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Supplementary Materials for

Assessing the land resource–food price nexus of the Sustainable

Development Goals

Michael Obersteiner, Brian Walsh, Stefan Frank, Petr Havlík, Matthew Cantele, Junguo Liu,

Amanda Palazzo, Mario Herrero, Yonglong Lu, Aline Mosnier, Hugo Valin, Keywan Riahi,

Florian Kraxner, Steffen Fritz, Detlef van Vuuren

Published 16 September 2016, Sci. Adv. 2, e1501499 (2016)

DOI: 10.1126/sciadv.1501499

This PDF file includes:

section S1.1. Overview of the general approach to the SDG land resource nexus.

section S1.2. Quantitative approach to modeling trade-offs and cobenefits.

section S1.3. Developing policy clusters in association with SDG targets.

section S1.4. Defining SDG strategies from individual policy alternatives.

section S2.1. The analytical framework for modeling SDG scenarios.

section S3.1. Indicators for assessing SDG strategies.

section S3.2. Normalized scores.

section S3.3. EI scores.

section S3.4. Fit statistics.

section S3.5. Complete GLOBIOM scenario results.

fig. S1. Diagram illustrating the conceptualization of the analysis.

fig. S2. SDG clusters, policies, and strategies.

fig. S3. Future primary energy supply from biomass.

fig. S4. Short-rotation tree plantation areas.

fig. S5. Indicators of environmental performance of Global Energy Assessment

scenarios.

fig. S6. Aggregate crop yield projections by SSP.

fig. S7. Historical and projected global food consumption.

fig. S8. Global consumption patterns of livestock products.

fig. S9. Diagrammatic illustration of the GLOBIOM model.

fig. S10. Graphical representation of interactions among policy clusters.

fig. S11. Representation of SDG scenario output and evaluation.

fig. S12. Spider charts showing GLOBIOM results for three SDG strategies (in

three SSPs).

fig. S13. Normality test on fit residuals from single-policy strategies.

fig. S14. Normality test on fit residuals from depressurizing strategies.

fig. S15. Normality test on fit residuals from pressurizing strategies.

fig. S16. Circular plots for SSPs 1 and 3.

table S1. Overview of definitions for three food system resilience policy cluster

policies.

table S2. Projected change in meat consumption per capita.

table S3. SDG strategy definition table.

table S4. GLOBIOM scenario results (values and normalized scores) for SSP1

scenarios.


scenarios.


scenarios.

table S7. GLOBIOM scenario results (values and percent deviation from 2010

value, at top) for SSP1 scenarios.


value, top) for SSP2 scenarios.


value, top) for SSP3 scenarios.

References (49–72)

1 SUPPLEMENTARY INFORMATION I (SI I)

1.1 OVERVIEW OF THE GENERAL APPROACH TO THE SDG LAND RESOURCE NEXUS

In this assessment we followed a stepwise approach (cf. fig. S1):

1. Translate qualitative SDGs and their component targets into a range of potential policies:

i. Policies are defined as discrete shifts from business-as-usual (BAU) undertaken on a global scale

in service of individual environmental/developmental targets or subsets of targets.

2. Policies are grouped into policy clusters according to the SDG target(s) they affect most directly.

i. A null policy in each policy cluster projects business-as-usual—i.e. inaction—on the targets in

the cluster.

ii. Each policy cluster includes two active policies, each representing one degree of ambition vis-à-

vis the relevant environmental/developmental target(s).

3. Policies are combined into strategies, defined as any and all policies enacted on a global scale in service

of any subset of SDG targets:

i. The null strategy projects a future in which zero active policies are enacted.

ii. Single-policy strategies are comprised of exactly one active policy from one policy cluster and

the null policy in the remaining policy clusters.

iii. Compound strategies include active policies in two or more policy clusters and the null policy in

the remaining policy clusters.

4. Each strategy is combined with one of three benchmark Shared Socio-economic Pathways (SSP) to form

a complete GLOBIOM scenario.

i. Each SSP contains a set of assumptions about global socioeconomic drivers, including especially

population growth and per capita income (26).

ii. Model results for each scenario are projected decennially through 2050.

iii. Use sets of GLOBIOM scenarios to study relationship between planetary boundaries and food

price projections.

5. Synthesize effects into multi-sectorial assessment with focus on linkages and interdependencies among

individual SDGs in the land system.

i. Draw conclusions about the underlying basis for tradeoffs among policies in service of

generalized subsets of SDGs.

1.2 QUANTITATIVE APPROACH TO MODELING TRADEOFFS AND COBENEFITS

At this point in the process of SDG formulation, only 30% of SDG targets have been quantified. Building on this

and other foundations, we use qualitative SDG formulations to assign quantitative GLOBIOM model parameters

to SDG targets in a manner consistent with negotiations in their respective fora (e.g., the UNFCCC and CBD).

Figure S2 lists the Sustainable Development Goals and targets pertinent to the land system and maps these to

the following land resource policy clusters: Energy & Climate; Food System Resilience; Agricultural Productivity;

Terrestrial Ecosystems; Biodiversity Conservation; Land Use, Land Use Change, and Forestry (LULUCF) Climate

Change Mitigation; and Sustainable Consumption. Due to the all-encompassing vision of the SDGs and

interdependencies in the land system, several policy clusters are relevant to multiple SDGs. In particular, the

Food System Resilience cluster factors into the operationalization of several SDGs. A full description of each

policy cluster and policy is provided in the scenario description section.

In each of these seven resource policy clusters, we formulate two active policies and one null policy (cf. fig. S2).

Together, the three policies within each policy cluster span a range of ambitions vis-à-vis SDG operationalization:

null policies project a continuation of business-as-usual (BAU), while active policies in each cluster represent

more ambitious, SDG-informed policy options. By comparing the results of strategies based on policies within

each cluster, we quantify and gain insight into the effects of SDG implementation on the land system.

One policy from each policy cluster is selected to define an integrated SDG strategy, which is studied in

GLOBIOM as a unique scenario. The BAU (null) strategy is formed from the combination of BAU (null) policies in

all seven clusters (cf. fig. S2).

fig. S1. Diagram illustrating the conceptualization of the analysis. Quadrants of the analysis can be mapped to steps in the policy process.

fig. S2. SDG clusters, policies, and strategies. Integrated SDG strategies are constructed by selecting one policy from each of seven policy clusters. The BAU (nominal or null) strategy includes the null policy in each cluster. Single-policy strategies include exactly one active policy in one policy cluster and null policies in the remaining six clusters, while compound strategies include active policies in two or more policy clusters.

Recognizing population and GDP growth as important-but-indirect scenario drivers, we consider each SDG

strategy in the context of three possible socio-economic pressure scenarios (SPS), or development trajectories.

The low, medium, and high socio-economic pressure scenarios are based on Shared Socio-economic Pathways

(SSPs) 1, 2, and 3, respectively (26). In this analysis, we define SSP2 (medium pressure) as the nominal case.

1.3 DEVELOPING POLICY CLUSTERS IN ASSOCIATION WITH SDG TARGETS

In this section, we elaborate on the correspondence between the qualitative targets comprising the seventeen

SDGs in their present form and the policies encoding strategies for pursuit of these same targets. Text in the

green brackets cites the targets that policies are designed to achieve in their respective clusters. Listed in bold,

each policy is accompanied by a description of the model parameters involved as well as an interpretation of its

role in SDG implementation.

1.3.1 Climate & Energy: low-carbon energy & climate mitigation policy pathways

The energy sector and climate policies in this work reference the Global Energy Assessment (GEA), a major

comprehensive analysis of this sector (9). For the null policy {BAU}, we use information from the GEA reference

scenario. We adopt as active policies—{Climate-BE} and {Climate-BE+}—two that are in line with the ambitions

of the SDGs both in terms of energy and climate policy goals.

All three policies in this cluster assume a demand development for first generation biofuels consistent with

assumptions made under the agriculture model inter-comparison project (31), as it is believed that short term

7.1: By 2030, ensure universal access to affordable, reliable and modern energy services

7.2: By 2030, increase substantially the share of renewable energy in the global energy mix

7.3: By 2030, double the global rate of improvement in energy efficiency

13: Take urgent action to combat climate change

14.3: Minimize and address the impacts of ocean acidification, including through enhanced

scientific cooperation at all levels

biofuel market developments are better captured under these assumptions and also allow for a better

representation of the interactions with the food sector.

In the context of the GEA, these active policies were formulated explicitly to be consistent with the sustainable

energy for all (SE4ALL) objectives and hence also goals 7 (energy) and 13 (climate). Specifically, both policies

achieve emissions reductions consistent with the 2°C target, reduce air pollution, and greatly expand access to

energy through reductions in fossil fuel consumption and expansion of renewable energies.

Both the {Climate-BE} and {Climate-BE+} policies represent large, new demands for biomass from the land use

system. Both assume that woody biomass demand is met by biomass extraction from managed forests and

dedicated bioenergy plantations. Differences between these policies correspond to sensitivity cases for

technological limitations and depict futures with divergent biomass potentials. The two policies also differ with

respect to the future availability of specific technologies—most notably, nuclear energy.

Brief descriptions of policies in this cluster (in curly brackets) are given below. A full discussion of the

corresponding SE4ALL scenarios (in square brackets) is provided in Chapter 17 of the GEA (9), and detailed

descriptors and quantitative outputs of these energy scenarios are downloadable at the GEA database.1

{BAU}

SE4ALL correspondent: [geah_counterfactual]

Null policy: baseline not consistent with SE4ALL objectives.

{Climate-BE}

SE4ALL correspondent: [geala_450_btr_nbecs_nsink_limbe]

Compatible with the 2 degree target by aiming at roughly 450 ppmVeq CO2 by 2100;

GEA-compatible efficiency assumptions;

Conventional transport sector assumptions;

No bioenergy and carbon capture and sequestration technology allowed;

No forest sinks are accounted for and;

Maximum limit on total biomass use.

{Climate-BE+}

SE4ALL correspondent: [geala_450_btr_noccs_nonuc]

Compatible with the 2 degree target by aiming at roughly 450 ppmVeq CO2 by 2100;

GEA-compatible efficiency assumptions;

Conventional transport sector assumptions;

Carbon capture and sequestration technology allowed (CCS);

No nuclear energy allowed.

1 http://www.iiasa.ac.at/web-apps/ene/geadb/dsd?Action=htmlpage&page=welcome

http://www.iiasa.ac.at/web-apps/ene/geadb/dsd?Action=htmlpage&page=welcome

By incorporating GEA scenarios into this analysis, we introduced a soft linkage between the MESSAGE energy

systems model and the GLOBIOM land use model. In this way, the land resource focus of this SDG assessment is

made broadly compatible with the targets contained in SDG 7 and SDG 13.

fig. S4. Short-rotation tree plantation areas. Projections made by the GLOBIOM model of short rotation tree plantations in million hectares dedicated to supply biomass for energy conversion in the BAU, Climate-BE, and Climate-BE+ policies for the years 2030 and 2050. The active policies in this cluster are projected to satisfy the constituent targets in SDGs 7 and 13.

0

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100

150

200

250

300

350

400

Tota

l Pla

nta

tio

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[Mh

a]

2030 2050

BAU Climate-BE Climate-BE+

fig. S3. Future primary energy supply from biomass. Decadal assumptions for the BAU, Climate-BE, and Climate-BE+ policies are shown in EJ/yr. (Source: GEA database)

fig. S5. Indicators of environmental performance of Global Energy Assessment scenarios. Performance is indicated by CO2 equivalent concentration (incl. all forcing agents) for the BAU, Climate-BE and Climate-BE+ policies.

1.3.2 LULUCF Climate Change Mitigation: land use change and associated GHG emissions from the

land use, land use change, and forestry sectors

While policies in the Clean Energy for All aim at 450ppm atmospheric carbon equivalent concentrations, we

study carbon taxes as additional land use and climate change mitigation policies for the land use, land use

change, and forestry (LULUCF) sectors. In addition to the null policy, two active policies are evaluated which

address the following SDG targets:

Mitigation of land use change and associated emissions is achieved with a GHG emission tax policy with full spatial and sectorial coverage of all major emission sources and sinks. Active policies in this cluster impose a tax of 10 US$2000/tCOeq {GHG $10} or 50 US$2000/tCOeq {GHG $50} on the following major land-based carbon emission-sink accounts:

13: Take urgent action to combat climate change

14.3: Minimize and address the impacts of ocean acidification, including through enhanced

scientific cooperation at all levels

15.1: By 2020, ensure the conservation, restoration and sustainable use of terrestrial and inland freshwater ecosystems and their services, in particular forests, wetlands, mountains and drylands

15.9: By 2020, integrate ecosystem and biodiversity values into national and local planning…

1. Biofuel emissions and savings 2. Cropland-based mitigation 3. Livestock-based mitigation 4. Land use change-based mitigation (incl. REDD+) 5. Peatland emissions

We note that emissions and emission savings from 1st generation biofuel emission savings are accounted for in this study, while biomass substitution effects in the bioenergy sector are not accounted for. Furthermore, emissions due to “unnecessary deforestation” (defined in Section 1.3.5) are not accounted for in this study.

1.3.3 Food System Resilience: maintaining resilience and efficiency in agricultural production

In this policy cluster, we conceive of active policies as investments in the resilience, robustness, and efficiency of

agricultural production systems. Specifically, these policies bolster the ability of farms and markets to satisfy

demand for food, feed, fuel, and fiber under evolving demographic, economic, hydrospheric pedospheric, and

climactic conditions. Waste reduction—within both production and consumption processes—is also integrated

into these policies. This policy cluster is relevant to a large number of SDG targets:

Rates of change within and among agricultural production systems respond to socioeconomic pressures: relative

to SSP3, SSP1 leads to improved structural agility (i.e. better access to credits, public investment in

infrastructure, capacity building). To reflect this, the SSPs inform the construction of policies from variations on

the following GLOBIOM model parameters (cf. Table 1):

1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters

2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production…

6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals…

8.4: Improve progressively, through 2030, global resource efficiency in consumption and production … with developed countries taking the lead

9.4 By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency

12.2: By 2030, achieve the sustainable management and efficient use of natural resources

12.3: By 2030, halve per capita global food waste at the retail and consumer levels and reduce food losses along production and supply chains, including post-harvest losses.

Summary of policies in the LULUCF Climate Change Mitigation policy cluster:

{BAU} GHG tax of 0 US$(2000)/tCO2eq on LULUCF GHG accounts

{GHG $10} GHG tax of 10 US$(2000)/tCO2eq on LULUCF GHG accounts

{GHG $50} GHG tax of 50 US$(2000)/tCO2eq on LULUCF GHG accounts

MAX crop system switch: maximum fractional increase of each crop production system per decade per

SimU (a geospatial pixel unit; see model description of GLOBIOM);

MAX livestock numbers shift: maximum fractional change in livestock numbers per decade per SimU.

Livestock expansion: expansion of livestock systems in the {Low Flexibility} policy is restricted to their

historic distribution of management systems. The distribution and expansion of livestock production

systems is unrestricted under {BAU} and {High Flexibility}.

Marginal return on fertilizer intensification: percentage increase in crop yield resulting from 1% fertilizer

intensification. We assume in the null policy, {BAU}, that a 1% increase in fertilizer input produces a 1.33%

increase in agricultural yields. Alternatively, under {High Flexibility} and {Low Flexibility} policies, 1%

fertilizer intensification generates a 2.0% or 0.8% yield increase, respectively.

Marginal return on water and investment intensification: percentage increase in crop yield resulting from

1% water or investment intensification.

table S1. Overview of definitions for three food system resilience policy cluster policies.

Waste reduction policies in this analysis are based on an in-depth FAO study on the same topic (11). The latter study identifies and probes spoilage at three points in production chains—agricultural production, postharvest handling and storage, and processing—and during the two stages of consumption: before and after retail distribution. FAOSTAT Food Balance Sheets report gross consumption, which explicitly includes post-production waste. As a result, this loss point is built into food demand projections and is studied here.

We examine the correlation between losses and wastes (LW) and GDP per capita to project future LW rates at the regional level. Variations in per capita demand for food and feed among the three socio-economic pressure scenarios (SSPS1, 2, and 3) are reconceived as degrees of post-production waste for each of the crops and regions included in GLOBIOM. SSP3 and SSP1 are mapped to the active policies in this cluster ({Low Flexibility} and {High Flexibility}, respectively), and the nominal (SSP2) projection is mapped to the null {BAU} policy. Overall, the global effects of GDP growth are small compared to the crop yield and feed efficiency developments, but they can play an important role in the food security some regions. In particular, we note:

Oilseeds & pulses sector

Among all regions, nominal (SSP2) LW projections are highest in Africa & the Middle East (17%) and lowest in Europe (6%).

Globally, LW in this sector decreases between 7% (SSP3) and 12% (SSP1) relative to 2000.

Dairy sector

Among all regions, nominal (SSP2) LW projections are highest in Africa & the Middle East (4-9%). Globally, LW decreases in this sector between 3% (SSP3) and 5% (SSP1) relative to 2000.

1.3.4 Agricultural Productivity: waste reduction & input-neutral yield growth

To project technological progress and its effects on agricultural production, we examine the historical

correlation between GDP per capita and growth rates in crop and livestock productivities. The resulting

econometric analysis (12) showed that economic income groups are a significant variable when projecting crop

yields. Data on country-level yields are collected from FAOSTAT, and GDP per capita data are reported by World

Bank (1980-2009).

We estimate yield response functions to GDP per capita separately for each of the 18 crops included in

GLOBIOM. Specifically, crop yields are fitted to countries’ logarithmic GDP per capita over the period [1980-

2009] using a fixed effects model with panel data (31). Countries are partitioned into four income groups, which

are defined using the World Bank’s GDP per capita classification system: < 1,500 USD; 1,500-4,000 USD; 4,000-

10,000 USD; and >10,000 USD (modulo small changes to group thresholds to ensure a sufficient number of

observations in each group). Within each economic group, the coefficient linking yields with GDP per capita is

derived from aggregated results.

Relative to 2010, yields in Europe are projected to grow between 35% (SSP3) and 50% (SSP1). Because of larger

present yield gaps, yield growth in Latin America and Africa & Middle East is in all cases much greater: 123% in

SSP1, 106% in SSP2, and 66% in SSP3. The resulting yield growth projections are shown in below.

In the livestock sector, efficiency increases for five products (pork, poultry, and ruminant meat, milk, and eggs)

are calculated similarly (49). As a result of GDP per capita growth, feed-to-ruminant meat and feed-to-dairy

conversion efficiencies (unit of product output per unit of feed input) grow most quickly for the period [2010-

2050] in Sub-Saharan Africa: +70% and +30%, respectively, in SSP1 (+50% and +20%, respectively, in SSP2).

Similar growth is projected for Latin America in both SSP scenarios. Swine and poultry feed efficiencies in these

regions increase by less than 5% over the whole projection period. In developed economies, where livestock

production systems are already fully-industrialized, efficiency growth rates do not exceed 5% in any product

categories over the full simulation period.

2.3: By 2030, double the agricultural productivity….

12.3: By 2030, halve per capita global food waste at the retail and consumer levels and reduce food losses along production and supply chains, including post-harvest losses.

The yield increases in the two active policies {+30% Yield} and {+50% Yield} assume yield boosts of 30% or 50%

“on top” of the projected yields described above. These yield increases were made across all periods and are

input-neutral, meaning they do not require additional fertilizer or water inputs. Conceived as active policies,

yield boosts can be achieved through supply-side spoilage and waste reductions (i.e. investments in on-farm

storage, infrastructure, and other capital improvements) as well as through research and development of new

crop varietals.

fig. S6. Aggregate crop yield projections by SSP. Ex ante projections [giga-calories per ha] are based on econometric analysis. Global (WRD), European (EUR), Latin American (LAM), and African & Middle Eastern (AFM) averages are shown for each SSP.

1.3.5 Terrestrial Ecosystems: avoiding land use change

In this study, we implement three policies governing land use. The null policy {BAU} functions as a no-policy

scenario wherein no new restrictions are imposed on conversion of any land type in the GLOBIOM model.

The first active policy, {Zero Def}, excludes conversion of primary and secondary managed forest. Conversion of

all other land use categories is allowed in unprotected areas. This policy is implemented on a gross level,

meaning that deforestation in one location cannot be compensated for by afforestation or reforestation in

another location. Further, deforestation is excluded throughout all simulation periods, implying that no

agricultural reserve lands can be built up due to deforestation in previous periods.

The second active policy {Zero Def/Grslnd} eliminates deforestation as well as any conversion of grasslands

starting in 2010. Again, full protection of forests and grasslands is assumed to hold on a gross level and

throughout each simulation period. However, conversion of natural vegetation to grassland is still allowed.

We note additionally that “deforestation” as defined here excludes the concept of unnecessary deforestation,

which is forest loss resulting from failure to optimize land use in ways that the GLOBIOM model suggests are

technically possible (50). These forests are “squandered” because social and political constraints mean that not

all the optimized land uses proposed by the model will be achieved. Constraints include lack of knowledge,

conflict, poor governance, perverse incentives and shortage of capital and poverty. These can create the

following sub-optimal land uses as defined in the Living Forest Report:

Poor forest management: destructive harvesting and poor silviculture leading to declining timber yields, poor regeneration or vulnerability to disease, fire or encroachment;

6.6: By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakes

15.2: By 2020, promote the implementation of sustainable management of all types of

forests, halt deforestation, restore degraded forests and increase afforestation and

reforestation by [x] per cent globally

15.3: By 2020, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land-degradation-neutral world

15.4: By 2030, ensure the conservation of mountain ecosystems, including their

biodiversity, in order to enhance their capacity to provide benefits that are essential for

sustainable development

15.9: By 2020, integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts

Summary of policies in the Terrestrial Ecosystems policy cluster:

{BAU}: No restrictions on land use change for agriculture and bioenergy production

{Zero Def}: no deforestation allowed

{Zero Def/Grslnd}: no deforestation or grassland conversion allowed

Inefficient livestock production: either low-stocking density causing more forests to be cleared, or high-stocking density in or near forests leading to degradation;

Unregulated forest conversion: to secure land for crops or settlement, often due to absence or weak enforcement of planning laws and inequitable or insecure land tenure and user rights;

Low-yield crop production: some forms of subsistence or (“slash and burn”) farming on marginal land or using less productive land to avoid reliance on imported commodities;

High-impact fuelwood collection: over-harvesting for domestic use or for commercial trade in charcoal; Reluctance to use idle, yet suitable land: due to armed conflicts, unresolved land disputes, insecure

tenure, and dysfunctional zoning or permit allocation processes.

1.3.6 Biodiversity Conservation: protecting global hotspots

In the biodiversity conservation policy cluster, the UNEP World Conservation Monitoring Centre (UNEP-WCMC)

global biodiversity dataset is used to identify ecosystems that are important for biodiversity conservation (16).

This dataset incorporates data from six different global conservation assessments: Conservation International

Hotspots (38), WWF Global 200 Ecoregions (51), Birdlife International Endemic Bird Areas (52), WWF/IUCN

Centres of Plant Diversity (53), Amphibian Diversity Areas (54), and Alliance for Zero Extinction Sites (55).

These data serve in this study as proxies for ecosystems mentioned in the SDG targets listed above, as precise

definitions of the targets and derivative mapping products are currently unavailable. In addition to these areas,

the following land categories are excluded from land conversion in the active policies in this cluster: bare areas,

snow and ice, and artificial areas. Finally, productive and potentially productive areas are classified according to

slope classes, where production costs for both agriculture and forestry are strongly related to slope and soil

conditions. In simulations, this results in the economic self-protection of mountain ecosystems.

We define active biodiversity protection policies by excluding areas from land conversion according to the

following criteria listed below. Even when identified in multiple layers of the biodiversity dataset, ecosystems

that have already been converted to commodities production are not restored under either of the active

policies.

14.5: By 2020, conserve at least 10 per cent of coastal and marine areas 15.1: By 2020, ensure the conservation, restoration and sustainable use of terrestrial and

inland freshwater ecosystems and their services, in particular forests, wetlands, mountains

and drylands, in line with obligations under international agreements

15.4: By 2030, ensure the conservation of mountain ecosystems, including their biodiversity, in order to enhance their capacity to provide benefits that are essential for sustainable development

15.5: Take urgent and significant action to reduce the degradation of natural habitats, halt

the loss of biodiversity and, by 2020, protect and prevent the extinction of threatened

species

15.7: Take urgent action to end poaching and trafficking of protected species of flora and fauna and address both demand and supply of illegal wildlife products

15.9: By 2020, integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts

1.3.7 Sustainable Consumption: livestock, food security, and equitability

In GLOBIOM, food demand projections are calculated from the interaction among: (i) population growth, (ii)

income per capita growth, and (iii) price elasticities of demand, which quantify market responses to price

changes. Demand increases proportionally with population within each of the 30 GLOBIOM regions, while GDP

per capita changes are translated into demand shifts as a function of policy-specific income elasticity values.

The Diet+ policy simulates changes in dietary behavior, a lifestyle change, toward efficiency and sustainability

relative to the FAO baseline. This active policy forecasts net decreases in per capita meat consumption in

developed countries, which can be achieved in large part through minimization of waste and spoilage. More

specifically, animal protein demand is reduced in regions where more than 75 g protein/capita/day are

consumed for animal and vegetal products. A minimum consumption of 25 g protein/capita/day of animal

calories is ensured, but red meat consumption is reduced to 5 g protein/capita/day (target remains possible

through non-ruminant meat, eggs and milk). In this policy, diets in developing economies shift toward greater

protein intake, defined as a minimum of 75 g protein/capita/day and maximum root consumption of 100

kcal/capita/day.

Price effects on consumption (iii) are endogenously computed. As a result, final demand is by construction

influenced by supply-side drivers such as technology and state of natural resources.

In Diet- and Diet+, income elasticities are shifted above and below the nominal (BAU) assumption as shown

below. The net effect of these policies in 2030 and 2050 is calculated and presented as percent changes in Table

2.

Summary of policies in the Biodiversity Conservation policy cluster:

{BAU}: no biodiversity protection—all land use conversions are allowed;

{Biodiversity}: moderate protection—no land cover conversions are allowed in a SimU if it is present

in at least 4 of 6 global conservation assessments;

{Biodiversity+}: strict protection—no land cover conversions are allowed in a SimU if it is present in

at least 2 of 6 global conservation assessments.

2.1: By 2030, end hunger and ensure access by all people…

2.2: By 2030, end all forms of malnutrition….

2.4 By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production…

8.4 Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation

12.1 Implement the 10-year framework of programmes on sustainable consumption and production, all countries taking action, with developed countries taking the lead, taking into account the development and capabilities of developing countries

12.2 By 2030, achieve the sustainable management and efficient use of natural resources

Summary of policies in the Sustainable Consumption policy cluster:

{BAU}: Future diets follow FAO projections through 2050, as detailed in table S2 and (56), along with

its 2006 and 2012 revisions.

{DIET-}: Per capita meat demand increases relative to BAU in both developed and developing

economies.

{DIET+}: Reduced per capita meat consumption in developed economies paired with increased meat

consumption in developing economies.

table S2. Projected change in meat consumption per capita. Shifts are shown (as percentages relative to 2010) in 2030 and 2050 under the BAU, Diet-, and Diet+ policies. GLOBIOM results are shown for Sub-Saharan Africa, the European Union, and total global consumption.

fig. S7. Historical and projected global food consumption. Demand is shown in [kcal/cap/day] in three alternative socio-economic pressure scenarios (High (HSPS): SSP3; Medium (MSPS): SSP2; Low (LSPS): SSP1). In each plot, grey represents demand for meat, milk, and eggs; orange represents crops and vegetation, and blue represents calorie streams not explicitly modeled in GLOBIOM (including most notably fish).

fig. S8. Global consumption patterns of livestock products. Trends are projected for the three SSPs in 2030, and expressed in kCal/cap/day. MSPS (SSP2) maps to the BAU policy, HSPS (SSP3) maps to the Diet- policy, and LSPS (SSP1) maps to the Diet+ policy in this cluster.

1.4 DEFINING SDG STRATEGIES FROM INDIVIDUAL POLICY ALTERNATIVES

As diagrammed in fig. S2, one policy (either null or active) in each policy cluster is specified to define each SDG

strategy. Each SDG strategy is subsequently combined with one of three SSPs and implemented as a GLOBIOM

model scenario as described in SI II.

table S3. SDG strategy definition table. Each SDG strategy is defined by specifying one of the three policies in

each policy cluster. The first row defines the reference BAU (nominal or null) strategy.

2 SUPPLEMENTARY INFORMATION II (SI II)

2.1 THE ANALYTICAL FRAMEWORK FOR MODELLING SDG SCENARIOS

The Global Biosphere Management Model (GLOBIOM) is a global recursive dynamic partial equilibrium model of

the agriculture and forest sectors, where economic optimization is based on the spatial equilibrium modeling

approach (27–30). The model adopts a “bottom-up” approach wherein supply forecasts are constructed in

successive steps from raw resources (land cover, land use, management systems) to finished products traded on

developed markets (see fig. S9 for an overview of the model framework). Biophysical models are used to

simulate agricultural and forest productivities on a grid with cell dimensions ranging from 5 x 5 to 30 x 30

arcminutes. Cells are subsequently aggregated into larger blocks, called Simulation Units, on the basis of

country, altitude, slope, and soil class (57). Demand for and international trade of commodities is simulated at a

regional level (57 regions with global coverage) (58). In addition to primary products, the model defines

processing activities in detail for several final products and by-products.

The model computes market equilibrium for agricultural and forest products by allocating land use among

production activities to maximize the sum of producer and consumer surplus within resource, technological and

policy constraints. Production in each grid cell is determined by cell-specific agricultural and silvicultural yields

(dependent on suitability and management), international and regional market prices and access (reflecting the

level of demand), and the conditions and cost associated with land conversion and production expansion. Trade

is modeled using a spatial equilibrium approach, which means that the trade flows are balanced out between

different specific geographical regions. This allows tracing of bilateral trade flows between individual regions.

The model includes six land cover types: cropland, grassland, other natural vegetation land, managed forests,

unmanaged forests and plantations. Depending on the relative profitability of the different production activities,

producers in the model can switch from one land cover type to another. Biomass demand for energy purposes

enters GLOBIOM as an exogenous variable. It can be defined through quantities by linking with energy sector

models like POLES or MESSAGE (29, 59), or by applying different biomass/bioenergy prices. For forests, mean

annual increments and growing stocks for GLOBIOM are obtained from the Global Forest Model (G4M), which is

a spatially explicit process-based forest management model (60, 61). For the agricultural sector, GLOBIOM

draws on results from the crop model EPIC (Environmental Policy Integrated Climate Model), which provides the

detailed biophysical processes of water, carbon and nitrogen cycling, as well as erosion and impacts of

management practices on these cycles. GLOBIOM therefore incorporates all inputs that affect yield

heterogeneity and can also represent a different marginal yield for different crops in a shared grid cell.

Plantation yields are based on internal calculations (29).

By integrating cropland, livestock and forest management into a single framework, the model allows for a

comprehensive account of agriculture and forestry GHG sources. GLOBIOM accounts for ten sources of GHG

emissions, including crop cultivation N2O emissions from fertilizer use, CH4 from rice cultivation, CH4 enteric

fermentation emissions from ruminant, CH4 and N2O emissions from manure management and N2O from

manure applied on pasture. Changes in C stocks are also traced for above and below ground biomass when

converting forest and natural land to cropland, as well as changes in soil. These emissions inventories are based

on IPCC accounting guidelines (62).

fig. S9. Diagrammatic illustration of the GLOBIOM model.

2.1.1 Applications of the model

The GLOBIOM model has been applied to many different issues related to land use change.

One major area of research has been the feasibility and effectiveness of various sectorial policy options for

emissions mitigation. Analyses have examined the potential consequences of bioenergy deployment both

globally (29), and regionally in the European Union (63) and the United States (64). The extent to which the

forestry sector could contribute to bioenergy supply has been analyzed in terms of various forestry activities (65)

and the social and environmental tradeoffs these activities entail (66).

Reducing emissions from deforestation in tropical areas is a key component of mitigation strategies. GLOBIOM

analyses have illustrated how different development pathways could reinforce or challenge the efficiency of

mitigation efforts. The model has been applied at national and regional scales in the Congo Basin (36) and Brazil

(67). Preparations are currently underway to expand the scope of analysis to South-East Asia in the context of

the IIASA Tropical Flagship Initiative.

GLOBIOM has been used to examine the co-benefits of joint mitigation of agriculture and forestry emissions (28,

30), an area in which the emissions mitigation and food security potential of production system transitions have

been clearly demonstrated, as well as the direct and indirect effects of crop intensification on these indicators

(49).

Beside climate change mitigation, the impacts of climate change on agriculture have also been widely explored

with the GLOBIOM model. Analysis of these impacts on crop yields and implications for the world food system

has been conducted in the context of AgMIP, a global comparison project (31), and at regional scale. Additional

ongoing analyses are examining the impact of climate change on livestock, a largely overlooked issue, as well as

adaptation strategies.

GLOBIOM has been deployed at the global level to analyze future challenges facing agricultural systems due to

anticipated trends in both supply and demand (49, 68). Some regional analyses have been undertaken in

cooperation with regional research centers and the intense consultation of local stakeholders (69). The explicit

inclusion of water as a resource (along with land and irrigated land) also makes GLOBIOM a particularly suitable

tool for analyzing water-related impacts of different development scenarios (70).

Finally, GLOBIOM has been used to support numerous policy exercises on the evolution of land use and carbon

stocks in the European Union (71, 72).

3 SUPPLEMENTARY INFORMATION III (SI III)

With this framework in place, we assess the impacts of a range of SDG strategies. Figure S10 provides an

overview of the complex anticipated interactions among these policy clusters.

fig. S10. Graphical representation of interactions among policy clusters. Green connectors represent positive or mutually reinforcing interactions between clusters, while negative interactions are shown in red. Arrows pointing from the Sustainable Consumption cluster symbolize a unidirectional relationship. Energy and climate systems (SDGs 7 and 13, respectively) are not directly modeled by the GLOBIOM model in this study, but are represented here because policies in the Clean Energy for All cluster have been made consistent with the energy and climate SDGs (7 and 13, respectively).

Changes towards diets lower in meat consumption reduce pressure on land resources such that land use,

biodiversity, climate and bioenergy policies can more easily be implemented. If pressure on land resources

lessens, production systems become more extensive, and technological progress vis-à-vis land management

slows. Bioenergy is used as an external driver in this study associated with the energy for all scenarios within the

global energy assessment. Increased bioenergy supply has positive effects on energy system performance

particularly if biomass conversion is combined with carbon capture and sequestration (BECCS), enabling

attainment of ambitious climate change mitigation goals. At the same time, we observe negative interactions of

an expanding BECCS sector with climate change mitigation measures in the land use, land use change, and

forestry (LULUCF) sectors. It should be noted that the negative effect of reduced CO2 concentrations on biomass

growth from lower carbon concentrations due to BECCS and climate feedbacks in terms of temperature,

precipitation and energy efficiency changes are not included in this study.

In order to study these complex feedbacks systematically, we examine tradeoffs in the land system using two

food security pressure indicators and five environmental pressure indicators. Each of these indicators is rooted

in environmental science and in the SDGs themselves, and collectively they provide valuable and actionable

insights into the direct and indirect effects of SDG policies and strategies.

fig. S11. Representation of SDG scenario output and evaluation. The results of all GLOBIOM scenarios are projected decennially through 2050 using the GLOBIOM model. Model results for each scenario are evaluated using five planetary indicators and their effect on food prices.

3.1 INDICATORS FOR ASSESSING SDG STRATEGIES

3.1.1 Food Price Index (FPI)

Global food price index is one of the two pressure indicators used to assess the effects of SDG strategies of food

security. Though it masks trends in specific commodities, this indicator captures the net effect of resource

scarcity and demographic and economic growth on food prices worldwide.

2000 value: 309.7

2010 value: 320.7

Indicator range in 2030: 268.3 – 376.8 [dimensionless]

3.1.2 LULUCF Emissions (GHG)

Greenhouse gas emissions from the land use, land use change, and forestry (LULUCF) sectors is one of five

pressure indicators used to assess the effects of SDG strategies on sustainable environmental management. The

net sum of emissions from biofuels, agricultural processes, peatland, and land use change are included in this

indicator, which represents roughly 15% of total carbon emissions. These emissions are the major driver of

climate change, one of eight planetary boundaries (6). Energy sector emissions are excluded from the GLOBIOM

model and this analysis.

2010 value:

4571.3 Mt CO2eq/year

Indicator range in 2030:

2631.9 – 6918.2 [Mt CO2eq/year]

At left: the relationship between model

results on food prices and LULUCF

emissions in year 2030. Model results,

linear regression fit, and fit statistics for

high pressure scenarios are indicated in

red. Single-policy strategy results are

indicated in grey, and low-pressure

strategies are indicated in green.

3.1.3 Agricultural Irrigation (WAT)

Water demand due to irrigation is one of five pressure indicators used to assess the effects of SDG strategies on

sustainable environmental management. In this analysis, we interpret the spread of reliance on irrigation as one

signature of food insecurity and environmentally problematic agricultural practices. By diverting water from

rivers, lakes, and aquifers, irrigation can contribute to the degradation of terrestrial and freshwater ecosystems.

Relative to rain-fed systems, irrigated systems require both greater initial investments and recurrent inputs. This

cost contributes to rising food, fiber, and fuel costs, and reduces the resilience of agricultural systems by

degrading the agility of their responses to climactic, pedospheric, and economic trends. For these reasons,

“Freshwater use” is included among critical planetary boundaries (6).

2010 value: 456.4 km3


451.2 – 567.9 [km3]


results on food prices and agricultural

water use in irrigation (in cubic

kilometers) in year 2030. Model results,






6918

(Wors

t )2632

(Best

)

LULUCF Emissions [MtCO2eq yr ¡ 1 ]

-20%

-15%

-10%

-5%

0%

+ 5%

+ 10%

+ 15%

+ 20%

Fo

od

Pri

ce

Ch

an

ge

r = 0 .6 4 p < 0 .0 1

r = 0 .6 7 p < 0 .0 1

r = 0 .8 1 p < 0 .0 1Ye ar 2 0 3 0

568

(Wors

t )451

(Best

)

Ag. Water Use [km 3 ]

-20%

-15%

-10%

-5%

0%

+ 5%

+ 10%

+ 15%

+ 20%

Fo

od

Pri

ce

Ch

an

ge

r = -0 .5 2 p < 0 .0 1

r = -0 .6 8 p < 0 .0 1

r = -0 .8 3 p < 0 .0 1Ye ar 2 0 3 0

3.1.4 Deforestation (FOR)

Sustainable management of forest ecosystems is a necessary condition for both economic development and

environmental conservation, as forests provide many ecosystems services essential to life on Earth.

Deforestation is a major component of “Land-system change,” one of eight critical planetary boundaries (6).

2010 value

(global forest area):

3,844,918 103 ha

Indicator range in 2030 (global

forest loss relative to 2010):

0 – 107,674 [103 ha]


results on food prices and deforestation

(cumulative) in year 2030. Model results,






3.1.5 Biodiversity Loss (BIO)

Land use change within global biodiversity hotspots is used as a proxy for biodiversity loss, one of the pressure

indicators of environmental functioning in this analysis. Biodiversity provides a direct measure of “Biosphere

integrity”—one of eight critical planetary boundaries—and its subcomponents, functional and genetic diversity

(6).

2010 value (global biodiversity

hotspots loss): 109,442 103 ha

Indicator range in 2030

(additional hotspot loss,

beyond land use change through

2010):

3,103.4 – 258,864.8 [103 ha]


results on food prices and biodiversity

hotspot conversion/loss (cumulative) in

year 2030. Model results, linear

regression fit, and fit statistics for high

pressure scenarios are indicated in red.

Single-policy strategy results are indicated

in grey, and low-pressure strategies are

indicated in green.

107674

(Wors

t ) 0

(Best

)

Deforestat ion [1000 ha]

-20%

-15%

-10%

-5%

0%

+ 5%

+ 10%

+ 15%

+ 20%

Fo

od

Pri

ce

Ch

an

ge

r = 0 .5 8 p < 0 .0 1

r = 0 .6 2 p < 0 .0 1

r = 0 .7 8 p < 0 .0 1Ye ar 2 0 3 0

258865

(Wors

t )3103

(Best

)

Biodiversit y Hotspot Loss [1000 ha]

-20%

-15%

-10%

-5%

0%

+ 5%

+ 10%

+ 15%

+ 20%

Fo

od

Pri

ce

Ch

an

ge

r = 0 .7 2 p < 0 .0 1

r = 0 .6 9 p < 0 .0 1

r = 0 .9 3 p < 0 .0 1Ye ar 2 0 3 0

3.1.6 Fertilizer Use (NTR)

Fertilizer use includes both nitrates and phosphorous agricultural inputs. These “biochemical flows,”—a major

source of GHG emissions and contributors to the degradation of freshwater ecosystems—are included among

critical planetary boundaries (6).

2010 value: 131,384.6 103 tons


117,419.6 – 219,796.2 [103 ton]


results on food prices and annual global

fertilizer (N and P) use in agriculture in

year 2030. Model results, linear

regression fit, and fit statistics for high

pressure scenarios are indicated in red.

Single-policy strategy results are indicated

in grey, and low-pressure strategies are

indicated in green.

3.1.7 Total Calorie Intake (CAL)

Per capita calorie consumption in Sub-Saharan Africa is also used to assess the effects of SDG strategies on food

security. This focus on one of the most impoverished regions of the world provides insight into the effect of

conservation policies on the eradication of extreme poverty and hunger, two essential components of inclusive

development. However, the relationship between SSA calorie consumption and environmental outcomes is not

as clear-cut as for global food prices. Specifically, the effect of SSP assumptions is at least as large as the effect of

SDG strategies on this indicator, so calorie intake is not included in the results of this analysis.

2010 value: 1851 kCal/cap/day

Indicator range in 2030: 1,635 – 2,293 [kCal/cap/day]

3.2 NORMALIZED SCORES

To facilitate comparisons among strategies across six diverse indicators, we normalize all indicator scores to the

range [0-1]. Within each indicator (independent of all others), “0” corresponds to the worst outcome among all

scenarios analyzed, and “1” corresponds to the best outcome.

For the Fertilizer Use indicator, for example, a normalized score of 0.0 corresponds to 219,796.3 [103 ton] annual

application, because this is the highest-pressure outcome (ca. 2030) in terms of nutrient use among all

GLOBIOM scenarios in this analysis. A normalized score of 1.0 corresponds to 117,419.6 [103 ton] annual

application because this is the lowest-pressure outcome (ca. 2030) among all scenarios.

Intermediate results are normalized to this range. Within the Fertilizer Use indicator, for example, the

normalized score for a result Xobs = 150,000 [103 ton] annual nutrient use would be calculated as follows:

219796

(Wors

t )

117420

(Best

)

Fert ilizer Use [kt ]

-20%

-15%

-10%

-5%

0%

+ 5%

+ 10%

+ 15%

+ 20%

Fo

od

Pri

ce

Ch

an

ge

r = 0 .7 2 p < 0 .0 1

r = 0 .3 4 p = 0 .0 3 3

r = 0 .2 3 p = 0 .2 1 5Ye ar 2 0 3 0

|𝐻𝑖𝑔ℎ 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑉𝑎𝑙𝑢𝑒−𝑋𝑜𝑏𝑠

𝐻𝑖𝑔ℎ 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑉𝑎𝑙𝑢𝑒−𝐿𝑜𝑤 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑉𝑎𝑙𝑢𝑒| = |

219,796.2−150,000

219,796.2−117,419.6| = 0.68

fig. S12. Spider charts showing GLOBIOM results for three SDG strategies (in three SSPs). Each of the seven pressure indicators is displayed on a radial axis, where the center of the plot corresponds to a normalized score of zero, and the outer radius corresponds to a score of one. From left: high-pressure (Low Flexibility & Climate-BE+), pressure-neutral (GHG $50), and low-pressure (Diet+ & Climate-BE) SDG strategies are plotted.

Strategy/GLOBIOM model results and normalized scores for all seven indicators in all GLOBIOM scenarios are

tabulated below.

3.3 ENVIRONMENTAL INDEX (EI) SCORES

The table below presents correlations between seven pressure indicators for single-policy SDG strategies in year

2030. For all except the food price index (FPI), a positive correlation indicates a co-benefit, and a negative

correlation indicated tradeoffs between two indicators. For FPI, a positive correlation indicates a tradeoff

(higher food prices), and a negative correlation indicates a co-benefit (lower food prices).

CAL FPI GHG WAT FOR BIO NTR

CAL 1.00 -0.39 -0.20 0.74 -0.20 -0.15 -0.30 FPI -0.39 1.00 0.67 -0.68 0.62 0.69 0.34 GHG -0.20 0.67 1.00 -0.22 0.90 0.88 0.57 WAT 0.74 -0.68 -0.22 1.00 -0.33 -0.24 -0.25 FOR -0.20 0.62 0.90 -0.33 1.00 0.83 0.45 BIO -0.15 0.69 0.88 -0.24 0.83 1.00 0.48 NTR -0.30 0.34 0.57 -0.25 0.45 0.48 1.00

Despite strong correlations between some indicators (GHG, FOR, and BIO), we average the normalized scores for

indicators corresponding to all five planetary boundaries to calculate Environmental Index (EI) scores for several

reasons. First, all five boundaries have been proposed and discussed as independent, co-equal boundaries of

ecosystem functioning; transgression of any individual boundary will carry negative-but-unpredictable

consequences. Secondly, each indicator is separately correlated to food prices (as shown in the pairwise

correlations in Section 3.1). Thirdly—and perhaps most importantly for this analysis—what matters is that we

can establish any metric of environmental functioning with an unambiguous relationship to food security. There

is no correct metric, as each captures a different aspect of a complicated system.

To illustrate this point, we re-ran this analysis five additional times, each time excluding one of the five

indicators from the definition of Environmental Index (EI) scores. In all cases, we see tradeoffs between

environmental outcomes and food prices, as indicated by statistically significant (p < 0.01) correlations and

tabulated below. This indicates that the tradeoffs identified and discussed here are not merely consequences of

the way EI scores have been defined, but indeed anticipate real tradeoffs and challenges that will be

encountered in the operationalization of the SDGs.

Indicator Excluded Standard Deviation R value P value

GHG 3.4% 0.49 0.001 WATR 2.9% 0.66 < 0.001 FOR 3.5% 0.43 0.004 BIO 3.4% 0.46 0.002 NTR 3.3% 0.53 < 0.001

3.4 FIT STATISTICS

We use a probability plot of fit residuals to assess the appropriateness of a linear regression fit to single-policy

strategies. An r2 value near one (cf. fig. S13) indicates that the correlation is significant and that our finding of a

relationship between these scenario results is appropriate.

fig. S13. Normality test on fit residuals from single-policy strategies. Linearity of results and a high r2-value indicate that a linear regression fit is appropriate for the data. Diet+ strategies in all three SSPs are excluded as outliers from this fit.

In figs. S14 and S15, we repeat this test for low- and high-pressure strategies, respectively. In high pressure-

strategies, the underlying structure of the scenario results is more apparent, but the probability plot indicates

that the linear regression analysis is apt in both cases.

fig. S14. Normality test on fit residuals from depressurizing strategies. This probability plot tests the normality of residuals from a linear regression fit to low-pressure strategies. Linearity and high r2-value indicate the aptness of the linear regression fit.

fig. S15. Normality test on fit residuals from pressurizing strategies. Probability plot testing normality of residuals from linear regression fit to high-pressure strategies. Results display an S-shape, indicating divergence from the normal distribution.

3.5 COMPLETE GLOBIOM SCENARIO RESULTS

table S4. GLOBIOM scenario results (values and normalized scores) for SSP1 scenarios.

table S7. GLOBIOM scenario results (values and percent deviation from 2010 value, at top) for SSP1 scenarios.

fig. S16. Circular plots for SSPs 1 and 3. These plots present the effects of depressurizing (left col.) and pressurizing (right col) SDG strategies in SSP1 (top row) and SSP3 (bottom row) as measured by five environmental indicators and global food prices. Policies on the outer ring of each circle indicate the third policy in each three-policy strategy. In the left (right) hemisphere of each circle, strategies are ranked from top to bottom by EI score (food price). Colors and percentages in each cell indicate deviations in year 2030 from 2010 values.

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