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Supply of carbon sequestration and biodiversity services from Australia’s agricultural land under global change B.A. Bryan a, *, M. Nolan a , T.D. Harwood b , J.D. Connor a , J. Navarro-Garcia c , D. King a , D.M. Summers a , D. Newth d , Y. Cai d , N. Grigg e , I. Harman d , N.D. Crossman a , M.J. Grundy f , J.J. Finnigan d , S. Ferrier b , K.J. Williams b , K.A. Wilson g , E.A. Law g , S. Hatfield-Dodds b a CSIRO Ecosystem Sciences, Waite Campus, Urrbrae, SA 5064, Australia b CSIRO Ecosystem Sciences, Black Mountain, Canberra, ACT 2601, Australia c CSIRO Ecosystem Sciences, Dutton Park, QLD 4102, Australia d CSIRO Marine and Atmospheric Research, Black Mountain, Canberra, ACT 2601, Australia e CSIRO Land and Water, Black Mountain, Canberra, ACT 2601, Australia f CSIRO Land and Water, Dutton Park, QLD 4102, Australia g School of Biological Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia 1. Introduction Beyond food provision, agroecosystems can provide services that can contribute to addressing the dual challenges of biodiversity decline and global climate change (Bateman et al., 2013; Power, 2010). Reforestation of agricultural land can remove Global Environmental Change 28 (2014) 166–181 A R T I C L E I N F O Article history: Received 18 September 2013 Received in revised form 12 May 2014 Accepted 18 June 2014 Available online Keywords: Scenarios Biodiversity conservation Ecosystem services Climate change Land use change Carbon sequestration A B S T R A C T Global agroecosystems can contribute to both climate change mitigation and biodiversity conservation, and market mechanisms provide a highly prospective means of achieving these outcomes. However, the ability of markets to motivate the supply of carbon sequestration and biodiversity services from agricultural land is uncertain, especially given the future changes in environmental, economic, and social drivers. We quantified the potential supply of these services from the intensive agricultural land of Australia from 2013 to 2050 under four global outlooks in response to a carbon price and biodiversity payment scheme. Each global outlook specified emissions pathways, climate, food demand, energy price, and carbon price modeled using the Global Integrated Assessment Model (GIAM). Using a simplified version of the Land Use Trade-Offs (LUTO) model, economic returns to agriculture, carbon plantings, and environmental plantings were calculated each year. The supply of carbon sequestration and biodiversity services was then quantified given potential land use change under each global outlook, and the sensitivity of the results to key parameters was assessed. We found that carbon supply curves were similar across global outlooks. Sharp increases in carbon sequestration supply occurred at carbon prices exceeding 50 $ tCO 2 1 in 2015 and exceeding 65 $ tCO 2 1 in 2050. Based on GIAM-modeled carbon prices, little carbon sequestration was expected at 2015 under any global outlook. However, at 2050 expected carbon supply under each outlook differed markedly, ranging from 0 to 189 MtCO 2 yr 1 . Biodiversity services of 3.32% of the maximum may be achieved in 2050 for a 1 $B investment under median scenario settings. We conclude that a carbon market can motivate supply of substantial carbon sequestration but only modest amounts of biodiversity services from agricultural land. A complementary biodiversity payment can synergistically increase the supply of biodiversity services but will not provide much additional carbon sequestration. The results were sensitive to global drivers, especially the carbon price, and the domestic drivers of adoption hurdle rate and agricultural productivity. The results can inform the design of an effective national policy and institutional portfolio addressing the dual objectives of climate change and biodiversity conservation that is robust to future uncertainty in both national and global drivers. ß 2014 Elsevier Ltd. All rights reserved. * Corresponding author. Tel.: +61 402 881 598. E-mail address: [email protected] (B.A. Bryan). Contents lists available at ScienceDirect Global Environmental Change jo ur n al h o mep ag e: www .elsevier .co m /loc ate/g lo envc h a http://dx.doi.org/10.1016/j.gloenvcha.2014.06.013 0959-3780/ß 2014 Elsevier Ltd. All rights reserved.

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Global Environmental Change 28 (2014) 166–181

Supply of carbon sequestration and biodiversity services fromAustralia’s agricultural land under global change

B.A. Bryan a,*, M. Nolan a, T.D. Harwood b, J.D. Connor a, J. Navarro-Garcia c, D. King a,D.M. Summers a, D. Newth d, Y. Cai d, N. Grigg e, I. Harman d, N.D. Crossman a, M.J. Grundy f,J.J. Finnigan d, S. Ferrier b, K.J. Williams b, K.A. Wilson g, E.A. Law g, S. Hatfield-Dodds b

a CSIRO Ecosystem Sciences, Waite Campus, Urrbrae, SA 5064, Australiab CSIRO Ecosystem Sciences, Black Mountain, Canberra, ACT 2601, Australiac CSIRO Ecosystem Sciences, Dutton Park, QLD 4102, Australiad CSIRO Marine and Atmospheric Research, Black Mountain, Canberra, ACT 2601, Australiae CSIRO Land and Water, Black Mountain, Canberra, ACT 2601, Australiaf CSIRO Land and Water, Dutton Park, QLD 4102, Australiag School of Biological Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia

A R T I C L E I N F O

Article history:

Received 18 September 2013

Received in revised form 12 May 2014

Accepted 18 June 2014

Available online

Keywords:

Scenarios

Biodiversity conservation

Ecosystem services

Climate change

Land use change

Carbon sequestration

A B S T R A C T

Global agroecosystems can contribute to both climate change mitigation and biodiversity conservation,

and market mechanisms provide a highly prospective means of achieving these outcomes. However, the

ability of markets to motivate the supply of carbon sequestration and biodiversity services from

agricultural land is uncertain, especially given the future changes in environmental, economic, and social

drivers. We quantified the potential supply of these services from the intensive agricultural land of

Australia from 2013 to 2050 under four global outlooks in response to a carbon price and biodiversity

payment scheme. Each global outlook specified emissions pathways, climate, food demand, energy price,

and carbon price modeled using the Global Integrated Assessment Model (GIAM). Using a simplified

version of the Land Use Trade-Offs (LUTO) model, economic returns to agriculture, carbon plantings, and

environmental plantings were calculated each year. The supply of carbon sequestration and biodiversity

services was then quantified given potential land use change under each global outlook, and the

sensitivity of the results to key parameters was assessed. We found that carbon supply curves were

similar across global outlooks. Sharp increases in carbon sequestration supply occurred at carbon prices

exceeding 50 $ tCO2�1 in 2015 and exceeding 65 $ tCO2

�1 in 2050. Based on GIAM-modeled carbon

prices, little carbon sequestration was expected at 2015 under any global outlook. However, at 2050

expected carbon supply under each outlook differed markedly, ranging from 0 to 189 MtCO2 yr�1.

Biodiversity services of 3.32% of the maximum may be achieved in 2050 for a 1 $B investment under

median scenario settings. We conclude that a carbon market can motivate supply of substantial carbon

sequestration but only modest amounts of biodiversity services from agricultural land. A

complementary biodiversity payment can synergistically increase the supply of biodiversity services

but will not provide much additional carbon sequestration. The results were sensitive to global drivers,

especially the carbon price, and the domestic drivers of adoption hurdle rate and agricultural

productivity. The results can inform the design of an effective national policy and institutional portfolio

addressing the dual objectives of climate change and biodiversity conservation that is robust to future

uncertainty in both national and global drivers.

� 2014 Elsevier Ltd. All rights reserved.

Contents lists available at ScienceDirect

Global Environmental Change

jo ur n al h o mep ag e: www .e lsev ier . co m / loc ate /g lo envc h a

* Corresponding author. Tel.: +61 402 881 598.

E-mail address: [email protected] (B.A. Bryan).

http://dx.doi.org/10.1016/j.gloenvcha.2014.06.013

0959-3780/� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Beyond food provision, agroecosystems can provide servicesthat can contribute to addressing the dual challenges ofbiodiversity decline and global climate change (Bateman et al.,2013; Power, 2010). Reforestation of agricultural land can remove

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181 167

significant amounts of carbon dioxide from the atmosphere, storeit in plants and soils (Harper et al., 2012; Rhemtulla et al., 2009),and thereby help mitigate climate change (Mackey et al., 2013;Smith et al., 2008). Reforestation of diverse, local, nativeecosystems (environmental plantings) may also accrue co-benefitsfor biodiversity (Benayas et al., 2009; Lin et al., 2013; Mackey et al.,2013; Pichancourt et al., 2014) by increasing habitat area,improving landscape connectivity, and enhancing species persis-tence under climate change (Renton et al., 2012; Summers et al.,2012). Market mechanisms, such as a price on carbon, are seen asan essential component of the policy armory for addressing globalclimate change (Benıtez et al., 2007; IPCC, 2014; Rogelj et al.,2013). Market-based incentives encouraging reforestation forsupplying carbon sequestration, biodiversity, and other servicesfrom agroecosystems have become increasingly common (Farleyand Costanza, 2010). However, the individual and combinedinfluence of market-based incentives is complex and uncertain(Bryan, 2013; Dumortier, 2013). While opportunities for carbonand biodiversity co-benefits from market-based policies have beenidentified (Crossman et al., 2011; Venter et al., 2009), trade-offsand perverse outcomes are a risk (Bradshaw et al., 2013; Dickieet al., 2011; Lindenmayer et al., 2012). Understanding the likelyinfluence of incentives on the supply of carbon and biodiversityfrom agricultural land is necessary to support effective policyinterventions (Fensham and Guymer, 2009; Freedman et al., 2009;Lindenmayer et al., 2012).

Markets present landholders with opportunities for newincome streams from the sale of credits for supplying additional,permanently sequestered carbon and the conservation andenhancement of native biodiversity (Yang et al., 2010). Economicopportunities, mediated by institutional factors, drive land usechange (Lambin et al., 2001; Mann et al., 2010) and hence, thesupply of both carbon sequestration and biodiversity services(Bryan, 2013; Patrick et al., 2009). Policy impacts on land usechange and the supply of ecosystem services have been widelyassessed based on the effects on economic returns from land use(Antle and Stoorvogel, 2006; Bryan et al., 2008, 2011b; Crossmanet al., 2011; Flugge and Abadi, 2006; Paul et al., 2013a; Polglase etal., 2013). In practice though, rates of land use change diverge fromthat predicted by profit-maximizing economic theory (Lubowskiet al., 2008). Barriers to adoption include: a lack of key structuraland relational mechanisms such as capital, knowledge, expertise,technology, land, and labor (Corbera and Brown, 2010; Upton etal., 2014); competing objectives; negative perceptions of environ-mental objectives; and policy and institutional risk and uncertain-ty (Dilling and Failey, 2013; Dumortier, 2013; Raymond andRobinson, 2013). Conversely, landholders also derive other non-market benefits from reforestation such as recreation, aesthetic,bequest, intrinsic, and other values (Raymond et al., 2009; Shaikhet al., 2007). These barriers and benefits generate significantuncertainty around the influence of market policy on reforestationand the supply of carbon sequestration and biodiversity fromagroecosystems.

The supply of carbon from agricultural land depends on therelative prices for crops and carbon, as well as assumptions arounddiscount rates, growth rates, and costs (Birch et al., 2010; Patersonand Bryan, 2012; Wise et al., 2007). Two earlier reviews reported thatunder the cost range of 10–150 $ tC�1 it may be possible to sequester250–500 MtC yr�1 in the US, and upwards of 2000 MtC yr�1 globallyfor several decades (Richards and Stokes, 2004); and that the costs ofsequestering carbon through tree planting and agroforestry weremore than double the costs of forest conservation (12.71–70.99 US$ tCO2

�1) (van Kooten et al., 2004). Recent studies atglobal, regional, and local scales have produced a range of resultsconsistent with these earlier syntheses and concluded that thereceipt of realistic carbon-related payments by landowners can have

substantial impacts on future land use patterns and terrestrialcarbon sequestration (Ahn, 2008; Alig et al., 2010; Antle and Valdivia,2006; Benitez et al., 2007; Funk et al., 2014; Golub et al., 2009;Jackson and Baker, 2010; Lubowski et al., 2006; Povellato et al., 2007;Torres et al., 2010; Townsend et al., 2012). Reforestation inAustralia’s agricultural regions has been found to be more profitablethan existing rain-fed agriculture – particularly cereal cropping andgrazing systems – even at relatively low carbon prices (Flugge andAbadi, 2006; Flugge and Schilizzi, 2005; Harper et al., 2007; Maraseniand Cockfield, 2011; Paterson and Bryan, 2012; Paul et al., 2013a,b;Polglase et al., 2013; Renwick et al., 2014). For example, Flugge andAbadi (2006) found that reforestation was more profitable thancropping-grazing systems in south-west Western Australia at acarbon price from 45 to 66 $ tCO2

�1. In the Lower Murray region ofsouthern Australia, Paterson and Bryan (2012) found that carbonsupply began at 20 $ tCO2

�1 with reforestation more profitable thanrain-fed agriculture in most areas at carbon prices above 60 $ tCO2

�1.Under their most plausible cost assumptions, Polglase et al. (2013)found that environmental plantings started to become profitable(including opportunity cost) at carbon prices above 40 $ tCO2

�1.A carbon market alone will not automatically generate

biodiversity co-benefits from reforestation (Hall et al., 2012;Nelson et al., 2008; Thomas et al., 2013). While environmentalplantings provide biodiversity benefits and are compatible withcarbon markets (Bradshaw et al., 2013), there is a risk that thesemarkets will favor fast-growing monocultures (carbon plantings) orother agroforestry options as they are more profitable – beingcheaper to establish, and sequestering more carbon much faster(Hunt, 2008; Kanowski and Catterall, 2010). Monocultureshowever, typically provide little biodiversity benefit (Hall et al.,2012; Smith, 2009). Financial incentives, administered throughprograms such as agri-environment schemes and payments forecosystem services, can supplement carbon incomes and achievebiodiversity co-benefits by fine-tuning the location and type ofreforestation occurring in agricultural land and closing the gap ineconomic returns from environmental plantings (Bryan andCrossman, 2013; Crossman et al., 2011; George et al., 2012;Thomas et al., 2013). Enabling landholders to bundle ecosystemservices payments with carbon credits for reforestation canreconnect biodiversity and climate change policy objectives(Bekessy and Wintle, 2008; Hunt, 2008; van Oosterzee et al.,2010). Carbon markets can boost inadequate conservation budgetsand help finance the substantial restoration task required tomaintain biodiversity in agroecosystems (Hein et al., 2013). Inreturn, biodiverse environmental plantings can contribute to thepermanence of stored carbon by enhancing the value placed onnew forests by society (Diaz et al., 2009).

A few studies have undertaken an integrated assessment ofmarket policy on the supply of carbon and biodiversity fromagroecosystems. Several studies have found significant opportu-nity to generate both carbon sequestration and biodiversityservices under relatively modest carbon prices in Australia throughenvironmental plantings (Carwardine et al., unpublished manu-script; Paul et al., 2013a; Polglase et al., 2013; Renwick et al.,2014). In assessing five conservation payment targeting strategiesfor the Willamette Basin, Oregon, Nelson et al. (2008) foundpersistent trade-offs between carbon sequestration and speciesconservation. In assessing economic returns from agriculture, andfrom carbon and environmental plantings under six carbon pricescenarios, Crossman et al. (2011) found that annual payments of6–120 US$ ha�1 yr�1 may cover the opportunity cost of environ-mental plantings in high priority biodiversity areas, depending onthe carbon price. Bryan and Crossman (2013) found thatagricultural commodity prices and carbon price drove the supplyof carbon sequestration through reforestation and, along withbiodiversity payments, also influenced biodiversity benefits. They

Fig. 1. Location and extent of the study area in Australia.

Fig. 2. Broad agricultural land use in the study area.

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181168

also found that biodiversity payments did not influence carbonsequestration. While these studies have assessed the influence offuture variability in some economic parameters (e.g. carbon price,payment levels) on carbon and biodiversity, uncertainty in otherkey economic and environmental parameters has not beenaddressed (e.g. climate change, adoption rates, productivity levels).Given the long time frames associated with the supply of bothcarbon sequestration and biodiversity services from reforestation,there is a need to quantify the influence of market policies underalternative global change scenarios (de Chazal and Rounsevell,2009; Rittenhouse and Rissman, 2012), and to understand theimpact of land use competition and uncertainty in key variables.

In this study, we assessed the supply of carbon sequestrationand biodiversity services motivated by a carbon market and abiodiversity payment scheme from Australia’s 85 M ha intensiveagricultural land under global change from 2013 to 2050. Toachieve this, we integrated a range of national and global scale,spatio-temporal, environmental and economic models. Integratedassessment of four global outlooks using the Global IntegratedAssessment Model (GIAM) produced internally consistent emis-sions pathways and population scenarios, and provided futureprojections of global climate, food demand, energy price, andcarbon price. GIAM outputs were then used to parameterize theenvironmental and economic modeling of three land uses – existingagriculture, carbon plantings, and environmental plantings – in asimplified version of the Land Use Trade-Offs (LUTO) model (Connoret al., unpublished manuscript). The spatial distribution of neteconomic returns to these land uses were quantified each year usinga profit function given new markets for carbon and biodiversity. Weestimated supply curves for carbon sequestration and biodiversityservices over time, given the potential for land use change fromcurrent agriculture to carbon plantings or environmental plantingswith the evolving conditions under each global outlook. Sensitivityof the results to variation in the land use change adoption hurdle rateand in agricultural productivity assumptions was assessed. Theresults provide insights into the effectiveness of market policies forcarbon sequestration and biodiversity services under a range ofplausible futures.

2. Methods

Key climatic and economic computer modeling was done usingraster data in Python (van Rossum and The Python Community,2013) and Numpy (Jones et al., 2001) at a resolution of �1.1 km2 gridcells (812 383 cells). All currency is in Australian dollars (2010) andcarbon units are in metric tons of carbon dioxide equivalent (tCO2).Variables are summarized in Supporting Information A and boldmathematical notation denotes spatial layers.

2.1. Study area

The study area is the non-contiguous 85 347 440 ha intensiveagricultural land (i.e. cleared intensive cropping/grazing land) ofAustralia (Fig. 1). Agricultural land is interspersed by areas ofnatural ecosystems, water bodies, urban development, and otherland uses such as extensive grazing. Agricultural land use by area(Fig. 2) is dominated by sheep and beef grazing, and cerealcropping with isolated areas of high-value, irrigated agriculture(Bryan et al., 2009a; Marinoni et al., 2012). Agriculture in the studyarea is also globally significant – accounting for around 3% and 24%of global wheat and wool production, respectively. Remnantnatural ecosystems are generally of conservation significance.

Policy settings around agriculture, climate change, emissionsabatement, and biodiversity in Australia are linked. Agriculturalpolicy has been consistently geared toward increasing productivity,market competitiveness, and sustainability. Direct public support

for agriculture through subsidies, quotas and other mechanisms islow relative to other countries (OECD, 2013), and largely focused onassistance in exceptional circumstances (e.g. drought payments).Climate change policy has included a price on carbon (DCCEE, 2011).Payments for biodiversity conservation have been trialed exten-sively over the last two decades (Hajkowicz, 2009), and the previousGovernment’s climate change policy package included a BiodiversityFund (DCCEE, 2011). While Australian agricultural, biodiversity, andclimate change policy is currently in review and is uncertain over themedium term, there is current bipartisan support for allowingfarmers to supply carbon offsets from reforestation. Due to thispolicy uncertainty, we analyzed generic carbon price and biodiver-sity payment policies in this study.

2.2. Global outlooks

Four global outlooks used in this study (L1, M3, M2, H3) weredeveloped within CSIRO’s Australian National Outlook (ANO)initiative through stakeholder interviews and workshops (Hat-field-Dodds, 2013). The outlooks were designed as plausible,internally consistent futures for Australia shaped by stakeholder-informed, prescient global drivers and domestic policy settings,

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181 169

coupled with specific natural, economic, and social trends. For theperiod 2013–2050, outlooks include different global settings forpopulation, greenhouse gas emissions, and radiative forcing (vanVuuren et al., 2011) which have implications for average income,the size of the economy, and per capita emissions (Table 1).Integrated assessment of these scenarios using the GlobalIntegrated Assessment Model (GIAM) accounted for climateimpacts on economic activity, competition for land, and theimpacts on producer prices for crops and livestock (Hatfield-Dodds, 2013; Newth et al., 2013). GIAM modeling providedprojections of climate and changes in the price of key variablesinfluencing land use including carbon, crops, livestock, and oil.

2.3. Sensitivity analysis

We assessed the sensitivity of carbon sequestration andbiodiversity supply to variation in the adoption of new land uses,and in agricultural productivity. Adoption was specified as threehurdle rates h in H{1�, 2�, 5� multipliers} to cover the range ofpossibilities from the land use change literature (Bullard et al.,2002; Dumortier, 2013; Murray-Rust et al., 2013; Prestemon andWear, 2000; Schroter et al., 2005). Adoption hurdle rates wereused to adjust agricultural profit to capture the inertia commonlyseen in adopting new land uses. For example, for a 2� hurdle rate,new land uses must be more than twice as profitable as agricultureto be adopted. We also specified three agricultural productivityscenarios u in U{0.0, 1.5, 3.0% pa. (simple)} to represent the range ofproductivity trends over the past 35 years (Nossal and Sheng,2010).

2.4. Climate change modeling

Spatial layers of climate change were created at a coarse spatialresolution using pattern regression based on the MPI-ESM–LRGeneral Circulation Model (GCM) (Harman, 2013). These climatechange delta layers represented the change in mean annual rainfall4R(y)o, temperature 4T(y)o, and run-off 4W(y)o from 2007 foreach year y from 2013 to 2050 under each global outlook. The �1.88resolution annual rainfall and temperature delta grid cell mid-points were then interpolated to 1.1 km grid cell resolution usingsplines and used to adjust ANUCLIM-modeled mean annual rainfalland temperature data layers, thereby creating layers of meanannual temperature T(y)o and rainfall R(y)o for each year y, undereach of the four global outlooks o.

Table 1Dimensions of the four global outlooks assessed in this study.

Indicator Units Value in 2010 Scenario

L1

Population in 2050 Billion people 6.9 8.1

World GDP per capita

in 2050

US$ ‘000

2010 cap�1

8.8 20.0

World GDP in 2050 US$ trillion 61.0 161.6

Benchmark

Representative

Concentration Pathway

(RCP)

– RCP3-PD

Radiative forcing in

2100

Wm�2 – Peak at 3.0 then

decline to 2.6

Atmospheric

concentration in 2100

CO2 – 445 ppm (declini

Emissions per capita in

2050

CO2 cap�1 7.0 3.1

Coverage of abatement

policy

– All sources

Global abatement

effort

– Very strong

2.5. Carbon sequestration

Carbon sequestration as calculated here includes the carbonstored in accumulated plant biomass following reforestation anddoes not consider soil carbon or agricultural emissions. We used3-PG2-modeled spatial layers of the 20-year carbon sequestra-tion potential (t CO2 ha�1) for mixed environmental carbon

plantings (S7) and hardwood carbon plantations (S8) (Polglaseet al., 2008) for environmental plantings and carbon plantings,respectively. These were converted to 100 year carbon accumu-lation layers C f

T for f in F{CP (carbon plantings), EP (environmen-tal plantings)} over the growth period of T = 100 years. Tocalculate carbon sequestration under each global outlook o wemodified the total 100-year cumulative carbon sequestrationpotential according to the likely impacts of climate changeDyð0Þ f

o calculated using regression (Supporting Information B). Arisk buffer e = 20% was also applied to capture the risk tosequestration from a range of factors including carbon modelinguncertainty, natural hazard, and moral hazard. Thus, the totalcumulative carbon sequestration potential for f in F{CP, EP} wascalculated as CðyÞ f

t;o ¼ DðyÞ fo � ð1 � eÞ � C f

T . Using a von Berta-lanffy–Chapman–Richards (vBCR) function of tree growth overtime (Zhao-gang and Feng-ri, 2003), these layers provided a basisfor calculating a climate- and risk-adjusted annual cumulativecarbon sequestration layer CðyÞ f

t;o and annual marginal (incre-ment) carbon sequestration layer cðyÞ f

t;o (tCO2 ha�1 yr�1) fromenvironmental plantings and carbon plantings for each year t andglobal outlook o (Supporting Information B).

2.6. Agricultural yields

The spatial distribution of 23 irrigated and rain-fedagricultural commodities was mapped for the year 2006 byMarinoni et al. (2012). Layers of long term average yield, price,and cost of production were assembled for each commoditycalculated over the 1996, 2001, and 2006 agricultural censuses.The impact of climate change on agricultural productivity wascalculated in a similar way to carbon sequestration throughregression with climate data (Supporting Information C).Agricultural climate impact delta layers DðyÞAG

o were used toadjust yield and cost parameters in calculating economicreturns to rain-fed agriculture over time (Section 2.8). Wespecified that irrigated agricultural production was not affectedby climate change.

M3 M2 H3

10.6 9.3 10.6

18.6 19.3 18.6

197.0 179.1 197.8

RCP4.5 RCP4.5 RCP8.5

4.5 4.5 8.5

ng) 650 ppm (stable) 650 ppm

(stable)

1360 ppm

(rising)

4.3 5.0 8.7

All, excluding emissions

from livestock

All, excluding emissions

from livestock

No sources

Strong Modest No action

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181170

2.7. Biodiversity priority layer

We identified spatial biodiversity priorities using a GeneralizedDissimilarity Model (GDM) (Ferrier et al., 2007). Biodiversitypriority layers were calculated under multiple climate futures for2050 and combined into a single biodiversity priority layer using alimited degree of confidence approach (McInerney et al., 2012;Supporting Information D). Higher biodiversity priority areasincrease the representation of plant communities in existingvegetation and new environmental plantings by providing futureenvironments which are similar to those found in the present, and/or help connect existing habitat. Biodiversity priority areas are alsorobust such that ecological restoration in these areas will providethe most consistent biodiversity benefits across the range ofpossible future climate outcomes.

2.8. Economic returns

Economic returns to agriculture, carbon plantings, and envi-ronmental plantings were calculated in net present value (NPV)terms over the rolling 100-year horizon (T = 100) starting fromeach year y, under each global outlook o. We used a high andconstant discount rate r = 10% reflecting the high risk and costlyreversibility of converting agricultural land to tree-based landuses. We used year-end discounting where revenue and costsoccurring in year 0 were undiscounted such that the present valueof an annual payment k was calculated as:

PVðkÞ ¼ k � 1 þ 1 � 1=ð1 þ rÞT�1

r

!(1)

Economic returns to agriculture were calculated as profit at fullequity (the economic return to land, capital, and management,exclusive of financial debt) using a widely used and validated profitfunction (Bryan et al., 2008, 2009b, 2010, 2011b). We adapted theMarinoni et al. (2012) profit layer for each agricultural commodityk in the set of 23 agricultural commodities K mapped by ABARES(2010) for the most recent agricultural census year (2006).Irrigated and rain-fed variants of each commodity were treatedseparately. The profit function incorporates multiple productsfrom a single enterprise (e.g. sheep produce both meat and wool).Spatial layers of baseline production (Q1k, Q2k) and price (P1k, P2k)data were derived from agricultural census data. Variable and fixedcost data were assembled from over 300 gross margin handbooks(crop- and region-specific state government agro-economicextension information).

In calculating PV ($ ha�1), layers of agricultural revenue TRðyÞAGu;o

for each year y, global outlook o, and productivity rate u, baselinecommodity prices P1k and P2k were adjusted by GIAM-modeledglobal food price trajectory multipliers w(y)k,o for livestock andgrains, and the baseline production layers Q1k and Q2k wereadjusted by the layer of climate change impacts DðyÞAG

o andproductivity multiplier for each year u(y), such that:

TRðyÞAGu;o ¼ PVðP1k’ðyÞk;o � Q 1kDðyÞAG

o uðyÞTRNk þ P2k’ðyÞk;o� Q 1kDðyÞAG

o uðyÞQ 2kÞ (2)

where TRNk is the proportion of livestock herds sold each year(TRNk = 1 for crops).

Layers of present value of agricultural costs TCðyÞAGu;o for included

quantity dependent costs QCk, area dependent costs ACk, and watercosts for irrigated commodities, in addition to fixed costs includingoperating (FOCk), depreciation (FDCk), and labor costs (FLCk).Quantity dependent costs were adjusted for climate change impactDðyÞAG

o and productivity multiplier u(y). Water costs werecalculated as crop water requirements WRk (ML ha�1) multiplied

by the price of water delivery WP ($ ML�1) adjusted for changes inrun-off DW(y)o under climate change. Variable costs wereadjusted according to changes in energy price represented bythe GIAM-modeled oil price multiplier v(y)o. Total agriculturalcosts ($ ha�1) for year y, under each productivity scenario u, andglobal outlook o were calculated as:

TCðyÞAGu;o ¼ PVðvðyÞo � Q CkQ 1kDðyÞAG

o uðyÞ þ ACk þWRkWP

DWðyÞo

� �

þ FOCk þ FDCk þ FLCkÞ (3)

Net economic returns to agriculture were then calculated foreach grid cell, modified by the adoption hurdle rate h in H (Section2.3):

pðyÞAGh;u;o ¼ h � ðTRðyÞAG

u;o � TCðyÞAGu;oÞ � a0 (4)

where a0 is a layer quantifying the area (ha) currently underagricultural production in each grid cell (Marinoni et al., 2012).

We then calculated the economic returns in NPV terms from thesale of credits for carbon sequestered by the two reforestation landuses f in F{CP, EP} over the same 100-year period, using a profitfunction, variants of which have been described and appliedelsewhere (Bryan and Crossman, 2013; Crossman et al., 2011;Paterson and Bryan, 2012). Revenue was calculated as the globalcarbon price p multiplied by the climate change- and risk-adjustedannual marginal carbon sequestration layer cðyÞ f

t;o under eachglobal outlook o, over the T = 100 year rolling horizon starting ateach year y:

TRðyÞ fp;o ¼

XT�1

t¼0

p � cðyÞ ft;o

ð1 þ rÞt(5)

From a landholder perspective, this is equivalent to being paid aguaranteed annuity over 100 years, based on the expected capitalvalue of the carbon yield calculated using prices and sequestrationrates at the year of planting.

The costs of reforestation of carbon plantings and environmen-tal plantings included costs of establishment, water, and annualmaintenance and transactions. Establishment costs ECf ($ ha�1) forcarbon plantings and environmental plantings were incurredupfront and were estimated using a spatially explicit model(Summers et al., unpublished manuscript). Costs of waterentitlements were also incurred upfront (t = 0) in water-limitedcatchments. Water costs were calculated as the product of waterrequirements of plantations WRf (ML ha�1 yr�1) (van Dijk andRenzullo, 2011) and the price of general security entitlements WE($ ML�1) which vary by catchment (Burns et al., 2011), modified bythe impact of climate change on run-off 4W(y)0 for each year y

under each global outlook o (Section 2.4). Annual maintenance andtransaction costs MTCf were applied uniformly across the studyarea (here MTCf = 120 $ ha�1 yr�1 for all f in F{CP, EP}). Establish-ment, and maintenance and transactions costs were adjusted bythe GIAM-modeled oil price modifier v(y)0 for each year y andglobal outlook o. The present value of reforestation costs ($ ha�1)was calculated as:

TCðyÞ fo ¼ vðyÞo½PVðMTC f Þ þ EC f � þWR f WE

DWðyÞo(6)

Layers quantifying the NPV of economic returns for carbonplantings and environmental plantings (f) were then calculated foreach year y, carbon price p, and global outlook o as:

pðyÞ fp;o ¼ ðTRðyÞ f

p;o � TCðyÞ fo Þ � a (7)

where a is the layer quantifying the area of each grid cell.

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181 171

2.9. Carbon sequestration and biodiversity supply

In culmination, we calculated the supply of carbon sequestra-tion and biodiversity in Australia under a carbon price andbiodiversity payment scheme. In this paper, we illustrate supplycurves for the years y = 2015 and 2050 only.

We modeled the potential for land use to change fromagriculture to carbon plantings and environmental plantings andquantified the resultant carbon sequestration supply over a rangeof carbon prices p in P{0, 1,. . .,300 $ tCO2

�1}, for each year y, forthe four global outlooks o, three productivity scenarios u, andthree adoption hurdle rates h, and assuming no biodiversitypayment. Areas of economic potential for land use change fromagriculture to carbon plantings xcðyÞCP

p;h;u;o were those where theNPV of returns to carbon plantings pðyÞCP

p;o were greater thanthose to both agriculture pðyÞAG

p;o and environmental plantingspðyÞEP

h;u;o.

xcðyÞCPp;h;u;o ¼

1 where pðyÞCPp;o > maxðpðyÞEP

p:o; pðyÞAGh;u;oÞ

0 otherwise

�(8)

Similarly, areas of potential for land use change to environ-mental plantings xcðyÞEP

p;h;u;o occurred where they were moreprofitable than carbon plantings and agriculture:

xcðyÞEPp;h;u;o ¼

1 where pðyÞEPp;o > maxðpðyÞCP

p:o; pðyÞAGh;u;oÞ

0 otherwise

�(9)

Carbon supply SC(y)p,h,u,o was calculated as the annual averageof the 100-year climate- and risk-adjusted carbon sequestrationCðyÞ f

T;o (Section 2.5) for carbon or environmental plantingsmultiplied by the area of each grid cell a, summed over all gridcells (represented by the inner summation in Eq. (10)) where theywere the most profitable land use, given the carbon price p, hurdlerate h, productivity rate u, and global outlook o:

SCðyÞp;h;u;o ¼Xf 2 F

Xa CðyÞ f

T;oxcðyÞ fp;h;u;o (10)

We also modeled a biodiversity payment policy where agovernment could invest a fixed annual budget in cost-effectivelytargeted payments to landholders for adopting environmentalplantings in higher biodiversity benefit areas (Chen et al., 2010;Connor et al., 2008). Biodiversity supply was calculated for eachyear y, for the four global outlooks o (including the GIAM-modeledcarbon price), three productivity scenarios u, and three adoptionhurdle rates h. Following Crossman et al. (2011), paymentsequaled the NPV of the opportunity cost – the gap in economicreturns between environmental plantings and the most profitableland use – over the 100 year period calculated as:

dðyÞp;h;u;o ¼ maxðpðyÞCPp;o; pðyÞEP

p;o; pðyÞAGh;u;oÞ � pðyÞEP

p;o (11)

Those cells receiving a payment were identified using an integerprogram which maximized the total biodiversity benefits achieved

Table 2Carbon price, food demand, and oil prices at 2015 and 2050 from GIAM used in this st

GIAM output L1

2015 2050

Carbon price (global) 2010 AUD 46.93 199.74

Grains demand (Australia) Change from 2007 9% 75%

Livestock demand (Australia) Change from 2007 31% 147%

Oil price (Australia) Change from 2007 30% 42%

under a constrained annual biodiversity budget m in M{0, 1, 2, . . .,5000 $M yr�1}:

maximzeXða � B � xbðyÞEP

m; p;h;u;oÞsubject to :

XxbðyÞEP

m; p;h;u;o � ðdðyÞp;h;u;oÞ � m(12)

where xbðyÞEPm; p;h;u;o is a binary spatial variable indicating which

grid cells receive a payment, B is the biodiversity priority layer(Section 2.7), a is the cell area, and the summation occurs over allgrid cells in the study area.

Annual supply of biodiversity benefits SB(y)m,p,h,u,o wascalculated as the area a of environmental plantings weighted bybiodiversity priority score B, summed over all grid cells for whichenvironmental plantings were most profitable. This was expressedas a percentage of the total biodiversity services achievable if allgrid cells were converted to environmental plantings:

SBðyÞm; p;h;u;o ¼P

a � B � xbðyÞEPm; p;h;u;oP

a � B� 100 (13)

Finally, we assessed the joint influence and interactions ofcarbon price and biodiversity payments on carbon sequestrationand biodiversity supply using an illustrative example at mediumagricultural productivity and 2� adoption hurdle rate. For the fourglobal outlooks o, both carbon sequestration and biodiversitysupply were calculated for the year y = 2050, carbon price p in P{0,1,. . ., 300 $ tCO2

�1} and annual biodiversity budget m in M{0, 1, 2,. . ., 5000 $M yr�1}.

3. Results

Below we present the results in the following order: scenarioparameters from global integrated assessment, agriculturalproduction and carbon sequestration rates, spatial biodiversitypriority areas, economic analyses, and finally the supply of carbonsequestration and biodiversity services.

3.1. Global outlook parameters

The modeled global grains prices for 2050 were higher than2015 levels across the four scenarios (Table 2), with the growth infood demand and increased competition for land outweighingimprovements in agricultural productivity. Changes in livestockprice were similar within each scenario, with the exception of L1,where livestock emissions were subject to the carbon price,incurring a significant input cost. The real oil price increasedslightly by 2050, with little variation across the four scenarios.

3.2. Agricultural production and carbon sequestration

Using regression, we were able to predict wheat yield as afunction of climate with a weak but significant R2 of 0.168. Theimpact of climate change on agricultural production varied

udy. Positive percentages indicate an increase.

M3 M2 H3

2015 2050 2015 2050 2015 2050

28.04 118.73 13.98 59.31 0 0

9% 118% 7% 11% 8% 61%

8% 112% 7% 22% 8% 61%

30% 44% 30% 45% 30% 43%

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181172

between each of the global outlooks and across the study area(Supporting Information E).

Unadjusted for climate change and risk, the total modeledcarbon sequestration over 100 years of tree growth for carbonplantings ranged from 27 to 1352 tCO2 ha�1, with a median of541 tCO2 ha�1. Higher levels of sequestration occurred in thewetter southern, eastern, and North-Eastern parts of the study area(Fig. 3). For environmental plantings, total carbon sequestrationlevels were typically much lower than for carbon plantings,ranging from 5 to 1290 tCO2 ha�1, with a median of 284 tCO2 ha�1.Higher sequestration rates occurred in the South-Eastern parts ofthe study area (Fig. 3).

We were able to predict carbon sequestration as a functionof climate with strong R2 values of 0.784 and 0.852 for carbonplantings and environmental plantings, respectively. Climatechange had a very small influence on carbon sequestrationrates with impact varying over the study area (SupportingInformation F).

3.3. Biodiversity priority areas

High priority biodiversity areas for environmental plantingswere concentrated in the south-west of Western Australia and inthe southern agricultural regions of South Australia, with other

Fig. 3. Non-adjusted carbon sequestration for carbon plantings and environmental

plantings over 100 years (C fT ).

priority areas scattered throughout the eastern parts of the studyarea (Fig. 4).

3.4. Economic returns

Economic returns to all land uses were lower in 2015 than in2050 (Supporting Information G; Fig. 5). Carbon plantings andenvironmental plantings made an economic loss over much of thestudy area across global outlooks in 2015 but were substantiallyhigher by 2050. Returns to all land uses were highest under the L1outlook and lowest under the H3 outlook. Economic returns toagriculture were higher in the south (Fig. 5). Economic returns tocarbon plantings were higher in the north and east, and in thewetter parts of the southern states. Economic returns toenvironmental plantings were higher in New South Wales andthe wetter parts of the southern states.

3.5. Carbon sequestration supply

In 2015, there was almost no supply of carbon sequestrationfrom carbon plantings or environmental plantings in Australia’sagricultural lands for carbon prices below 50 $ tCO2

�1. Supplyincreased dramatically with prices up to 125 $ tCO2

�1, abovewhich little additional supply was induced. At 150 $ tCO2

�1, over80% of the maximum sequesterable carbon (350 MtCO2) wassupplied (Fig. 6). There was negligible difference in carbon supplycurves between global outlooks. At GIAM-modeled carbon prices,only small amounts of carbon were sequestered (maximum of4.7 MtCO2 yr�1 in L1).

Differences in both biophysical and economic parameters underthe four global outlooks had a substantial impact on the 2050carbon supply curves, relative to 2015. In 2050, negligible carbonwas sequestered below 65 $ tCO2

�1, above which sequestrationincreased more gradually with price (Fig. 6). At 150 $ tCO2

�1 only46–67% of the maximum sequesterable carbon was supplied(Fig. 6) and there were differences between outlooks. At GIAM-modeled carbon prices in 2050, at total of 189, 110, 13, and0 MtCO2 yr�1 was sequestered under L1, M3, M2, and H3,respectively.

The choice of adoption hurdle rate and agricultural productivityrate had a strong influence on the supply of carbon sequestration(Fig. 7). For example, at 2050, under M3 at the GIAM-modeledcarbon price of 119 $ tCO2

�1, medium productivity (1.5% pa.) andhurdle rate (2�), 110 MtCO2 yr�1 was sequestered. At low and highagricultural productivity settings, 145 and 82 MtCO2 yr�1 weresequestered, respectively, and at 1� and 5� adoption hurdle rates161 and 37 MtCO2 yr�1 were sequestered, respectively.

3.6. Biodiversity supply

Providing a gap payment to landholders for adopting environ-mental plantings in high biodiversity priority areas enhanced thesupply of biodiversity services in the study area. Substantialdifferences were found under the four global outlooks with thegreatest supply following the carbon price (Fig. 8). Under themedium productivity and adoption hurdle rate, a 1 $B budget in2015 could supply 3.31%, 2.71%, 2.36%, and 2.11% of the maximumbiodiversity services achievable under L1, M3, M2, and H3outlooks, respectively. Wider variance occurred in 2050, with4.14%, 3.32%, 2.72%, and 1.48% of the maximum biodiversityservices achieved under the four outlooks, respectively.

Adoption hurdle rate and agricultural productivity rate also had astrong influence on the supply of biodiversity services in the studyarea (Fig. 9). For example, at 2050, a 1 $B biodiversity fund under M3at the GIAM-modeled carbon price of 119 $ tCO2

�1 and a 2� hurdlerate, achieved a total of 3.32% of the maximum attainable

Fig. 4. Biodiversity priority areas for ecological restoration with environmental plantings in the study area (higher score = higher priority).

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181 173

biodiversity services at medium agricultural productivity. At highand low productivity settings, 2.65% and 4.59% of the maximum wasachieved, respectively. At 1� and 5� adoption hurdle rates, 5.32%and 1.65% of the maximum was achieved, respectively.

3.7. Policy interaction effects

Carbon sequestration supply was overwhelmingly dominatedby carbon price (Supporting Information H). Biodiversity paymentsfor environmental plantings generated comparatively little addi-tional carbon sequestration (<1.07 MtCO2 yr�1) in 2050 irrespec-tive of the carbon price and only minor variation occurred betweenglobal outlooks. For example, for a 1 $B biodiversity budget, atGIAM-modeled carbon prices for L1, M3, M2, and H3, an additional0.284, 0.419, 0.896, and 0.276 MtCO2 yr�1 was sequestered,respectively. With no carbon price, a 1 $B biodiversity budgetsequestered an additional 0.241, 0.253, 0.317, and0.276 MtCO2 yr�1 for L1, M3, M2, and H3, respectively.

Biodiversity supply was jointly influenced by the biodiversitypayment and carbon price (Fig. 10). The carbon price weaklyincreased biodiversity supply at carbon prices <100 $ tCO2

�1 andhad negligible effect at higher carbon prices. The biodiversitypayment weakly increased biodiversity supply at carbon prices<100 $ tCO2

�1 but had a strong effect at higher carbon prices. This

influence was consistent across global outlooks. Together, thecarbon price and biodiversity payment supplied greater biodiversityservices than the sum of both policies operating alone. For example,under the M3 outlook including the GIAM-modeled carbon price(119 $ tCO2

�1), medium productivity and 2� adoption hurdle rate, a1 $B biodiversity payment scheme alone achieved 3.32% of themaximum biodiversity services in 2050. With no carbon price, thebiodiversity payment alone achieved 1.30%. With no biodiversitypayment, a carbon price alone supplied just 0.32% of the maximum.

4. Discussion

4.1. Supply of carbon sequestration

Our finding that substantial amounts of carbon may besequestered in Australia’s agricultural land under a carbon marketechoes previous findings (Paterson and Bryan, 2012; Paul et al.,2013a,b; Polglase et al., 2013; Renwick et al., 2014). Carbonsequestration was supplied from the conversion of agricultural landto carbon plantings and environmental plantings which becomemore profitable under carbon and biodiversity markets. Supply wasreduced in 2050 compared to 2015. Consistent with previous studies(Bryan et al., 2011a; Lehmann et al., 2013), the decline in thecompetitiveness of land sector sequestration over the period to 2050

Fig. 5. Annualized economic returns to agriculture (medium productivity) and economic returns to carbon plantings and environmental plantings (adjusted for climate

change and risk), under the four global outlooks using GIAM-modeled carbon prices and assuming no biodiversity payment for environmental plantings.

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181174

Fig. 6. Carbon sequestration supply curves for four global outlooks for 2015 and 2050 under central values for productivity (medium), adoption hurdle rate (2�), and

assuming no biodiversity payment. Gray arrows indicate GIAM-modeled carbon prices under each global outlook.

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181 175

was largely driven by changes in economic parameters (e.g.increases in real commodity prices following global food demand,and increases in agricultural productivity driving up economicreturns from agriculture; increasing costs of reforestation), ratherthan environmental (e.g. climate change). Minor variation in the2050 supply curves between global outlooks was driven by climatechange impacts, global commodity prices (particularly the low foodprice in M2), and oil prices. However, under the 2050 GIAM-modeledcarbon prices projected for each of the four global outlooks, expected

Fig. 7. Carbon sequestration supply curves for 2050 under the four global outlooks inclu

adoption hurdle rates (1�, 2�, 5�), and assuming no biodiversity payment. Gray horiz

carbon supply differed substantially. Different adoption hurdle ratesand agricultural productivity assumptions also affected the supplyof carbon sequestration. These parameters are both very uncertainand very influential aspects of future land use. Broadly consistentwith the global analysis of (West et al., 2010), carbon sequestrationsupply was concentrated in wetter climes mostly used for lowervalue livestock grazing – in this case southern Queensland, EasternNew South Wales and the southern parts of Victoria, South Australia,and Western Australia.

ding sensitivity analysis across three agricultural productivity rates (L, M, H), three

ontal lines indicate the GIAM-modeled carbon price under each global outlook.

Fig. 8. Biodiversity supply curves for four global outlooks for 2015 and 2050 under central values for productivity and adoption hurdle rate, and assuming GIAM-modeled

carbon prices for each global outlook.

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181176

Overall, the impact of the biodiversity payment on carbonsupply was limited. Biodiversity investment produced only a verysmall net increase in carbon sequestration. The greatest additionalcarbon sequestration occurred at moderate carbon prices, andparticularly in the M2 outlook with lower food prices. The limitedinfluence of biodiversity payments on carbon sequestration is dueto the convergence of economic returns to environmentalplantings, carbon plantings, and agriculture at moderate carbon

Fig. 9. Biodiversity supply curves for 2050 under the four global outlooks including sensiti

rates (1�, 2�, 5�), and assuming GIAM-modeled carbon prices for each global outlook

prices, thereby lessening the biodiversity payment required andincreasing the area of environmental plantings and carbonsequestered. At higher carbon prices, land converted to environ-mental plantings would otherwise be converted to carbonplantings in the absence of the payment. As carbon plantingstended to sequester more carbon than environmental plantings,even with the injection of additional funds for biodiversity, therewas little net gain in sequestration.

vity analysis for three agricultural productivity rates (L, M, H), three adoption hurdle

.

Fig. 10. Illustrative 2050 biodiversity supply surfaces for the four global outlooks in response to co-variation in carbon price and biodiversity payment under medium

agricultural productivity rates and 2� adoption hurdle rate. GIAM-modeled carbon prices for each global outlook are marked by the dark vertical line.

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181 177

4.2. Supply of biodiversity services

Mirroring findings for Britain and the Americas (Thomas et al.,2013), parts of South America and Vietnam (Hall et al., 2012), andOregon (Nelson et al., 2008) – a carbon market alone did notgenerate substantial biodiversity services from Australia’s agricul-tural land. Over most of the study area, the sequestration abilityand thus, the earning potential, of environmental plantings was farexceeded by the high growth tree species typically used in carbonplantings. However, when complemented by public investmentthrough targeted payments to landholders, the area of biologicallyimportant land under native vegetation could be substantiallyincreased through environmental plantings. The biodiversitypayment scheme leveraged the carbon market by boosting theeconomic returns from carbon sequestered by environmentalplantings. However, even a highly cost-effective program such asthat modeled in this study would require a large investment tohave a significant impact such that securing 5% of the maximumachievable biodiversity services would require investment in theorder of 1.8–3.4 $B in 2015, and between 1.5–>5 $B in 2050.Differences in supply of biodiversity services from 2015 to 2050were partly due to increased costs of reforestation, increasedeconomic price and productivity of agriculture, and increasedcarbon price (except H3). Areas typically selected for paymentswere concentrated in the high biodiversity priority areas insouthwest Western Australia, the agricultural regions of SouthAustralia, and in the upland areas of the eastern states.

Differences in biodiversity supply under the four globaloutlooks, emphasized by 2050, were largely due to differencesin the carbon price – with greater biodiversity supplied at higher

carbon prices, and tempered by adoption hurdle and agriculturalproductivity assumptions. Greatest supply, but also greatestvariation from productivity and adoption hurdle rates, was seenin the L1 and M3 scenarios, with lowest supply and least variationunder H3. Differences across outlooks reflected two interactionsbetween biodiversity payments and carbon price. The firstinteraction was straightforward – higher carbon prices resultedin more environmental plantings, shifting the supply curve to theright in Fig. 8. Here, under the higher carbon prices of the L1 andM3 outlooks, some biodiversity services could be achieved with nopayment scheme where the returns to environmental plantingswere greater than both carbon plantings and agricultural produc-tion. The second interaction was more complex. Because additionalbiodiversity services were secured through an opportunity cost (orgap) payment, the slope of the supply curve reflects largely theimpact of the carbon price on the returns to environmentalplantings relative to that from carbon plantings and agriculture.Over large areas, as the carbon price increased from zero, theeconomic returns from carbon plantings and environmentalplantings approached those from agriculture. Thus, lower gappayments were required to encourage environmental plantingswith increasing carbon price – to a point – beyond which highercarbon prices further increased economic returns from carbonplantings, far exceeding those from agriculture, widening the gapbetween economic returns to environmental plantings and carbonplantings, and increasing the biodiversity payment required. Thus,payments were targeted at those areas where returns from carbonplantings and environmental plantings converged with agricultureat the given carbon price. Targeting adapted over time to followthese areas of higher biodiversity benefit, and lower gap payment.

Fig. 11. Summary of the influence of the carbon price and biodiversity payment

market policies on carbon sequestration and biodiversity services. Large plus signs

are illustrative of carbon sequestration and biodiversity services. The small plus

sign indicates a small amount.

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181178

4.3. Policy synthesis

Complex interactions occurred between the carbon price andthe biodiversity payment schemes in their effect on motivatingpotential land use change and the subsequent supply ofecosystem services. To synthesize, reflecting previous results(Bryan and Crossman, 2013; Nelson et al., 2008; Thomas et al.,2013), a carbon price alone was likely to generate substantialcarbon sequestration but little biodiversity services, while abiodiversity payment alone was likely to motivate the supply ofsome biodiversity services but very little carbon sequestration.While it had little effect on carbon supply, a biodiversity paymentwas able to motivate greater supply of biodiversity services with acarbon price than without (Fig. 11).

These results suggest an opportunity for synergy betweencarbon and biodiversity policy, particularly with respect tobiodiversity (Lin et al., 2013). This supports the need to understandsynergies and tensions between multiple policy instruments andtheir influence on co-benefits and trade-offs identified in thegrowing ecosystem services literature (Bryan, 2013; Lin et al.,2013; Robertson et al., 2014). It also reinforces the recentdirections of global climate and biodiversity policy initiatives. Forexample, the ambit of the UN REDD program was recentlyextended from reducing carbon emissions from deforestation toalso consider forest degradation and the role of conservation,sustainable management, and enhancement of carbon stocks inforests of developing countries (REDD+, UNFCCC, 2011). WorkingGroup III in the recently released Assessment Report 5 of theIntergovernmental Panel on Climate Change also focused onsynergies and trade-offs among policies and the need to considerinteractions and multiple objectives (IPCC, 2014). Our study giveseffect to these global change policy imperatives.

4.4. Comparison with previous assessments

Our results have both similarities and differences with previousAustralian assessments of carbon and biodiversity. Significantly,our results differ from the key Burns et al. (2011) assessment ofland use change and carbon sequestration potential. Despiteanalyzing higher carbon prices (up to 286 $ tCO2

�1 by 2050) thanwe did, Burns et al. (2011) found only modest amounts of land tobe economically viable for reforestation and therefore modestamounts of carbon supply. These large differences are drivenprimarily by variation in several important assumptions. Burns etal. (2011) considered environmental plantings and long rotationhardwood forestry but omitted fast growing monocultures (orcarbon plantings) which dominated land use change in ourassessment. Different choices for a range of other sensitiveeconomic and behavioral parameters – most notably, the lowerrates of carbon sequestration by trees – also contributed to themuch lower rate of potential land use change and carbon

sequestration. Our carbon sequestration results broadly agreewith the recent Polglase et al. (2013) assessment. Despite a focuson lower-yielding environmental plantings and low-end carbonprices (10–50 $ tCO2

�1), Polglase et al. (2013) found substantialeconomic potential for carbon sequestration although concludedthat its realization would be unlikely and highly constrained. Withregard to carbon-biodiversity assessment, the findings of Carwar-dine et al. (unpublished manuscript) were broadly consistent with,although less conservative than ours in that biodiversity targetscould be achieved through targeted payments with the budgetrequired highly dependent upon carbon price (138 $M yr�1 at20 $ tCO2

�1). However, the spatial priorities identified weresomewhat different to ours, due to the different methods ofbiodiversity prioritization. The differences in the results discussedabove illustrate the sensitivity of results to study design andassumptions about domestic and global drivers.

4.5. Methodological strengths and limitations

There are several strengths of the methods used here comparedto other recent studies. The spatial resolution used here (1 km2 gridcells) enabled us to both capture important heterogeneity inbiophysical (particularly ecological) and economic drivers, andcover a large geographical extent. The annual time-step allowsincorporation of more realistic gradual changes in parameters suchas carbon price and growth rates and is a significant advance (cf.Bryan and Crossman, 2013; Paul et al., 2013a). To increase realism,we considered a greater level of complexity in the land-economysystem than many previous studies (e.g. Paterson and Bryan, 2012;Paul et al., 2013a,b; Polglase et al., 2013; Renwick et al., 2014)including competition from three key land uses, and dual policyinstruments and objectives. While some studies have consideredthe impacts of global change (Bryan et al., 2010, 2011a) this is thefirst time that the influence of internally consistent global outlookson landscape scale carbon and biodiversity has been assessed bylinking to an integrated assessment model. We also innovated inthe treatment of uncertainty. While many studies have considereduncertainty through sensitivity analyses or scenarios (Nelson et al.,2008; Paterson and Bryan, 2012; Paul et al., 2013a), we examinedthe effect of variation in key processes of land use change adoptionand agricultural productivity on the results. The use of the hurdlerate in particular is a simple way of exploring rarely consideredeffects of option value and behavioral inertia in land use change(Dumortier, 2013; Lubowski et al., 2006, 2008). Finally, theparticular characteristics of the land-economy system assessedhere is likely to be common to other agricultural regions, and themethods developed herein to assess the supply of carbonsequestration and biodiversity services are broadly applicable tothese areas.

The most significant limitation of this study and others like it isthe uncertainty around many of the model parameters. Much efforthas been made to reduce uncertainty for several of the mostsensitive environmental and economic model parameters such asagricultural profit mapping (Marinoni et al., 2012) and theestablishment costs of reforestation (Summers et al., unpublishedmanuscript). However, further improvements in environmentaland economic modeling are also required, such as the impact ofclimate change on crops, livestock, and carbon sequestration rates.Climate impacts need to be quantified using process models suchas APSIM and 3-PG2, which will improve the regression-basedestimates used here. Modeling of carbon sequestration rates ofcarbon plantings and environmental plantings needs particularattention (Strengers et al., 2008). Here, we used sensitivityanalysis to assess the influence of two of the most influential anduncertain parameters – agricultural productivity and land usechange adoption. Although minor in comparison, the influence of

B.A. Bryan et al. / Global Environmental Change 28 (2014) 166–181 179

uncertainty in other parameters such as climate change projec-tions by different GCMs should also be quantified.

Another significant limitation is that we assume landholdersmake long run decisions on the basis of the simulated pricetrajectories. This abstracts from real world stochasticity anduncertainty about future prices and the implications of these forrelative profitability of competing land use, particularly in theperiod before market settings mature and risk managementarrangements are established. Techniques such as real optionsanalysis have potential to improve the valuation of economicreturns under uncertainty (Dumortier, 2013). Further, for simplic-ity, the analysis presented here assumed exogenous agriculturalcommodity prices, such that a decrease in supply of agriculturalcommodities in Australia had no influence on price over time. Amore realistic but more complex approach is the solution to priceequilibrium, where endogenous agricultural prices respond to landuse change through elasticity of demand and agriculturalcompetitiveness increases (Connor et al., unpublished manu-script). Future enhancements might also explore the merit ofmodeling adoption hurdle rates and agricultural productivity ratesas endogenous. Enhanced policy sophistication could also beintroduced to capture real-world complexity (Bryan, 2013) andconnections such as leakage and indirect effects of land use change(Lambin and Meyfroidt, 2011) also need to be captured. Realismcould be further increased by considering a broader range of landuses and ecosystem service impacts (Lin et al., 2013).

5. Conclusion

We have presented the first integrated assessment of thepotential supply of carbon sequestration and biodiversity servicesunder a range of global outlooks, with a focus on Australia’sintensive agricultural land. We conclude that there is economicpotential for the substantial supply of carbon sequestration fromthe study area under a carbon market. Considering GIAM-modeledcarbon prices under the four global outlooks assessed, little carbonsequestration was likely by 2015 under any outlook. But by 2050,in response to the higher carbon prices under the L1 and M3outlooks, which assume strong and medium global action onclimate change, substantial carbon sequestration may be supplied.We also conclude that there is economic potential for the supply ofbiodiversity services when a targeted payment scheme is used tocomplement the carbon price, although it will not generate muchadditional carbon sequestration. However, used in isolation, acarbon market is unlikely to provide substantial biodiversityservices and a biodiversity payment is unlikely to supply muchadditional carbon sequestration. All of these results were sensitiveto the trajectories of economic and environmental parameters inthe global outlooks, especially the carbon price and how this affectsrevenue from new land uses relative to the costs of production(including opportunity costs of foregone agricultural production).Assumptions about agricultural productivity growth and rates ofland use change adoption were also influential. The resultsillustrate the utility of considering global and domestic outlooksand the need for policy and management to be robust to futureuncertainty. This information can enable society to understand thekey drivers and their effects, and provide a basis for the design andimplementation of institutional responses (e.g. market rules,regulatory institutions) which guard against undesirable outcomesand steer us toward more desirable ones.

Acknowledgements

We are very grateful to CSIROs Sustainable Agriculture Flagshipand Australian National Outlook initiative for supporting this work.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.gloenvcha.2014.06.013.

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