JRC (2011) International Reference Life Cycle Data System (ILCD) Handbook ‐ Recommendations for Life Cycle Impact Assessment in the European context. First edition. Luxemburg: European Commission‐Joint Research Centre ‐ Institute for Environment and Sustainability
Erisman JW, Grinsven H, Leip A, Mosier A, Bleeker A (2009) Nitrogen and biofuels; an overview of the current state of knowledge. Nutr Cycl Agroecosyst 86:211–223
European Commission (2009) Directive 2009/28/EC of the European Parliament and of the council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC, Official Journal of the European Union, L140/16 of 5.6.2009
European Commission (2010a) Report from the Commission on indirect land-use change related to biofuels and bioliquids, COM (2010) 811 final. Brussels 22(12):2010
European Commission (2010) Commission Decision of 10 June 2010 on guidelines for the calculation of land carbon stocks for the purpose of Annex V to Directive 2009/28/EC, Official Journal of the European Union, L151/19 of 17.6.2010
Faist Emmenegger M, Heck T, Jungbluth N (2007) Erdgas. Sachbilanzen von Energiesystemen. Final report No. 6 ecoinvent data v2.0. Editors: Dones R.. Volume: 6. Swiss Centre for LCI, PSI. Dübendorf and Villigen, Switzerland
Faist Emmenegger M, Reinhard J, Zah R (2009) Sustainability Quick Check for Biofuels – intermediate background report. With contributions from T. Ziep, R. Weichbrodt, Prof. Dr. V. Wohlgemuth, FHTW Berlin and A. Roches, R. Freiermuth Knuchel, Dr. G. Gaillard, Agroscope Reckenholz-Tänikon. Dübendorf, Switzerland
FAO and IFA (2001) Global estimates of gaseous emissions of NH3, NO and N2O from agricultural land. First version, published by Food and Agriculture Organization of the United Nations (FAO) and International Fertilizer Industry Association (IFA). Rome, 2001
FAO (2013) FAOSTAT. Food and Agriculture Organization of the United Nations, Available at: http://www.fao.org/economic/est/prices
Fedepalma (2009) Anuario estadistico 2009—la agroindustria de la palma de aceite en Colombia. Bogota, Colombia
Finkbeiner M (2013) Indirect land use change (iLUC) within life cycle assessment (LCA) e scientific robustness and consistency with international standards. Berlin, Germany: Association of the German Biofuel Industry, Verband der ӧlsaatenverarbeitenden Industriein Deutschland; 2013
Frischknecht R, Fantke P, Tschümperlin L, Niero M, Antón A, Bare J, Boulay AM, Cherubini F, Hauschild MZ, Henderson A, Levasseur A, McKone TE, Michelsen O, Milà i Canals L, Pfister S, Ridoutt B, Rosenbaum RK, Verones F, Vigon B, Jolliet O (2016) Global guidance on environmental life cycle impact assessment indicators: progress and case study. Int J Life Cycle Assess 21:429–442
Garcia R, Marques P, Freire F (2014) Life-cycle assessment of electricity in Portugal. Appl Energ 134:563–572
Goedkoop M, Heijungs R, Huijbregts M, Schryver A, Struijs J, van Zelm R (2012) 2012) ReCiPe 2008: a life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level; First edition (revised. Characterisation, Report I
Guinée J, Gorrée M, Heijungs R, Huppes G, Kleijn R, de Koning A, Huijbregts M (2002) Handbook on Life Cycle Assessment – Operational Guide to the ISO Standards. I. LCA in Perspective; IIa: Guide; IIb: Operational Annex III: Scientific Background. Dordrecht: Kluwer Academic Publishers
Hansen SB, Olsen SI, Ujang Z (2012) Greenhouse gas reductions through enhanced use of residues in the life cycle of Malaysian palm oil derived biodiesel. Bioresour Technol 104:358–366
Harsono SS, Prochnow A, Grundmann P, Hansen A, Hallmann C (2012) Energy balances and greenhouse gas emissions of palm oil biodiesel in Indonesia. GCB Bioenergy 4:213–228
Hassan MNA, Jaramillo P, Griffin WM (2011) Life cycle GHG emissions from Malaysian oil palm bioenergy development: the impact on transportation sector’s energy security. Energy Policy 39:2615–2625
Henson IE, Ruiz RR, Romero HM (2012) The greenhouse gas balance of the oil palm industry in Colombia : a preliminary analysis. II. Greenhouse gas emissions and the carbon budget. Agron Colombiana 30:370–378
IEA (2009) Electricity/Heat in Colombia in 2008. International Energy Agency-IEA, Paris, France
IFA (2013) Statistics-IFADATA. IFA-International Fertilizer Industry Association, Available at: http://www.fertilizer.org/ifa/ifadata/results
IPCC (2006) IPCC guidelines for national greenhouse gas inventories. Prepared by the National Greenhouse Gas Inventories Programme, Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. (Eds.). Hayama, Japan: Institute for Global Environmental Strategies
IPCC (2007) IPCC Fourth Assessment Report: Climate Change 2007. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007. Cambridge, United Kingdom and New York, USA: Cambridge University Press
Jolliet O, Frischknecht R, Bare J, Boulay A-M, Bulle C, Fantke P, Gheewala S, Hauschild M, Itsubo N, Margni M, McKone TE, Canals LM, Postuma L, Prado-Lopez V, Ridoutt B, Sonnemann G, Rosenbaum RK, Seager T, Struijs J, van Zelm R, Vigon B, Weisbrod A (2014) Global guidance on environmental life cycle impact assessment indicators: findings of the scoping phase. Int J Life Cycle Assess 19:962–967
Jungbluth N (2007) Erdöl. Sachbilanzen von Energiesystemen. Final report No. 6 ecoinvent data v2.0. Editors: Dones R. Volume: 6. Swiss Centre for LCI, PSI. Dübendorf and Villigen, Switzerland
Jungbluth N, Chudacoff M, Dauriat A, Dinkel F, Doka G, Faist Emmenegger M, Gnansounou E, Kljun N, Spielmann M, Stettler C, Sutter J (2007) Life Cycle Inventories of Bioenergy. Final report ecoinvent data v2.0. Volume: 17. Swiss Centre for LCI, ESU. Duebendorf and Uster, Switzerland
Kaewmai R, Kittikun H, Musikavong C (2012) Greenhouse gas emissions of palm oil mills in Thailand. Int J Greenh Gas Con 11:141–151
Klepper O, Beusen AHW, Meinardi CR (1995) Modelling the flow of nitrogen and phosphorus in Europe: from loads to coastal seas. RIVM report 451501004, RIVM, Bilthoven, the Nederlands
Lam MK, Lee KT (2011) Renewable and sustainable bioenergies production from palm oil mill effluent (POME): win-win strategies toward better environmental protection. Biotechnol Adv 29:124–41
Lam MK, Lee KT, Mohamed AR (2009) Life cycle assessment for the production of biodiesel: a case study in Malaysia for palm oil versus jatropha oil. Biofuels Bioprod Bioref 3:601–12
Lechon Y, Cabal H, Sáez R (2011) Life cycle greenhouse gas emissions impacts of the adoption of the EU Directive on biofuels in Spain. Effect of the import of raw materials and land use changes. Biomass Bioenerg 35:2374–2384
Malça J, Freire F (2010) Uncertainty analysis in biofuel systems: an application to the life cycle of rapeseed oil. J Ind Ecol 14:322–334
Malça J, Freire F (2011) Life-cycle studies of biodiesel in Europe: a review addressing the variability of results and modeling issues. Renew Sustain Energy Rev 15:338–351
Manik Y, Halog A (2012) A meta-analytic review of life cycle assessment and flow analyses studies of palm oil biodiesel. Integr Environ Assess Manag 9(1):134–141
Muñoz I, Schmidt JH, Brandão M, Weidema BP (2014) Avoiding the streetlight effect: Rebuttal to ‘Indirect land use change (iLUC) within life cycle assessment (LCA) – scientific robustness and consistency with international standards’ by prof. Dr. Matthias Finkbeiner. 2.‐0 LCA consultants, Aalborg 17th September 2014
Nemecek T, Kägi T (2007) Life Cycle Inventories of Swiss and European Agricultural Production Systems. Final report ecoinvent V2.0 No. 15a., Agroscope Reckenholz-Taenikon Research Station ART, Swiss Centre for Life Cycle Inventories, Zurich and Dübendorf, Switzerland
OECD-FAO (2013) OECD – FAO Agricultural Outlook 2013–2022 Highlights. Available at: http://www.oecd.org/
Papong S, Chom-In T, Noksa-nga S, Malakul P (2010) Life cycle energy efficiency and potentials of biodiesel production from palm oil in Thailand. Energy Policy 38:226–233
Patthanaissaranukool W, Polprasert C, Englande AJ (2013) Potential reduction of carbon emissions from Crude Palm Oil production based on energy and carbon balances. Appl Energy 102:710–717
Pinzon L (2012) Colombia: Biofuels annual. Colombian Biofuels Use Close to reaching E10 and B10 Levels. Gain Report. US Department of Agriculture, Washington, DC
Pleanjai S, Gheewala SH (2009) Full chain energy analysis of biodiesel production from palm oil in Thailand. Appl Energy 86:S209–S214
Prasuhn V (2006) Erfassung der PO4-Austräge für die Ökobilanzierung SALCA Phosphor. Agroscope Reckenholz - Tänikon ART, 20 p
Queiroz AG, França L, Ponte MX (2012) The life cycle assessment of biodiesel from palm oil (“dendê”) in the Amazon. Biomass Bioenerg 36:50–59
Reijnders L, Huijbregts MAJ (2008) Palm oil and the emission of carbon-based greenhouse gases. J Clean Prod 16:477–482
Reijnders L, Huijbregts MAJ (2011) Nitrous oxide emissions from liquid biofuel production in life cycle assessment. Curr Opin Environ Sust 3(5):432–437
Reinhard J, Zah R (2009) Global environmental consequences of increased biodiesel consumption in Switzerland: consequential life cycle assessment. J Clean Prod 17:S46–S56
Rincón V (2009) Dinámica de la expansión del área cultivada com palma de aceite y su impacto en la cobertura del suelo: Zona Oriental palmera de Colombia (1972–2009). MSc. Universitat de Girona, Girona, Spain
Rodrigues TO, Caldeira-Pires A, Luz S, Frate CA (2014) GHG balance of crude palm oil for biodiesel production in the northern region of Brazil. Renew Energ 62:516–521
Romero-Ruiz MH, Flantua SG, Tansey K, Berrio JC (2012) Landscape transformations in savannas of northern South America: land use/cover changes since 1987 in the Llanos Orientales of Colombia. Appl Geogr 32:766–776
Schmidt JH (2007) Life cycle assessment of rapeseed oil and palm oil. Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil. Department of Development and Planning, Aalborg University
Schmidt JH (2010) Comparative life cycle assessment of rapeseed oil and palm oil. Int J Life Cycle Assess 15:183–197
Schmidt JH, Weidema BP, Brandão M (2015) A framework for modelling indirect land use changes in life cycle assessment. J Clean Prod 99:230–238
Siangjaeo S, Gheewala SH, Unnanon K, Chidthaisong A (2011) Implications of land use change on the life cycle greenhouse gas emissions from palm biodiesel production in Thailand. Energy Sustain Dev 15:1–7
Silalertruksa T, Gheewala SH (2012) Environmental sustainability assessment of palm biodiesel production in Thailand. Energy 43:306–314
Souza SP, Pacca S, Ávila MT, Borges JLB (2010) Greenhouse gas emissions and energy balance of palm oil biofuel. Renew Energ 35:2552–2561
Souza SP, Ávila MT, Pacca S (2012) Life cycle assessment of sugarcane ethanol and palm oil biodiesel joint production. Biomass Bioenerg 44:70–79
Spielmann M, Dones R, Bauer C (2007) Life Cycle Inventories of Transport Services. Final report ecoinvent Data v2.0, Vol. 14, Dübendorf and Villigen, Switzerland, Swiss Centre for LCI, PSI
Stichnothe H, Schuchardt F (2010) Comparison of different treatment options for palm oil production waste on a life cycle basis. Int J Life Cycle Assess 15(9):907–915
Stichnothe H, Schuchardt F (2011) Life cycle assessment of two palm oil production systems. Biomass Bioenerg 35(9):3976–3984
Sutter J (2007) Life Cycle Inventories of Highly Pure Chemicals. Final report ecoinvent Data v2.0. Editors: 0. Volume: 19. Swiss Centre for LCI, ETHZ. Duebendorf and St. Gallen, Switzerland
Thamsiriroj T, Murphy JD (2009) Is it better to import palm oil from Thailand to produce biodiesel in Ireland than to produce biodiesel from indigenous Irish rape seed? Appl Energy 86:595–604
USDA (1999) Soil Taxonomy. A Basic System of Soil Classification for Making and Interpreting Soil Surveys. Agriculture Handbook. Number 436, United States Department of Agriculture Natural Resources Conservation Service
van der Voet E, Lifset RJ, Luo L (2010) Life-cycle assessment of biofuels, convergence and divergence. Biofuels 1(3):435–449
Van Zelm R, Huijbregts MAJ, Den Hollander HA, Van Jaarsveld HA, Sauter FJ, Struijs J, Van Wijnen HJ, Van de Meent D (2008) European characterization factors for human health damage of PM10 and ozone in life cycle impact assessment. Atmos Environ 42:441–453
Vázquez-Rowe I, Rege S, Marvuglia A, Thénie J, Haurie A, Benetto E (2013) Application of three independent consequential LCA approaches to the agricultural sector in Luxembourg. Int J Life Cycle Assess 18(8):1593–1604
von Uexküll HR, Fairhurst TH (1991) Fertilizing for high yield and quality. The oil palm. IPI, Bern, 79 p
Wicke B, Dornburg V, Junginger M, Faaij A (2008) Different palm oil production systems for energy purposes and their greenhouse gas implications. Biomass Bioenerg 32:1322–1337
World Bank (2013) World Bank Commodity Price Data. World Bank. Available at: http://www.indexmundi.com/commodities/?commodity=food-price-index
Yee KF, Tan KT, Abdullah AZ, Lee KT (2009) Life cycle assessment of palm biodiesel: revealing facts and benefits for sustainability. Appl Energy 86:S189–S196
Acknowledgments
The research presented in this article was supported by the Portuguese Science and Technology Foundation (FCT) projects: PTDC/SEN-TRA/117251/2010 (Extended “well-to-wheels” assessment of biodiesel for heavy transport vehicles) and PTDC/EMS-ENE/1839/2012 (Sustainable mobility: Perspectives for the future of biofuel production). This work was also framed under the Energy for Sustainability Initiative of the University of Coimbra-Portugal, MIT-Portugal Program and supported by the R&D project EMSURE (Energy and Mobility for Sustainable Regions, CENTRO 07 0224 FEDER 002004). Érica Castanheira gratefully acknowledges financial support from FCT, through grant SFRH/BD/60328/2009.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Responsible editor: Isabel Quispe
Rights and permissions
About this article
Cite this article
Castanheira, É.G., Freire, F. Environmental life cycle assessment of biodiesel produced with palm oil from Colombia. Int J Life Cycle Assess 22, 587–600 (2017). https://doi.org/10.1007/s11367-016-1097-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11367-016-1097-6