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Calculating land use change in carbon footprints of agricultural products as an impact of current land use
Journal of Cleaner Production 28 (2012) 120e126
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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Calculating land use change in carbon footprints of agricultural products as an impact of current land use T.C. Ponsioen*, T.J. Blonk Blonk Milieu Advies (Blonk Environmental Consultants), Gravin Beatrixlaan 34, 2805 PJ Gouda, The Netherlands
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 April 2011 Received in revised form 26 September 2011 Accepted 11 October 2011 Available online 21 October 2011
Land use change causes large amounts of greenhouse gas emissions, but there is no generally agreed method yet for attributing those emissions to food products. The so called direct land use change method has serious weaknesses (for example, the way of amortization). Impact modelling, often referred to as indirect land use change, where land use change is calculated as a function of land use, is complex and subject to precarious assumptions. We present a simple impact model that is based on statistical trends in land use developments within countries. Estimated global warming potential of annual burning and decay of natural aboveground biomass for agricultural expansion in a country is divided between timber harvest and agriculture, based on revenue estimates from selling timber and agricultural land use returns. Estimated global warming potential of soil organic carbon decay due to land use change is all allocated to agriculture. The total global warming potential of land use change is then allocated to agricultural activities that show a trend of area expansion. Although this method has some points of discussion, it works with publicly available data and it can be improved when more detailed information becomes available. In our opinion, it also gives more sensible results than the currently used direct land use change method(s). Furthermore, it can provide better grounds for motivating producers and consumers to improve their behaviour in relation to global warming. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Lifecycle assessment Carbon footprint Global warming Land use change Food Agriculture
1. Introduction Recently, there is a rapidly increasing interest in attributing global warming potential to products in carbon footprints to give producers and consumers insight in their contribution to global warming and help them identify possible mitigation options. The global warming potential that is related to agricultural activities (e.g. nitrous oxide emission from the soil and manure storage, methane emissions from manure storage and enteric fermentation) is about 13% of the annual global warming potential that is related to all human activities (Olivier et al., 2005). Greenhouse gas emissions from converting natural habitats into permanent agriculture contribute about 7% to the global warming potential of anthropogenic greenhouse gas emissions, and soil organic carbon decay and peat oxidation contribute about 10% (based on IPCC, 2000, 2007; FAO, 2001; Olivier et al., 2005). The question of how emissions from burning and decay of aboveground natural biomass and soil organic carbon decay should be attributed to agricultural
* Corresponding author. Tel.: þ31 182 579970; fax: þ31 182 579971. E-mail addresses: [email protected] (T.C. Ponsioen), blonkmilieuadvies.nl (T.J. Blonk). 0959-6526/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jclepro.2011.10.014
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production in lifecycle assessment studies is therefore of high interest and has occupied many lifecycle assessment practitioners (see, for example: Börjesson and Tufvesson, 2011; Nguyen et al., 2010; and their references). When an activity in the product’s lifecycle that results in greenhouse gas emissions serves more than one product, the resulting global warming potential is generally allocated to the products based on a logical characteristic or function according to the international lifecycle assessment standards ISO 14040/44 (ISO, 2006). Physical characteristics should be preferred before non physical, but in case of agricultural food products, the revenue of products is often applied (Guinée, 2001). However, allocating the global warming potential of deforestation that leads to timber/fuel wood and a large number of agricultural products is problematic, mainly because the deforested land can be used for an indefinite time period. The objective of this paper is to analyse existing solutions to this problem and describe an alternative approach. The enormous increase in land use for bio-fuel production initiated a widespread debate among policy makers and researchers on how to take land use change into account in lifecycle assessments of agricultural products. As a consequence, most research papers on the land use change issue are related to bio-fuels (see for example: Börjesson and Tufvesson, 2011; Kim and Dale, 2009; Searchinger
T.C. Ponsioen, T.J. Blonk / Journal of Cleaner Production 28 (2012) 120e126
et al., 2008; Fargione et al., 2008; Gallagher, 2008). However, it is relevant for all agricultural products, regardless of their application. Especially in case of animal products, land use change can be an important issue (for example: Nguyen et al., 2010; Steinfeld et al., 2006). The British Standards Institute (BSI, 2008) and the European Parliament (EP, 2009) dictate in their protocols to allocate onetwentieth of the global warming potential from burning and decay of aboveground natural biomass and soil organic carbon decay to each year of agricultural production in the first twenty years after the event. This approach is based on the question of who is to blame for the land conversion that took place in the past. Proponents appear to consider converted land as a type of historical investment for agricultural production: the emissions from burning and decay of aboveground natural biomass can be amortized over a production period, like a capital good. Besides that, soil organic carbon is assumed to decay at a linear rate during the same period, as suggested by the IPCC (1996, 2006). Attributing the sum of the annually amortized burning and decay of aboveground natural biomass and annual soil organic carbon decay to each year of agricultural production is referred to as direct land use change. There are three important concerns for applying the direct land use change method. The most pressing concern is that any amortization period is arbitrary (Croezen and Kampman, 2008; Nguyen et al., 2010). The twenty years of the BSI and the EP was chosen based on IPCC (1996), who use this period to enable the assumption of a linear rate of soil organic carbon stock change (rather than a nonlinear rate, which would complicate calculations considerably). It can be discussed if this makes sense for soil organic carbon, but e in our opinion e it is not sensible to apply it to the global warming potential from burning and decay of aboveground biomass, unless it is certain that the land will be abandoned after (exactly) twenty years. The second is a more practical concern. Detailed information is needed, such as the exact location where the agricultural product under study was produced and when the land was converted. When such information is lacking, worst case situations are prescribed (BSI, 2008) or procedures are suggested to make estimates of land use change in a certain area (WRI, 2010). Using the method could easily lead to large differences between products that belong to the category of within the amortization period and products for which can be proven they do not belong to that category. Moreover, the consequence is that the sum of the carbon footprints of all land use activities with the direct land use change method is not in line with the IPCC compliant calculations on country or global scale (for example: Olivier et al., 2005). A third concern is that the direct land use change method does not take any actual displacement effects into account (Börjesson and Tufvesson, 2011). For example, crop growing area may extend on existing fertile grasslands, pushing livestock to recently deforested marginal land. An alternative approach, often referred to as indirect land use change, is future orientated and focuses on the mechanism of land use change as a function of land use in a situation of globally increasing demand of agricultural products (see, for example: Kim and Dale, 2009; Searchinger et al., 2008; Fargione et al., 2008; Gallagher, 2008). The use of land puts pressure on the availability of land and therefore contributes to future land use change (regardless of whether it takes place on recently deforested land or elsewhere). This can be considered as a type of impact modelling, comparable with other types of impact modelling within lifecycle assessment. For example, the global warming potentials of greenhouse gases are derived against a background of current and future atmospheric concentrations of gasses. Another example is the depletion of mineral resources, which is calculated against the backdrop of developments in current and future demand and reserves in several impact models (Guinée et al., 2002; Goedkoop et al., 2009). The land use change impact models start with
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current land use and calculate the potential land use change against the backdrop of the trends in growing demand for crop products, changes in productivity, and clearing of natural areas. The models are ideally able to determine how a certain amount of land use and crop consumption will influence future clearing of natural habitats and related greenhouse gas emissions. Especially the paper by Searchinger et al. (2008) triggered a debate on the indirect effects of increasing land use for crop based fuel production on land use change in the world. The most important issue in this debate is that scenario studies such as the Searchinger et al. study include many assumptions based on slender scientific evidence. For example, the relation between the soybean price and deforestation rate in the Amazon region e a crucial factor in the complex econometric calculation model e is based on only four data points from Morton et al. (2006). Nevertheless, such studies provide a useful indication on what the impact could be when policy makers promote the production of bio-fuels. Some examples of governmental bodies that make use of this type of studies are the Californian Air Resource Board (CARB), the U.S. Environmental Protection Agency (EPA), the U.K. Renewable Transport Fuel Obligation (RTFO) program, and the European Parliament. Some of those governmental bodies are also involved in the current process of defining land use change impact factors from the complex scenario studies to be used in carbon footprint assessments of bio-fuel products. However, we think that as long as scenario studies do not result in more unambiguous conclusions, this way of calculating the global warming potential of land use change is not ready yet for inclusion in carbon footprints of products. Thus, the research question that we put forward is: can we develop a simple method for calculating and allocating the global warming potential from land conversion to agricultural products, which gives a sensible picture and, at the same time, can be a starting point for designing improvement options? In this paper, we describe a method and present results for some cases, continued by a discussion. 2. Methods, data and results 2.1. General outline of a method for calculating the GWP of land use change The method described here aims to calculate product GWP values of land use change that are in line with the global assessments of the IPCC (IPCC, 2000, 2007; Olivier et al., 2005). This method still has some arbitrary elements and causality remains an issue (see Discussion). Therefore, we propose to present the GWP results of land use change separately from the other GWP results, which is also recommended in the draft ISO standard for carbon footprints (ISO, 2010). The method includes greenhouse gas emissions from aboveground biomass as a one-time event where the carbon stock drops from its natural values to the value in the agricultural system (Fig. 1). The greenhouse gas emissions from soil organic carbon stock change is related to crop management and, therefore, included separately. Equation (1) is used for calculating land use change GWP value of aboveground carbon stock change that is attributed to a crop in a country per hectare of land use. The resulting values are based on historic and current data.
GWP LUCAði; cÞ ¼ GWP ACCðcÞ*f agr:ðcÞ*f nat: landðcÞ*exp: rateði; cÞ=areaði; cÞ (1) where GWP-LUCA is the attributed land use change GWP value of aboveground carbon stock change for crop i in country c [tonnes
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Aboveground
CO2eq per ha year]; GWP-BCC is the weighted average GWP value of belowground carbon stock change in country c [tonnes CO2eq per ha year]; T-eq. is the time to reach a new equilibrium level of soil organic carbon.
Belowground biomass
2.2. Global warming potential of aboveground carbon stock change The annual land conversion rate (from natural land to agriculture) is assumed here equal to the annual agricultural expansion rate in a country. Because it is not known how much of each type of natural land (natural grassland, steppe, shrub-land, and different types of forest land) is converted to agriculture, shares of existing forest types (including steppe and shrub-land) are used (Table 1). The weighted average aboveground biomass is then calculated by using IPCC default values for aboveground biomass of each forest type per continent (Table 1). The share of land conversion from natural grassland to agriculture is difficult to determine, because part of the natural grassland may be converted to cultivated grassland and part to cropland. As we are focussing on cropland (and not on cultivated grassland), we assume that a contraction of total grassland in a country is equal to conversion from natural grassland to cropland (providing that total cropland is expanding in the country). The belowground carbon stock change was calculated by taking the default reference soil organic carbon stock from IPCC (2006) based on the predominant climate zone and soil type in a country and assuming all converted land is managed with full tillage and medium inputs. This means that the carbon stock change depends only on the shares of land use change to paddy rice (10% increase in soil organic carbon), perennial crops (no change) and long-term cultivation (decrease, depending on climate zone). Based on IPCC publications (IPCC, 2006; Andreae and Merlet, 2001), we calculated that the global warming potential is about 1.81, 1.76, and 1.73 kg CO2 equivalents per kg biomass from burning tropical forest, extra tropical forest and savannah/grassland, respectively. This is almost equal to the amount of CO2 that would be released from the oxidation of carbon in biomass (assuming 50% of the biomass is carbon, 1.83 kg CO2 per kg biomass is released). We used a rounded value of 1.8 and applied the factor for biomass decay as well, assuming that methane emission from biomass decay does not significantly contribute to the total global warming potential. For example, the average natural biomass in Brazilian forests is about 280 tonnes per ha (based on the values in Table 1). The global warming potential of burning and decay of aboveground natural biomass is therefore about 500 tonnes CO2 equivalents per ha: the result of 280 tonnes per ha multiplied by 1.8 kg CO2eq per kg biomass.
Time
Fig. 1. Aboveground and belowground biomass in a natural habitat and changes over time after a land conversion event.
CO2eq per ha year]; GWP-ACC is the weighted average GWP value of aboveground carbon stock change in country c [tonnes CO2eq per ha]; f-agr. is the allocation fraction of agriculture, where one minus allocation is the allocation fraction of timber [-]; f-nat. land is the fraction of crop area expansion that is at the cost of natural land, where one minus the f-nat. land is at the cost of other crop area [-]; exp. rate is the annual increase in area for crop i in country c [ha per year]; area is the actual area of crop i in country c [ha]. In short, the equation is related to: a) country weighted average GWP values of aboveground carbon stock change per hectare, b) allocation between timber and agriculture based on timber revenue and net present value of deforested land, c) a fraction of area expansion for a crop in a country that is at the cost of forest, and d) a relative area expansion rate of a crop in a country (annual expansion rate divided by actual crop area). In the following paragraphs, the data sources and methods for deriving the parameters in the equation are explained. Equation (2) is used for calculating land use change GWP value of belowground carbon stock change that is attributed to a crop in a country per hectare of land use.
GWP LUCBði; cÞ ¼ GWP BCCðcÞ*f nat: landðcÞ*T eq:*exp: rateði; cÞ=areaði; cÞ
(2)
where GWP-LUCB is the attributed land use change GWP value of belowground carbon stock change for crop i in country c [tonnes
Table 1 Shares of different types of forests, average aboveground biomass values and weighted averages of five important countries for oil palm and soybean and land conversion (sources: FAO Global Forest Resources Assessment 2000 and 2006 IPCC guidelines and own calculations). Forest type
Indonesia
Malaysia
Argentina
Brazil
[Share of forest type] Tropical rainforest Tropical moist Tropical dry Tropical shrub Tropical mountain Subtropical humid Subtropical steppe Subtropical mountain Temperate oceanic Temperate steppe Temperate mountain Average [tonne/ha]
88% 2% e 1% 9% e e e e e e 333
Southeast Asia
South America
[tonne biomass/ha] 94% e e e 6% e e e e e e 341
4% 22% 61% e 5% 3% 1% 1% 2% 1% 2% 211
76% 14% 8% e 1% 2% e e e e e 281
350 290 160 70 205 290 70 205 120 0 130 e
300 220 210 80 145 220 80 145 180 0 130 e
T.C. Ponsioen, T.J. Blonk / Journal of Cleaner Production 28 (2012) 120e126 25
Area (million hectares)
20
Soybeans
15
Maize
Other crops (contracting) 10
Other crops (expanding) Sugar cane
5
0 1985
1990
1995
2000
2005
2010
Fig. 2. Area development of important crops in Brazil (source: FAO, 2011).
2.3. The relative expansion rate per crop and country The global warming potential is only allocated to agricultural land use activities that increase in area within a country. However, the use of annual increases could result in very high fluctuations in the allocation fractions from year to year. Therefore, we propose the use of expected increases from a trend analysis. The allocation fractions are then equal to the area expansion trends in proportion to the sum of those expansion trends. Using FAO (2011) statistics, the trend analysis of area expansion resulted in 0.76 million ha per year for soybean, 0.19 for sugar cane and 0.17 for other crops in Brazil with expanding area between 1990 and 2009. This period was chosen as most representative for the expected trend based on OECD-FAO Agricultural Outlook reports (OECD, 2010). We realize that the choice for this period is subject to debate (see Discussion section). Fig. 2 shows the area development of important crops and pastures of the past twenty years in Brazil as an example. The area of meadows and pastures expanded during that period in Brazil, but as there is a clear trend towards stabilisation (Fig. 3). Therefore, no expansion is expected in the meadow and pasture area in Brazil. This results in a relative area expansion for soybean in Brazil of 0.68 (0.76/[0.76 þ 0.19 þ 0.17]). The total net expected agricultural area expansion in Brazil is 0.90 million ha per year. This means that part of the area expansion for soybean, sugar cane and other crops with expected expanding area is due to the contraction of the area under other crops, such as rice, beans, cotton, wheat and coffee. This part is equal to 0.22 million ha per year divided by 0.90 million ha per year, which yields 0.19 (so, 1 0.19 ¼ 0.81 of the expanded area is at the expense of natural land). 2.4. Allocation to timber and agriculture
Permanent meadows and pastures area (Mio ha)
The calculated annual global warming potential from burning and decay of aboveground biomass in a country is allocated to different economic activities. First, the emissions are allocated to 200
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timber harvest, based on the economic value of timber and of cleared land for agricultural purposes. For timber, prices can be used. Prices of cleared land, on the other hand, do not necessarily represent the economic value, because of a lacking or underdeveloped land market (no clear land ownership and few documented transactions). Therefore, we suggest the use of agricultural returns converted to net present value. The average volume of timber that is extracted from deforestation areas in Brazil is about 20 m3 per hectare and its stumpage value is about US$ 13 per m3. The average income from timber extraction is therefore about US$ 250 per deforested hectare. An analysis by Grieg-Gran (2008) gives a realistic indication of the value of cleared land based on agricultural land use returns from deforested areas, converted to net present value in the year 2007 with a discount rate of 10% and a time horizon of thirty years. The choice of this time horizon is common for this type of analysis, because the results do not change significantly when increasing the time horizon. This amounts to about US$ 460 per hectare (Table 2). The allocation fraction for timber is therefore calculated to be 0.35 (251 US$/ha/[251 US$/ha þ 462 US$/ha]), and so the allocation fraction for agricultural land use activities is 0.65. For Argentina, we assumed the same values as for Brazil. For Malaysia, we assumed the same values as for Indonesia.
2.5. Calculating GWP land use change values The land use change GWP of aboveground biomass per hectare of soybean in Brazil is calculated as follows: 500 tonnes CO2eq/ha year (GWP-ACC), multiplied by 0.65 (the allocation to agricultural land use activities), multiplied by 0.81 for expansion from forest (where the remaining fraction if from contraction of other crops’ area), multiplied by 0.76 million ha per year, and divided by the actual soybean area (23.2 million ha in 2010 according to the trend), which gives 8.5 tonnes CO2 equivalents per ha (Table 3; Equation (1)). The land use change GWP of belowground biomass per hectare of soybean in Brazil is calculated as follows: 3.6 tonnes CO2eq/ha year (GWP-BCC), Multiplied by 20 years (time to reach new soil organic carbon equilibrium) multiplied by 0.81 for expansion from forest (where the remaining fraction is from contraction of other crops’ area), multiplied by 0.76 million ha per year, and divided by the actual soybean area (23.2 million ha in 2010 according to the trend), which gives 2.6 tonnes CO2 equivalents per ha (Table 3; Equation (2)).
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In Table 4, the calculated land use change GWP values for the most important crop products in 2010 in Brazil, Argentina,
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Table 2 Deforested land use returns in Brazil and Indonesia (source: Grieg-Gran, 2008).
185 180 1990
1995
2000
2005
2010
Fig. 3. Area development of permanent meadows and pastures in Brazil (source: FAO, 2011).
Land use
Brazil
Indonesia
Total agriculture returns (US$/ha) One-time timber harvesting (US$/ha) Total returns (US$/ha) Allocation fraction agriculture (e)
462 251 713 0.65
909 1099 2008 0.45
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Table 3 Example parameter values for soybean and oil palm in Brazil, Argentina, Indonesia and Malaysia and stepwise calculations of the GWP land conversion default values for the production year 2010 (see Equations (1) and (2)). Parameter
Units
Soybean Brazil
Soybean Argentina
Oil palm Malaysia
Oil palm Indonesia
GWP-ACC (aboveground) GWP-BCC (belowground) Time to soil carbon equilibrium (T-eq.) Allocation fraction to agriculture (f-agr.) Fraction expansion (f-nat. land) Expected expansion (exp. rate) Expected area in 2010 (area) GWP-LUCA (aboveground e Eq. (1)) GWP-LUCB (belowground e Eq. (2))
tonnes CO2eq/ha year tonnes CO2eq/ha year Years e ha/ha 106 ha/year 106 ha tonnes CO2eq/ha tonnes CO2eq/ha
500 3.6 20 0.65 0.81 0.76 23.2 8.5 2.6
380 6.1 20 0.65 0.86 0.73 17.3 8.9 3.3
600 0.2 20 0.45 0.60 0.13 4.2 4.9 0.0
600 1.7 20 0.45 0.83 0.24 4.9 11.0 0.1
Indonesia and Malaysia are shown. The values of other crops and countries are available on request or can be produced by following the above described method.
2.6. Carbon footprint of meat products For illustration of possible results in a carbon footprint study, we calculated the carbon footprints of several meat products as sold in Dutch supermarkets: Irish beef, Dutch beef from dairy cows, veal, lamb, pork and chicken meat. The calculation method and used data (excluding land use and land use change) are described in detail by Blonk et al. (2011). The method is in line with the ISO 14040/44 standards (ISO, 2006) and largely with PAS2050 (BSI, 2008), where the main difference with the latter concerns land use and land use change. The reference flow is a kg of average fresh meat at the gate of the supermarket (excluding package weight). The consumer phase is excluded, but waste processing of packaging is included within the system boundaries. In Table 5, the carbon footprint results for several meat products as sold by Dutch supermarkets are shown for illustration of possible outcomes of a carbon footprint study including land use change
GWP values. The main findings from these results are that a) types of meat with relatively small carbon footprints, pork and chicken meat, have high land use change scores when using the presented method, and b) land use change scores are roughly half of the carbon footprint in case of pork and chicken meat and much lower in case of the other meat types in this analysis.
3. Discussion We have presented a simple method for calculating and allocating the global warming potential from land conversion to agricultural products, but the question remains: does it give sensible results and can it be a starting point for designing improvement options? In the Introduction we argued that the so called direct land use change method does not give sensible results and so called indirect land use change impact models still result in ambiguous conclusions, which makes them difficult to use for designing improvement options. We believe that our method gives more sensible results than the direct land use change method, which is based on the amortization concept. Our results are less sensitive to arbitrary choices and
Table 4 Land use change GWP values for the most important crop products in 2010 in Brazil, Argentina, Indonesia and Malaysia. Crop name
Brazil (abovea)
Brazil (belowb)
Argentina (abovea)
Argentina (belowb)
Indonesia (abovea)
Indonesia (belowb)
Malaysia (abovea)
Malaysia (belowb)
0.2 7.2 1.9 e 6.8 e e e e 0.7 2.4 2.3 e e e 2.2 2.3 e e 8.9 2.8 e e 0.1
0.1 3.6 0.9 e 3.4 e e e e 0.4 1.2 1.1 e e e 1.1 1.1 e e 4.4 1.4 e e 0.1
2.9 e e 7.1 e 10.2
0.4 e e 0.9 e 1.3
4.4 e e 0.2 1.8 4.5 11.0 e e 1.7 1.2 e e 0.1 e e e
0.6 e e 0.0 0.2 0.6 1.4 e e 0.2 0.2 e e 0.0 e e e
e e e 3.4 e e e 6.6 e e e e e 4.9 3.5 0.0 e e e
e e e 0.1 e 0.7 e 0.1 e e 1.6 e e 0.1 0.1 0.0 e e e
e e 0.3 e
e e 0.0 e
[tonnes CO2eq per hectare] Bananas Barley Beans, dry Cashew nuts, with shell Cassava Cocoa beans Coconuts Coffee, green Fruit, tropical freshness Grapes Groundnuts, with shell Maize Natural rubber Oil palm fruit Oilseeds, Nes Oranges Rice, paddy Seed cotton Sorghum Soybeans Sugar cane Sunflower seed Vegetables freshness Wheat a b
e 3.0 e 1.9 e e 3.6 e 1.0 4.1 3.8 0.8 7.6 9.3 e e e e 12.6 8.5 6.8 12.6 2.4 3.3
e 0.7 e 0.4 e e 0.8 e 0.2 0.9 0.8 0.2 1.7 2.1 e e e e 2.8 1.9 1.5 2.8 0.5 0.7
Above refers to the land use change GWP of aboveground carbon stock change. Below refers to land use change GWP of belowground carbon stock change.
T.C. Ponsioen, T.J. Blonk / Journal of Cleaner Production 28 (2012) 120e126 Table 5 Carbon footprint results for several meat products as sold by Dutch supermarkets. Product
Land use change (aboveground)
Land use change (belowground)
Carbon footprint (excl. land use change)
Beef, Irish Beef, Dutch (dairy cows) Veal Lamb Pork Chicken meat
0.76 0.29
0.14 0.05
38.3 9.2
0.30 0.49 1.42 1.46
0.10 0.09 0.70 0.58
7.8 15.5 4.8 3.6
kg CO2 eq per kg fresh meat
independent of data on the exact location of agricultural production and the history of a certain area of land. However, we also introduced several choices that may be subject to debate. For example, the trend analysis of crop area expansion is based on twenty years of production data. A choice of ten or thirty years gives different results (not shown in this paper). On the other hand, the choice is based on an analysis of crop area development in a country and OECD-FAO outlook reports (OECD, 2010) and needs to be reconsidered for each country and new insights. Another point of contention in our method is the calculation of allocation fractions to timber and agriculture. Data on revenue from timber harvest from natural forests and the value of deforested land are scarce and difficult to judge on reliability. The study by GriegGran (2008) provides data for the most important countries where large-scale land conversion takes place (for example Brazil, Indonesia and Malaysia). For other countries, additional analyses may be required. On the other hand, it is also arbitrary to allocate 100% of the deforestation GWP to agriculture and none to timber as is done in all other carbon footprint studies (which are known to us). The concept of relating land use change to crop area expansion was also introduced by Leip et al. (2010). However, they estimated several scenarios to the cost of what fractions of each type of natural vegetation is converted into crop area based on studies such as Morton et al. (2006). The problem is that such data are scarce and difficult to judge on reliability and completeness. Our approach is to use weighted average aboveground biomass of natural vegetation in a country. This may overestimate the actual aboveground biomass that is cleared for land conversion (and in some cases it may underestimate), but it results in reproducible and consistent results. Contrary to indirect land use change impact models, which relate land use change to increasing demand for certain crop products anywhere in the world, our method identifies production of specific crops in countries where large-scale deforestation for timber harvest and land use change takes place. This may be debatable, because in the global market agricultural activities all over the world are completely interrelated. Our arguments to choose for country specific calculations are partly because those relations are too complex to model and partly because deforestation can be stimulated or discouraged by governmental policies. Road construction and new settlements motivate people to start timber businesses, while agricultural business is attracted to exploit the resulting cleared land in a later stage. Government policies have a large influence on such processes by infrastructure development and market interventions (Margulis, 2004). On the other hand, policies on knowledge development, nature conservation and law enforcement may prevent the exploitation of the most vulnerable natural habitats. In the end, governments need to choose between stimulating economic development/securing energy supply and implementing policies against deforestation. From an agricultural perspective, a combination is possible when efforts are made to use the available land more efficiently and reduce pressure on land.
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A risk, however, with our country specific method is that buyers of certain crops decide to switch from the same crop product from a country where large-scale land conversion takes place to a country where this problem does not occur. Unless the crop yield is higher in the latter country, this does not lead to any mitigation of greenhouse gas emissions. So, reduced pressure on land by improved yields and moving crop production to more productive areas (in other countries) are the main options that result in improvements. We must emphasize that assessing carbon footprints of products does not give complete answers to what is the best strategy for mitigating greenhouse gas emissions. It merely gives insight into the greenhouse gas emissions that are related to existing or hypothetical production chains. To fully evaluate the positive and negative effects of mitigation strategies on greenhouse gas emissions in production chains, consequential lifecycle assessments or scenario models that are specific to the case of interest are more appropriate. 4. Conclusion In this paper, we presented a method for including global warming potentials of land use change into lifecycle assessment of products. We suggest this method as a better basis for including land use change in carbon footprints of products than the currently used direct land use change factor as used in several carbon footprint protocols, which is based on an amortization period. The method gives an indication of the carbon footprint of land use change in relation to crop demand and region of crop production that is in line with annual global warming potential on a global scale. This GWP is allocated to timber and crops with expanding areas within countries. Although the method has some debatable issues, it largely works with publicly available data, it can be improved when more detailed information is available. It also gives more sensible and consistent results than currently used methods for calculating the GWP of land use change in carbon footprints. It could also provide better grounds for motivating producers and consumers to improve their behaviour in relation to greenhouse gas emissions. References Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles 15 (4). Blonk, H., Ponsioen, T., Kool, A., Marinussen, M., 2011. The Agri-footprint Method. Methodological LCA Framework, Assumptions and Applied Data. Version 1.0. Blonk Milieu Advies, Gouda. www.agri-footprint.com. Börjesson, P., Tufvesson, L.M., 2011. Agricultural crop-based biofuels e resource efficiency and environmental performance including direct land use changes. Journal of Cleaner Production 19, 108e120. BSI, 2008. PAS 2050:2008 e Specification for the Assessment of the Life Cycle Greenhouse Gas Emissions of Goods and Services. British Standards, UK. Croezen, H., Kampman, B., 2008. Calculating greenhouse gas emissions of EU biofuels. An assessment of the EU methodology proposal for biofuels CO2 calculations. Delft, October 2008. EP (European Parliament), 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 Law 5.6.2009 L 140/16 EN. FAO, 2001. Global Forest Resources Assessment 2000: Main Report. FAO Forestry Paper 140, Rome. FAO, 2011. FAOSTAT. Food and Agricultural Organization of the United Nations. Available at: faostat.fao.org (accessed August 2011). Fargione, J., Hill, J., Tilman, D., Polasky, S., Hawthorne, P., 2008. Land clearing and the biofuel carbon debt. Science 319 (5867), 1235e1238. Gallagher, E., 2008. The Gallagher Review of the Indirect Effects of Biofuel Production. Renewable Fuels Agency, London, U.K. Goedkoop, M.J., Heijungs, R., Huijbregts, M., De Schryver, A., Struijs, J., Van Zelm, R., 2009. ReCiPe 2008, A Life Cycle Impact Assessment Method which Comprises Harmonised Category Indicators at the Midpoint and the Endpoint Level, first ed.. http://www.lcia-recipe.net Report I: Characterisation; 6 January 2009.
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Grieg-Gran, M., 2008. The Cost of Avoiding Deforestation. Update of the Report Prepared for the Stern Review of the Economics of Climate Change. International Institute for Environment and Development. Guinée, J., 2001. Handbook on life cycle assessment d operational guide to the ISO standards. International Journal of Life Cycle Assessment 6 (5), 255. Guinée, J.B., Gorrée, M., Heijungs, R., Huppes, G., Kleijn, R., De Koning, A., Van Oers, L., Wegener Sleeswijk, A., Suh, S., Udo de Haes, H.A., De Bruijn, H., Van Duin, R., Huijbregts, M.A.J., 2002. Handbook on Life Cycle Assessment. Operational Guide to the ISO Standards. I: LCA in Perspective. IIa: Guide. IIb: Operational Annex. III: Scientific Background. Kluwer Academic Publishers, Dordrecht, ISBN 1-4020-0228-9, 692 pp. IPCC, 1996. Revised 1996 IPCC guidelines for national greenhouse gas inventories. In: Houghton, J.T., Meira Filho, L.G., Lim, B., Treanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J., Callender, B.A. (Eds.), IPCC/OECD/IEA. UK Meteorological Office, Bracknell. IPCC, 2000. Land use. In: Watson, R.T., Noble, I.R., Bolin, B., et al. (Eds.), Land-use Change and Forestry. Cambridge University Press, UK, 375 pp. IPCC, 2006. Prepared by the National Greenhouse Gas Inventories Programme. In: Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. (Eds.), Guidelines for National Greenhouse Gas Inventories. IGES, Japan. http://www.ipccnggip.iges. or.jp/public/2006gl/index.htm. IPCC, 2007. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In: Core Writing Team, Pachauri, R.K., Reisinger, A. (Eds.), Climate Change 2007: Synthesis Report. IPCC, Geneva, Switzerland, 104 pp. ISO (International Standardization Organization), 2006. SS-EN ISO 14044. Environmental Management e Life Cycle Assessment e Requirements and Guidelines. ISO. ISO (International Standardization Organization), 2010. ISO/CD 14067-1. Carbon Footprint of Products d Part 1: Quantification. ISO.
Kim, S., Dale, B.E., 2009. Regional variations in greenhouse gas emissions of biobased products in the United Statesdcorn-based ethanol. International Journal of Life Cycle Assessment 14, 540e546. Leip, A., Weiss, F., Wassenaar, T., Perez, I., Fellmann, T., Loudjani, P., Tubiello, F., Grandgirard, D., Monni, S., Biala, K., 2010. Evaluation of the Livestock Sector’s Contribution to the EU Greenhouse Gas Emissions (GGELS) e Final Report. European Commission, Joint Research Centre. Margulis, S., 2004. Causes of Deforestation in the Brazilian Amazon. World Bank, Washington DC. Morton, D.C., DeFries, R., Shimabukuro, Y.E., Anderson, L.O., Arai, E., Espirito-Santo, F., Freitas, R., Morisette, J., 2006. Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon. PNAS 103 (39), 14637e14641. Nguyen, T.L.T., Hermansen, J.E., Mogensen, L., 2010. Environmental consequences of different beef production systems in the EU. Journal of Cleaner Production 18, 756e766. OECD, 2010. OECD-FAO Agricultural Outlook 2010e2019. OECD, Paris. Olivier, J., Van Aardenne, J., Dentener, F., Pagliari, V., Ganzeveld, L., Peters, J., 2005. Recent trends in global greenhouse gas emissions: regional trends 1970e2000 and spatial distribution of key sources. Journal of Integrative Environmental Sciences 2 (2e3), 81e99. Searchinger, T., Heimlich, R., Houghton, R.A., Dong, F., Elobeid, A., Fabiosa, J., Tokgoz, S., Hayes, D., Yu, T.H., 2008. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319 (5867), 1238e1240. Steinfeld, H., Gerber, P., Wassenaar, T., Castel, V., Rosales, M., de Haan, C., 2006. Livestock’s long shadow e Environmental issues and options. FAO document, 390 pp. WRI, 2010. Product Accounting & Reporting Standard. Draft for stakeholder review. November 2010. World Resources Institute and World Business Council for Sustainable Development.
Contents lists available at SciVerse ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Calculating land use change in carbon footprints of agricultural products as an impact of current land use T.C. Ponsioen*, T.J. Blonk Blonk Milieu Advies (Blonk Environmental Consultants), Gravin Beatrixlaan 34, 2805 PJ Gouda, The Netherlands
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 April 2011 Received in revised form 26 September 2011 Accepted 11 October 2011 Available online 21 October 2011
Land use change causes large amounts of greenhouse gas emissions, but there is no generally agreed method yet for attributing those emissions to food products. The so called direct land use change method has serious weaknesses (for example, the way of amortization). Impact modelling, often referred to as indirect land use change, where land use change is calculated as a function of land use, is complex and subject to precarious assumptions. We present a simple impact model that is based on statistical trends in land use developments within countries. Estimated global warming potential of annual burning and decay of natural aboveground biomass for agricultural expansion in a country is divided between timber harvest and agriculture, based on revenue estimates from selling timber and agricultural land use returns. Estimated global warming potential of soil organic carbon decay due to land use change is all allocated to agriculture. The total global warming potential of land use change is then allocated to agricultural activities that show a trend of area expansion. Although this method has some points of discussion, it works with publicly available data and it can be improved when more detailed information becomes available. In our opinion, it also gives more sensible results than the currently used direct land use change method(s). Furthermore, it can provide better grounds for motivating producers and consumers to improve their behaviour in relation to global warming. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Lifecycle assessment Carbon footprint Global warming Land use change Food Agriculture
1. Introduction Recently, there is a rapidly increasing interest in attributing global warming potential to products in carbon footprints to give producers and consumers insight in their contribution to global warming and help them identify possible mitigation options. The global warming potential that is related to agricultural activities (e.g. nitrous oxide emission from the soil and manure storage, methane emissions from manure storage and enteric fermentation) is about 13% of the annual global warming potential that is related to all human activities (Olivier et al., 2005). Greenhouse gas emissions from converting natural habitats into permanent agriculture contribute about 7% to the global warming potential of anthropogenic greenhouse gas emissions, and soil organic carbon decay and peat oxidation contribute about 10% (based on IPCC, 2000, 2007; FAO, 2001; Olivier et al., 2005). The question of how emissions from burning and decay of aboveground natural biomass and soil organic carbon decay should be attributed to agricultural
* Corresponding author. Tel.: þ31 182 579970; fax: þ31 182 579971. E-mail addresses: [email protected] (T.C. Ponsioen), blonkmilieuadvies.nl (T.J. Blonk). 0959-6526/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jclepro.2011.10.014
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production in lifecycle assessment studies is therefore of high interest and has occupied many lifecycle assessment practitioners (see, for example: Börjesson and Tufvesson, 2011; Nguyen et al., 2010; and their references). When an activity in the product’s lifecycle that results in greenhouse gas emissions serves more than one product, the resulting global warming potential is generally allocated to the products based on a logical characteristic or function according to the international lifecycle assessment standards ISO 14040/44 (ISO, 2006). Physical characteristics should be preferred before non physical, but in case of agricultural food products, the revenue of products is often applied (Guinée, 2001). However, allocating the global warming potential of deforestation that leads to timber/fuel wood and a large number of agricultural products is problematic, mainly because the deforested land can be used for an indefinite time period. The objective of this paper is to analyse existing solutions to this problem and describe an alternative approach. The enormous increase in land use for bio-fuel production initiated a widespread debate among policy makers and researchers on how to take land use change into account in lifecycle assessments of agricultural products. As a consequence, most research papers on the land use change issue are related to bio-fuels (see for example: Börjesson and Tufvesson, 2011; Kim and Dale, 2009; Searchinger
T.C. Ponsioen, T.J. Blonk / Journal of Cleaner Production 28 (2012) 120e126
et al., 2008; Fargione et al., 2008; Gallagher, 2008). However, it is relevant for all agricultural products, regardless of their application. Especially in case of animal products, land use change can be an important issue (for example: Nguyen et al., 2010; Steinfeld et al., 2006). The British Standards Institute (BSI, 2008) and the European Parliament (EP, 2009) dictate in their protocols to allocate onetwentieth of the global warming potential from burning and decay of aboveground natural biomass and soil organic carbon decay to each year of agricultural production in the first twenty years after the event. This approach is based on the question of who is to blame for the land conversion that took place in the past. Proponents appear to consider converted land as a type of historical investment for agricultural production: the emissions from burning and decay of aboveground natural biomass can be amortized over a production period, like a capital good. Besides that, soil organic carbon is assumed to decay at a linear rate during the same period, as suggested by the IPCC (1996, 2006). Attributing the sum of the annually amortized burning and decay of aboveground natural biomass and annual soil organic carbon decay to each year of agricultural production is referred to as direct land use change. There are three important concerns for applying the direct land use change method. The most pressing concern is that any amortization period is arbitrary (Croezen and Kampman, 2008; Nguyen et al., 2010). The twenty years of the BSI and the EP was chosen based on IPCC (1996), who use this period to enable the assumption of a linear rate of soil organic carbon stock change (rather than a nonlinear rate, which would complicate calculations considerably). It can be discussed if this makes sense for soil organic carbon, but e in our opinion e it is not sensible to apply it to the global warming potential from burning and decay of aboveground biomass, unless it is certain that the land will be abandoned after (exactly) twenty years. The second is a more practical concern. Detailed information is needed, such as the exact location where the agricultural product under study was produced and when the land was converted. When such information is lacking, worst case situations are prescribed (BSI, 2008) or procedures are suggested to make estimates of land use change in a certain area (WRI, 2010). Using the method could easily lead to large differences between products that belong to the category of within the amortization period and products for which can be proven they do not belong to that category. Moreover, the consequence is that the sum of the carbon footprints of all land use activities with the direct land use change method is not in line with the IPCC compliant calculations on country or global scale (for example: Olivier et al., 2005). A third concern is that the direct land use change method does not take any actual displacement effects into account (Börjesson and Tufvesson, 2011). For example, crop growing area may extend on existing fertile grasslands, pushing livestock to recently deforested marginal land. An alternative approach, often referred to as indirect land use change, is future orientated and focuses on the mechanism of land use change as a function of land use in a situation of globally increasing demand of agricultural products (see, for example: Kim and Dale, 2009; Searchinger et al., 2008; Fargione et al., 2008; Gallagher, 2008). The use of land puts pressure on the availability of land and therefore contributes to future land use change (regardless of whether it takes place on recently deforested land or elsewhere). This can be considered as a type of impact modelling, comparable with other types of impact modelling within lifecycle assessment. For example, the global warming potentials of greenhouse gases are derived against a background of current and future atmospheric concentrations of gasses. Another example is the depletion of mineral resources, which is calculated against the backdrop of developments in current and future demand and reserves in several impact models (Guinée et al., 2002; Goedkoop et al., 2009). The land use change impact models start with
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current land use and calculate the potential land use change against the backdrop of the trends in growing demand for crop products, changes in productivity, and clearing of natural areas. The models are ideally able to determine how a certain amount of land use and crop consumption will influence future clearing of natural habitats and related greenhouse gas emissions. Especially the paper by Searchinger et al. (2008) triggered a debate on the indirect effects of increasing land use for crop based fuel production on land use change in the world. The most important issue in this debate is that scenario studies such as the Searchinger et al. study include many assumptions based on slender scientific evidence. For example, the relation between the soybean price and deforestation rate in the Amazon region e a crucial factor in the complex econometric calculation model e is based on only four data points from Morton et al. (2006). Nevertheless, such studies provide a useful indication on what the impact could be when policy makers promote the production of bio-fuels. Some examples of governmental bodies that make use of this type of studies are the Californian Air Resource Board (CARB), the U.S. Environmental Protection Agency (EPA), the U.K. Renewable Transport Fuel Obligation (RTFO) program, and the European Parliament. Some of those governmental bodies are also involved in the current process of defining land use change impact factors from the complex scenario studies to be used in carbon footprint assessments of bio-fuel products. However, we think that as long as scenario studies do not result in more unambiguous conclusions, this way of calculating the global warming potential of land use change is not ready yet for inclusion in carbon footprints of products. Thus, the research question that we put forward is: can we develop a simple method for calculating and allocating the global warming potential from land conversion to agricultural products, which gives a sensible picture and, at the same time, can be a starting point for designing improvement options? In this paper, we describe a method and present results for some cases, continued by a discussion. 2. Methods, data and results 2.1. General outline of a method for calculating the GWP of land use change The method described here aims to calculate product GWP values of land use change that are in line with the global assessments of the IPCC (IPCC, 2000, 2007; Olivier et al., 2005). This method still has some arbitrary elements and causality remains an issue (see Discussion). Therefore, we propose to present the GWP results of land use change separately from the other GWP results, which is also recommended in the draft ISO standard for carbon footprints (ISO, 2010). The method includes greenhouse gas emissions from aboveground biomass as a one-time event where the carbon stock drops from its natural values to the value in the agricultural system (Fig. 1). The greenhouse gas emissions from soil organic carbon stock change is related to crop management and, therefore, included separately. Equation (1) is used for calculating land use change GWP value of aboveground carbon stock change that is attributed to a crop in a country per hectare of land use. The resulting values are based on historic and current data.
GWP LUCAði; cÞ ¼ GWP ACCðcÞ*f agr:ðcÞ*f nat: landðcÞ*exp: rateði; cÞ=areaði; cÞ (1) where GWP-LUCA is the attributed land use change GWP value of aboveground carbon stock change for crop i in country c [tonnes
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Aboveground
CO2eq per ha year]; GWP-BCC is the weighted average GWP value of belowground carbon stock change in country c [tonnes CO2eq per ha year]; T-eq. is the time to reach a new equilibrium level of soil organic carbon.
Belowground biomass
2.2. Global warming potential of aboveground carbon stock change The annual land conversion rate (from natural land to agriculture) is assumed here equal to the annual agricultural expansion rate in a country. Because it is not known how much of each type of natural land (natural grassland, steppe, shrub-land, and different types of forest land) is converted to agriculture, shares of existing forest types (including steppe and shrub-land) are used (Table 1). The weighted average aboveground biomass is then calculated by using IPCC default values for aboveground biomass of each forest type per continent (Table 1). The share of land conversion from natural grassland to agriculture is difficult to determine, because part of the natural grassland may be converted to cultivated grassland and part to cropland. As we are focussing on cropland (and not on cultivated grassland), we assume that a contraction of total grassland in a country is equal to conversion from natural grassland to cropland (providing that total cropland is expanding in the country). The belowground carbon stock change was calculated by taking the default reference soil organic carbon stock from IPCC (2006) based on the predominant climate zone and soil type in a country and assuming all converted land is managed with full tillage and medium inputs. This means that the carbon stock change depends only on the shares of land use change to paddy rice (10% increase in soil organic carbon), perennial crops (no change) and long-term cultivation (decrease, depending on climate zone). Based on IPCC publications (IPCC, 2006; Andreae and Merlet, 2001), we calculated that the global warming potential is about 1.81, 1.76, and 1.73 kg CO2 equivalents per kg biomass from burning tropical forest, extra tropical forest and savannah/grassland, respectively. This is almost equal to the amount of CO2 that would be released from the oxidation of carbon in biomass (assuming 50% of the biomass is carbon, 1.83 kg CO2 per kg biomass is released). We used a rounded value of 1.8 and applied the factor for biomass decay as well, assuming that methane emission from biomass decay does not significantly contribute to the total global warming potential. For example, the average natural biomass in Brazilian forests is about 280 tonnes per ha (based on the values in Table 1). The global warming potential of burning and decay of aboveground natural biomass is therefore about 500 tonnes CO2 equivalents per ha: the result of 280 tonnes per ha multiplied by 1.8 kg CO2eq per kg biomass.
Time
Fig. 1. Aboveground and belowground biomass in a natural habitat and changes over time after a land conversion event.
CO2eq per ha year]; GWP-ACC is the weighted average GWP value of aboveground carbon stock change in country c [tonnes CO2eq per ha]; f-agr. is the allocation fraction of agriculture, where one minus allocation is the allocation fraction of timber [-]; f-nat. land is the fraction of crop area expansion that is at the cost of natural land, where one minus the f-nat. land is at the cost of other crop area [-]; exp. rate is the annual increase in area for crop i in country c [ha per year]; area is the actual area of crop i in country c [ha]. In short, the equation is related to: a) country weighted average GWP values of aboveground carbon stock change per hectare, b) allocation between timber and agriculture based on timber revenue and net present value of deforested land, c) a fraction of area expansion for a crop in a country that is at the cost of forest, and d) a relative area expansion rate of a crop in a country (annual expansion rate divided by actual crop area). In the following paragraphs, the data sources and methods for deriving the parameters in the equation are explained. Equation (2) is used for calculating land use change GWP value of belowground carbon stock change that is attributed to a crop in a country per hectare of land use.
GWP LUCBði; cÞ ¼ GWP BCCðcÞ*f nat: landðcÞ*T eq:*exp: rateði; cÞ=areaði; cÞ
(2)
where GWP-LUCB is the attributed land use change GWP value of belowground carbon stock change for crop i in country c [tonnes
Table 1 Shares of different types of forests, average aboveground biomass values and weighted averages of five important countries for oil palm and soybean and land conversion (sources: FAO Global Forest Resources Assessment 2000 and 2006 IPCC guidelines and own calculations). Forest type
Indonesia
Malaysia
Argentina
Brazil
[Share of forest type] Tropical rainforest Tropical moist Tropical dry Tropical shrub Tropical mountain Subtropical humid Subtropical steppe Subtropical mountain Temperate oceanic Temperate steppe Temperate mountain Average [tonne/ha]
88% 2% e 1% 9% e e e e e e 333
Southeast Asia
South America
[tonne biomass/ha] 94% e e e 6% e e e e e e 341
4% 22% 61% e 5% 3% 1% 1% 2% 1% 2% 211
76% 14% 8% e 1% 2% e e e e e 281
350 290 160 70 205 290 70 205 120 0 130 e
300 220 210 80 145 220 80 145 180 0 130 e
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Area (million hectares)
20
Soybeans
15
Maize
Other crops (contracting) 10
Other crops (expanding) Sugar cane
5
0 1985
1990
1995
2000
2005
2010
Fig. 2. Area development of important crops in Brazil (source: FAO, 2011).
2.3. The relative expansion rate per crop and country The global warming potential is only allocated to agricultural land use activities that increase in area within a country. However, the use of annual increases could result in very high fluctuations in the allocation fractions from year to year. Therefore, we propose the use of expected increases from a trend analysis. The allocation fractions are then equal to the area expansion trends in proportion to the sum of those expansion trends. Using FAO (2011) statistics, the trend analysis of area expansion resulted in 0.76 million ha per year for soybean, 0.19 for sugar cane and 0.17 for other crops in Brazil with expanding area between 1990 and 2009. This period was chosen as most representative for the expected trend based on OECD-FAO Agricultural Outlook reports (OECD, 2010). We realize that the choice for this period is subject to debate (see Discussion section). Fig. 2 shows the area development of important crops and pastures of the past twenty years in Brazil as an example. The area of meadows and pastures expanded during that period in Brazil, but as there is a clear trend towards stabilisation (Fig. 3). Therefore, no expansion is expected in the meadow and pasture area in Brazil. This results in a relative area expansion for soybean in Brazil of 0.68 (0.76/[0.76 þ 0.19 þ 0.17]). The total net expected agricultural area expansion in Brazil is 0.90 million ha per year. This means that part of the area expansion for soybean, sugar cane and other crops with expected expanding area is due to the contraction of the area under other crops, such as rice, beans, cotton, wheat and coffee. This part is equal to 0.22 million ha per year divided by 0.90 million ha per year, which yields 0.19 (so, 1 0.19 ¼ 0.81 of the expanded area is at the expense of natural land). 2.4. Allocation to timber and agriculture
Permanent meadows and pastures area (Mio ha)
The calculated annual global warming potential from burning and decay of aboveground biomass in a country is allocated to different economic activities. First, the emissions are allocated to 200
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timber harvest, based on the economic value of timber and of cleared land for agricultural purposes. For timber, prices can be used. Prices of cleared land, on the other hand, do not necessarily represent the economic value, because of a lacking or underdeveloped land market (no clear land ownership and few documented transactions). Therefore, we suggest the use of agricultural returns converted to net present value. The average volume of timber that is extracted from deforestation areas in Brazil is about 20 m3 per hectare and its stumpage value is about US$ 13 per m3. The average income from timber extraction is therefore about US$ 250 per deforested hectare. An analysis by Grieg-Gran (2008) gives a realistic indication of the value of cleared land based on agricultural land use returns from deforested areas, converted to net present value in the year 2007 with a discount rate of 10% and a time horizon of thirty years. The choice of this time horizon is common for this type of analysis, because the results do not change significantly when increasing the time horizon. This amounts to about US$ 460 per hectare (Table 2). The allocation fraction for timber is therefore calculated to be 0.35 (251 US$/ha/[251 US$/ha þ 462 US$/ha]), and so the allocation fraction for agricultural land use activities is 0.65. For Argentina, we assumed the same values as for Brazil. For Malaysia, we assumed the same values as for Indonesia.
2.5. Calculating GWP land use change values The land use change GWP of aboveground biomass per hectare of soybean in Brazil is calculated as follows: 500 tonnes CO2eq/ha year (GWP-ACC), multiplied by 0.65 (the allocation to agricultural land use activities), multiplied by 0.81 for expansion from forest (where the remaining fraction if from contraction of other crops’ area), multiplied by 0.76 million ha per year, and divided by the actual soybean area (23.2 million ha in 2010 according to the trend), which gives 8.5 tonnes CO2 equivalents per ha (Table 3; Equation (1)). The land use change GWP of belowground biomass per hectare of soybean in Brazil is calculated as follows: 3.6 tonnes CO2eq/ha year (GWP-BCC), Multiplied by 20 years (time to reach new soil organic carbon equilibrium) multiplied by 0.81 for expansion from forest (where the remaining fraction is from contraction of other crops’ area), multiplied by 0.76 million ha per year, and divided by the actual soybean area (23.2 million ha in 2010 according to the trend), which gives 2.6 tonnes CO2 equivalents per ha (Table 3; Equation (2)).
195
In Table 4, the calculated land use change GWP values for the most important crop products in 2010 in Brazil, Argentina,
190
Table 2 Deforested land use returns in Brazil and Indonesia (source: Grieg-Gran, 2008).
185 180 1990
1995
2000
2005
2010
Fig. 3. Area development of permanent meadows and pastures in Brazil (source: FAO, 2011).
Land use
Brazil
Indonesia
Total agriculture returns (US$/ha) One-time timber harvesting (US$/ha) Total returns (US$/ha) Allocation fraction agriculture (e)
462 251 713 0.65
909 1099 2008 0.45
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Table 3 Example parameter values for soybean and oil palm in Brazil, Argentina, Indonesia and Malaysia and stepwise calculations of the GWP land conversion default values for the production year 2010 (see Equations (1) and (2)). Parameter
Units
Soybean Brazil
Soybean Argentina
Oil palm Malaysia
Oil palm Indonesia
GWP-ACC (aboveground) GWP-BCC (belowground) Time to soil carbon equilibrium (T-eq.) Allocation fraction to agriculture (f-agr.) Fraction expansion (f-nat. land) Expected expansion (exp. rate) Expected area in 2010 (area) GWP-LUCA (aboveground e Eq. (1)) GWP-LUCB (belowground e Eq. (2))
tonnes CO2eq/ha year tonnes CO2eq/ha year Years e ha/ha 106 ha/year 106 ha tonnes CO2eq/ha tonnes CO2eq/ha
500 3.6 20 0.65 0.81 0.76 23.2 8.5 2.6
380 6.1 20 0.65 0.86 0.73 17.3 8.9 3.3
600 0.2 20 0.45 0.60 0.13 4.2 4.9 0.0
600 1.7 20 0.45 0.83 0.24 4.9 11.0 0.1
Indonesia and Malaysia are shown. The values of other crops and countries are available on request or can be produced by following the above described method.
2.6. Carbon footprint of meat products For illustration of possible results in a carbon footprint study, we calculated the carbon footprints of several meat products as sold in Dutch supermarkets: Irish beef, Dutch beef from dairy cows, veal, lamb, pork and chicken meat. The calculation method and used data (excluding land use and land use change) are described in detail by Blonk et al. (2011). The method is in line with the ISO 14040/44 standards (ISO, 2006) and largely with PAS2050 (BSI, 2008), where the main difference with the latter concerns land use and land use change. The reference flow is a kg of average fresh meat at the gate of the supermarket (excluding package weight). The consumer phase is excluded, but waste processing of packaging is included within the system boundaries. In Table 5, the carbon footprint results for several meat products as sold by Dutch supermarkets are shown for illustration of possible outcomes of a carbon footprint study including land use change
GWP values. The main findings from these results are that a) types of meat with relatively small carbon footprints, pork and chicken meat, have high land use change scores when using the presented method, and b) land use change scores are roughly half of the carbon footprint in case of pork and chicken meat and much lower in case of the other meat types in this analysis.
3. Discussion We have presented a simple method for calculating and allocating the global warming potential from land conversion to agricultural products, but the question remains: does it give sensible results and can it be a starting point for designing improvement options? In the Introduction we argued that the so called direct land use change method does not give sensible results and so called indirect land use change impact models still result in ambiguous conclusions, which makes them difficult to use for designing improvement options. We believe that our method gives more sensible results than the direct land use change method, which is based on the amortization concept. Our results are less sensitive to arbitrary choices and
Table 4 Land use change GWP values for the most important crop products in 2010 in Brazil, Argentina, Indonesia and Malaysia. Crop name
Brazil (abovea)
Brazil (belowb)
Argentina (abovea)
Argentina (belowb)
Indonesia (abovea)
Indonesia (belowb)
Malaysia (abovea)
Malaysia (belowb)
0.2 7.2 1.9 e 6.8 e e e e 0.7 2.4 2.3 e e e 2.2 2.3 e e 8.9 2.8 e e 0.1
0.1 3.6 0.9 e 3.4 e e e e 0.4 1.2 1.1 e e e 1.1 1.1 e e 4.4 1.4 e e 0.1
2.9 e e 7.1 e 10.2
0.4 e e 0.9 e 1.3
4.4 e e 0.2 1.8 4.5 11.0 e e 1.7 1.2 e e 0.1 e e e
0.6 e e 0.0 0.2 0.6 1.4 e e 0.2 0.2 e e 0.0 e e e
e e e 3.4 e e e 6.6 e e e e e 4.9 3.5 0.0 e e e
e e e 0.1 e 0.7 e 0.1 e e 1.6 e e 0.1 0.1 0.0 e e e
e e 0.3 e
e e 0.0 e
[tonnes CO2eq per hectare] Bananas Barley Beans, dry Cashew nuts, with shell Cassava Cocoa beans Coconuts Coffee, green Fruit, tropical freshness Grapes Groundnuts, with shell Maize Natural rubber Oil palm fruit Oilseeds, Nes Oranges Rice, paddy Seed cotton Sorghum Soybeans Sugar cane Sunflower seed Vegetables freshness Wheat a b
e 3.0 e 1.9 e e 3.6 e 1.0 4.1 3.8 0.8 7.6 9.3 e e e e 12.6 8.5 6.8 12.6 2.4 3.3
e 0.7 e 0.4 e e 0.8 e 0.2 0.9 0.8 0.2 1.7 2.1 e e e e 2.8 1.9 1.5 2.8 0.5 0.7
Above refers to the land use change GWP of aboveground carbon stock change. Below refers to land use change GWP of belowground carbon stock change.
T.C. Ponsioen, T.J. Blonk / Journal of Cleaner Production 28 (2012) 120e126 Table 5 Carbon footprint results for several meat products as sold by Dutch supermarkets. Product
Land use change (aboveground)
Land use change (belowground)
Carbon footprint (excl. land use change)
Beef, Irish Beef, Dutch (dairy cows) Veal Lamb Pork Chicken meat
0.76 0.29
0.14 0.05
38.3 9.2
0.30 0.49 1.42 1.46
0.10 0.09 0.70 0.58
7.8 15.5 4.8 3.6
kg CO2 eq per kg fresh meat
independent of data on the exact location of agricultural production and the history of a certain area of land. However, we also introduced several choices that may be subject to debate. For example, the trend analysis of crop area expansion is based on twenty years of production data. A choice of ten or thirty years gives different results (not shown in this paper). On the other hand, the choice is based on an analysis of crop area development in a country and OECD-FAO outlook reports (OECD, 2010) and needs to be reconsidered for each country and new insights. Another point of contention in our method is the calculation of allocation fractions to timber and agriculture. Data on revenue from timber harvest from natural forests and the value of deforested land are scarce and difficult to judge on reliability. The study by GriegGran (2008) provides data for the most important countries where large-scale land conversion takes place (for example Brazil, Indonesia and Malaysia). For other countries, additional analyses may be required. On the other hand, it is also arbitrary to allocate 100% of the deforestation GWP to agriculture and none to timber as is done in all other carbon footprint studies (which are known to us). The concept of relating land use change to crop area expansion was also introduced by Leip et al. (2010). However, they estimated several scenarios to the cost of what fractions of each type of natural vegetation is converted into crop area based on studies such as Morton et al. (2006). The problem is that such data are scarce and difficult to judge on reliability and completeness. Our approach is to use weighted average aboveground biomass of natural vegetation in a country. This may overestimate the actual aboveground biomass that is cleared for land conversion (and in some cases it may underestimate), but it results in reproducible and consistent results. Contrary to indirect land use change impact models, which relate land use change to increasing demand for certain crop products anywhere in the world, our method identifies production of specific crops in countries where large-scale deforestation for timber harvest and land use change takes place. This may be debatable, because in the global market agricultural activities all over the world are completely interrelated. Our arguments to choose for country specific calculations are partly because those relations are too complex to model and partly because deforestation can be stimulated or discouraged by governmental policies. Road construction and new settlements motivate people to start timber businesses, while agricultural business is attracted to exploit the resulting cleared land in a later stage. Government policies have a large influence on such processes by infrastructure development and market interventions (Margulis, 2004). On the other hand, policies on knowledge development, nature conservation and law enforcement may prevent the exploitation of the most vulnerable natural habitats. In the end, governments need to choose between stimulating economic development/securing energy supply and implementing policies against deforestation. From an agricultural perspective, a combination is possible when efforts are made to use the available land more efficiently and reduce pressure on land.
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A risk, however, with our country specific method is that buyers of certain crops decide to switch from the same crop product from a country where large-scale land conversion takes place to a country where this problem does not occur. Unless the crop yield is higher in the latter country, this does not lead to any mitigation of greenhouse gas emissions. So, reduced pressure on land by improved yields and moving crop production to more productive areas (in other countries) are the main options that result in improvements. We must emphasize that assessing carbon footprints of products does not give complete answers to what is the best strategy for mitigating greenhouse gas emissions. It merely gives insight into the greenhouse gas emissions that are related to existing or hypothetical production chains. To fully evaluate the positive and negative effects of mitigation strategies on greenhouse gas emissions in production chains, consequential lifecycle assessments or scenario models that are specific to the case of interest are more appropriate. 4. Conclusion In this paper, we presented a method for including global warming potentials of land use change into lifecycle assessment of products. We suggest this method as a better basis for including land use change in carbon footprints of products than the currently used direct land use change factor as used in several carbon footprint protocols, which is based on an amortization period. The method gives an indication of the carbon footprint of land use change in relation to crop demand and region of crop production that is in line with annual global warming potential on a global scale. This GWP is allocated to timber and crops with expanding areas within countries. Although the method has some debatable issues, it largely works with publicly available data, it can be improved when more detailed information is available. It also gives more sensible and consistent results than currently used methods for calculating the GWP of land use change in carbon footprints. It could also provide better grounds for motivating producers and consumers to improve their behaviour in relation to greenhouse gas emissions. References Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles 15 (4). Blonk, H., Ponsioen, T., Kool, A., Marinussen, M., 2011. The Agri-footprint Method. Methodological LCA Framework, Assumptions and Applied Data. Version 1.0. Blonk Milieu Advies, Gouda. www.agri-footprint.com. Börjesson, P., Tufvesson, L.M., 2011. Agricultural crop-based biofuels e resource efficiency and environmental performance including direct land use changes. Journal of Cleaner Production 19, 108e120. BSI, 2008. PAS 2050:2008 e Specification for the Assessment of the Life Cycle Greenhouse Gas Emissions of Goods and Services. British Standards, UK. Croezen, H., Kampman, B., 2008. Calculating greenhouse gas emissions of EU biofuels. An assessment of the EU methodology proposal for biofuels CO2 calculations. Delft, October 2008. EP (European Parliament), 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 Law 5.6.2009 L 140/16 EN. FAO, 2001. Global Forest Resources Assessment 2000: Main Report. FAO Forestry Paper 140, Rome. FAO, 2011. FAOSTAT. Food and Agricultural Organization of the United Nations. Available at: faostat.fao.org (accessed August 2011). Fargione, J., Hill, J., Tilman, D., Polasky, S., Hawthorne, P., 2008. Land clearing and the biofuel carbon debt. Science 319 (5867), 1235e1238. Gallagher, E., 2008. The Gallagher Review of the Indirect Effects of Biofuel Production. Renewable Fuels Agency, London, U.K. Goedkoop, M.J., Heijungs, R., Huijbregts, M., De Schryver, A., Struijs, J., Van Zelm, R., 2009. ReCiPe 2008, A Life Cycle Impact Assessment Method which Comprises Harmonised Category Indicators at the Midpoint and the Endpoint Level, first ed.. http://www.lcia-recipe.net Report I: Characterisation; 6 January 2009.
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