Food Production and Amazon Preservation are Not Mutually

Exclusive: Exploring Feasible Avenues from the Perspective of Land

Use Related NDCs in Brazil

WILLIAM WILLS1, MARCELO MOREIRA2, MARTIN OBERMAIER1, JULIEN LEFEVRE3,

ROMULO ELY4

Energy Planning Program, COPPE, Universidade Federal do Rio de Janeiro, Centro de Tecnologia,

Bloco C, Sala 211, Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ 21941-972,

BRAZIL

2Agroicone, Avenida Angélica, no 2447 set 173 - Higienópolis

CEP 01227-200 São Paulo / SP,

BRAZIL

3Centre International de Recherche sur l´Environnement et le Développement (CIRED),

Campus du Jardin Tropical, 45 bis, avenue de la Belle Gabrielle, 94736 Nogent-sur-Marne Cedex,

FRANCE

4Management Science & Information Systems, Rutgers Business School, Rutgers – The State

University of New Jersey, 100 Rockafeller Road, Piscataway, NJ 08854,

USA

Abstract: The COP21 Paris Agreement and Nationally Determined Contributions (NDC) have proven to be

milestones in the operationalization of climate action at the country-level, particularly in the Agriculture,

Forestry and Other Land Use (AFOLU) sector. In Brazil, AFOLU-related NDC actions in its first version

envisaged the elimination of, for instance: illegal deforestation in the Amazon region, the restoration and

reforestation of 12 million hectares of severely degraded lands, and the substantial expansion of sustainably

produced biofuels by 2030. While Brazilian Government commitment to these NDC targets soon vanished, a

specific analytical question concerns as to how far sustainable land use scenarios can contribute to non-climate

benefits (i.e., socioeconomic development) despite their internal challenges of harmonizing them with

environmental protection and climate change mitigation. In this paper, we analyse the potential socioeconomic

and environmental synergies and trade-offs in NDC implementation, given the possibility of the demand for

land in Brazil increase due to agricultural and livestock expansion. We focus our analysis on GDP growth,

income distribution, and food security. We do so by integrating land-use with computable general equilibrium

(CGE) models and running policy scenarios, emulating different levels of NDC and development policy

implementation. Our analysis helps us to understand how social and economic outcomes do change under

varying levels of commitment by decision-makers. It shows that NDC implementation in Brazil is preferable to

a business-as-usual development pathway, particularly when climate change is taken into consideration. While

GDP and household income appear unaffected by NDC implementation, reducing the pressure over Amazon

deforestation along with other social benefits appears under more stringent NDC implementation. Those results

are particularly important given the significant upward trend in Amazon deforestation recorded in the last few

years and the loss of purchasing power of low-income classes observed since 2015 in Brazil. In the AFOLU

sector, NDC implementation, as in its first version, could thus act as entry points for alternative development

pathways. For instance, such as the ones discussed under green economy or low carbon growth paradigms -

although, possible trade-offs between agricultural and other sectors will still need to be closely monitored.

Key-Words: Climate policies, NDCs, Land-use modelling, CGE modelling, Food security, Brazil

Received: June 14, 2022. Revised: December 23, 2022. Accepted: January 25, 2023. Published: February 17, 2023.

1 Introduction

The COP21 Paris Agreement and NDC submission

have been a milestone for operationalizing climate

action at country-level, both for adaptation and

mitigation. However, it is still an open question how

actions targeting to keep global warming to <1.5°C

can be consolidated with dominant national

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development goals. This is particularly the case of

agriculture, forestry, and other land use (AFOLU).

Climate policy decisions have considerable

consequences on agricultural production, food

prices, deforestation, social welfare, and

macroeconomic development, [9], [10], [12], [14],

[22], [45], [56], [57]. Some relevant study can be

found in [59]. Our focus is to investigate to what

extent different levels of NDC and NDC-related

AFOLU policies in Brazil would affect the social

and macroeconomic objectives in the country.

Agriculture plays a central role in Brazil’s economy

and has a series of very relevant social and

environmental impacts which extend beyond its

national frontier. That is the case of climate change.

While the country has the merit of having kept the

world largest forest budget, with about 65% of its

851 million ha territory covered by natural

vegetation, the AFOLU sector is today responsible

for 60% of the country’s net GHG emissions, [26],

[34].

Climate change mitigation policies directly designed

for land-use-related sectors, including conservation,

could help to reduce such emissions. However, the

relative costs for the operationalisation of these

policies may be comparatively high to other

countries and regions [11]. As a result, it is argued

that they could threaten key development goals such

as food security.

Even under lower warming scenarios in line with

the 1.5°C Paris Agreement objective, climate

change will affect agricultural production and food

security, [8], [39]. Although some crops (including

sugarcane) may benefit from increasing

temperatures and positive effects from higher CO2

fertilisation, higher surface temperature, reduced

precipitation, and higher frequency and intensity of

climatic extremes such as droughts, are projected to

decrease crop yields by 2030 for staple crops, [39],

[45]. If agricultural yield is further reduced by

climate change, ongoing agricultural expansion

(including biofuels) may cause indirect distributive

effects on poorer households, which spend a

significant share of their income on food [43].

Brazil’s AFOLU-related NDCs are ambitious and

include: (1) the implementation of Brazil’s Forest

Code

; (2) elimination of illegal deforestation in the

Amazon region; (3) restoring and reforesting 12

million hectares of forests; (4) and substantial

expansion of biofuels in the Brazilian energy mix by

The Forest Code governs the use and protection of

private lands in Brazil. It provides a way to monitor

restoration and control deforestation in private

landholdings.

2030. These actions are to be supported by other

already ongoing policies, including the Low Carbon

Emission Agriculture Program (ABC). The ABC

stipulates the restoration of 15 million hectares of

degraded pasturelands and the enhancing of 5

million hectares of integrated cropland-livestock-

forestry systems (ICLFS) by 2030 [3]. While it is

clear that NDC implementation in Brazil could offer

much-needed leverage for realising agricultural and

socioeconomic development, it is also clear that

finding ways to balance those objectives in

agriculture (including biofuels and livestock

production) to avoid negative trade-offs in climate

and biodiversity protection continues to be a major

challenge, [12], [26], [39].

Besides the importance of the Brazilian NDC to

GHG emissions and the objectives of the Paris

Agreement, it is important to highlight recent trends

involving Amazon deforestation in Brazil. After an

84% decrease in Amazon deforestation from 2004

to 2012, when it reached its record low at 4571

Km2, Amazon deforestation started to increase

slowly until 2019, when it seems to have gained

momentum (73% increase from 2019 to 2021),

although still smaller, it seems to be getting close to

the level of 2006, [19]. Reducing deforestation rates

in Brazil is crucial not only for the country to reach

its NDC goals up to 2030 given the very high GHG

emissions associated to it, but also to preserve

biodiversity and maintain ecosystem services that

are key for the agriculture sector in the country.

In this paper, we assess the potential trade-offs and

synergies of AFOLU NDC-related policy

development in Brazil until 2030 (the timeframe for

NDC implementation). Our focus relies on the

macroeconomic and social development,

particularly: GDP growth, income distribution, and

food security. We develop and run five policy

scenarios, emulating different levels of NDC and

associated policies’ implementation (ABC, ICLFS).

Our aim is to understand how outcomes may change

under varying levels of commitment by decision

makers towards NDC implementation. We develop

and apply a modelling framework for integrating

macroeconomic, land use change, and climate

impact assessment, based on already established and

peer-reviewed models developed by the authors,

[15], [28], [29], [56], [57], [58]. In addition, we

integrate the impacts of climate change on

agricultural yield. The climate change impacts are

often ignored in studies that simulate different

mitigation scenarios. We find this integration

particularly relevant regarding Brazil’s historically

high-income inequality and the potential impacts on

food prices. Also, we assess how this could reflect

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on household budgets, especially those of the

poorest income groups. Using this framework, we

aim to understand to what extent agricultural and

biofuels expansion under NDC implementation in

Brazil could contribute to environmental, social, and

economic objectives. Furthermore, which set of

policies could reduce, or potentially offset, adverse

negative outcomes caused by these isolated policies.

2 Approach

2.1 Integrating Computable General

Equilibrium (CGE) and Land Use Modelling

Land-use economic models in partial equilibrium

have been extensively explored in history. Since

their use covers a broad range of applications, they

have been employed to measure impacts of land-use

policies, implementation of new energy systems and

technologies, food demand shocks or biophysical

constraints on agricultural development, commodity

prices, land allocation, among other, [16], [32], [48].

A positive attribute of them is that they allow

considering the spatial heterogeneity of land

characteristics and include various levels of detail

about biophysical processes.

However, these models ignore the rest of the

economy. They fail to capture economy-wide

feedbacks on sectors that are directly and indirectly

linked with land-use. For instance, they usually

ignore crucial interactions from energy industries,

commodities, labour, and capital markets. Also, they

do not perform broad socio-economical assessments

of land-use related issues. Conversely, one of the

main facets of CGE models is their ability to

connect agricultural markets and land-use choices to

the rest of the economy. Their pitfall relies of the

fact that they usually include only rough

representations of land allocation, with the land

modelled as a homogenous and perfectly mobile

production factor between a limited number of

agriculture sectors. The strength of one is the

weakness of other.

Two lines of research aim to overcome the

limitations of partial and general equilibrium

approaches. The first line of research consists in

directly improving CGE models with added details

on crops, commodities, technology, and better land

supply representation [21]. For instance, models

may include bioenergy technologies as latent

technologies [46] or rely on more disaggregated

databases, [1], [52]. In addition, Land supply

representation can be based on more advanced land

supply functions [11] or distinct agro-ecological

zones (AEZ) [27] which contributes to greater

representativity of the models. Advanced CGE

models usually include a mix of these features, [25],

[53]. The second line of research links a detailed

land-use model in partial equilibrium to the

economy-wide model or CGE. Here, models can be

either “hard-linked” through direct integration of

both models [20] or “soft-coupled”, with the land-

use model linked to a full multi-sector CGE model

through iterative runs, exchanging data until final

convergence is reached, [47], [54]. Soft-coupling

has the advantage that a higher level of detail about

both land-use and economy-wide processes is kept.

This is also the approach we adopt in this paper.

2.2 Integrating CGE and Land Use

Modelling for Brazil

We use a soft-link to couple the IMACLIM-BR

CGE with the Brazilian Land Use Model (BLUM).

IMACLIM-BR is a hybrid, recursive CGE; BLUM

is a partial equilibrium model dedicated to land use.

Between both, data on GDP and demand for

biofuels, agriculture, and livestock goods is

exchanged, as is information on investments needs

by each sector to support its way of production.

Interactive runs are conducted until reaching

convergence between the two models. In practice, a

data template was used to exchange the datasets

between both models. This template was filled in

with each model’s outputs: the outputs of one model

functioned as inputs for the other model, and the

latter’s outputs then served as inputs to the first

model, leading to the iterative runs.

2.3 The Brazilian Land Use Model

(BLUM)

The BLUM is a dynamic, partial equilibrium, multi-

regional, and multi-market economic model

specifically tailored for the Brazilian agricultural

sectors. It is composed by two modules, both

dedicated to model the following aspects of

agricultural commodities: supply-demand and land

use. BLUM works with the global system of Food

and Agricultural Policy Research Institute (FAPRI)

[35] but considering only the Brazilian territory

[15]. The model has been instrumental in the

Renewable Fuel Standard 2 [5] and to substantiate

the Brazilian government’s proposal for NDC at

COP21, [24], [36].

Dynamic interactions are key to BLUM. They

simultaneously define a vector of equilibrium prices

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and quantities in the Supply-Demand module [38].

National demand nis composed of domestic

consumption, exports, and final stocks. Its main

determinants are prices, income, population,

consumption patterns. Supply at national level is

defined by the sum of production in Brazil’s six

geographic regions, as well as initial stocks (equal

to the final stocks of the previous year) and imports.

Feedstock production itself is represented as the

result of simple multiplication of the area harvested

by productivity. For agroindustry goods, BLUM

considers efficiencies and production costs on the

industrial part as well as migration between

technologies. BLUM’s land use module allows

simulating for competition between agricultural

crops, pastures, and native vegetation. The results

can then be further detailed using a spatial allocation

proprietary model. Finally, competition elasticities

simulate intraregional land competition in

agricultural activities (i.e., competition between the

country’ six geographical regions).

Expansion elasticities in BLUM indicate a need for

producers to increase the total agricultural area of a

region, generating deforestation. For our analysis,

we estimated the elasticities under symmetry,

homogeneity, and additionality constraints [17]

using geo-referenced and regional production data

as a primary information source [15].

Yields we projected as functions of the past ten

years, allowing for small responses to long-term

profitability (reflecting additional investments in

R&D) and short-term profitability (which induces

higher use of agricultural inputs). Yields are also

affected by climate change. Average yield reduction

expected for the agricultural crop (as a percentage of

yield potential) was calculated for the six

geographic regions using plant growth models

subjected to future climate scenario and downscaled

weather data [58]. The average percentage yield

reduction due to climate change was then integrated

into our study’s baseline scenario, described in the

next section of this paper.

2.3.1 CGE model: IMACLIM-BR

IMACLIM-BR is a CGE model designed to assess

medium or long-term macroeconomic and social

implications of climate and energy policies in

Brazil, [22], [23], [55], [57]. Built under a social

accounting matrix framework, it details not only

Brazilian economic flows, but also physical flows

(as energy, industry, and food commodities) are

fully described to embark technical information

from Brazilian energy and land-use scenarios. This

is important to assure the consistency with mid to

long-term energy-economy projections. IMACLIM-

BR represents Brazil as an open economy but

includes specific structural assumptions, helping

overcome some of the shortcomings of closed

economy models and improving empirical and

policy relevance.

The base year of the model is 2015. IMACLIM-BR

has 19 productive sectors, including energy

(biomass, oil, oil derivatives, and electricity),

passenger transport, agriculture, livestock, and

services. We disaggregated ten income classes for

our analysis, helping to explain the implications of

each scenario on income distribution, consumption,

and the impact on other inequalities.

Models based on static production frontiers are

represented by constant elasticity of substitution

production functions (CES) and endogenously

calibrated based on past data. This is not the case of

IMACLIM-BR Of course, it is very difficult to

accurately portray long-term production frontiers

which are the consequences of different price

vectors and linked to technologies that will only be

available in the long-term future. The way

IMACLIM-BR deals with this is by exogenously

incorporating long-term production frontiers into the

model. Data and information are collected from

experts and dedicated sectoral models (bottom-up or

engineering models) which then help describe

relevant innovation potential curves and allow for

data exchanges between different sectors, their

production and consumption, [24], [57].

Additionally, IMACLIM-BR can model cash flows,

total investments per period, fuel substitution,

energy efficiency, among other components. As a

result, we have a model capable to properly set up

changes in the technical coefficients making robust

projections of available technologies, costs and its

impacts over the economy during the time horizon

of the study, [28], [29], [56], [57]. A detailed

description of the model can be found on [30].

2.4 Scenarios

We construct a set of five scenarios to understand

the consequences of NDC and associated policy

implementation until 2030 under different levels of

commitment to policy implementation. Scenario 1 is

the baseline or reference scenario (REF). It does not

consider climate change. Table 1 shows the main

hypotheses and data used for the scenario.

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Table 1. Main hypotheses and data for reference

scenario (REF)

Sources: [3], [18], [42], [44].

The four remaining scenarios are the alternative

scenarios. They consider different aspects relevant

to NDC/climate change policy implementation.

Scenarios 1 and 2 (CC1 and CC2) allow for an

increase of pressure on land use, whereas scenarios

3 and 4 (CC3 and CC4) focus on solutions to

alleviate that pressure. Table 2 describes the

hypotheses that we have used to build each one of

these alternative scenarios.

Table 2. Main hypotheses for alternative scenarios

(CC1 to CC4)

Sources: [3], [24], [37].

Changes in agricultural yield due to, for instance,

reshaping temperature and precipitation patterns, are

equally contemplated from CC1 to CC4. However,

CC2 represents a more intense picture in terms of

the climate change pressure on land use. The 12

million hectares of forest represent the

implementation of Brazil’s Forest Code and the

Brazilian NDC, with the specific objective for forest

recovery of those areas according to regional

requirements for law compliance, [37], [49], [50].

3 Results

3.1 Scenarios

3.1.1 Reference Scenario

Figure 1 shows the used area and levels of

agricultural production until 2030 for the REF

scenario. We group agricultural production into

grains, meat (beef, pigs, and poultry), and biofuels

(ethanol, with some part coming from sugar

production, and biodiesel, produced mainly from

soybean). The agricultural area includes annual

crops (grains, except 2nd season and winter crops),

sugarcane, commercial forest, and pastures.

Production outcomes are presented in % growth,

and area outcomes in in absolute values.

Fig. 1: Agricultural production and land use change

in the reference scenario (REF).

Source: Authors

Grain production increases significantly until 2030

in the REF scenario, almost doubling in 20 years.

This can be mainly explained by an increase of

soybean exports, demand for meat (indirectly

affecting demand for grains) and biodiesel

(particularly soybean oil). The meat sector (linked to

pasture demand) shows a less steeped growth

compared with grains but still performs a strong

expansion of 36%.

Ethanol projection is marked by two distinct

periods: up to 2020 and from 2020 to 2030. At first,

the sector shows only timid growth of less than one

billion litre/year. This is mainly due to structural

difficulties. The sector grows at a more accelerated

pace from 2020 to 2030, with production reaching

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40 billion litres. The production of biodiesel follows

diesel demand and Brazil’s biodiesel blending

mandate of 10% in volumetric terms, not showing

any growth potential beyond this blending mandate.

From the land use perspective, annual crops expand

from 37 million hectares in 2010 to 52 million

hectares in 2030. The increase in terms of

production is therefore 2.5 times higher than the

increase of the area used in this period. The

difference is explained by the expansion of second

crops (which do not require additional area for

production) and yields. In 2030, corn second crop

accounts for almost 75% of domestic corn

production, contrasting with 40% in 2010. The

expansion of sugarcane area is relatively modest

(24%), especially when compared with the

sugarcane crush expansion (38%). The area planted

with commercial forest grows from less than 7

million hectares in 2010 to over 11 million hectares

by 2030. Only pasture faces area reduction

compared with 2010. Pasture experiences a

reduction of 15 million hectares due to the need of

additional area for agricultural production. Despite a

35% increase in beef production, total pasture area

is reduced by 8% between 2010 and 2030. The trend

to adopt medium and high technology systems in

livestock production, as observed in recent years,

mainly explains this.

In REF, total land used by agriculture and livestock

(production of grains, sugar cane, forest, and

pastures) grows from 236 to 243 million hectares

between 2010 and 2030, resulting in a total

expansion of 7 million hectares. For the sake of

simplicity, we assume that 7 million hectares of

native vegetation would be necessary to satisfy this

demand for land between 2010 and 2030.

3.1.2 Alternative Scenario

In Figure 2, the agricultural production (Figure 2.A),

area (Figure 2.B), and prices (Figure 2.C) are

presented in relative terms, whereas GHG emissions

are presented in absolute terms (Figure 2.D).

Fig. 2: Results of reference and alternative scenarios

for production, area, prices and annual GHG

emissions in 2030.

Source: Authors

In annual crops markets, prices tend to increase due

to inelastic demand curves for agricultural

commodities while production area tends to increase

with higher associated AFOLU emissions from land

use (see Figure 2.C, 2.B, and 2.D). Ethanol is an

exception of these trends. It is an almost perfect

substitute for gasoline and thus represents higher

demand elasticities. In this context, scenario CC1

would lead to a limited impact on agricultural

output, except for ethanol. For the regional

estimates of crop yield variations due to climate

change, only two regions show yield decreases

greater than 5%. Of course, significant climatic

impact can still be expected at the micro-regional

level, and long-run climate change is likely to affect

agricultural outputs more strongly. CC2 scenario is

the first of our scenarios for which we consider

partial NDC implementation. Compared to REF,

CC2 is marked by two major developments: strong

expansion of ethanol and afforestation. Ethanol

production increases over 35% (Figure 2.A) which

requires about 15% more land (Figure 2.B) as part

of sugarcane is also used for sugar production.

Afforestation reduces the total area available to

agriculture, with particular impact on pastureland

and annual crops (Figure 2.B). The combination of

climate change, higher ethanol production, and

large-scale afforestation thus result in a stress in

terms of commodity markets, with average prices

growing between 8% (grains) and 15% (sugarcane)

– see Figure 2.C. This leads to a significant

reduction of GHG emissions in the AFOLU sector,

dropping it to about 125 million tons of CO2eq per

year in 2030. We should note that substitution of

gasoline by ethanol is not accounted in this figure

meaning that overall emissions reductions in this

scenario and CC3 and CC4 would likely be even

higher.

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CC3 scenario represents the additional recovery of

15 million ha of degraded pastures and

implementation of 5 million ha of ICLFS. Both

measures enhance the productivity in the livestock

sector (which is currently the sector using the largest

parcel of land). Higher efficiency in the livestock

sector releases pressure in the land market, reducing

prices (mostly meat, but also grains and sugarcane),

and allowing not only higher consumption but also

an increase in terms of agricultural production. The

GHG emissions in this scenario also decrease to 344

million tons of CO2eq by 2030 (195 lower than

REF).

In CC4 we consider additional improvements in the

transport sector. In addition, more ethanol is

produced due to the use of sugarcane bagasse for

ethanol production, including for co-generation.

Adding second generation biofuels drives gains in

terms of productivity in bioenergy production. All

these assumptions result into a lesser demand for

agricultural feedstock which consequently reduces

pressures on land use. This, in turn, leads to

deceases in grains and ethanol prices.

Counterintuitively, meat prices increase. As

biodiesel reduces its demand for soybean oil, its

production becomes less attractive, affecting the

feed availability. The raise of feed prices indirectly

increases the prices of proteins since feed plays a

relevant role in its cost structure.

3.2 Macroeconomic and Social Results

Table 3 shows the macroeconomic outcomes of the

climate change scenario (CC1) and the

implementation of land use related mitigation

measures (CC2 to CC4). Overall, macroeconomic

outcomes are rather limited until 2030. For instance,

all of the five scenarios present GDP values for

2030 at around 2.3 Billion 2015 USD. Annual GDP

growth rates vary only between 2.09% (CC1

scenario) to 2.11% (REF and CC4 scenarios). As a

result, total GDP per capita is also similar across all

five scenarios (US$ 10.286 per capita to US$ 10.311

per capita).

Table 3. Macroeconomic results

Source: Authors

For 2030, all alternative scenarios have a higher

price index compared to REF, with the smallest and

highest values being respectively represented by

CC1 and CC2. Price indexes are directly linked with

the land use results. As climate change induces a

slight increase of agriculture prices in the CC1

scenario, it spreads within the rest of the economy,

which culminates into general prices increase of

1.8%. The NDC policies also add pressure on land

in the CC2 scenario, leading to an additional

increase of agriculture prices and thus the general

price index of by 3.1%. As investments in the

recovery of degraded pastures and on ICLFS reduce

pressure on land demand, CC3 and CC4 scenarios

have a lower price index than CC2 and close to the

price index level of CC1.

The slight variations found in trade balance follow

the price index. The smallest trade balance

variations occur in CC2, where the price index is at

its lowest and Brazil becomes less competitive

compared to the rest of the world. Overall, climate

change impacts on prices compare to those impacts

caused by the implementation of the expansive

mitigation policies implemented under the Brazilian

NDC scenarios (CC2 to CC4).

As we go from scenario CC1 to scenario CC4, a

fuller NDC implementation in fact holds

macroeconomic gains for the Brazilian economy.

Although those effects would still be rather small,

they almost fully offset the losses caused by climate

change. Even in scenario CC4, where we have

simulated an extensive low carbon program for the

transport sector, accumulated investments in

mitigation for this sector in the period from 2016 to

2030 (35 billion 2015 USD) are only a small

fraction of how much was accumulated in terms of

GDP (0.1%) or what was accumulated from the

perspective of investments in the whole economy in

the same period (0.5%).

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Figure 3.A presents socioeconomic outcomes from

REF on household expenses for each one of the six

IMACLIM-BR household classes. While class 1

represents the 10% poorest families roughly

corresponding to the population under the poverty

line with less than US$5.5 per day, 2-5 classes stand

for the intermediate category, divided into 20%

groups. Class 6 represents the 10% of richest

families. We notice that the average food expense

share across classes in 2030 (around 13%) is rather

low and similar to today’s France (12% in 2018

even bearing a three times lower GDP per capita.

This means that reference food prices are lower in

Brazil compared to international benchmarks.

However, food budget shares sharply differ between

classes by 2030: the poorest households (class 1)

use 17% of their disposable income to buy food,

while the richest households (class 6) use only 5,3%

of their disposable income for the same purposes

(with a greater share dedicated to “other expenses”,

which includes going to restaurants). We can expect

that an increase of food prices will lead to increased

food insecurity as food access becomes more and

more difficult, especially for poorer households.

Fig. 3: Household´s expenses in 2030

Source: Authors

Figure 3.C shows the variations of the physical

consumption of food across households’ classes and

scenarios. Higher food prices from CC1 to CC4

compared to REF lead to lower relative food

consumption, which decreases less for the lower

than higher classes. The lower price elasticity of

These data can be found in OECD, 2022.

The literature on poverty and food security, [40], [45]

consent that poorer households consume food near

minimum security limits, and thus have less possibility of

further reducing it than well-off households.

Observation: as class 1 (poor households) do not have

private cars or expensive home appliances, they keep

their energy consumption per capita lower (including

biofuels) than richer classes. Because of this no changes

in energy consumption are observed.

poorer households’ food consumption is expected to

intensify the regressive impact of food price

increase.

As food prices increase, so does the amount people

would have to spend on buying food, constraining

food consumption. Figure 3.B shows the resulting

food expense share of household class 1 across all

five scenarios. Class 1 food expense share reaches

its peak under the partial implementation of the

NDC case (CC2), resulting in a 12% increase

compared to REF and a 2-point increase of budget

share. These are directly linked to our agriculture

and food price increase projections for that scenario.

Taking a different course, CC3 and particularly

CC4, alleviate the pressure on land use and food

prices, substantially reducing the tension on

household budgets. Overall, the food expense shares

of class 1 in our scenarios stay below 20% which is

still far from the food insecurity thresholds usually

considered by previous studies

Figure 3.D shows the resulting impacts on

household consumption of services and other goods.

The results reflect the purchasing power of

households outside food and energy expenses. We

can first notice that average consumption impacts of

climate change and NDC policies on households are

significantly higher than GDP impacts. Also, in

CC2 consumption 2% lower in average than REF

(3.3% lower for class 1). However, the additional

policies to alleviate pressure on land and decreased

biofuel demand in CC4 offset almost half of the

average consumption losses in CC1, increasing the

average well-being of families in a climate change

context. Notwithstanding, as expected, climate

change and NDC policies have a regressive impact

on households’ consumption. The poorer classes are

relatively more impacted than the richer classes.

Poorer households significantly reduce their

expenses on services and other goods in order to

secure their disposable income for buying food. This

is even more evident in scenarios where food prices

are more impacted, such as CC2. On the other hand,

richer households need to do little to change their

consumption patterns. From the CC3 and CC4

policies perspective, although providing an average

consumption dividend compared to CC2 (also,

compared to CC1 with CC4), they do not reduce the

regressivity of the impacts. Furthermore, the

consumption surplus of CC4 compared to CC1 is

In some studies, households are considered as food

insecure if their food expense share is higher than 50%

[51].

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Martin Obermaier, Julien Lefevre, Romulo Ely

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moved even more to richer households than the

poorer. Additional policies are needed to correct the

distributive implications of NDC policies.

4 Discussion and Conclusions

Our paper shows how the agricultural sector is

affected by AFOLU-related NDC implementation

and its consequences at the macroeconomic and

social level in Brazil. When degraded lands are

recovered, ICLFS are implemented, and strong 2nd

generation biofuels growth is put in place, these

negative effects can be mostly offset. This is

particularly the case in more sustainably oriented

scenarios CC3 and CC4 which also present several

important co-benefits in well-being, food security

and reducing the pressure over Amazon

deforestation. There are also considerable changes

in AFOLU and energy related GHG emissions, with

both CC3 and CC4 scenarios presenting

considerable emissions reductions compared to

REF. Our analysis thus shows that NDC

implementation in Brazil is considerably preferable

to a business-as-usual development pathway,

particularly once climate change impacts are taken

into consideration. While economic sectors are quite

differently affected (e.g., lower meat and grains

production against substantially higher ethanol

production in all NDC scenarios except CC4), we

find only a limited macroeconomic impact for all

scenarios which consider climate change (CC1-

CC4), despite heavy investments in conservation,

land restoration, and biofuels deployment (both

soybean and ethanol production). This limited

impact may be due to the following: (1) we have not

analysed any climate change scenario considered

extreme (which makes sense given our horizon is

2030); (2 and related) our simulations period is short

(from up to 2030); (3i) we have considered a limited

weight in terms of the investments in public

transport and energy efficiency in relation to the

total investments of the economy while (4) there is a

positive “corrective nature” of these investments in

promoting efficiency across the Brazilian economy.

We also do only find a limited impact for all climate

change scenarios for total family income by class

which varies little across the scenarios.

In fact, across all scenarios, when considering

climate change impact, the partial implementation

of the NDC actions increases the pressure on land

and prices of goods that require land as input (very

clear in the CC2 scenario). However, as further

steps into full implementation of NDC are taken,

land pressure is reduced, decreasing also GHG

emissions. In the same way, less pressure in land

use leads to a lower share of income locked in food

consumption.

On the other hand, our analysis shows that the way

families spend their income across scenarios reveals

important impacts. As poorer classes already

consume food near their lower subsistence limit,

they have less room to further reduce their

consumption in case of food prices increase. As a

result, poorer families end up spending a larger

share of their income on food while reducing their

physical consumption. With a higher share of

income compromised for food, to balance back their

budget, poorer families need to reduce the

consumption of services and other goods. This result

brings to light the loss of well-being in most critical

scenarios (especially CC2). Richer families suffer

much less the impacts of food price increases as

only a minor part of their income is allocated to

food. Similarly, the share of income committed to

services and other goods is much less affected in

higher classes. It confirms that poorer household

classes would suffer more from climate change

impacts and higher food prices.

Scenarios CC3 and particularly CC4 present slightly

different development trajectories for Brazil that are

worth to consider. They do not show negative

impacts on GDP growth and other macroeconomic

indicators and, in fact, perform better from a

socioeconomic and environmental perspective, with

extensive investment programs aiming to encourage

public transportation and promote energy efficiency

gains for light and heavy vehicles which would

lessen the need for fossil fuels and biofuels. These

scenarios would also reduce pressure on land use,

Amazon deforestation, food prices, and GHG

emissions. This is the case of scenarios CC3 and,

particularly, CC4. Poorer families would be

benefited from such “new” development trajectories

as their food consumption and well-being would be

closer to what was observed in the hypothetical REF

scenario which does not take climate change into the

analysis (and is thus truly hypothetical, as a no

climate change scenario is no longer a reality). Not

to mention that these scenarios would be preferable

given their important leverage for the realization of

synergies between socioeconomic development,

conservation, and climate protection. These

scenarios provide clear links not only to the

COP/UNFCCC process but also green or low

carbon growth concepts or the 2030 Agenda for

Sustainable Development, [12], [13]. Our results

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William Wills, Marcelo Moreira,

Martin Obermaier, Julien Lefevre, Romulo Ely

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indicate that little would be lost by turning the

Brazilian economy towards these alternative

development trajectories which would likely

contribute to these targets.

Our analysis requires two words of caution: first,

our scenarios rely on a conservative interpretation of

the future which does not reflect a true potential of

either economic growth rates or climate change

impacts. Putting more pressure on our simulations

by using higher economic growth rates, severer

climate change impacts, also, stricter land use

policies could amplify existing trade-offs and make

decisions on policy implementations such as NDC

more complicated. Second, the current policy

situation in Brazil regarding implementations of

environmental protection (e.g., zeroing in on illegal

deforestation) and agricultural development has

changed considerably in the past years.

To conclude, our modelling exercise shows that

green or low carbon growth scenarios bring

important social and environmental benefits. With

the new federal administration recently elected, and

its compromise to reduce Amazon deforestation and

GHG emissions, the results found on this research

shows a highly desirable pathway for Brazil.

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Contribution of individual authors to the

creation of a scientific article (ghostwriting

policy)

- William Wills: experiment design, CGE modeling,

- CGE and land-use link, policy analysis, and

writing.

- Marcelo Moreira: land-use modeling.

- Martin Obermaier: policy analysis and writing.

- Julien Lefèvre: CGE modeling.

- Romulo Ely: Input-Output database hybridization,

CGE and land-use link, and writing.

Sources of funding for research presented in a

scientific article or scientific article itself

Not applied.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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William Wills, Marcelo Moreira,

Martin Obermaier, Julien Lefevre, Romulo Ely

E-ISSN: 2224-2899

504

Volume 20, 2023

Conflict of Interest

The authors have no conflicts of interest to declare

that are relevant to the content of this article.