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Life cycle assessment of sugarcane ethanol production in India in comparison to Brazil

  • LCA FOR ENERGY SYSTEMS AND FOOD PRODUCTS
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Abstract

Purpose

India’s biofuel programme relies on ethanol production from sugarcane molasses. However, there is limited insight on environmental impacts across the Indian ethanol production chain. This study closes this gap by assessing the environmental impacts of ethanol production from sugarcane molasses in Uttar Pradesh, India. A comparative analysis with south-central Brazilian sugarcane ethanol is also presented to compare the performance of sugarcane molasses-based ethanol with sugarcane juice-based ethanol.

Methods

The production process is assessed by a cradle-to-gate life cycle assessment. The multifunctionality problem is solved by applying two variants of system expansion and economic allocation. Environmental impacts are assessed with Impact 2002+ and results are presented at the midpoint level for greenhouse gas emissions, non-renewable energy use, freshwater eutrophication and water use. Furthermore, results include impacts on human health and ecosystem quality at the damage level. Sensitivity analysis is also performed on key contributing parameters such as pesticides, stillage treatment and irrigation water use.

Results and discussion

It is found that, compared to Brazilian ethanol, Indian ethanol causes lower or comparable greenhouse gas emissions (0.09–0.64 kgCO2eq/kgethanolIN, 0.46–0.63 kgCO2eq/kgethanolBR), non-renewable energy use (−0.3–6.3 MJ/kgethanolIN, 1–4 MJ/kgethanolBR), human health impacts (3.6 · 10−6 DALY/kgethanolIN, 4 · 10−6 DALY/kgethanolBR) and ecosystem impairment (2.5 PDF · m2 · year/kgethanolIN, 3.3 PDF · m2 · year/kgethanolBR). One reason is that Indian ethanol is exclusively produced from molasses, a co-product of sugar production, resulting in allocation of the environmental burden. Additionally, Indian sugar mills and distilleries produce surplus electricity for which they receive credits for displacing grid electricity of relatively high CO2 emission intensity. When economic allocation is applied, the greenhouse gas emissions for Indian and Brazilian ethanol are comparable. Non-renewable energy use is higher for Indian ethanol, primarily due to energy requirements for irrigation. For water use and related impacts, Indian ethanol scores worse due groundwater irrigation, despite the dampening effect of allocation. The variation on greenhouse gas emissions and non-renewable energy use of Indian mills is much larger for high and low performance than the respective systems in Brazil.

Conclusions

Important measures can be taken across the production chain to improve the environmental performance of Indian ethanol production (e.g. avoiding the use of specific pesticides, avoiding the disposal of untreated stillage, transition to water efficient crops). However, to meet the targets of the Indian ethanol blending programme, displacement effects are likely to occur in countries which export ethanol. To assess such effects, a consequential study needs to be prepared.

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Notes

  1. The system’s energy efficiency can be estimated either by accounting for the energy conversion efficiency (i.e. primary to secondary energy) and the process energy requirements (e.g. heat demand per unit of output) or by accounting for products (and co-products) per unit of total energy input, which is covered by bagasse. We assess a range based on conversion efficiency. However, efficiency improvements at the agricultural production phase (sugarcane productivity) are also important to assess the system’s environmental and cost performance (van den Wall Bake et al. 2009).

  2. System expansion is associated with consequential modelling. However, there are situations where it is applied to solve multifunctionality of foreground systems modelled by an attributional approach. These situations are encountered in product-related decision support studies that assess the life cycle of existing supply chains (EC 2010), similar to this study.

  3. In comparative life cycle assessment, the two systems should have aligned regional scope. This entails for India and Brazil that each SE approach should consider displacement of national average electricity (based on EC 2010) or marginal electricity production. Instead, in this study, we compare the systems on the basis of the credits assigned. This choice is made because the national average electricity fuel mix of the two countries differs significantly (fossil fuel-based and hydropower in India and Brazil, respectively). Considering the geographical context of the two systems and the EC (2010) guidelines, the SE-O approach for India should be compared to the SE-C approach for Brazil.

  4. Typically, coal boilers have higher efficiencies than biomass boilers. This study assumes similar efficiencies, which is likely for new biomass boilers that displace vintage coal-based boilers.

  5. Alternatively, for surplus bagasse in Brazil, we (a) assume that it is combusted to increase power output with 25 % efficiency (1.1 kWh/kgbagasse,dry basis) and (b) assess pellet production, export to Europe and use in co-firing power plants where it displaces coal (Section 5.1). Background information is included in the Supporting information.

  6. 1.325 % of nitrogen in N-fertilisers and 1.225 % of nitrogen in unburned trash is converted to N in N2O (Macedo et al. 2008).

  7. Distilleries in Uttar Pradesh also produce fuel-grade (anhydrous) ethanol. Due to aggregated reporting of hydrous and anhydrous ethanol by available statistics, the approach of this study might lead to an underestimation of co-products associated with hydrous ethanol. To assess the influence of our assumption (i.e. all production reported for Uttar Pradesh is hydrous ethanol), we correct the avoided energy requirement related to the conversion of hydrous to anhydrous ethanol based on the values in Prakash et al. (2005). The underestimation would be in the range of 1 % for surplus electricity and 3 % for surplus bagasse, which would only slightly affect the results of SE-O, i.e. GHG emissions and NREU would be lower by 0.007 kgCO2eq/kgethanolIN and 0.05 MJ/kgethanolIN, respectively.

  8. GHG emissions from Brazilian dams are controversial. Fearnside and Pueyo (2012) estimate higher emissions than those published by the national Brazilian electricity authority. The latter are also used in the Ecoinvent inventories (Dos Santos et al. 2006) and have been used in this study. Upward correction of these values in our analysis would entail that the CO2 intensity of the national average Brazilian electricity mix was higher. By analogy, higher credits would be assigned to surplus electricity provided by the sugar mills, therefore reducing the relative difference between Indian and Brazilian ethanol.

  9. Due to allocation (Table 6), Indian ethanol is associated with the impacts of 8 kgcane/kgethanoIN (based on \( \frac{19.9\kern2.77695pt \mathrm{kg}\kern0.5em \mathrm{sugarcane}}{\mathrm{kg}\kern0.5em \mathrm{molasses}}\times \frac{5.06\kern2.77695pt \mathrm{kg}\kern0.5em \mathrm{molasses}}{\mathrm{kg}\kern0.5em \mathrm{ethanol}}\times 0.08 \)), while Brazilian ethanol is associated with the impacts of 15 kgcane/kgethanolBR. The difference in GHG emissions associated with the agricultural phase between the two product systems is 45 % in SE-C and 10 % in SE-O between Indian and Brazilian ethanol, respectively.

  10. In 2008, non-harvested land was as high as 28 % primarily due to bad weather conditions. In that year, compared to Indian ethanol, Brazilian ethanol would show higher EQ by 45 %, instead of 30 % as shown in Fig. 6.

Abbreviations

BOD:

Biological oxygen demand

COD:

Chemical oxygen demand

DAP:

Diammonium phosphate

EA:

Economic allocation

EBP:

Ethanol blending programme

EQ:

Ecosystem quality

GHG:

Greenhouse gas

HH:

Human health

ISO:

International Standardization Organization

SE-C:

System expansion conservative

SE-O:

System expansion optimistic

SSP:

Single super phosphate

TSP:

Triple super phosphate

UP:

Uttar Pradesh

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Acknowledgments

We would like to thank the Indian Sugar Mill Association for the fruitful cooperation during this work. We also would like to thank the three anonymous reviewers for their valuable contributions to this article.

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Authors and Affiliations

  1. Energy and Resources, Copernicus Institute of Sustainable Development, Utrecht University, Heidelberglaan 2, 3584 CS, Utrecht, Netherlands

    Ioannis Tsiropoulos & André P. C. Faaij

  2. Department of Energy, Faculty of Mechanical Energy, University of Campinas, Rua Mendeleyev 200, Cidade Universitária “Zeferino Vaz”, Campinas, 13083-860, SP, Brazil

    Joaquim E. A. Seabra

  3. Nestlé Research Center, Lausanne, Nestlé LTC, Vers-chez-les-Blanc, 1000, Lausanne 26, Switzerland

    Lars Lundquist & Urs Schenker

  4. Nestlé Waters Management and Technology, PTC Water, B.P. 101, 88804, Vittel Cedex, France

    Jean-François Briois

  5. Energy Group, Institute for Environmental Sciences and Forel Institute, University of Geneva, 1227, Carouge, Geneva, Switzerland

    Martin K. Patel

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  1. Ioannis Tsiropoulos
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  2. André P. C. Faaij
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Tsiropoulos, I., Faaij, A.P.C., Seabra, J.E.A. et al. Life cycle assessment of sugarcane ethanol production in India in comparison to Brazil. Int J Life Cycle Assess 19, 1049–1067 (2014). https://doi.org/10.1007/s11367-014-0714-5

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