# Food Production and Amazon Preservation are Not Mutually Exclusive: Exploring Feasible Avenues from the Perspective of Land Use Related NDCs in Brazil

William Wills, Marcelo Moreira, Martin Obermaier, Julien Lefevre, Romulo Ely

<figure><img src="image_1.png" /></figure>

## To cite this version:

William Wills, Marcelo Moreira, Martin Obermaier, Julien Lefevre, Romulo Ely. Food Production and Amazon Preservation are Not Mutually Exclusive: Exploring Feasible Avenues from the Perspective of Land Use Related NDCs in Brazil. WSEAS Transactions on Business and Economics, 2023, 20, pp.492 - 504. 10.37394/23207.2023.20.45. hal-04047476

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Submitted on 1 Dec 2023

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Food Production and Amazon Preservation are Not Mutually
Exclusive: Exploring Feasible Avenues from the Perspective of Land
Use Related NDCs in Brazil

WILLIAM WILLS<fnref n="1" />, MARCELO MOREIRA<fnref n="2" />, MARTIN OBERMAIER<fnref n="1" />, JULIEN LEFEVRE<fnref n="3" />,
ROMULO ELY<fnref n="4" />

*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

<footnote n="1">¹Energy 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</footnote>

<footnote n="2">²Agroicone, Avenida Angélica, no 2447 set 173 - Higienópolis
CEP 01227-200 São Paulo / SP,
BRAZIL</footnote>

<footnote n="3">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</footnote>

<footnote n="4">⁴Management Science & Information Systems, Rutgers Business School, Rutgers – The State
University of New Jersey, 100 Rockafeller Road, Piscataway, NJ 08854,
USA</footnote>
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<fnref n="1" />; (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

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

<footnote n="1">¹ 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.</footnote>
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
and quantities in the Supply-Demand module [38]. National demand is 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's 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.
<table><caption>Table 1. Main hypotheses and data for reference scenario (REF)</caption><thead><tr><th></th><th>Unit</th><th>2010</th><th>2020</th><th>2030</th></tr></thead><tbody><tr><td>Population</td><td>Millions</td><td>191</td><td>212</td><td>223</td></tr><tr><td>GDP Growth (World)</td><td>% per year</td><td>5.2</td><td>4.0</td><td>3.5</td></tr><tr><td>GDP Growth (Brazil)</td><td>% per year</td><td>7.5</td><td>2.5</td><td>2.5</td></tr><tr><td>Inflation rate (Brazil)</td><td>% per year</td><td>7.3</td><td>4.5</td><td>4.5</td></tr><tr><td>Exchange rate</td><td>R$/USD</td><td>1.8</td><td>4.1</td><td>5.2</td></tr><tr><td>Oil price</td><td>USD/Barrel</td><td>77</td><td>61</td><td>95</td></tr></tbody></table>

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><caption>Table 2. Main hypotheses for alternative scenarios (CC1 to CC4)</caption><thead><tr><th>Scenario</th><th>Description</th></tr></thead><tbody><tr><td>Alternative Scenario 1 (CC1)</td><td>Identical to the reference scenario, except:<br/>+ changes in agricultural yield due to climate change (mainly due to changes in temperature and precipitation patterns)</td></tr><tr><td>Alternative Scenario 2 (CC2)</td><td>Identical to CC1, but with intensified pressures on land use/availability of land for agricultural/biofuels production. Includes:<br/>+ 12 million ha restoration<br/>+ Fixed ethanol mandate (54 bi litres of ethanol, of which 2,5 are second generation)<br/>+ 68 TWh of bioelectricity</td></tr><tr><td>Alternative Scenario 3 (CC3)</td><td>Identical to CC2, but in addition including:<br/>+ recovery of 15 million ha of degraded pastures<br/>+ 5 million ha of ICLFS</td></tr><tr><td>Alternative Scenario 4 (CC4)</td><td>Identical to CC3, but in addition including:<br/>+ improvements in mobility: Implementations of a low carbon program for the transport sector as described in, [24], including measures like: energy efficiency programs for light and heavy-duty vehicles, investments in railways, waterways and subways, BRTs, etc (leading to a smaller consumption of ethanol and biodiesel). Total accumulated cost of this investment program, from 2016 to 2030, is estimated at 35 billion dollars (2015 USD).<br/>+ Improvements in 2G ethanol technology.</td></tr></tbody></table>

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 absolute values.

<figure><img src="image_2.png" /><figcaption>Fig. 1: Agricultural production and land use change in the reference scenario (REF).</figcaption></figure>

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
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).

<figure><img src="image_3.png" /><figcaption>Fig. 2: Results of reference and alternative scenarios for production, area, prices and annual GHG emissions in 2030.</figcaption></figure>

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.
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 decreases 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><caption>Table 3. Macroeconomic results</caption><thead><tr><th></th><th>Unit</th><th>2015</th><th>REF</th><th>CC1</th><th>CC2</th><th>CC3</th><th>CC4</th></tr><tr><th></th><th></th><th></th><th>2030</th><th>2030</th><th>2030</th><th>2030</th><th>2030</th></tr></thead><tbody><tr><td>GDP</td><td>Billion 2015 USD</td><td>1,681</td><td>2,299</td><td>2,294</td><td>2,294</td><td>2,296</td><td>2,298</td></tr><tr><td>GDP growth per year</td><td>% per year</td><td>-</td><td>2.11</td><td>2.09</td><td>2.10</td><td>2.10</td><td>2.11</td></tr><tr><td>GDP/capita</td><td>Thousand 2015 USD/capita</td><td>7.93</td><td>10.31</td><td>10.29</td><td>10.29</td><td>10.30</td><td>10.31</td></tr><tr><td>Investment rate</td><td>% of GDP</td><td>16.4</td><td>19.2</td><td>18.4</td><td>18.5</td><td>18.8</td><td>19.0</td></tr><tr><td>Trade balance</td><td>% of GDP</td><td>4.3</td><td>3.8</td><td>3.4</td><td>2.9</td><td>3.3</td><td>3.6</td></tr><tr><td>Price index</td><td>% increase in relation to REF</td><td>-</td><td>-</td><td>1.8</td><td>3.1</td><td>2.5</td><td>2.3</td></tr><tr><td>Number of full-time jobs</td><td>Million jobs</td><td>99.8</td><td>117.8</td><td>117.2</td><td>117.3</td><td>117.5</td><td>117.7</td></tr><tr><td>Unemployment rate</td><td>%</td><td>8.5</td><td>7.5</td><td>7.9</td><td>7.9</td><td>7.7</td><td>7.6</td></tr></tbody></table>

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%).
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 2018²), 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.³⁴

<figure><img src="image_4.png" /><figcaption>Fig. 3: Household's expenses in 2030
Source: Authors</figcaption></figure>

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

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 <fnref n="5" />.

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

<footnote n="2">² These data can be found in OECD, 2022.</footnote>

<footnote n="3">³ 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.</footnote>

<footnote n="4">⁴ 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.</footnote>

<footnote n="5">⁵ In some studies, households are considered as food insecure if their food expense share is higher than 50% [51].</footnote>
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
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.

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