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Cutting the cake: Supporting environmental fund allocation decisions
Journal of Environmental Management 90 (2009) 2737–2745
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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Cutting the cake: Supporting environmental fund allocation decisions Stefan Hajkowicz* CSIRO Sustainable Ecosystems, 306 Carmody Rd, St Lucia, QLD 4067, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 April 2008 Received in revised form 9 February 2009 Accepted 7 March 2009 Available online 16 April 2009
This paper describes a decision support model for allocating financial resources amongst multiple user groups in environmental management problems. The model is based on the multiple criteria analysis (MCA) method of compromise programming. It was used to inform the allocation of Natural Heritage Trust funds across 14 regions in Queensland, Australia. The model targets funding to those regions with greater natural resource management needs. Need is determined by 19 weighted criteria relating to natural resource assets and threats. The model was accepted by the Australian Government, Queensland Government and regional groups as an appropriate means for allocating program funds; first in 2005 and then again, with improvements, in 2007. This paper shows that an MCA model can improve the transparency, auditability and acceptance of allocation decisions which would otherwise be heavily politicised. Crown Copyright Ó 2009 Published by Elsevier Ltd. All rights reserved.
Keywords: Environmental fund allocation Multiple criteria analysis Decision support Conflict resolution
1. Introduction One of the most common and challenging dilemmas facing natural resource managers is how to allocate a fixed resource (e.g. money, land, water) amongst competing users (e.g. regions, stakeholders, countries). Such decisions will typically involve multiple stakeholders, multiple policy objectives and considerable uncertainty (Gough and Ward, 1996). Under these conditions, a formalised decision procedure can improve transparency, analytic rigour, stakeholder involvement and auditability (Hajkowicz, 2007a). In this paper we describe a decision model for allocating environmental funds amongst 14 regions in Queensland, Australia under the proposed third phase of the Natural Heritage Trust (NHT) program1. The model builds upon earlier work by Hajkowicz (2007b) in Phase Two of the NHT. Since this time the algorithms, decision procedure and stakeholder engagement process have improved substantially. The present paper has an emphasis how the decision was made and influenced by stakeholders. An analysis is presented of the stakeholder groups who guided the decision, how the decision problem was structured, how the model gained acceptance and how the model compares to an optimisation
* Tel.: þ61 7 3214 2327; fax: þ61 7 3214 2308. E-mail address: [email protected] URL: http://www.csiro.au/cse 1 Following the 2007 Federal Election the Natural Heritage Trust (NHT) program was replaced by a new program, with similar policy objectives, called ‘‘Caring for Our Country’’. This study was completed when the NHT was still in effect and its continuation in some form was anticipated by most stakeholders.
approach. An iterative process for identifying, filtering and weighting criteria was introduced via a series of workshops. With the benefit of hindsight we can now see how the evolving decision models are changing historical patterns of investment. The paper presents a framework, and principles, for the allocation of fixed resources amongst competing users in an environmental management setting. It provides an operational definition of ‘‘sharing’’ via the concept of equalisation. The aim is to equalise the financial capability of each region to address its natural resource management needs. The model uses an index of need based on 19 weighted criteria relating to natural resource assets and threats. The allocation is determined such that dollars-per-unit need is equal for each region. This means funding is targeted towards those regions with greatest need. Compromise programming (CP; Zeleny, 1973, 1982), a multiple criteria analysis (MCA) technique, is used to weight indicators and construct a needs index. In this case the fixed resource being allocated is money. However, the model design, decision process and many of the issues we encountered would be similar for other resource types.
2. Decision models for resource allocation The question of how to allocate fixed resources amongst multiple users has received much attention in the field of environmental management. Much effort has focused on the allocation of water resources. For example, Syme et al. (1999) present an operational definition of ‘‘fairness’’ in water allocation decisions based on social psychological theories of justice and equity. Ballestero et al. (2002) look at the question of how to allocate water
0301-4797/$ – see front matter Crown Copyright Ó 2009 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2009.03.002
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use rights prior to the establishment of water markets with a case study of the Lorca region in Spain. In addition to water there have been studies on the allocation of many other fixed resources such as pollution rights (Diem and Comrie, 2001), resource extraction or harvesting rights (Merrifield and Firoozi, 1995) and access to recreational sites for tourism operators (Davis and Tisdell, 1996). Some of the allocation models are based on economic efficiency whereby the aim is to maximise benefit subject to a resource availability threshold (Burton, 1996; Chang et al., 1995). Other models are primarily concerned with fairness and equity issues and aim to share the resource (Kampas and White, 2003). Yet other models combine both elements of sharing and optimisation (Vesterdal and Svendsen, 2004). Whether to optimise or share is a common theme running through the literature on environmental resource allocation. It is given further consideration later in this paper. Whilst much has been written on the subject of resource allocation there have been fewer attempts to operationalise concepts within decision models and even fewer cases of their real-world application. In one case Mimi and Sawalhi (2003) design a multicriteria decision model to allocate the waters of the Jordon River amongst Israel, Palestine, Syria and Lebanon. Their model is based on 9 criteria for equitable water use and they suggest it may help negotiators reach solutions. Their algorithms provide an operational definition of ‘‘fair’’. However, the model is presented as ‘‘food for thought’’ and has not yet been implemented. One model, which has been implemented to allocate large resources, was developed by the United States Department of Agriculture (USDA). The USDA uses a multi-criteria model, containing 30 weighted criteria, to allocate funding under the Environmental Quality Incentives Program (EQIP) to each of the 50 states in the US. In fiscal 2006 around US$652 million were allocated using this model. The US General Accounting Office reviewed the model and called for some improvements (GAO, 2006). In particular the GAO suggested providing a better rationale for each criterion, such that it was linked to policy objectives, improving the criteria weighting process and using more up-to-date datasets. Johansson and Cattaneo (2006) explored the impact of changing the functional form of the EQIP allocation formula. They show that millions of dollars can be displaced by changing weights or by changing the functional form from an additive to geometric index. The sensitivity of the result to changes in weights highlights the importance of accurately capturing decision maker preferences. But in natural resource management programs the weighting task is often completed by technical staff with little reference to broader societal opinions (Hajkowicz et al., 2009). 3. Allocating environmental funds across Queensland’s regions This case study is concerned with allocating fiscal resources amongst regional bodies in Queensland under Australia’s Natural Heritage Trust (NHT) program. In 2007 the Australian Government announced a five year extension of the NHT program running from 2008/09 to 2013/14 funded at A$2 billion nationally (Australian Government, 2007). In preparation for this program the regional bodies, Queensland Government and Australian Government agreed to develop a decision model to inform the allocation of funds across Queensland regions. However, an election on 24 November 2007 resulted in a change of government with a new prime minister and cabinet. On 14 March 2008 the newly formed cabinet announced that NHT would be replaced by the ‘‘Caring for our Country’’ program (CfOC) (DAFF, 2008). This removed the immediate need to apply a decision model because CfOC involves
a different model for regional funding. Nevertheless, the concepts of resource sharing and allocation expressed through the decision model were real. All parties were prepared to apply the model to allocate NHT funds. Therefore, the modelling concepts, conflict resolution and consensus building exercises were real for the Queensland Government, Federal Government and regional NRM bodies. At the time of writing, prior to the announcement of CfOC, a series of bilateral agreements, which would define the objectives and operation of the NHT in each Australian State and Territory, were in the final stages of negotiation but not yet signed and released. In 2006 the Australian Natural Resource Management Ministerial Council endorsed a framework for future natural resource management programs. This agreement between the Queensland and Australian Governments provides most authoritative statements about what the NHT seeks to achieve in coming years. The agreement states that investments under future natural resource management programs will be focused on six themes (Ministerial Council, 2006): 1. Biodiversity decline. This involves protecting Australia’s flora and fauna assets from threats such as invasive species, climate change, habitat loss and other forms of land degradation. 2. Salinity and water quality. This includes land and water salinisation, and other forms of water pollution such as nutrient and sediment runoff. 3. Coastal and peri-urban areas. Rapid population growth and urban development in many of Australia’s peri-urban and coastal areas has created environmental, social and economic pressures. 4. Productive sustainable landscapes. The maintenance and enhancement of Australia’s productive agricultural landscapes is a key outcome for the Natural Heritage Trust. 5. Capacity-building and institutional change. This involves building the capacity of individual people (e.g. farmers), communities and institutions to manage natural resources. This includes indigenous land management. 6. Cross cutting themes. This includes matters such as adaptation to climate change, enhanced indigenous land management, the design of market based instruments and achieving greater participation by local government. Under current arrangements the NHT is delivered through some 56 regional bodies across Australia. In the state of Queensland there are 14 regions (Fig. 1). The Queensland and Australian Governments aim to provide each regional body with an indicative budget prior to the commencement of the third phase of the NHT program on 1 July 2008. The indicative budget provides guidance on fiscal resources that will be available to the region in coming years. This is crucial information for effective planning and management. Setting regional budgets introduces a resource allocation problem: How much money should each region be given? This has much in common with other environmental resource allocation where a fixed quantity is being assigned to alternative users to achieve ecological and social objectives. Our task was to build a decision support model that informed, but did not replace, decision makers in setting budgets. The construction and application of the decision model was guided by three main groups: 1. The Queensland and Australian Government cabinet ministers with environmental and natural resource management portfolios relevant to the NHT. These democratically elected persons hold final accountability for decisions on indicative regional NHT budgets in Queensland. They will consider the recommendation of the joint steering committee.
S. Hajkowicz / Journal of Environmental Management 90 (2009) 2737–2745
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Fig. 1. Natural resource management regions in Queensland and modelled budget shares (note: these shares have not been implemented in policy and future programs are likely to have different requirements).
2. The joint steering committee (JSC). The JSC comprises senior government staff and is responsible for managing the NHT in Queensland. The JSC provides advice to the relevant ministers on the indicative budgets. The JSC were the ‘‘hands on’’ decision makers guiding the design and application of the model. 3. The regional groups collective (RGC). The RGC comprises a representative from each of Queensland’s 14 natural resource management regions. Each regional body has a board of community members, appointed by the State and Federal government ministers, who appoint an executive officer and staff. The regional bodies represent the key stakeholder group as they are heavily impacted by the decision. Their opinions and concerns were sought in the design and application of the decision model. The selection of stakeholders in this study was not part of the research design. The stakeholders were, instead, ‘‘supplied’’ through existing political and institutional structures. For some, this may raise questions of representativeness. However, I argue that these processes, borne of a live democratic system, often provide the best possible stakeholder representation. Selection of persons to guide the decision by researchers can introduce other elements of bias. Questions of putting multiple stakeholder input into an MCA model are given more detailed attention in prior
research (De Marchi et al., 2000; Messner et al., 2006; Stagl, 2006; Hajkowicz, 2008). Here we assume that Australia’s current political institutions, elected representatives and the persons they appoint provide adequate stakeholder representation. The decision model was not required to find an ‘‘answer’’ but rather to inform the joint steering committee’s recommendation along with other relevant information. Consequently, it was important that the model and decision making process provided a learning tool which helped decision makers explore complex trade-offs. Prior to the application of formalised decision procedures in 2005 the allocations of funding were decided by processes of negotiation and arbitration between all three governance levels mentioned above (Hajkowicz, 2007b). There was no well defined or accepted procedure. In reviews of the NHT the Australian National Audit Office have been critical about the lack of analytic rigour and transparent decision processes to target investments (ANAO, 1997, 2008). 4. Computing and applying the needs index Building an environmental index involves identifying measurable criteria, weighting them, transforming them into commensurate units and combining them via some type of multi-criteria function (Hajkowicz, 2006). In a review of the US Environmental
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Protection Agency’s Index of Watershed Indicators, Schultz (2001) argues that formalisation of indices using multi-attribute utility theory (i.e. multiple criteria analysis) improves analytic rigour, repeatability and transparency. Today a wide range of multi-criteria decision support tools are available to construct environmental indices, see Figueira et al. (2005) for a recent review. We used the MCA method of compromise programming (CP) to compute the NHT needs index in Queensland. Widely applied in the field of environmental management (Bojo´rquez-Tapia et al., 2005; Marshall and Homans, 2006), compromise programming permits the specification of a best (ideal) and worst (anti-ideal) value for each criterion. Compromise programming seeks to minimise the distance from the ideal solution across all criteria. The ability to set, and control, performance benchmarks within criteria is a key strength of compromise programming in environmental problems where thresholds are often of much importance (Hajkowicz and Higgins, 2007). Ballestero et al. (2002) also use compromise programming to allocate water licenses prior to the establishment of markets in Spain. In this study we adapt the compromise programming algorithm to assign a higher score (ui) to a region with greater need as follows (Hajkowicz et al., 2008):
2 ui ¼ 4
m X j¼1
wjc 1
c 31=c fbj xij 5 fbj fwj
(1)
2 ui ¼ 4
m X
31=c qffiffiffiffiffiffiffiffiffiffiffiffiffiffi c wj 1 gij2 5
2 ui ¼ 4
m X
wj
3 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffic 1=c 2 5 1 1 ðgij 1Þ
(3)
j¼1
0 1c 31=c loge ð0:5Þ*gijd 7 6X B C 7 6 m B C 7 md ui ¼ 6 wj Be C 7 6 @ A 5 4 2
(4)
j¼1
In Equation (4) the parameters m and d control the shape of the sigmoid and in Equations (2)–(4) gij is determined by:
gij ¼
fbj xij fbj fwj
(5)
Once the needs index has been calculated for each region the equalisation of financial resources is simply (Hajkowicz, 2007b):
mi ¼ M
ui n P
(6) ui
i¼1
In this equation there is a finite set of criteria (j ¼ 1, ., m) and a finite set of decision options (i ¼ 1, ., n). Percentage weights (wj) show the importance of each criterion. The raw performance score for each option against each criterion is given by xi,j. This is transformed into a utility metric using a best (ideal) value (fbj) and a worst (anti-ideal) value (fwj) for each criterion. In this model the best value was typically equal to the maximum across all regions. The worst value was typically set to zero. The parameter c controls the importance of the maximal deviation from the ideal. In the final analysis the linear transformation shown in Equation (1) above was used for each criterion, but we did make available four alternative transformations when the relationship was not linear. These included a concave, convex and sigmoidal transformation:
Here mi is the budget assigned to region i and M is the total funding available. This equation assures that the financial capability of each region to address natural resource management problems is equalised. Each region is given the same dollars-per-unit need. During the project we emphasised that this did not mean equal shares to counter some perceptions that emerged. The money was targeted to where needs were greatest and regions with greater need attracted greater investment. 5. Identifying the criteria Following Ridgley and Lumpkin (2000) the criteria used to define the needs index were represented to decision makers in
Endangered regional ecosystems (C1) Ecosystem health
Regional ecosystems of concern (C2) Remnant habitat as a percentage of pre-clear extent (C3) Area of important wetlands (C4) Soil erosion (C5) Nitrogen leaching (C6)
Natural resource management needs score
(2)
j¼1
Soil and water quality
Phosphorus leaching (C7) Area of salinity risk (C8) Acid sulphate soils (C9) Length of coastline (C10)
Geographic extent
Population pressures
Area of region (C14) Length of major rivers (C15) Rural residential area (C11) Population growth rate (absolute value) (C12)
Indigenous natural resource management
Portion of population that is indigenous (C16)
Economic importance of agriculture
Gross value of primary production (C13)
Area of indigenous land tenure and use agreements (C17)
Percent of workforce employed in primary industries (C19) Area of heritage sites (C18)
Fig. 2. Final criteria used in the analysis.
S. Hajkowicz / Journal of Environmental Management 90 (2009) 2737–2745
hierarchical form (Fig. 2). Identifying evaluation criteria is a crucial, and oftentimes challenging, task in group MCA problems with multiple decision makers (Hajkowicz, 2008). The criteria set the agenda for the decision analyst. They determine what data is collected and how the decision problem is defined. Several authors have suggested that the selection of criteria is of far greater importance than the choice of MCA algorithm, but the question of how to choose criteria has received comparatively little attention in the scientific literature (Kasanen et al., 2000; Janssen, 2001; Hajkowicz and Higgins, 2007). In recent times formalised approaches for criteria selection have emerged through research on problem structuring methods and problem structuring software (Mingers and Rosenhead, 2004; Scheubrein and Zionts, 2006). The approach for identifying criteria adapted the ‘‘Brainstorming and Voting Process’’ used by Mendoza and Prabhu (2000) in an application of MCA for forest management. This involves briefing the decision making group on an initial set of criteria and explaining the MCA process. In our process the group was then able to assess (brainstorm) each criterion individually and vote for those they thought should be retained, ousted or introduced. Consultation on the criteria was conducted with the regional bodies, Queensland Government agencies and Australian Government agencies. It commenced with a workshop in Brisbane on 25 September 2007 with a representative of each regional body present. Typically this was the chief executive officer although in some cases a member of staff attended in his/her place. Also present were around a dozen Queensland and Australian Government staff with responsibility for managing or informing the NHT program. Prior to the meeting all attendees received a set of 18 proposed evaluation criteria. At the meeting the MCA model, and how it would be used to help set regional NHT budgets, was explained. Every criterion was then discussed in detail. Each regional body was given 5 red ticks to place next to criteria they thought important. There was consensus on most of the criteria that should be retained and removed. The meeting attendees also identified 5 additional criteria for which datasets should be sought, and if suitable data were found, included in the MCA model. The criteria before and after the brainstorming workshop are shown in Table 1. These were then translated into the final set of criteria show in hierarchical form earlier (Fig. 2). The data were drawn from geographic information system datasets held by State and Federal Government agencies. Each of the 14 regional bodies supplied one set of criteria weights. There was a requirement that weights add to 100 percent at each branch of the hierarchy. In most cases several staff in the region worked on the criteria weighting exercise and it was coordinated by the chief executive officer. Regional bodies were given detailed descriptions of the criteria and each criterion was discussed at a seminar beforehand. Most criteria were explored interactively on a geographic information system which displayed the range of values across Queensland. However, decision makers were not explicitly shown the performance data, or minimum and maximum values, for each criterion at the time of weighting. They were given a chance to interactively change their weights when the full model was revealed later in the decision process. Whilst this limited options for strategic bias in criteria weighting, i.e. placing higher weight on those criteria which give your region more money, it may have introduced ambiguity in the weight setting exercise. Decision makers should see performance data, or the range (minimum–maximum) at the time of weighting (Keeney, 1992). This will clearly influence the weights that are chosen. In this study we relied on detailed descriptions, and a good understanding, of each criterion with an opportunity for interactive adjustment in light of performance data at a later time. Decision makers should
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Table 1 Identifying the criteria through stakeholder consultation. First Iteration (prior to stakeholder meeting)
Second Iteration (following voting and brainstorming session)
1. Amount of soil erosion 2. Area of medium or high salinity risk 3. Area of native woody vegetation outside reserves 4. Area of priority habitat for cassowaries, mahogany gliders, koalas and other priority species if data is available 5. Number of Endangered Vulnerable and Rare (EVR) species sightings within the region 6. Extent of heritage (world heritage and register of the national estate) areas 7. Area of important wetlands 8. Gross value of agricultural production 9. Area of agricultural land use 10. Number of persons employed in primary industries 11. Area of indigenous land tenure within the region 12. Area of indigenous land use agreements (ILUAs) within the reigon 13. Number of indigenous persons in region 14. Area of the NRM region. 15. Length of rivers/streams within the NRM region 16. Length of coastline within the NRM region 17. Human population within the region 18. Climate change (drought/rainfall variability extreme weather)
To Be Excluded 1. Ground and surface water use 2. Area of native woody veg from SLATS (satellite data) 3. Sightings of endangered vulnerable or rare species 4. Area of priority habitat (koalas, cassowaries, mahogany gliders) 5. Area of agriculture 6. Area of indigenous land tenure 7. Area of indigenous land use agreements 8. Climate change To Be Included 1. Soil erosion 2. Area of salinity risk 3. Natural and indigenous heritage area 4. Area of important wetlands 5. Gross value of agricultural production 6. Percentage of population that is indigenous 7. Terrestrial area of region, excluding ocean 8. Length of rivers 9. Length of coastline 10. Absolute value of population growth rate 11. A criterion relating to priority and/or threatened species or habitats 12. A criterion relating to native remnant vegetation (including both woody vegetation and grasslands) outside protected areas To be Subject to Further Analysis 1. Area of weeds and pests 2. Area of acid sulphate soils 3. Rural residential area as a proxy for peri-urban land 4. Number of persons employed in extractive industries (or some other measure of mining activity). 5. Nitrogen and phosphorus runoff (tonnes per year)
have been explicitly shown the defined criteria performance ranges at the time of weighting. Doing so may have led to greater agreement on the weights. This shortcoming perhaps highlights some of the challenges of designing an MCA model within the confines of a real-world decision process with inflexible timeframes. 6. Results The spread of weights for branches and criteria is shown in boxplots (Figs. 3 and 4). The average (median) importance ordering of the branches, from most important to least important, was: 1. 2. 3. 4. 5. 6. 7.
Ecosystem health Soil and water quality Economic value of agriculture Geographic extent Population pressures Indigenous natural resource management Area of heritage sights
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S. Hajkowicz / Journal of Environmental Management 90 (2009) 2737–2745 45% Max 40% 3rd quartile
35%
Weight
30% 25% Median
20% 15% 10%
Min
1st quartile
5% 0% Ecosystem health
Soil and water quality
Geographic extent
Population pressures
Indigenous NRM
Economic value Area of heritage of agriculture sites
Fig. 3. Box plot of branch weights for regional bodies (n ¼ 14). A higher weight indicates greater importance.
The criteria receiving the highest weights were the gross value of agricultural production (C13), soil erosion (C5) and endangered regional ecosystems (C1). Those receiving the lowest weights were nitrogen leaching (C6); phosphorus leaching (C7) and acid sulphate soils (C9). It is interesting to note the considerable spread in weights across all 14 groups. For example, the most important criterion – the gross value of agricultural production – ranges in weight from 0.3% to 35%. The groups were also in disagreement about the relative importance of the area of the region (C14) with the weight varying from 0% to 36%. These large variations may have resulted because decision makers were not shown criteria ranges (minimum–maximum) at the time of weighting and had different understandings. A check was made to see whether there may have been strategic bias whereby regions with larger areas assigned greater weight to this criterion to increase their budget. This was considered a possibility for area (C14) as a region’s area is easy to compute and/ or see on a map. A correlation test between area of region and
weight produced an r-square (RSQ) of 0.1. Correlations between all other criteria and their weights did not reveal strong evidence of weighting bias. The median RSQ was 0.15, the minimum was 0.001 and the maximum, for length of rivers (C15) was 0.575. Therefore, the results do not provide evidence of strategic bias. Regions did not appear to be weighting criteria strategically to maximise their budget. The break-up of funding resulting from the average (arithmetic mean) of the regional body weights was shown previously in Fig. 1. This represents a significant change from historical funding. Table 2 shows the change in relative share for each regional body compared to historical funding. The historical funding is for the period 2000– 2007 and includes phase two of the NHT and another program called the National Action Plan for Salinity and Water Quality. Two regions, Desert Channels and Cape York, increase their relative shares by over 6 percent. The relative share decreases by 4 percent for 4 regions of Burdekin, Condamine, Border Rivers and Southern Gulf. These swings show that adoption of the MCA model would cause significant changes for regional bodies.
40%
35%
30%
Weight
25%
20%
15%
10%
5%
0% C1
C2
C3
C4
C5
C6
C7
C8
C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19
Fig. 4. Box plot of criteria weights (n ¼ 14; see Fig. 2 for criteria descriptions). A higher weight indicates greater importance.
S. Hajkowicz / Journal of Environmental Management 90 (2009) 2737–2745 Table 2 Change in relative shares under historic funding (Note: these are modelled shares and have not been implemented in policy due to the change in program design by government. Future programs will respond to different policy objectives and datasets). Regional Body
Proposed Share (percent)
Border Rivers Maranoa Balonne Burdekin Burnett Mary Cape York Condamine Desert Channels Fitzroy Mackay Whitsunday Northern Gulf South East Queensland South West Queensland Southern Gulf Torres Strait Wet Tropics
Change in Historic Share (percent)
8
5.1
8 7 10 4 12 11 4 7 6 7 8 3 5
4.3 2.4 6.1 4.1 6.6 3.1 0.4 2.9 3.6 2.2 4.7 1.1 0.5
predict, at the time of the decision. Under the NHT regional bodies may fund activities as diverse as data gathering, policy development, publicity campaigns, tree planting, weed removal and creek bank restoration. Without knowledge of these activities it is impossible to define the functional relationship between expenditure and benefits (i.e. Equation (7)). Secondly, even if the activities were known the scientific models and datasets required to link expenditure to outcomes are seldom available. This same issue was found to be an obstacle in targeting payments under US conservation programs. The United States Department of Agriculture USDA reports that process models to link investments to outcomes often do not exist (Weinberg and Claassen, 2006; p2): ‘‘Ideally, a measurement tool would exist that links conservation program incentives to the environmental indicator of interest. However, agricultural emissions from individual farms generally cannot be monitored at a reasonable cost. The path from policy incentives to environmental outcomes is complex and multistage.’’
It is stressed that due to the shift in program design, from NHT to CfOC, these allocations no longer apply. But at the time of analysis this was a real decision problem for the regional groups and there was an expectation that the result would be used by policy makers. Sensitivity analysis was conducted by computing elasticities for each region and criterion. Here we define elasticity as the percentage change in a region’s budget resulting from a one percentage increase in each criterion’s weight. The criteria with the highest average elasticities (shown in parentheses) across all regions included C9 acid sulphate soils (þ1.1), C13 gross value of agriculture (þ1.1) and C12 population growth rate (þ1). The model was least sensitive to changes in C14 area of region and C11 (þ0.6) rural residential area (þ0.6).
Given these obstacles the concept of fiscal equalisation (Hajkowicz, 2007b) will often be more appropriate than the optimisation form expressed by Wu and Boggess (1999). A fiscal equalisation model targets financial resources, or any other environmental resource, on the basis of need. Each region is allocated the same dollars-per-unit need. This provides each region with the same financial capability to address its natural resource management problems. This approach has potential for application in some of the world’s largest natural resource management and conservation programs such as: -
-
7. Policy implications The MCA model designed and applied in this study does not search for an optimal solution given the common definition of ‘‘optimal’’. With respect to the US Conservation Reserve Program Wu and Boggess (1999) describe a model for optimising outcomes over multiple regions. For the problem of inter-regional allocation this requires defining a relationship between environmental expenditure and environmental benefit:
bi ¼ f ðmi ; a; b; c.Þ
(7)
In this case parameters a, b, c . are environmental or institutional factors which influence the return on investment. The optimisation model would then be to set mi for each region such that we:
Maximise
n X
bi
(8)
mi M
(9)
i¼1
Subject to
n X
2743
i¼1
Many economists would argue this is the most appropriate model structure. It aims to solve for the optimal outcome. However, in the case of setting regional NHT budgets in Queensland, and many other such cases, solving this problem formulation will not be feasible. There are two main reasons. Firstly, the actual environmental management activities that will occur within a region are unknown, and almost impossible to
-
The Environmental Quality Incentives Program. In fiscal 2006 around US$652 million were allocated to the 50 States using 30 weighted indicators (GAO, 2006). The Farm and Ranch Lands Protection Program. In fiscal 2007 around $73 million were allocated to the States using 11 weighted indicators. (NRCS, 2004, 2007a; Cattaneo et al., 2006). The Wetlands Restoration Program. In fiscal 2007 around US$228 million were allocated amongst the States using 4 weighted indicators (NRCS, 2007b).
It could also be applied in the allocation of scarce land, water or other environmental resources. These decisions are likely to involve unclear project activities (or uses of the resource) and have similar limitations with respect to datasets and scientific models. Formalisation of the allocation problem using MCA in these, and many other such programs, could help improve transparency and auditability (Gamper and Turcanu, 2007). Increasingly researchers are seeing models as a learning tool within a broader decision process than a means of producing a definitive answer (Ramanathan, 2001; Herath, 2004; Hermans et al., 2007). In this study it was found that stakeholders, i.e. the regional bodies, opted to use the MCA model even though it was proposing a major re-allocation of funds with winners and losers. Furthermore, the stakeholders did not introduce strategic bias into criteria weights. There was no evidence that regions were weighting more heavily those criteria which they knew would increase their fund allocation. This suggests a willingness to consider the common good through the MCA decision model. It appeared that MCA was not only a decision tool but was changing the way people approached the decision problem. Whether this differs from what would have occurred in an unaided, or other type of, decision environment is mostly
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untested. There have been some studies to compare MCA with unaided decisions which give evidence of different outcomes (Hajkowicz, 2007a). But we do not yet have a comprehensive understanding of how the decision procedure is changed by MCA in both positive and negative ways. Future research could examine how and whether an MCA model, or other rational model, can help decision makers separate their own objectives from broader policy objectives. 8. Conclusion Decisions involving the allocation of environmental resources amongst regions will always be challenging. They will involve multiple stakeholders holding multiple objectives and are often characterised by conflict, uncertainty and, sometimes, confusion about purpose and policy objectives. Unproductive conflict and political deadlock, where decisions do not occur and new policies are blocked, can easily occur. A multi-criteria decision model can provide an effective means for stakeholders to explore and debate a resource sharing problem. Whilst it is difficult to reach unanimous agreement the decision model can provide structure for the debate. This study found that stakeholders supported the multiple criteria analysis (MCA) model even when it disadvantaged them compared to historical decisions. There was no evidence of strategic bias where regions weighted more heavily those criteria which increased their funding. The main benefit of MCA was not in the provision of an ‘‘answer’’ to the allocation problem, but in the provision of a process acceptable to all stakeholders which could be used to generate a solution in a transparent, robust and auditable manner. This can be improved by ensuring decision makers are shown criteria ranges (minimum–maximum) at the time of weighting. An MCA has been adopted by stakeholders in phase two and the proposed phase three of the Australian Natural Heritage Trust. An MCA model is now being developed by CSIRO to inform investment decisions under the federal government’s A$200 million rescue package for the Great Barrier Reef. The acceptance of MCA, and formalised decision procedures, over ad-hoc methods represents a major advance in how Australians are making natural resource management choices. Further research is required to investigate the extent to which, and how, the MCA model helped build consensus and how it impacts, positively and negatively, an otherwise unaided decision procedure. This may be initially addressed by a more in-depth analysis strategic bias in criteria weighting by stakeholders. Acknowledgements This study was funded by the Queensland Government Department of Natural Resources and Water. It was made possible by the involvement of all fourteen of Queensland’s Natural Resource Management Groups through the Regional Groups Collective. It was also supported by the Australian Government whose staff were actively involved in the meetings. The author is very grateful for the detailed and helpful review comments of several anonymous reviewers. References ANAO, 1997. Commonwealth Natural Resource Management and Environment Programs. Audit Report No. 36 1996–97. The Australian National Audit Office, Canberra. ANAO, 2008. Regional Delivery Model for the Natural Heritage Trust and the National Action Plan for Salinity and Water Quality. Audit Report No. 21 2007– 08. The Australian National Audit Office, Canberra.
Australian Government, 2007. Budget Paper No. 2, Budget Measures 2007–08. Australian Government, Canberra. Ballestero, E., Alarco´n, S., Garcı´a-Bernabeu, A., 2002. Establishing politically feasible water markets: a multi-criteria approach. Journal of Environmental Management 65, 411–429. Bojo´rquez-Tapia, L.A., Sa´nchez-Colon, S., Florez, A.F., 2005. Building consensus in environmental impact assessment through multicriteria modeling and sensitivity analysis. Environmental Management 36, 469–481. Burton, P.S., 1996. Land use externalities: mechanism design for the allocation of environmental resources. Journal of Environmental Economics and Management 30, 174–185. Cattaneo, A., Hellerstein, D., Nickerson, C., Myers, C., 2006. Balancing the Multiple Objectives of Conservation Programs. Economic Research Service, U.S. Department of Agriculture, Washington DC, pp. 55. Chang, N.B., Wen, C.G., Wu, S.L., 1995. Optimal management of environmental and land resources in a reservoir watershed by multiobjective programming. Journal of Environmental Management 44, 144–161. DAFF, 2008. Caring for Our Country – Better Land Management, Less Red Tape. The Hon Tony Burke MP, Media Release DAFF08/024BJ. Department for Agriculture, Fisheries and Forestry, Canberra. Davis, D., Tisdell, C., 1996. Economic management of recreational scuba diving and the environment. Journal of Environmental Management 48, 229–248. De Marchi, B., Funtowicz, S.O., Lo Cascio, S., Munda, G., 2000. Combining participative and institutional approaches with multicriteria evaluation: an empirical study for water issues in Troina, Sicily. Ecological Economics 34, 267–282. Diem, J.E., Comrie, A.C., 2001. Allocating anthropogenic pollutant emissions over space: application to ozone pollution management. Journal of Environmental Management 63, 425–447. Figueira, J., Greco, S., Ehrgott, M. (Eds.), 2005. Multiple Criteria Decision Analysis: State of the Art Surveys. Springer, New York. Gamper, C.D., Turcanu, C., 2007. On the governmental use of multi-criteria analysis. Ecological Economics 62, 298–307. GAO, 2006. UDSA Should Improve Its Process for Allocating Funds to States for the Environmental Quality Incentives Program. United States Government Accountability Office, Washington DC, pp. 1–57. Gough, J.D., Ward, J.C., 1996. Environmental decision making and lake management. Journal of Environmental Management 48, 1–15. Hajkowicz, S.A., 2006. Multi-attributed environmental index construction. Ecological Economics 57, 122–139. Hajkowicz, S.A., 2007a. A comparison of multiple criteria analysis and unaided approaches to environmental decision making. Environmental Science and Policy 10, 177–184. Hajkowicz, S.A., 2007b. Allocating scarce financial resources across regions for environmental management in Queensland, Australia. Ecological Economics 61, 208–216. Hajkowicz, S.A., Higgins, A., 2007. A comparison of multiple criteria analysis techniques for water resource management. European Journal of Operational Research 184, 255–265. Hajkowicz, S.A., 2008. Supporting multi-stakeholder environmental decisions. Journal of Environmental Management 88, 607–614. Hajkowicz, S.A., Spencer, R., Higgins, A., Marinoni, O., 2008. Evaluating water quality investments using cost utility analysis. Journal of Environmental Management 88, 1601–1610. Hajkowicz, S., Collins, K., Cattaneo, A., 2009. Review of agri-environment indexes and Stewardship payments. Environmental Management 43, 221–236. Herath, G., 2004. Incorporating community objectives in improved wetland management: the use of the analytic hierarchy process. Journal of Environmental Management 70, 263–273. Hermans, C., Erickson, J., Noordewier, T., Sheldon, A., Kline, M., 2007. Collaborative environmental planning in river management: an application of multicriteria decision analysis in the White River Watershed in Vermont. Journal of Environmental Management 84, 534–546. Janssen, R., 2001. On the use of multi-criteria analysis in environmental impact assessment in The Netherlands. Journal of Multi-Criteria Decision Analysis 10, 101–109. Johansson, R.C., Cattaneo, A., 2006. Indices for working land conservation: form affects function. Review of Agricultural Economics 28, 567–584. Kampas, A., White, B., 2003. Selecting permit allocation rules for agricultural pollution control: a bargaining solution. Ecological Economics 47, 135–147. Kasanen, E., Wallenius, H., Wallenius, J., Zionts, S., 2000. A study of high-level managerial decision processes, with implications for MCDM research. European Journal of Operational Research 120, 496–510. Keeney, R.L., 1992. Value Focused Decision Making: a Path to Creative Decision Making. Harvard University Press, Cambridge, MA. Marshall, E.P., Homans, F.R., 2006. Juggling land retirement objectives on an agricultural landscape: coordination, conflict, or compromise? Environmental Management 38, 37–47. Mendoza, G.A., Prabhu, R., 2000. Development of a methodology for selecting criteria and indicators of sustainable forest management: a case study on participatory assessment. Environmental Management 36, 469–481. Merrifield, J., Firoozi, F., 1995. Renewable resource use: transition from capture to allocation and optimal stock recovery. Journal of Environmental Management 44, 195–211. Messner, F., Zwirner, O., Karkuschke, M., 2006. Participation in multi-criteria decision support for the resolution of a water allocation problem in the Spree River basin. Land Use Policy 23, 63–75.
S. Hajkowicz / Journal of Environmental Management 90 (2009) 2737–2745 Mimi, Z.A., Sawalhi, B.I., 2003. A decision tool for allocating the waters of the Jordan River Basin between all Riparian parties. Water Resources Management 17, 447–461. Mingers, J., Rosenhead, J., 2004. Problem structuring methods in action. European Journal of Operational Research 152, 530–554. Ministerial Council, 2006. Framework for Future NRM Programmes. Natural Resource Management Ministerial Council, Australian Government, Canberra. p. 10. NRCS, 2004. Farm and Ranch Lands Protection Program: Farm Bill 2002 Fact Sheet. Natural Resource Conservation Service, United States Department of Agriculture, Washington DC, p. 2. NRCS, 2007a. Farm and Ranch Lands Protection Program FY 2007 Financial and Technical Assistance Dollars to States. Natural Resource Conservation Service, United States Department of Agriculture, Washington DC. NRCS, 2007b. Wetland Reserve Program: Farm Bill 2002, Fact Sheet. Natural Resource Conservation Service, United States Department of Agriculture, Washington DC, p. 3. Ramanathan, R., 2001. A note on the use of the analytic hierarchy process for environmental impact assessment. Journal of Environmental Management 63, 27–35. Ridgley, M., Lumpkin, C.A., 2000. The bi-polar resource-allocation problem under uncertainty and conflict: a general methodology for the public decision-maker. Journal of Environmental Management 59, 89–105.
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Scheubrein, R., Zionts, S., 2006. A problem structuring front end for a multiple criteria decision support system. Computers and Operations Research 33, 18–31. Schultz, M.T., 2001. A critique of EPA’s index of watershed indicators. Journal of Environmental Management 62, 429–442. Stagl, S., 2006. Multicriteria evaluation and public participation: the case of UK energy policy. Land Use Policy 23, 53–62. Syme, G.J., Nancarrow, B.E., McCreddin, J.A., 1999. Defining the components of fairness in the allocation of water to environmental and human uses. Journal of Environmental Management 57 (1), 51–70. Vesterdal, M., Svendsen, G.T., 2004. How should greenhouse gas permits be allocated in the EU? Energy Policy 32, 961–968. Weinberg, M., Claassen, R., 2006. Conservation Program Design: Rewarding Farm Practices Versus Environmental Performance. Economic Brief Number 5. Economic Research Service, United States Department of Agriculture, Washington DC, pp. 6. Wu, J., Boggess, W.G., 1999. The optimal allocation of conservation funds. Journal of Environmental Economics and Management 38, 302–321. Zeleny, M., 1973. Compromise programming. In: Cocharane, J.L., Zeleny, M. (Eds.), Multiple Criteria Decision Making. University of Southern Carolina Press, Columbia, S.C, pp. 262–301. Zeleny, M., 1982. Multiple Criteria Decision Making. McGraw-Hill, New York.
Contents lists available at ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Cutting the cake: Supporting environmental fund allocation decisions Stefan Hajkowicz* CSIRO Sustainable Ecosystems, 306 Carmody Rd, St Lucia, QLD 4067, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 April 2008 Received in revised form 9 February 2009 Accepted 7 March 2009 Available online 16 April 2009
This paper describes a decision support model for allocating financial resources amongst multiple user groups in environmental management problems. The model is based on the multiple criteria analysis (MCA) method of compromise programming. It was used to inform the allocation of Natural Heritage Trust funds across 14 regions in Queensland, Australia. The model targets funding to those regions with greater natural resource management needs. Need is determined by 19 weighted criteria relating to natural resource assets and threats. The model was accepted by the Australian Government, Queensland Government and regional groups as an appropriate means for allocating program funds; first in 2005 and then again, with improvements, in 2007. This paper shows that an MCA model can improve the transparency, auditability and acceptance of allocation decisions which would otherwise be heavily politicised. Crown Copyright Ó 2009 Published by Elsevier Ltd. All rights reserved.
Keywords: Environmental fund allocation Multiple criteria analysis Decision support Conflict resolution
1. Introduction One of the most common and challenging dilemmas facing natural resource managers is how to allocate a fixed resource (e.g. money, land, water) amongst competing users (e.g. regions, stakeholders, countries). Such decisions will typically involve multiple stakeholders, multiple policy objectives and considerable uncertainty (Gough and Ward, 1996). Under these conditions, a formalised decision procedure can improve transparency, analytic rigour, stakeholder involvement and auditability (Hajkowicz, 2007a). In this paper we describe a decision model for allocating environmental funds amongst 14 regions in Queensland, Australia under the proposed third phase of the Natural Heritage Trust (NHT) program1. The model builds upon earlier work by Hajkowicz (2007b) in Phase Two of the NHT. Since this time the algorithms, decision procedure and stakeholder engagement process have improved substantially. The present paper has an emphasis how the decision was made and influenced by stakeholders. An analysis is presented of the stakeholder groups who guided the decision, how the decision problem was structured, how the model gained acceptance and how the model compares to an optimisation
* Tel.: þ61 7 3214 2327; fax: þ61 7 3214 2308. E-mail address: [email protected] URL: http://www.csiro.au/cse 1 Following the 2007 Federal Election the Natural Heritage Trust (NHT) program was replaced by a new program, with similar policy objectives, called ‘‘Caring for Our Country’’. This study was completed when the NHT was still in effect and its continuation in some form was anticipated by most stakeholders.
approach. An iterative process for identifying, filtering and weighting criteria was introduced via a series of workshops. With the benefit of hindsight we can now see how the evolving decision models are changing historical patterns of investment. The paper presents a framework, and principles, for the allocation of fixed resources amongst competing users in an environmental management setting. It provides an operational definition of ‘‘sharing’’ via the concept of equalisation. The aim is to equalise the financial capability of each region to address its natural resource management needs. The model uses an index of need based on 19 weighted criteria relating to natural resource assets and threats. The allocation is determined such that dollars-per-unit need is equal for each region. This means funding is targeted towards those regions with greatest need. Compromise programming (CP; Zeleny, 1973, 1982), a multiple criteria analysis (MCA) technique, is used to weight indicators and construct a needs index. In this case the fixed resource being allocated is money. However, the model design, decision process and many of the issues we encountered would be similar for other resource types.
2. Decision models for resource allocation The question of how to allocate fixed resources amongst multiple users has received much attention in the field of environmental management. Much effort has focused on the allocation of water resources. For example, Syme et al. (1999) present an operational definition of ‘‘fairness’’ in water allocation decisions based on social psychological theories of justice and equity. Ballestero et al. (2002) look at the question of how to allocate water
0301-4797/$ – see front matter Crown Copyright Ó 2009 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2009.03.002
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use rights prior to the establishment of water markets with a case study of the Lorca region in Spain. In addition to water there have been studies on the allocation of many other fixed resources such as pollution rights (Diem and Comrie, 2001), resource extraction or harvesting rights (Merrifield and Firoozi, 1995) and access to recreational sites for tourism operators (Davis and Tisdell, 1996). Some of the allocation models are based on economic efficiency whereby the aim is to maximise benefit subject to a resource availability threshold (Burton, 1996; Chang et al., 1995). Other models are primarily concerned with fairness and equity issues and aim to share the resource (Kampas and White, 2003). Yet other models combine both elements of sharing and optimisation (Vesterdal and Svendsen, 2004). Whether to optimise or share is a common theme running through the literature on environmental resource allocation. It is given further consideration later in this paper. Whilst much has been written on the subject of resource allocation there have been fewer attempts to operationalise concepts within decision models and even fewer cases of their real-world application. In one case Mimi and Sawalhi (2003) design a multicriteria decision model to allocate the waters of the Jordon River amongst Israel, Palestine, Syria and Lebanon. Their model is based on 9 criteria for equitable water use and they suggest it may help negotiators reach solutions. Their algorithms provide an operational definition of ‘‘fair’’. However, the model is presented as ‘‘food for thought’’ and has not yet been implemented. One model, which has been implemented to allocate large resources, was developed by the United States Department of Agriculture (USDA). The USDA uses a multi-criteria model, containing 30 weighted criteria, to allocate funding under the Environmental Quality Incentives Program (EQIP) to each of the 50 states in the US. In fiscal 2006 around US$652 million were allocated using this model. The US General Accounting Office reviewed the model and called for some improvements (GAO, 2006). In particular the GAO suggested providing a better rationale for each criterion, such that it was linked to policy objectives, improving the criteria weighting process and using more up-to-date datasets. Johansson and Cattaneo (2006) explored the impact of changing the functional form of the EQIP allocation formula. They show that millions of dollars can be displaced by changing weights or by changing the functional form from an additive to geometric index. The sensitivity of the result to changes in weights highlights the importance of accurately capturing decision maker preferences. But in natural resource management programs the weighting task is often completed by technical staff with little reference to broader societal opinions (Hajkowicz et al., 2009). 3. Allocating environmental funds across Queensland’s regions This case study is concerned with allocating fiscal resources amongst regional bodies in Queensland under Australia’s Natural Heritage Trust (NHT) program. In 2007 the Australian Government announced a five year extension of the NHT program running from 2008/09 to 2013/14 funded at A$2 billion nationally (Australian Government, 2007). In preparation for this program the regional bodies, Queensland Government and Australian Government agreed to develop a decision model to inform the allocation of funds across Queensland regions. However, an election on 24 November 2007 resulted in a change of government with a new prime minister and cabinet. On 14 March 2008 the newly formed cabinet announced that NHT would be replaced by the ‘‘Caring for our Country’’ program (CfOC) (DAFF, 2008). This removed the immediate need to apply a decision model because CfOC involves
a different model for regional funding. Nevertheless, the concepts of resource sharing and allocation expressed through the decision model were real. All parties were prepared to apply the model to allocate NHT funds. Therefore, the modelling concepts, conflict resolution and consensus building exercises were real for the Queensland Government, Federal Government and regional NRM bodies. At the time of writing, prior to the announcement of CfOC, a series of bilateral agreements, which would define the objectives and operation of the NHT in each Australian State and Territory, were in the final stages of negotiation but not yet signed and released. In 2006 the Australian Natural Resource Management Ministerial Council endorsed a framework for future natural resource management programs. This agreement between the Queensland and Australian Governments provides most authoritative statements about what the NHT seeks to achieve in coming years. The agreement states that investments under future natural resource management programs will be focused on six themes (Ministerial Council, 2006): 1. Biodiversity decline. This involves protecting Australia’s flora and fauna assets from threats such as invasive species, climate change, habitat loss and other forms of land degradation. 2. Salinity and water quality. This includes land and water salinisation, and other forms of water pollution such as nutrient and sediment runoff. 3. Coastal and peri-urban areas. Rapid population growth and urban development in many of Australia’s peri-urban and coastal areas has created environmental, social and economic pressures. 4. Productive sustainable landscapes. The maintenance and enhancement of Australia’s productive agricultural landscapes is a key outcome for the Natural Heritage Trust. 5. Capacity-building and institutional change. This involves building the capacity of individual people (e.g. farmers), communities and institutions to manage natural resources. This includes indigenous land management. 6. Cross cutting themes. This includes matters such as adaptation to climate change, enhanced indigenous land management, the design of market based instruments and achieving greater participation by local government. Under current arrangements the NHT is delivered through some 56 regional bodies across Australia. In the state of Queensland there are 14 regions (Fig. 1). The Queensland and Australian Governments aim to provide each regional body with an indicative budget prior to the commencement of the third phase of the NHT program on 1 July 2008. The indicative budget provides guidance on fiscal resources that will be available to the region in coming years. This is crucial information for effective planning and management. Setting regional budgets introduces a resource allocation problem: How much money should each region be given? This has much in common with other environmental resource allocation where a fixed quantity is being assigned to alternative users to achieve ecological and social objectives. Our task was to build a decision support model that informed, but did not replace, decision makers in setting budgets. The construction and application of the decision model was guided by three main groups: 1. The Queensland and Australian Government cabinet ministers with environmental and natural resource management portfolios relevant to the NHT. These democratically elected persons hold final accountability for decisions on indicative regional NHT budgets in Queensland. They will consider the recommendation of the joint steering committee.
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Fig. 1. Natural resource management regions in Queensland and modelled budget shares (note: these shares have not been implemented in policy and future programs are likely to have different requirements).
2. The joint steering committee (JSC). The JSC comprises senior government staff and is responsible for managing the NHT in Queensland. The JSC provides advice to the relevant ministers on the indicative budgets. The JSC were the ‘‘hands on’’ decision makers guiding the design and application of the model. 3. The regional groups collective (RGC). The RGC comprises a representative from each of Queensland’s 14 natural resource management regions. Each regional body has a board of community members, appointed by the State and Federal government ministers, who appoint an executive officer and staff. The regional bodies represent the key stakeholder group as they are heavily impacted by the decision. Their opinions and concerns were sought in the design and application of the decision model. The selection of stakeholders in this study was not part of the research design. The stakeholders were, instead, ‘‘supplied’’ through existing political and institutional structures. For some, this may raise questions of representativeness. However, I argue that these processes, borne of a live democratic system, often provide the best possible stakeholder representation. Selection of persons to guide the decision by researchers can introduce other elements of bias. Questions of putting multiple stakeholder input into an MCA model are given more detailed attention in prior
research (De Marchi et al., 2000; Messner et al., 2006; Stagl, 2006; Hajkowicz, 2008). Here we assume that Australia’s current political institutions, elected representatives and the persons they appoint provide adequate stakeholder representation. The decision model was not required to find an ‘‘answer’’ but rather to inform the joint steering committee’s recommendation along with other relevant information. Consequently, it was important that the model and decision making process provided a learning tool which helped decision makers explore complex trade-offs. Prior to the application of formalised decision procedures in 2005 the allocations of funding were decided by processes of negotiation and arbitration between all three governance levels mentioned above (Hajkowicz, 2007b). There was no well defined or accepted procedure. In reviews of the NHT the Australian National Audit Office have been critical about the lack of analytic rigour and transparent decision processes to target investments (ANAO, 1997, 2008). 4. Computing and applying the needs index Building an environmental index involves identifying measurable criteria, weighting them, transforming them into commensurate units and combining them via some type of multi-criteria function (Hajkowicz, 2006). In a review of the US Environmental
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Protection Agency’s Index of Watershed Indicators, Schultz (2001) argues that formalisation of indices using multi-attribute utility theory (i.e. multiple criteria analysis) improves analytic rigour, repeatability and transparency. Today a wide range of multi-criteria decision support tools are available to construct environmental indices, see Figueira et al. (2005) for a recent review. We used the MCA method of compromise programming (CP) to compute the NHT needs index in Queensland. Widely applied in the field of environmental management (Bojo´rquez-Tapia et al., 2005; Marshall and Homans, 2006), compromise programming permits the specification of a best (ideal) and worst (anti-ideal) value for each criterion. Compromise programming seeks to minimise the distance from the ideal solution across all criteria. The ability to set, and control, performance benchmarks within criteria is a key strength of compromise programming in environmental problems where thresholds are often of much importance (Hajkowicz and Higgins, 2007). Ballestero et al. (2002) also use compromise programming to allocate water licenses prior to the establishment of markets in Spain. In this study we adapt the compromise programming algorithm to assign a higher score (ui) to a region with greater need as follows (Hajkowicz et al., 2008):
2 ui ¼ 4
m X j¼1
wjc 1
c 31=c fbj xij 5 fbj fwj
(1)
2 ui ¼ 4
m X
31=c qffiffiffiffiffiffiffiffiffiffiffiffiffiffi c wj 1 gij2 5
2 ui ¼ 4
m X
wj
3 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffic 1=c 2 5 1 1 ðgij 1Þ
(3)
j¼1
0 1c 31=c loge ð0:5Þ*gijd 7 6X B C 7 6 m B C 7 md ui ¼ 6 wj Be C 7 6 @ A 5 4 2
(4)
j¼1
In Equation (4) the parameters m and d control the shape of the sigmoid and in Equations (2)–(4) gij is determined by:
gij ¼
fbj xij fbj fwj
(5)
Once the needs index has been calculated for each region the equalisation of financial resources is simply (Hajkowicz, 2007b):
mi ¼ M
ui n P
(6) ui
i¼1
In this equation there is a finite set of criteria (j ¼ 1, ., m) and a finite set of decision options (i ¼ 1, ., n). Percentage weights (wj) show the importance of each criterion. The raw performance score for each option against each criterion is given by xi,j. This is transformed into a utility metric using a best (ideal) value (fbj) and a worst (anti-ideal) value (fwj) for each criterion. In this model the best value was typically equal to the maximum across all regions. The worst value was typically set to zero. The parameter c controls the importance of the maximal deviation from the ideal. In the final analysis the linear transformation shown in Equation (1) above was used for each criterion, but we did make available four alternative transformations when the relationship was not linear. These included a concave, convex and sigmoidal transformation:
Here mi is the budget assigned to region i and M is the total funding available. This equation assures that the financial capability of each region to address natural resource management problems is equalised. Each region is given the same dollars-per-unit need. During the project we emphasised that this did not mean equal shares to counter some perceptions that emerged. The money was targeted to where needs were greatest and regions with greater need attracted greater investment. 5. Identifying the criteria Following Ridgley and Lumpkin (2000) the criteria used to define the needs index were represented to decision makers in
Endangered regional ecosystems (C1) Ecosystem health
Regional ecosystems of concern (C2) Remnant habitat as a percentage of pre-clear extent (C3) Area of important wetlands (C4) Soil erosion (C5) Nitrogen leaching (C6)
Natural resource management needs score
(2)
j¼1
Soil and water quality
Phosphorus leaching (C7) Area of salinity risk (C8) Acid sulphate soils (C9) Length of coastline (C10)
Geographic extent
Population pressures
Area of region (C14) Length of major rivers (C15) Rural residential area (C11) Population growth rate (absolute value) (C12)
Indigenous natural resource management
Portion of population that is indigenous (C16)
Economic importance of agriculture
Gross value of primary production (C13)
Area of indigenous land tenure and use agreements (C17)
Percent of workforce employed in primary industries (C19) Area of heritage sites (C18)
Fig. 2. Final criteria used in the analysis.
S. Hajkowicz / Journal of Environmental Management 90 (2009) 2737–2745
hierarchical form (Fig. 2). Identifying evaluation criteria is a crucial, and oftentimes challenging, task in group MCA problems with multiple decision makers (Hajkowicz, 2008). The criteria set the agenda for the decision analyst. They determine what data is collected and how the decision problem is defined. Several authors have suggested that the selection of criteria is of far greater importance than the choice of MCA algorithm, but the question of how to choose criteria has received comparatively little attention in the scientific literature (Kasanen et al., 2000; Janssen, 2001; Hajkowicz and Higgins, 2007). In recent times formalised approaches for criteria selection have emerged through research on problem structuring methods and problem structuring software (Mingers and Rosenhead, 2004; Scheubrein and Zionts, 2006). The approach for identifying criteria adapted the ‘‘Brainstorming and Voting Process’’ used by Mendoza and Prabhu (2000) in an application of MCA for forest management. This involves briefing the decision making group on an initial set of criteria and explaining the MCA process. In our process the group was then able to assess (brainstorm) each criterion individually and vote for those they thought should be retained, ousted or introduced. Consultation on the criteria was conducted with the regional bodies, Queensland Government agencies and Australian Government agencies. It commenced with a workshop in Brisbane on 25 September 2007 with a representative of each regional body present. Typically this was the chief executive officer although in some cases a member of staff attended in his/her place. Also present were around a dozen Queensland and Australian Government staff with responsibility for managing or informing the NHT program. Prior to the meeting all attendees received a set of 18 proposed evaluation criteria. At the meeting the MCA model, and how it would be used to help set regional NHT budgets, was explained. Every criterion was then discussed in detail. Each regional body was given 5 red ticks to place next to criteria they thought important. There was consensus on most of the criteria that should be retained and removed. The meeting attendees also identified 5 additional criteria for which datasets should be sought, and if suitable data were found, included in the MCA model. The criteria before and after the brainstorming workshop are shown in Table 1. These were then translated into the final set of criteria show in hierarchical form earlier (Fig. 2). The data were drawn from geographic information system datasets held by State and Federal Government agencies. Each of the 14 regional bodies supplied one set of criteria weights. There was a requirement that weights add to 100 percent at each branch of the hierarchy. In most cases several staff in the region worked on the criteria weighting exercise and it was coordinated by the chief executive officer. Regional bodies were given detailed descriptions of the criteria and each criterion was discussed at a seminar beforehand. Most criteria were explored interactively on a geographic information system which displayed the range of values across Queensland. However, decision makers were not explicitly shown the performance data, or minimum and maximum values, for each criterion at the time of weighting. They were given a chance to interactively change their weights when the full model was revealed later in the decision process. Whilst this limited options for strategic bias in criteria weighting, i.e. placing higher weight on those criteria which give your region more money, it may have introduced ambiguity in the weight setting exercise. Decision makers should see performance data, or the range (minimum–maximum) at the time of weighting (Keeney, 1992). This will clearly influence the weights that are chosen. In this study we relied on detailed descriptions, and a good understanding, of each criterion with an opportunity for interactive adjustment in light of performance data at a later time. Decision makers should
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Table 1 Identifying the criteria through stakeholder consultation. First Iteration (prior to stakeholder meeting)
Second Iteration (following voting and brainstorming session)
1. Amount of soil erosion 2. Area of medium or high salinity risk 3. Area of native woody vegetation outside reserves 4. Area of priority habitat for cassowaries, mahogany gliders, koalas and other priority species if data is available 5. Number of Endangered Vulnerable and Rare (EVR) species sightings within the region 6. Extent of heritage (world heritage and register of the national estate) areas 7. Area of important wetlands 8. Gross value of agricultural production 9. Area of agricultural land use 10. Number of persons employed in primary industries 11. Area of indigenous land tenure within the region 12. Area of indigenous land use agreements (ILUAs) within the reigon 13. Number of indigenous persons in region 14. Area of the NRM region. 15. Length of rivers/streams within the NRM region 16. Length of coastline within the NRM region 17. Human population within the region 18. Climate change (drought/rainfall variability extreme weather)
To Be Excluded 1. Ground and surface water use 2. Area of native woody veg from SLATS (satellite data) 3. Sightings of endangered vulnerable or rare species 4. Area of priority habitat (koalas, cassowaries, mahogany gliders) 5. Area of agriculture 6. Area of indigenous land tenure 7. Area of indigenous land use agreements 8. Climate change To Be Included 1. Soil erosion 2. Area of salinity risk 3. Natural and indigenous heritage area 4. Area of important wetlands 5. Gross value of agricultural production 6. Percentage of population that is indigenous 7. Terrestrial area of region, excluding ocean 8. Length of rivers 9. Length of coastline 10. Absolute value of population growth rate 11. A criterion relating to priority and/or threatened species or habitats 12. A criterion relating to native remnant vegetation (including both woody vegetation and grasslands) outside protected areas To be Subject to Further Analysis 1. Area of weeds and pests 2. Area of acid sulphate soils 3. Rural residential area as a proxy for peri-urban land 4. Number of persons employed in extractive industries (or some other measure of mining activity). 5. Nitrogen and phosphorus runoff (tonnes per year)
have been explicitly shown the defined criteria performance ranges at the time of weighting. Doing so may have led to greater agreement on the weights. This shortcoming perhaps highlights some of the challenges of designing an MCA model within the confines of a real-world decision process with inflexible timeframes. 6. Results The spread of weights for branches and criteria is shown in boxplots (Figs. 3 and 4). The average (median) importance ordering of the branches, from most important to least important, was: 1. 2. 3. 4. 5. 6. 7.
Ecosystem health Soil and water quality Economic value of agriculture Geographic extent Population pressures Indigenous natural resource management Area of heritage sights
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S. Hajkowicz / Journal of Environmental Management 90 (2009) 2737–2745 45% Max 40% 3rd quartile
35%
Weight
30% 25% Median
20% 15% 10%
Min
1st quartile
5% 0% Ecosystem health
Soil and water quality
Geographic extent
Population pressures
Indigenous NRM
Economic value Area of heritage of agriculture sites
Fig. 3. Box plot of branch weights for regional bodies (n ¼ 14). A higher weight indicates greater importance.
The criteria receiving the highest weights were the gross value of agricultural production (C13), soil erosion (C5) and endangered regional ecosystems (C1). Those receiving the lowest weights were nitrogen leaching (C6); phosphorus leaching (C7) and acid sulphate soils (C9). It is interesting to note the considerable spread in weights across all 14 groups. For example, the most important criterion – the gross value of agricultural production – ranges in weight from 0.3% to 35%. The groups were also in disagreement about the relative importance of the area of the region (C14) with the weight varying from 0% to 36%. These large variations may have resulted because decision makers were not shown criteria ranges (minimum–maximum) at the time of weighting and had different understandings. A check was made to see whether there may have been strategic bias whereby regions with larger areas assigned greater weight to this criterion to increase their budget. This was considered a possibility for area (C14) as a region’s area is easy to compute and/ or see on a map. A correlation test between area of region and
weight produced an r-square (RSQ) of 0.1. Correlations between all other criteria and their weights did not reveal strong evidence of weighting bias. The median RSQ was 0.15, the minimum was 0.001 and the maximum, for length of rivers (C15) was 0.575. Therefore, the results do not provide evidence of strategic bias. Regions did not appear to be weighting criteria strategically to maximise their budget. The break-up of funding resulting from the average (arithmetic mean) of the regional body weights was shown previously in Fig. 1. This represents a significant change from historical funding. Table 2 shows the change in relative share for each regional body compared to historical funding. The historical funding is for the period 2000– 2007 and includes phase two of the NHT and another program called the National Action Plan for Salinity and Water Quality. Two regions, Desert Channels and Cape York, increase their relative shares by over 6 percent. The relative share decreases by 4 percent for 4 regions of Burdekin, Condamine, Border Rivers and Southern Gulf. These swings show that adoption of the MCA model would cause significant changes for regional bodies.
40%
35%
30%
Weight
25%
20%
15%
10%
5%
0% C1
C2
C3
C4
C5
C6
C7
C8
C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19
Fig. 4. Box plot of criteria weights (n ¼ 14; see Fig. 2 for criteria descriptions). A higher weight indicates greater importance.
S. Hajkowicz / Journal of Environmental Management 90 (2009) 2737–2745 Table 2 Change in relative shares under historic funding (Note: these are modelled shares and have not been implemented in policy due to the change in program design by government. Future programs will respond to different policy objectives and datasets). Regional Body
Proposed Share (percent)
Border Rivers Maranoa Balonne Burdekin Burnett Mary Cape York Condamine Desert Channels Fitzroy Mackay Whitsunday Northern Gulf South East Queensland South West Queensland Southern Gulf Torres Strait Wet Tropics
Change in Historic Share (percent)
8
5.1
8 7 10 4 12 11 4 7 6 7 8 3 5
4.3 2.4 6.1 4.1 6.6 3.1 0.4 2.9 3.6 2.2 4.7 1.1 0.5
predict, at the time of the decision. Under the NHT regional bodies may fund activities as diverse as data gathering, policy development, publicity campaigns, tree planting, weed removal and creek bank restoration. Without knowledge of these activities it is impossible to define the functional relationship between expenditure and benefits (i.e. Equation (7)). Secondly, even if the activities were known the scientific models and datasets required to link expenditure to outcomes are seldom available. This same issue was found to be an obstacle in targeting payments under US conservation programs. The United States Department of Agriculture USDA reports that process models to link investments to outcomes often do not exist (Weinberg and Claassen, 2006; p2): ‘‘Ideally, a measurement tool would exist that links conservation program incentives to the environmental indicator of interest. However, agricultural emissions from individual farms generally cannot be monitored at a reasonable cost. The path from policy incentives to environmental outcomes is complex and multistage.’’
It is stressed that due to the shift in program design, from NHT to CfOC, these allocations no longer apply. But at the time of analysis this was a real decision problem for the regional groups and there was an expectation that the result would be used by policy makers. Sensitivity analysis was conducted by computing elasticities for each region and criterion. Here we define elasticity as the percentage change in a region’s budget resulting from a one percentage increase in each criterion’s weight. The criteria with the highest average elasticities (shown in parentheses) across all regions included C9 acid sulphate soils (þ1.1), C13 gross value of agriculture (þ1.1) and C12 population growth rate (þ1). The model was least sensitive to changes in C14 area of region and C11 (þ0.6) rural residential area (þ0.6).
Given these obstacles the concept of fiscal equalisation (Hajkowicz, 2007b) will often be more appropriate than the optimisation form expressed by Wu and Boggess (1999). A fiscal equalisation model targets financial resources, or any other environmental resource, on the basis of need. Each region is allocated the same dollars-per-unit need. This provides each region with the same financial capability to address its natural resource management problems. This approach has potential for application in some of the world’s largest natural resource management and conservation programs such as: -
-
7. Policy implications The MCA model designed and applied in this study does not search for an optimal solution given the common definition of ‘‘optimal’’. With respect to the US Conservation Reserve Program Wu and Boggess (1999) describe a model for optimising outcomes over multiple regions. For the problem of inter-regional allocation this requires defining a relationship between environmental expenditure and environmental benefit:
bi ¼ f ðmi ; a; b; c.Þ
(7)
In this case parameters a, b, c . are environmental or institutional factors which influence the return on investment. The optimisation model would then be to set mi for each region such that we:
Maximise
n X
bi
(8)
mi M
(9)
i¼1
Subject to
n X
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i¼1
Many economists would argue this is the most appropriate model structure. It aims to solve for the optimal outcome. However, in the case of setting regional NHT budgets in Queensland, and many other such cases, solving this problem formulation will not be feasible. There are two main reasons. Firstly, the actual environmental management activities that will occur within a region are unknown, and almost impossible to
-
The Environmental Quality Incentives Program. In fiscal 2006 around US$652 million were allocated to the 50 States using 30 weighted indicators (GAO, 2006). The Farm and Ranch Lands Protection Program. In fiscal 2007 around $73 million were allocated to the States using 11 weighted indicators. (NRCS, 2004, 2007a; Cattaneo et al., 2006). The Wetlands Restoration Program. In fiscal 2007 around US$228 million were allocated amongst the States using 4 weighted indicators (NRCS, 2007b).
It could also be applied in the allocation of scarce land, water or other environmental resources. These decisions are likely to involve unclear project activities (or uses of the resource) and have similar limitations with respect to datasets and scientific models. Formalisation of the allocation problem using MCA in these, and many other such programs, could help improve transparency and auditability (Gamper and Turcanu, 2007). Increasingly researchers are seeing models as a learning tool within a broader decision process than a means of producing a definitive answer (Ramanathan, 2001; Herath, 2004; Hermans et al., 2007). In this study it was found that stakeholders, i.e. the regional bodies, opted to use the MCA model even though it was proposing a major re-allocation of funds with winners and losers. Furthermore, the stakeholders did not introduce strategic bias into criteria weights. There was no evidence that regions were weighting more heavily those criteria which they knew would increase their fund allocation. This suggests a willingness to consider the common good through the MCA decision model. It appeared that MCA was not only a decision tool but was changing the way people approached the decision problem. Whether this differs from what would have occurred in an unaided, or other type of, decision environment is mostly
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untested. There have been some studies to compare MCA with unaided decisions which give evidence of different outcomes (Hajkowicz, 2007a). But we do not yet have a comprehensive understanding of how the decision procedure is changed by MCA in both positive and negative ways. Future research could examine how and whether an MCA model, or other rational model, can help decision makers separate their own objectives from broader policy objectives. 8. Conclusion Decisions involving the allocation of environmental resources amongst regions will always be challenging. They will involve multiple stakeholders holding multiple objectives and are often characterised by conflict, uncertainty and, sometimes, confusion about purpose and policy objectives. Unproductive conflict and political deadlock, where decisions do not occur and new policies are blocked, can easily occur. A multi-criteria decision model can provide an effective means for stakeholders to explore and debate a resource sharing problem. Whilst it is difficult to reach unanimous agreement the decision model can provide structure for the debate. This study found that stakeholders supported the multiple criteria analysis (MCA) model even when it disadvantaged them compared to historical decisions. There was no evidence of strategic bias where regions weighted more heavily those criteria which increased their funding. The main benefit of MCA was not in the provision of an ‘‘answer’’ to the allocation problem, but in the provision of a process acceptable to all stakeholders which could be used to generate a solution in a transparent, robust and auditable manner. This can be improved by ensuring decision makers are shown criteria ranges (minimum–maximum) at the time of weighting. An MCA has been adopted by stakeholders in phase two and the proposed phase three of the Australian Natural Heritage Trust. An MCA model is now being developed by CSIRO to inform investment decisions under the federal government’s A$200 million rescue package for the Great Barrier Reef. The acceptance of MCA, and formalised decision procedures, over ad-hoc methods represents a major advance in how Australians are making natural resource management choices. Further research is required to investigate the extent to which, and how, the MCA model helped build consensus and how it impacts, positively and negatively, an otherwise unaided decision procedure. This may be initially addressed by a more in-depth analysis strategic bias in criteria weighting by stakeholders. Acknowledgements This study was funded by the Queensland Government Department of Natural Resources and Water. It was made possible by the involvement of all fourteen of Queensland’s Natural Resource Management Groups through the Regional Groups Collective. It was also supported by the Australian Government whose staff were actively involved in the meetings. The author is very grateful for the detailed and helpful review comments of several anonymous reviewers. References ANAO, 1997. Commonwealth Natural Resource Management and Environment Programs. Audit Report No. 36 1996–97. The Australian National Audit Office, Canberra. ANAO, 2008. Regional Delivery Model for the Natural Heritage Trust and the National Action Plan for Salinity and Water Quality. Audit Report No. 21 2007– 08. The Australian National Audit Office, Canberra.
Australian Government, 2007. Budget Paper No. 2, Budget Measures 2007–08. Australian Government, Canberra. Ballestero, E., Alarco´n, S., Garcı´a-Bernabeu, A., 2002. Establishing politically feasible water markets: a multi-criteria approach. Journal of Environmental Management 65, 411–429. Bojo´rquez-Tapia, L.A., Sa´nchez-Colon, S., Florez, A.F., 2005. Building consensus in environmental impact assessment through multicriteria modeling and sensitivity analysis. Environmental Management 36, 469–481. Burton, P.S., 1996. Land use externalities: mechanism design for the allocation of environmental resources. Journal of Environmental Economics and Management 30, 174–185. Cattaneo, A., Hellerstein, D., Nickerson, C., Myers, C., 2006. Balancing the Multiple Objectives of Conservation Programs. Economic Research Service, U.S. Department of Agriculture, Washington DC, pp. 55. Chang, N.B., Wen, C.G., Wu, S.L., 1995. Optimal management of environmental and land resources in a reservoir watershed by multiobjective programming. Journal of Environmental Management 44, 144–161. DAFF, 2008. Caring for Our Country – Better Land Management, Less Red Tape. The Hon Tony Burke MP, Media Release DAFF08/024BJ. Department for Agriculture, Fisheries and Forestry, Canberra. Davis, D., Tisdell, C., 1996. Economic management of recreational scuba diving and the environment. Journal of Environmental Management 48, 229–248. De Marchi, B., Funtowicz, S.O., Lo Cascio, S., Munda, G., 2000. Combining participative and institutional approaches with multicriteria evaluation: an empirical study for water issues in Troina, Sicily. Ecological Economics 34, 267–282. Diem, J.E., Comrie, A.C., 2001. Allocating anthropogenic pollutant emissions over space: application to ozone pollution management. Journal of Environmental Management 63, 425–447. Figueira, J., Greco, S., Ehrgott, M. (Eds.), 2005. Multiple Criteria Decision Analysis: State of the Art Surveys. Springer, New York. Gamper, C.D., Turcanu, C., 2007. On the governmental use of multi-criteria analysis. Ecological Economics 62, 298–307. GAO, 2006. UDSA Should Improve Its Process for Allocating Funds to States for the Environmental Quality Incentives Program. United States Government Accountability Office, Washington DC, pp. 1–57. Gough, J.D., Ward, J.C., 1996. Environmental decision making and lake management. Journal of Environmental Management 48, 1–15. Hajkowicz, S.A., 2006. Multi-attributed environmental index construction. Ecological Economics 57, 122–139. Hajkowicz, S.A., 2007a. A comparison of multiple criteria analysis and unaided approaches to environmental decision making. Environmental Science and Policy 10, 177–184. Hajkowicz, S.A., 2007b. Allocating scarce financial resources across regions for environmental management in Queensland, Australia. Ecological Economics 61, 208–216. Hajkowicz, S.A., Higgins, A., 2007. A comparison of multiple criteria analysis techniques for water resource management. European Journal of Operational Research 184, 255–265. Hajkowicz, S.A., 2008. Supporting multi-stakeholder environmental decisions. Journal of Environmental Management 88, 607–614. Hajkowicz, S.A., Spencer, R., Higgins, A., Marinoni, O., 2008. Evaluating water quality investments using cost utility analysis. Journal of Environmental Management 88, 1601–1610. Hajkowicz, S., Collins, K., Cattaneo, A., 2009. Review of agri-environment indexes and Stewardship payments. Environmental Management 43, 221–236. Herath, G., 2004. Incorporating community objectives in improved wetland management: the use of the analytic hierarchy process. Journal of Environmental Management 70, 263–273. Hermans, C., Erickson, J., Noordewier, T., Sheldon, A., Kline, M., 2007. Collaborative environmental planning in river management: an application of multicriteria decision analysis in the White River Watershed in Vermont. Journal of Environmental Management 84, 534–546. Janssen, R., 2001. On the use of multi-criteria analysis in environmental impact assessment in The Netherlands. Journal of Multi-Criteria Decision Analysis 10, 101–109. Johansson, R.C., Cattaneo, A., 2006. Indices for working land conservation: form affects function. Review of Agricultural Economics 28, 567–584. Kampas, A., White, B., 2003. Selecting permit allocation rules for agricultural pollution control: a bargaining solution. Ecological Economics 47, 135–147. Kasanen, E., Wallenius, H., Wallenius, J., Zionts, S., 2000. A study of high-level managerial decision processes, with implications for MCDM research. European Journal of Operational Research 120, 496–510. Keeney, R.L., 1992. Value Focused Decision Making: a Path to Creative Decision Making. Harvard University Press, Cambridge, MA. Marshall, E.P., Homans, F.R., 2006. Juggling land retirement objectives on an agricultural landscape: coordination, conflict, or compromise? Environmental Management 38, 37–47. Mendoza, G.A., Prabhu, R., 2000. Development of a methodology for selecting criteria and indicators of sustainable forest management: a case study on participatory assessment. Environmental Management 36, 469–481. Merrifield, J., Firoozi, F., 1995. Renewable resource use: transition from capture to allocation and optimal stock recovery. Journal of Environmental Management 44, 195–211. Messner, F., Zwirner, O., Karkuschke, M., 2006. Participation in multi-criteria decision support for the resolution of a water allocation problem in the Spree River basin. Land Use Policy 23, 63–75.
S. Hajkowicz / Journal of Environmental Management 90 (2009) 2737–2745 Mimi, Z.A., Sawalhi, B.I., 2003. A decision tool for allocating the waters of the Jordan River Basin between all Riparian parties. Water Resources Management 17, 447–461. Mingers, J., Rosenhead, J., 2004. Problem structuring methods in action. European Journal of Operational Research 152, 530–554. Ministerial Council, 2006. Framework for Future NRM Programmes. Natural Resource Management Ministerial Council, Australian Government, Canberra. p. 10. NRCS, 2004. Farm and Ranch Lands Protection Program: Farm Bill 2002 Fact Sheet. Natural Resource Conservation Service, United States Department of Agriculture, Washington DC, p. 2. NRCS, 2007a. Farm and Ranch Lands Protection Program FY 2007 Financial and Technical Assistance Dollars to States. Natural Resource Conservation Service, United States Department of Agriculture, Washington DC. NRCS, 2007b. Wetland Reserve Program: Farm Bill 2002, Fact Sheet. Natural Resource Conservation Service, United States Department of Agriculture, Washington DC, p. 3. Ramanathan, R., 2001. A note on the use of the analytic hierarchy process for environmental impact assessment. Journal of Environmental Management 63, 27–35. Ridgley, M., Lumpkin, C.A., 2000. The bi-polar resource-allocation problem under uncertainty and conflict: a general methodology for the public decision-maker. Journal of Environmental Management 59, 89–105.
2745
Scheubrein, R., Zionts, S., 2006. A problem structuring front end for a multiple criteria decision support system. Computers and Operations Research 33, 18–31. Schultz, M.T., 2001. A critique of EPA’s index of watershed indicators. Journal of Environmental Management 62, 429–442. Stagl, S., 2006. Multicriteria evaluation and public participation: the case of UK energy policy. Land Use Policy 23, 53–62. Syme, G.J., Nancarrow, B.E., McCreddin, J.A., 1999. Defining the components of fairness in the allocation of water to environmental and human uses. Journal of Environmental Management 57 (1), 51–70. Vesterdal, M., Svendsen, G.T., 2004. How should greenhouse gas permits be allocated in the EU? Energy Policy 32, 961–968. Weinberg, M., Claassen, R., 2006. Conservation Program Design: Rewarding Farm Practices Versus Environmental Performance. Economic Brief Number 5. Economic Research Service, United States Department of Agriculture, Washington DC, pp. 6. Wu, J., Boggess, W.G., 1999. The optimal allocation of conservation funds. Journal of Environmental Economics and Management 38, 302–321. Zeleny, M., 1973. Compromise programming. In: Cocharane, J.L., Zeleny, M. (Eds.), Multiple Criteria Decision Making. University of Southern Carolina Press, Columbia, S.C, pp. 262–301. Zeleny, M., 1982. Multiple Criteria Decision Making. McGraw-Hill, New York.