Exploring a safe operating approach to weighting in ...

Journal of Cleaner Production 37 (2012) 147e153

Contents lists available at SciVerse ScienceDirect

Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Exploring a safe operating approach to weighting in life cycle impact assessment e a case study of organic, conventional and integrated farming systems H.L. Tuomisto a, *, I.D. Hodge b, P. Riordan a, D.W. Macdonald a a b

Wildlife Conservation Research Unit, University of Oxford, The Recanati-Kaplan Centre, Tubney House, Abingdon Road, Tubney, Oxon OX13 5QL, UK Department of Land Economy, University of Cambridge, Cambridge CB3 9EP, UK

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 June 2011 Received in revised form 27 June 2012 Accepted 28 June 2012 Available online 14 July 2012

Normalization and weighting steps used in life cycle impact assessment (LCIA) are often ignored as the weighting factors currently available are seen as being uncertain, subjective and unreliable. This article aims to contribute to the development of a new approach towards weighting, exploring the application of the concept of a planetary safe operating space for human welfare. Based on this approach, the boundaries included in this study relate to: climate change, rate of biodiversity loss, nitrogen cycle, phosphorus cycle, stratospheric ozone depletion, global freshwater use and change in land use. The weighting factors are then applied to a case study comparing environmental impacts of organic, conventional and integrated farming systems with alternative land uses. An integrated farming system that uses a part of the land for natural forest was found to have the lowest total impact score. Conventional farming systems with Miscanthus and managed forest had the highest total impact scores. The main source of uncertainty in the results arose from the wide range of assessments for the safe boundary of the biodiversity loss impact category. As the weighting factors proposed in this paper are not based on the common LCA impact categories, more work is needed to adjust the weighting factors to be suitable for use in LCA studies. More research is also needed for further defining the safe planetary boundaries. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Life cycle assessment Impact assessment Weighting factors Environmental impacts Organic farming Integrated farming

1. Introduction The purpose of life cycle impact assessment (LCIA) in Life Cycle Assessment (LCA) is to provide information for understanding the results from the life cycle inventory (LCI) (ISO 14040, 2006). Normalization and weighing steps in LCIA are used for comparing and aggregating the different environmental impact categories and providing a single impact score for the systems studied. In the normalization step, the LCI results for the different impact categories are divided by a reference value that can be based on the total emissions or resource use for a given area, the ratio of one alternative system to another or the highest value among all options. Weighting methods can be applied to the endpoints or midpoints of the causeeeffect chain. Endpoint indicators are defined at the level of the areas of protection, such as human health, natural environment and natural resources (Finnveden et al., 2009). Midpoint indicators refer to the impacts that are somewhere between the emission and the endpoint, for example changes in concentrations in the atmosphere. * Corresponding author. Tel.: þ39 0332786731; fax: þ39 0332785162. E-mail address: [email protected] (H.L. Tuomisto). 0959-6526/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jclepro.2012.06.025

Weighting factors are generally based on monetary or nonmonetary values (Ahlroth et al., 2011). Monetary values are represented by willingness to pay or to accept compensation, in terms of direct use values (e.g. timber value of a forest), indirect use values (e.g. value of carbon fixed by a forest) or non-use values that are based on the values that people attach to amenities regardless of whether they use them or not (Ahlroth et al., 2011). Direct use values can often be determined by market prices for calculating damage costs, loss of production and/or loss of capital. Non-use values must be estimated by means of expressed preference valuation methods, such as contingent valuation or choice experiments. Non-monetary weighting methods used in LCA studies include distance-to-target (Lin et al., 2005; Weiss et al., 2007) and panel weighting methods (Goedkoop and Spriensma, 2001; Guinee et al., 2011). Distance-to-target methods evaluate different environmental impact categories depending on the distance between the current level of environmental impact and an environmental target value (Weiss et al., 2007). These methods assume that all of the targets have an equal importance and therefore further weighting factors are not necessary. In panel weighting methods the valuations of the panel members are investigated through questionnaires and panel interviews where the interviewees are asked to

148

H.L. Tuomisto et al. / Journal of Cleaner Production 37 (2012) 147e153

rank different environmental impacts based on their perception of the importance of the impact categories. The panels can consist of experts, stakeholders or lay people. Weighting factors based on willingness to pay studies and panel methods reflect people’s subjective views on the importance of different environmental impact categories, either in terms of their personal preferences or in terms of their judgements as to the importance of a particular environmental impact. Those people contributing to the determination of the weights may not be personally affected, may have little direct experience or knowledge of the environmental impacts involved and will almost never have previous experience of attaching monetary values to them. Bruni and Sugden (2007) for instance have commented on individuals’ limited experience and ability to place realistic valuations on public goods. Studies have shown that rankings differ substantially even among experts (Bare, 2010; Guinee et al., 2011), perhaps not surprisingly, given the uncertainty as to the basis against which the relative significance of environmental impacts is to be judged. Any weighting of different environmental impact categories necessarily includes value judgements, including the choice of what weighting method to use. The choice of the weighting method also depends on the aim of the study. If, for instance, the aim is to find which of the studied systems optimizes the provision of long-term benefits for human health, scientific knowledge of health impacts will drive the selection of the weighting factors. There is however a more fundamental challenge to the selection of weighting methods where scientific understanding of the environmental system is very incomplete but where there is a non-zero probability of dramatic and potentially catastrophic and irreversible system change. In these circumstances, neither individual preferences nor expert judgements may be expected to identify ‘optimal’ weights for environmental impact. Rather, an appropriate analysis may start from a perspective of operating within certain defined constraints, based for instance on a Safe Minimum Standard (Toman, 1994; Ciriacy-Wantrup, 2007) or a precautionary principle (O’Riordan and Cameron, 1994). This logic lies behind arguments to analyse climate change policy in terms of insurance rather than terms of costebenefit analysis (Ackerman, 2008; Weitzman, 2010). Following this logic, Rockström et al. (2009a,b) have recently developed this approach in seeking to identify planetary boundaries within which humanity can operate safely. This paper explores a way in which this idea might be incorporated into LCA studies. According to the LCA guidelines, the two first steps of LCIA, classification and characterization are always included in LCA studies, whereas the last two steps, normalization and weighting, are optional (ISO 14044, 2006). If those two steps are ignored the results are left open to different personal interpretations (Zhou and Schoenung, 2007). A review published by European Union Joint Research Centre (JRC, 2010) compared 13 different methods used for LCIA in LCA. Amongst those methods only 6 included

a weighting step. However, some of the methods report results as endpoints rather than midpoints. When endpoints are used some de facto weighting happens already when impacts are grouped into the endpoint categories. For example, Eco-indicator 99 methodology uses 3 different endpoint categories: human health, ecosystem quality and resources (Goedkoop and Spriensma, 2001). The human health category uses Disability Adjusted Life Years (DALY) as an indicator describing the impact of toxic substances on human health (Goedkoop and Spriensma, 2001). However, that method assumes that there are three different problems (human health, ecosystem quality and resources) that are not connected, whereas the method proposed in this article suggests that all impacts ultimately have an impact on human welfare. This article contributes to the development of a potential new approach towards weighting in LCA, based on the concept of a planetary safe operating space for human welfare as propounded by Rockström et al. (2009b). This is adopted in developing novel weighting factors which are applied in a comparison of the environmental impacts of organic, conventional and integrated farming systems. 2. Methods 2.1. Development of the novel weighting factors The weighting method proposed here takes a distance-to-target approach (Weiss et al., 2007). Instead of using a variety of different governmental targets arising from different sources, the target levels used here are based on a single approach to identifying safe planetary boundaries determined by Rockström et al. (2009a). They argue that human driven global environmental change may push the Earth system outside the stable environmental state, which can potentially have disastrous consequences for human life. To meet the challenge of maintaining the stable state, they propose a framework based on nine planetary boundaries that define the safe operating space for humanity. Those nine boundaries are: climate change, rate of biodiversity loss, interference with nitrogen and phosphorus cycles, stratospheric ozone depletion, ocean acidification, global freshwater use, change in land use, chemical pollution and atmospheric aerosol loading. The definitions of these boundaries are explained in more detail in Rockström et al. (2009b) and a short summary is given here. The weighting factors for each impact category were generated by calculating the ratio between the current position and the estimated safe boundary (Table 1). 2.1.1. Climate change The climate change boundary is based on scientific understanding of the requirements that are needed for avoiding climate change driven problems that are difficult for society to cope with. Boundary values were placed at 350 ppm atmospheric CO2 concentration and 1 W m2 radiative forcing above pre-industrial

Table 1 Planetary boundaries, current status and weighting factors based on the data from Rockström et al. (2009b) (uncertainty ranges in parenthesis if known).

Climate change 1 Climate change 2 Climate change average Biodiversity loss Nitrogen cycle Phosphorus cycle Ozone depletion Ocean acidification Global freshwater use Land use

Unit

Planetary boundary

Current status

Weighting factor

Parts per million Watts per m3

350 1

387 1.6 (0.6e2.4)

Number of species per million species years Million tonnes N per yr Million tonnes P per yr Dobson units Saturation state of aragonite in surface sea water km3 water consumed per yr % of crop land

10 (10e100) 35 11 (11e101) 276 2.75 4000 (4000e6000) 15

100 121 9 (8.5e9.5) 283 2.90 2600 11.7

1.11 1.60 (0.6e2.4) 1.31 (0.86e1.76) 10 (1e10) 3.46 0.82 (0.08e0.86) 1.02 1.05 0.65 (0.43e0.65) 0.78

H.L. Tuomisto et al. / Journal of Cleaner Production 37 (2012) 147e153

levels. These were based on an analysis of the equilibrium sensitivity of the climate system to GHG forcing, the behaviour of the large polar ice sheets under different conditions and the observed behaviour of the climate system at the current CO2 concentration of about 387 ppm and 1.6 W m2 (þ0.8/1.0 W m2) radiative forcing. The weighting factor was calculated as an average of the two indicators. The only source of information about the uncertainty range related to the current level of radiative forcing. 2.1.2. Stratospheric ozone depletion The boundary for ozone levels is defined as a 5% decrease in column ozone levels for any particular latitude with respect to 1964e1980 values. An estimate of the uncertainty range was not provided. 2.1.3. Interference with the global nitrogen cycles Anthropogenic interference with nitrogen cycles and phosphorus flows has caused eutrophication in lakes and marine ecosystems. For nitrogen cycling the boundary is based on a ‘first guess’ due to lack of data. The boundary describes the amount of additional reactive nitrogen flowing into the Earth system as result of industrial fixation of atmospheric N2 to ammonia, agricultural fixation of atmospheric N2 via cultivation of leguminous crops, fossil fuel combustion and biomass burning. The boundary was initially set at approximately 25% of its current value. 2.1.4. Interference with the global phosphorus cycles The parameter for the phosphorus cycle describes the anthropogenic phosphorus flow into oceans. The boundary is placed at a maximum of 10 times the natural background weathering flux of phosphorus with an uncertainty range of 10e100 times. 2.1.5. Biodiversity loss The biodiversity loss boundary is based on the rate of extinctions. The planetary boundary is placed at 10 extinctions per million species-years (E/MSY) with an uncertainty range of 10e100 E/MSY. The boundary is an order of magnitude above the background rate. 2.1.6. Global freshwater use The parameter for freshwater use is based on the consumption of blue water (used and sifted runoff) per year. It is estimated that physical water scarcity is reached when withdrawals of blue water exceed 5000e6000 km3 yr1, and therefore the planetary boundary was placed at 4000 km3 yr1 with an uncertainty range of 4000e6000 km3 yr1. 2.1.7. Land use change The parameter for land use change is the percentage of global land cover converted to crop land. It is placed at 15% of the global ice-free land surface. This is based on the current farming practices and it is acknowledged that farming systems that mimic natural processes could allow an extension of this boundary. The weighting factor for each impact is given as the ratio of the current status divided by the planetary boundary. Thus the negative impacts are given greater weight, in excess of 1 where the planetary boundary is already exceeded, and less weight is given where there is judged to be some remaining capacity. 2.2. Case study 2.2.1. Farming systems The farming systems are explained in more detail in Tuomisto et al. (2012) and a short summary is given here. Each farm was assumed to utilize 100 ha land and produce food crop output of 460

149

tonnes (t) potatoes, 88 t winter wheat, 60 t field beans and 66 t spring barley. These crop outputs were determined by the yield output from 20 ha of organic land area available for each crop under a standard organic rotation in lowland farming in England. Higher yielding systems required less land for producing food crops and any land area that was not needed for production of the defined volumes of food crops and green manure crops to sustain fertility was then available for alternative uses. Three different alternative land uses for ‘the rest-of-the-land’ were included: cultivation of Miscanthus energy grass, managed forest and natural forest. In some of the systems biogas was produced from green manure, cover crops and straw. The energy produced from Miscanthus, wood and biogas was assumed to replace fossil fuels, and therefore, regarded as negative energy input and GHG emissions in the balance calculations. The model organic crop rotation consisted of: 1. grass-clover (GC); 2. potatoes (Solanium tuberosum); 3. winter wheat (Triticum aestivum) þ undersown overwinter cover crop (CC); 4. spring beans (Vicia faba) þ CC; and 5. spring barley (Hordeum vulgare) þ undersown GC. The model farming systems compared were: 1. Organic farm without biogas production (O). The GC, CC and crop residues (CR) were incorporated into the soil. Ploughing was used. 2. Organic farm with biogas production (OB). The GC, CC and CR (straw of wheat and bean crops) were harvested for biogas production. Ploughing was used. 3. Conventional farm (C). Used mineral fertilizers and non-organic pesticides. No GC, CC or biogas production. Ploughing was used. Crop rotation consisted of potatoes, winter wheat, spring beans and spring barley. 4. Integrated farm (IF). The crop rotation and biogas production were similar to the OB system, but non-organic pesticides were used. Ploughing was used. 5. Integrated special (IFS). As IF but instead of GC municipal biowaste was used as a fertilizer. Non-organic pesticides and notillage were used. Crop rotation consisted of potatoes, winter wheat, spring beans and spring barley. The GHG emissions, nutrient balances, land use and biodiversity impacts of these farming systems were quantified. The method for the biodiversity impact assessment was adapted from De Schryver et al. (2010). This method assesses the ecosystem damage by using the potentially disappeared fraction (PDF) of species as an indicator, describing the change in vascular plant species richness within the occupied area as compared with the baseline. 2.2.2. Normalization and weighting The results for the environmental impacts of GHG emissions, nitrogen use, phosphorus use, land use and biodiversity impacts of the organic, conventional and integrated farming systems were normalized and weighted. The following formula (Formula (1)) was used for normalization and weighting:

W ¼

X

ai Di =Ni

(1)

i

where W is the weighted score for all aggregated impact categories, ai the weighting factor for the individual impact categories (i), Di the result of the impact before weighting for individual impact category and Ni the normalization value for each individual impact category. The weighting factors presented in Section 2.1 were used. The results of the impacts before weighting were based on Tuomisto

150

H.L. Tuomisto et al. / Journal of Cleaner Production 37 (2012) 147e153

et al. (2012) and are presented in Table 2. For normalization values the highest values from each impact category were used, so that the normalized impact describes the proportion of the highest level found under any of the systems compared.

A

O OB_Miscanthus

OB_Managed forest

N input

OB_Natural forest P input

C_Miscanthus

2.3. Uncertainty and sensitivity analyses

GHG

C_Managed forest C_Natural forest

Monte Carlo analysis was used for an uncertainty analysis. The model was simulated by using 25,297 replications with randomly generated input values. Microsoft Excel 2007 software generated random numbers using a uniform distribution within the estimated uncertainty ranges of the input values and the weighting factors. For environmental impact data the uncertainty range was between 25 and 75 percentiles (Table 2), whereas for the weighting factors were as reported in Table 1. SPSS 14.0 software was used for the statistical analyses. In the sensitivity analysis the impacts of different weighting factors on the results were assessed by changing the base values in the primary data by uncertainty ranges of the weighing factors reported earlier in Table 1. The ‘low’ scenario used the lowest values, ‘high’ the highest values and ‘base’ the base values. In the sensitivity analysis the impact data were constant in each scenario. 3. Results When the impacts were weighted the IFS_Natural forest system had clearly the lowest total impact score, being over 50% lower compared to the second lowest system IFS_Managed forest (Fig. 1). C_Miscanthus and C_Managed forest had the highest impact scores. The differences between the rest of the systems were small. The relatively high impact scores of the organic systems were mainly due to their low GHG mitigation. GHG emissions had a negative impact score in most of the systems due to high GHG mitigation achieved through the production of biofuels. In all cases, biodiversity loss had the highest total impact score compared to the other impact categories, followed by N input with the second highest impact. The highest contributors to the uncertainty ranges of the OB_Miscanthus and IFS_Natural forest systems were uncertainties in the biodiversity and GHG impact categories (Fig. 2). The consequences of the uncertainty relating to the weighting factors can be seen in Fig. 3, where the base results of the impact categories are weighted by using the low, high and base weighting factors. This comparison shows that when the lowest weighting factors were used, C models had lower total impact than organic models and IF models, whereas when base and high weighting factors were used

Biodiversity

IF_Miscanthus

Land use

IF_Managed forest IF_Natural forest

IFS_Miscanthus IFS_Managed forest IFS_Natural forest -5

0

5 10 Weighted impacts

15

B

O OB_Miscanthus OB_Managed forest OB_Natural forest C_Miscanthus C_Managed forest C_Natural forest IF_Miscanthus IF_Managed forest IF_Natural forest IFS_Miscanthus IFS_Managed forest IFS_Natural forest 0

5

10

15

Total impact score

Fig. 1. Weighted impacts separated to different impact categories (A) and median weighted total impact score with uncertainty range (B). Error bars represent the 25 and 75 percentiles based on the Monte Carlo analysis.

C_Miscanthus and C_Managed forest had the highest total impact score over all options. Fig. 4 shows that the main reason for this difference was the wide uncertainty range of the biodiversity weighting factor. 4. Discussion The weighting factors proposed in this article suggest a basis for the development of a consistent approach to weighting factors for comparing the total impacts of different systems. The proposed approach based on the safe planetary boundaries is in effect

Table 2 Minimum and maximum values of the input values used in the Monte Carlo analysis (impacts per farm, based on the data from Tuomisto et al., 2012). N input (kg)

O OB_Miscanthus OB_Managed forest OB_Natural forest C_Miscanthus C_Managed forest C_Natural forest IF_Miscanthus IF_Managed forest IF_Natural forest IFS_Miscanthus IFS_Managed forest IFS_Natural forest

P input (kg)

GHG (t CO2-eq)

Biodiversity (index)

Land use (ha)

Min

Max

Min

Max

Min

Max

Min

Max

Min

Max

8680 10,074 10,074 10,074 6661 6661 6661 8670 8670 8670 3350 3350 3350

16,120 18,709 18,709 18,709 12,371 12,371 12,371 16,101 16,101 16,101 6221 6221 6221

602 722 722 722 561 561 561 585 585 585 280 280 280

1118 1342 1342 1342 1041 1041 1041 1086 1086 1086 520 520 520

140 285 267 269 630 425 450 559 463 473 822 617 642

170 210 192 194 545 255 273 485 374 382 737 447 465

33 33 33 32 72 66 36 46 45 35 65 60 30

44 46 45 42 81 75 40 56 53 41 76 69 34

84 80 80 80 48 48 48 65 65 65 48 48 48

96 91 91 91 52 52 52 74 74 74 52 52 52

H.L. Tuomisto et al. / Journal of Cleaner Production 37 (2012) 147e153

151

Fig. 2. The total uncertainty of impact data and weighting factors in each impact category.

a distance-to-target method that assumes that all of the targets have an equal importance. This assumption is consistent with the planetary boundaries as the idea is that it is necessary to stay within the safe boundaries in all of the impact categories. The main difference with the proposed weighting approach and other distance-to-target methods is that the targets are determined by scientists rather than being based on politically determined targets, although necessarily implying political judgements. The weighting factors proposed in this paper are not based on the common LCA impact categories, and therefore, more work is needed to adjust the weighting factors to be suitable for use in LCA studies. For instance, the weighting factors proposed in this article provide only a possibility of weighting N input and P input instead

Fig. 3. Weighted impacts when low, base and high weighting factors were used and the impact data was kept constant.

of eutrophication. For that reason it was not possible to compare the weighting method proposed in this paper with other LCA weighting methods. Also we note that this approach may fail to represent other aspects of environmental impacts that are neither related to the planetary system nor LCA impacts. For instance, alternative farming systems may provide different levels of visual amenity that would be appreciated by local residents and in principle influence preferences towards them without having implications for the Earth system more widely. Economic valuation or panel methods are likely to be more appropriate methodologies for determining weights for these types of impacts. Further adjustment will also be needed to make sure that the way in which the planetary boundary is characterised matches the way in which the environmental impact is measured. The biodiversity weighting factor in the approach adopted here is particularly problematic. The indicator used to estimate the biodiversity impacts of the farming systems compared was based on the species richness of vascular plants whereas the biodiversity weighting factor was based on the extinction rate of all species. Both of those biodiversity indicators have their own flaws. The species richness of vascular plants does not necessarily correlate with species richness of all species. It can also be argued that the extinction rate of all species is not a good indicator, since all species do not have the same value for the ecosystem. It can also be argued that the weighting method and the method for biodiversity assessment are not compatible as the indicators are different. Therefore, there is a particular need to develop the methods used for biodiversity assessment in LCA. As fully recognised by Rockström et al. (2009a), more research is needed for defining the planetary boundary limits and for estimating their uncertainty ranges. Some of the boundaries discussed have yet to be assessed and there may be further important boundaries yet to be identified. The boundaries proposed in by Rockström et al. (2009a) have received some criticism. For instance, Townsend and Porder (2011) have proposed alternative options for phosphorus cycle, including P-driven freshwater eutrophication,

152

H.L. Tuomisto et al. / Journal of Cleaner Production 37 (2012) 147e153

the terms in which the planetary boundaries and the environmental impacts are measured. There is also a significant social and political question as to how available planetary room for manoeuvre is to be allocated between and within different societies. This implies a socio-political aspect towards the determination of weights that should be applied in different contexts and constituencies. Clearly a great deal of work needs to be done in order to make the approach operational in practice. 5. Conclusions

Fig. 4. Contribution of each impact category to the total impact score in different weighting factor scenarios.

the potential for world P supply to place an ultimate limit on food production, and depletion of soil P stocks in some world regions. Others have questioned the whole idea of using thresholds as basis for determining safety boundaries (Schlesinger, 2009). Bass (2009) suggested that instead of using ‘land use’ as an indicator, the quality of the land use should be considered and therefore the indicator should rather be ‘soil degradation’ or ‘soil loss’. It has also been argued that the boundaries may have to be changed over time according to new discoveries. Allen (2009) argued in the case of climate change the indicators of planetary boundaries may need to be changed while more understanding about climate systems has been gained. The results of the case study suggested that IFS systems had the lowest total impacts under all weighting factor scenarios. However, due to the wide uncertainty range of the safe planetary boundary for biodiversity impact, the ranking order of the organic and conventional systems changed when different levels of weighting factors were used. Given the uncertainties and ranges in the approach as it stands, we cannot ensure that it offers definitive priorities for resource management. The estimates of planetary boundaries can in principle indicate the extent to which there is room for manoeuvre in policies and decisions taken towards natural resource management. However, what is absent is an institutional structure that can allocate the available capacity against the different boundaries amongst the many competing demands (Walker et al., 2009). The method illustrated here suggests a possible tool that can assist in institutional development whereby information on the relative capacity available within the planetary system can be incorporated into LCIA. But as it stands, it does not represent the way in which priorities might be given for the competing demands for ways in which the available capacity might be used. This needs to be allocated between countries and between projects and initiatives within countries, which would create different weighting factors for different regions or countries. The Climate Change Act 2008 in the UK offers a hint as to how this might be done. This Act sets targets for GHG emissions for the UK so that a UK rather than a planetary limit could be applied for analysis in the UK. Of course, the implication is that every country would need to undertake a similar exercise in respect of each of the planetary boundaries and then be prepared to stick to these limits. Further, the significance of an environmental impact depends on the particular circumstances under which it occurs in terms of both time and space. This illustrates the political elements of such decision-making. Better measures of some impacts are required, especially with regard to biodiversity, and greater consistency is needed between

The global nature of environmental problems and the major gaps in our understanding of the potential system changes associated with increasing environmental impacts provide a rationale for seeking to manage environmental impacts within a safe operating space. In some way or other individual decisions on resource management need to be set within the context of a globally safe operating space. This is addressed in this study by determining the weights in terms of the relationship between the current global status and the proposed planetary boundary. The approach faces many limitations and numerous challenges. Nevertheless, we believe that there is merit in the principle lying behind this approach that justifies further research and analysis. Acknowledgements We thank Holly Hill Charitable Trust for funding the project and Tom Curtis (LandShare) for commenting on the article. References Ackerman, F., 2008. Climate economics in four easy pieces. Development 51, 325e331. Ahlroth, S., Nilsson, M., Finnveden, G.R., Hjelm, O., Hochschorner, E., 2011. Weighting and valuation in selected environmental systems analysis tools e suggestions for further developments. Journal of Cleaner Production 19, 145e156. Allen, M., September 2009. Planetary boundaries: tangible targets are critical. Nature Reports Climate Change 23. Bare, J.C., 2010. Life cycle impact assessment research developments and needs. Clean Technologies and Environmental Policy 12, 341e351. Bass, S., September 2009. Planetary boundaries: keep off the grass. Nature Reports Climate Change 23. Bruni, L., Sugden, R., 2007. The road not taken: how psychology was removed from economics, and how it might be brought back. The Economic Journal 117, 146e173. Ciriacy-Wantrup, S.V., 2007. Resource Conservation: Economics and Policies. University of California Press, Berkeley. De Schryver, A., Goedkoop, M., Leuven, R., Huijbregts, M., 2010. Uncertainties in the application of the species area relationship for characterisation factors of land occupation in life cycle assessment. The International Journal of Life Cycle Assessment 15, 682e691. Finnveden, G.R., Hauschild, M.Z., Ekvall, T., Guinée, J., Heijungs, R., Hellweg, S., Koehler, A., Pennington, D., Suh, S., 2009. Recent developments in life cycle assessment. Journal of Environmental Management 91, 1e21. Goedkoop, M., Spriensma, R., 2001. The Eco-indicator 99 A Damage Oriented Method for Life Cycle Impact Assessment. Methodology report. PRe Consultants B.V., Amersfoort. Guinee, J.B., Heijungs, R., Huppes, G., Zamagni, A., Masoni, P., Buonamici, R., Ekvall, T., Rydberg, T., 2011. Life cycle assessment: past, present, and future. Environmental Science & Technology 45, 90e96. ISO 14040, 2006. Environmental Management e Life Cycle Assessment e Principles and Framework. International Organisation for Standardisation (ISO), Geneva. ISO 14044, 2006. Environmental Management e Life Cycle Assessment e Requirements and Guidelines. International Organisation for Standardisation (ISO), Geneva. JRC, 2010. Analysis of existing environmental impact assessment methodologies for use in life cycle assessment. In: ILCD Handbook International Reference Life Cycle Data System. European Union, Ispra. Lin, M.Y., Zhang, S.S., Chen, Y., 2005. Distance-to-target weighting in life cycle impact assessment based on Chinese environmental policy for the period 1995e2005. International Journal of Life Cycle Assessment 10, 393e398. O’Riordan, T., Cameron, J., 1994. Interpreting the Precautionary Principle. Earthscan, London.

H.L. Tuomisto et al. / Journal of Cleaner Production 37 (2012) 147e153 Rockström, J., Steffen, W., Noone, K., Persson, A., Chapin, F.S., Lambin, E.F., Lenton, T.M., Scheffer, M., Folke, C., Schellnhuber, H.J., Nykvist, B., de Wit, C.A., Hughes, T., van der Leeuw, S., Rodhe, H., Sorlin, S., Snyder, P.K., Costanza, R., Svedin, U., Falkenmark, M., Karlberg, L., Corell, R.W., Fabry, V.J., Hansen, J., Walker, B., Liverman, D., Richardson, K., Crutzen, P., Foley, J.A., 2009a. A safe operating space for humanity. Nature 461, 472e475. Rockström, J., Steffen, W., Noone, K., Persson, A., Chapin, F.S., Lambin, E., Lenton, T.M., Scheffer, M., Folke, C., Schellnhuber, H.J., Nykvist, B., de Wit, C.A., Hughes, T., van der Leeuw, S., Rodhe, H., Sorlin, S., Snyder, P.K., Costanza, R., Svedin, U., Falkenmark, M., Karlberg, L., Corell, R.W., Fabry, V.J., Hansen, J., Walker, B., Liverman, D., Richardson, K., Crutzen, P., Foley, J., 2009b. Planetary boundaries: exploring the safe operating space for humanity. Ecology and Society 14. Schlesinger, W.H., 2009. Planetary boundaries: thresholds risk prolonged degradation. Nature Reports Climate Change. Toman, M.A., 1994. Economics and “sustainability”: balancing trade-offs and imperatives. Land Economics 70, 399e413. Townsend, A.R., Porder, S., 2011. Boundary issues. Environmental Research Letters 6.

153

Tuomisto, H.L., Hodge, I.D., Riordan, P., Macdonald, D.W., 2012. Comparing energy balances, greenhouse gas balances and biodiversity impacts of contrasting farming systems with alternative land uses. Agricultural Systems 108, 42e49. Walker, B., Barrett, S., Polasky, S., Galaz, V., Folke, C., Engström, G., Ackerman, F., Arrow, K., Carpenter, S., Chopra, K., Daily, G., Ehrlich, P., Hughes, T., Kautsky, N., Levin, S., Mäler, K.-G.R., Shogren, J., Vincent, J., Xepapadeas, T., de Zeeuw, A., 2009. Looming global-scale failures and missing institutions. Science 325, 1345e1346. Weiss, M., Patel, M., Heilmeier, H., Bringezu, S., 2007. Applying distance-to-target weighing methodology to evaluate the environmental performance of biobased energy, fuels, and materials. Resources Conservation and Recycling 50, 260e281. Weitzman, M.L., 2010. Climate change: insurance for a warming planet. Nature 467, 784e785. Zhou, X., Schoenung, J.M., 2007. An integrated impact assessment and weighting methodology: evaluation of the environmental consequences of computer display technology substitution. Journal of Environmental Management 83, 1e24.