LIFE CYCLE ASSESSMENT (LCA) IN AGRO-RESIDUE BIOENERGY

Considering the environmental uncertainties associated with bioenergy production as  discussed in  the Introduction part, it  is imperative to  analyze the pros and cons  of bioenergy generation from a Life Cycle  Assessment (LCA) prospective. The  LCA is a tool to  define the environmental burdens from a  product, process or activity by identifying and quantifying energy and materials usage, as well  as waste discharges, assessing the impacts of these wastes on  the environment and it  also   evaluates the opportunities for environmental improvements over   the  whole life   cycle   (Singh and Olsen,  2012). LCA also  helps streamline the production process by  suggesting the best alternatives to  minimize the overall envi- ronmental impact. There are  four  main steps in LCA study: (a) goal and scope definition, where the goal and scope of the study, system boundary, functional unit are  identified and defined, (b)  life  cycle inventory (LCI), life  cycle  of the product under study is modelled considering all the inputs and outputs; (c) life cycle  inventory anal- ysis  (LCIA), where the environmental relevance of  all  the inputs and outputs of a  product is  assessed, and,  (d)  Interpretation, the results of the study are  reported and possible measures to  reduce burden on  the environment is suggested at this stage. The various phases of LCA study are  shown in Fig. 1 (Rathore et al., 2013). So, LCA is basically a systematic study of a product’s life cycle,  primar- ily  aiming at reducing its  environmental burden (in  other words increasing the product’s environmental acceptability).

In any  bioenergy production system, the farming stage results in  significant GHGs  emissions and other environmental impacts due to  the use  of energy intensive farm machinery for  irrigation, land preparation, sowing, harvest, collection and transportation of  feedstock.  Production  and  application  of  synthetic  fertilizer and pesticide also  lead to  emissions and impacts soil  and water quality. It  is  reported that, in  case  of sugarcane-based bioenergy generation,  the  farming  stage  has   the  highest  environmental impacts due to  land use,   fuel   and agro-chemicals consumption (Contreras et al., 2009). Sugarcane bagasse can  be used for produc- tion of  both bioelectricity and bioethanol. Lower  energy related emissions could be  achieved if bagasse is  used for  co-generation based electricity compared to  bagasse bioethanol or  fossil  energy system (Botha and von Blottnitz, 2006; Ramjeawon, 2008). Further, integrated production of first  generation (1G)  and second genera- tion (2G) bagasse bioethanol has  been proposed as an environmen- tally better option than 1G conventional production process (Gnansounou et al., 2015). In a comparative cradle-to-gate LCA of sugarcane bioethanol production in  India and Brazil,  Tsiropoulos et al. (2014) observed that Indian bioethanol causes lower or equal GHGs   emissions,  non-renewable   energy  use,    human  health impacts and ecosystem impairment compared to  Brazilian bioethanol. The  possible reason for  such lower emissions is  that the  Indian bioethanol program  is  largely based  on   sugarcane molasses,  a  by-product of  sugar production system, resulting in the allocation of environmental burden between product (sugar) and by-product (molasses). The  cradle-to-gate LCA is a partial LCA from resource extraction (cradle) to  factory gate (gate). On  the other hand, the cradle-to-grave is the full LCA from resource extrac- tion (cradle) to  waste disposal (grave).

LCA studies have also  demonstrated the environmental benefits of rice  and corn residue based bioenergy production (Shafie et al., 2014; Sanscartier et al.,  2014; Nguyen et al.,  2013; Soam   et al., 2017). Soam  et al. (2017) reported that electricity production from rice  straw produces a higher GHGs emissions reduction compared to  biogas production  in  Indian condition. In  Malaysia also,  rice straw based power generation was   indicated to  emit less  GHGs in comparison with coal  or natural gas  (Shafie et al., 2014). How- ever,  according to  Silalertruksa and Gheewala (2013), bioethanol production from rice  straw results in higher environmental benefit compared to combustion-based power production or thermo- chemical conversion of  straw to  Biomass-Dimethyl Ether (bio- DME). On the other hand, according to Tonini et al. (2016), biofuel production from agro-residues without involving land use  change is  a  promising emissions reduction option, but the feed-sector’s annual crops or  residues should not  be  used to  produce biofuel, as land use  change related GHGs emissions exceeds any  GHGs sav- ings  from displacing conventional energy sources. Using  corn cob as  fuel  pellets for  electricity generation, a 40% and 80% reduction in  GHGs  emissions compared to coal  and natural gas  combined cycle  (NGCC) has  been reported by  Sanscartier et al.  (2014). LCA study also  finds that co-firing of biomass with coal  results in sub- stantial emissions reduction (Sebastián et al., 2011). However, the quantity of  emissions reduction will  depend upon the degree of biomass pre-treatment  and coal  boiler efficiency and hence the use   biomass with low   pre-treatment  and with minimum effect on  boiler efficiency is suggested to  maximize emissions reduction (Sebastián et al., 2011). Furthermore, the size  and design parame- ters of biomass power plant also  influence the emission pattern. Butnar et al. (2010) reported that, biomass power plant with gen- eration capacity in  the range of 10–25 MW  yield better environ- mental performance, since for  bigger power plant (>25 MW),  the higher  efficiency of  electricity production  is  overtaken  by   the higher biomass transport distance and constraints of  land avail- ability for  biomass cultivation. A. Singh  et al.  (2011),  through a LCA study reported that the type of biogas reactor has  an influence on  emissions savings by  up  to  15%. Although the use  of biomass (such as  wheat straw, poplar and Ethiopian mustard) as  a substi- tute  to   coal    or   natural  gas    reduces  global  warming,  non- renewable energy use,  human toxicity and eco-toxicity, however, it  also   leads to  increased risk   of  eutrophication, photochemical ozone and respiratory inorganic (Nguyen et al.,  2013; Kimming et al., 2011).

The above LCA discussions suggest that, residue bioenergy have certain environmental superiorities over  conventional fuels,  if uti- lized in a sustainable way.  However, since the same biomass feed- stock can be used through multiple energy conversion routes (heat, electricity, bioethanol), therefore  it  is  important to  identify the most  beneficial route  of  conversion.  In  this  regard,  Cherubini et al.  (2009) reported that (i)  production of  heat and electricity from biomass has  higher GHGs  emissions reduction and energy saving benefits than the production of biofuel, (ii)  waste/residue biomass shows best environmental performance, since they avoid both the impacts of energy crop production and the emissions from waste management. For enhanced GHGs emissions reduction, Muench (2015) recommended  deployment  of  (i)  non-dedicated ligno-cellulosic biomass  with  thermo-chemical  conversion,  (ii) dedicated  ligno-cellulosic  biomass  with  thermo-chemical conversion and (iii)  dedicated ligno-cellulosic biomass with direct combustion. Use of biomass waste/residue has  also  been suggested as   a  better  emission reduction  option  than  replacing existing croplands  or   clearing  new  lands  for   energy  crop  plantation (Fargione et al., 2008; Searchinger et al., 2008; Ho et al., 2014).

Uncertainties in bioenergy LCA

As discussed in the previous section, though the environmental benefits of bioenergy over  fossil  energy has  been realized through LCA studies, however, there are  some uncertainties related to  LCA applicability (Hellweg and Canals, 2014). The  variations may be due to the differences in the type and management of raw materi- als,  conversion technologies, end use  technologies and the choice of  LCA methodologies (consequential/attributional/hybrid  LCA) (Cherubini et al.,  2009; Muench, 2015; Muench and Guenther, 2013). Lack  of  general consensus regarding the definition of  the system boundary, fossil  reference system, optimal functional unit, ideal allocation of environmental impact between products and co- products,  modelling  carbon  cycle    of   biomass  (biogenic/non- biogenic  carbon  flow)    also    lead  to   variations  (Muench  and Guenther, 2013; Choudhary et al., 2014). In addition, the collection of actual data for LCA study is a challenging task,  as these datasets may vary  temporally and spatially (Singh et al., 2013). Allocation is a very  sensitive issue in LCA which affects the results significantly. The  inappropriate allocations could lead to  incorrect LCA results. Allocation is a procedure of appropriately allocating the environ- mental burdens of a multi-functional process among its  functions or products (Reap  et al., 2008). Allocation step is one  of the deter- mining steps that tell   how much of  the environmental burden caused by  a  multi-functional  process should be  apportioned to each product or  function (Singh and Olsen,   2012). Plevin et  al. (2014) cautioned that LCA results should refrain from using unsup- ported claims, such as ‘‘using product X results in a Y% reduction in GHG emission compared to product Z” because such claims are  valid only  in rare cases.

The  limitations of LCA must be  taken into account while con- ducting bioenergy LCA. Hellweg and Canals (2014) suggested that it is a duty of the LCA practitioners to explain to the decision mak- ers  that LCA is not  always a tool  to provide a single answer, but it gives  a comprehensive understanding of a problem and its possible solutions.  Muench  (2015)  recommended  numbers  of  ways to increase the reliability of LCA results which includes: (i) LCA can be  further improved by  accounting for  heterogeneity among bio- mass systems, (ii) the strong influence of small differences in bio- mass systems  must  be   considered while interpreting  the  LCA results, (iii) transferability of LCA results between similar systems must always be investigated, (iv) adopting assumptions from other systems should be avoided since it may lead to errors, (v) analysis of  GHGs  emissions mitigation potential  is  only   a  first   step  in assessing the  sustainability of  biomass derived electricity and hence future LCA research should include additional environmen- tal,  economic and social impact categories. Further, it should also be noted that since a real  life situation is modelled in LCA, so, there is  a  possibility of distortion of the reality in  the model outcome (Goedkoop et al., 2013). Hence, developing LCA model with prior knowledge of the system under investigation and careful selection of the system parameters is important to  minimize bias  between reality and model output and gain  a true picture of the environ- mental benefits of bioenergy.

Spatial LCA in bioenergy and environmental planning

Due  to  the distributed nature of  biomass resources, environ- mental  impacts  of   bioenergy  may  have  spatial  consequences too,  especially with regards to  land use  and biodiversity. Exclud- ing  the impact on  biodiversity in  LCA studies can   significantly limit the applicability of  LCA findings (Geyer et al.,  2010; Baan et al.,  2013).  In  many  bioenergy LCA studies, average country level  values are  taken as input data. However, parameters such as grain productivity, residue to  grain production ratio, surplus resi- due availability, competing uses of residue may vary  from region to region. Thus,  considering country level  average data for local  or regional representation may lead to  erroneous result. This  is par- ticularly true for  large scale biomass power plants, where feed- stocks  are   collected  from  large  geographical  areas.  Traditional LCAs are  unable to  recognize the spatial dimension of  environ- mental impacts of  bioenergy but it  can  be  addressed if the LCA is conducted on  a GIS platform (Geyer et al., 2010). Use  of Infor- mation Technology supported LCA enables to  analyze and visual- ize  material flows,   processes or  products and the calculation of eco-balances  on   spatial  scale  (Dresen and  Jandewerth,  2012). Applying spatio-temporal   models  can   improve the  spatial and temporal depths of LCA analysis (Arodudu et al., 2017). Regional- ized  LCA using spatial platform increases the accuracy of assess- ment  by   accounting  site-specific  production  conditions  along with differences in  transport and the sensitivity of  ecosystems (Hellweg and Canals, 2014). However, limited literature  is  avail- able  on spatial LCA. Therefore, the following discussion is not  lim- ited to  residue bioenergy but various forms of bioenergy are  also covered.

Land  use  impact on  biodiversity is difficult to  predict because of  the spatial heterogeneity of  biodiversity and unavailability of precise impact assessment tool  (Geyer et al., 2010). The integrated use  of GIS and LCA (spatial LCA) could give  important insight into how  land  use   change  could  impact  biodiversity.  Geyer et  al. (2010) presented  a  proof-of-concept  (Fig.  2)  by  integrating GIS and LCA together for  impact assessment of  ethanol production on  land use  and biodiversity in  California. The  study found that GIS  based  inventory  modelling  of  land  use   allows important refinement in  LCA and using GIS, land use  can  be  modelled as  a geospatial and nonlinear function of  output. Humpenoder et al. (2013) combined GIS based LandSHIFT model with LCA to investi- gate the land use  impact on  the carbon balance of biofuel in  the European Union (EU).  The  results indicate that land use  change has  a  major impact on  the GHGs  emissions performance of  bio- fuel.  In  a  different study, Geyer et al.  (2013) presented a  spatial LCA approach of  sun-to-wheels  energy conversion pathways in the US considering land use  impacts, life  cycle  GHGs  emissions and fossil  fuel  demand for five different sun-to-wheels conversion pathways. Azapagic et  al.  (2013) developed a  decision support system called PUrE which integrates several environmental assessment  tools in  one   platform for  sustainability assessment of   human   activities  on    the   urban  environment.  The    sub- components of the tool  includes GIS, LCA, substance flow  analysis (SFA), air  dispersion modelling (ADM), health impact assessment (HIA)  and  multi-criteria  decision analysis (MCDA).  Gasol   et  al. (2011) conducted a spatial LCA study to  investigate decentralized bioenergy potential based on  Brassica  spp.  and Populus  spp.  The GIS is  used to  estimate bioenergy production while the LCA is applied to  estimate potential CO2  emission reduction from bioen- ergy  power plants. Mutel et al.  (2012) proposed a  GIS combined LCA method for regionalized LCA on spatial platform using Bright- way  software which directly includes GIS capabilities in  the LCA calculation. Dresen and Jandewerth (2012) combined geo- informatics with LCA to conduct spatial analysis of biogas produc- tion in  Germany. Under the  HEDGE-BIOMASS project, Ferrarini et al. (2014) proposed a combination of GIS, LCA and SWAT model to  investigate landscape level   bioenergy production in  order to identify how and where bioenergy can  be  produced sustainably and how to  optimize the trade-off between delivery of  multiple ecological services  and  farmers  benefit  within  limited  land resources. Integration of  GIS into LCA for  impact  assessment  of algal  biofuel production from wastewater has  also  been recently reported by  Roostaei and Zhang (2016).

Fig.  2.  A procedure to integrate GIS into LCA for  the assessment of environmental implications of biofuel production (Geyer et al., 2010). The  interface between GIS and LCA are indicated by  bold arrows. Here, GIS provides land use land cover and biodiversity habitat data to the LCA framework to identify the bioenergy impact on biodiversity elements.