Carolina Grottera

Giovanna Ferrazzo Naspolini

Emilio Lèbre

La Rovere

Daniel Neves

Schmitz Gonçalves

Tainan De

Farias Nogueira

Otto Hebeda

Carolina Burle

Schmidt Dubeux

George Vasconcelos Goes

Marcelo Melo

Ramalho Moreira

Gabriela Mota Da Cruz

Claudio Joaquim

Martagão Gesteira

William Wills

Gabriel Malta Castro

Márcio De Almeida D'agosto

Gaëlle Le Treut

Sergio Henrique Ferreira Da Cunha

Julien Lefèvre

Energy policy implications of carbon pricing scenarios for the Brazilian NDC implementation

Keywords: Brazil, emission scenarios, climate mitigation, carbon pricing, energy supply and demand, energy mix, energy modelling

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The carbon intensity of the Brazilian energy mix is fairly low, thanks to an 83% share of renewables in power generation (64% from hydropower) and significant use of biofuels. Coal, the most carbon-intensive fossil fuel, accounts for barely 5% of the Total Primary Energy Supply (TPES), against 12% for natural gas and 34% for oil (EPE, 2020a).

In contrast, high annual deforestation and degradation rates present the main challenge for Brazil to reduce its GHG emissions. Reducing land-use emissions is paramount if Brazil is to contribute to achieving global concentration levels consistent with limiting temperature increase to below 2 o Celsius above pre-industrial levels, as preconised by the Paris Agreement [START_REF] Postic | Energy contribution to Latin American INDCs: Analyzing subregional trends with a TIMES model[END_REF]. In fact, literature investigating mitigation options in Brazil often advocates that the most interesting opportunities lie in the landuse change and agricultural sectors [START_REF] Chen | Economic implications of reducing carbon emissions from energy use and industrial processes in Brazil[END_REF][START_REF] La Rovere | Climate change mitigation actions in Brazil[END_REF].

In 2015, Brazil presented its Intended Nationally Determined Contribution to the UNFCCC Conference of the Parties (COP21) (iNDC, hereafter referred to as NDC, following its ratification in 2016). More recently, during the Climate Leader Summit organised by United States Presidency in 2021, the Brazilian government announced the country's commitment to reach climate neutrality in 2050, anticipating from 2060 as previously defined 1 . The Brazilian NDC pledge consists of an economy-wide, absolute mitigation target. Brazilian GHG emissions were limited to 1.3 GtCO2e in 2025 and 1.2 GtCO2e in 2030, representing a reduction of 37% and 43% compared to 2005 2 (2.1 GtCO2e) (Brazil, 2015). Table 1 lists the sectoral measures and targets included in the Brazilian NDC.

Table 1

In a global context, Bertam et al. (2021) argue that ambitious climate targets require decisive emission cuts at the international and national levels until 2030, with the most substantial contribution to decarbonisation coming from the power sector, requiring a shift of energy investments in solar and wind technologies in the coming decade.

In Brazil, as much as the NDC pledge defines the land-use sector as the primary contributor to emissions reductions in absolute terms, the required efforts do not seem to be on track. After a sharp decrease in annual deforestation rates from 2004 to 2012 (including an 80% decrease in the Amazon), the reversal of this trend has led to high deforestation rates again in 2019 and 2020. Reducing emissions from land-use change will require resuming command-and-control policies, as the bulk of them results from illegal activities [START_REF] Bastidas | Reaching Brazil's Nationally Determined Contributions: An assessment of the key transitions in final demand and employment[END_REF][START_REF] Grottera | Impacts of carbon pricing on income inequality in Brazil[END_REF]. In fact, recent projections to the Brazilian case alert that further efforts may be needed in most economic sectors to achieve the Brazilian NDC targets since the country has not been able to control its deforestation rates [START_REF] Rochedo | The threat of political bargaining to climate mitigation in Brazil[END_REF]. Emissions from agriculture and energy have gained momentum and account for an increasing share of Brazil's emissions [START_REF] Carvalho | How likely is Brazil to achieve its NDC commitments in the energy sector? A review on Brazilian low-carbon energy perspectives[END_REF][START_REF] Oliveira | International market mechanisms under the Paris Agreement: A cooperation between Brazil and Europe[END_REF]. Furthermore, NDC targets achievement is crucial to follow a GHG emissions pathway beyond 2030, aligning the country's most recent announcement with the general objective of the Paris Agreement (net-zero GHG emissions in 2050).

The main objective of this study is to assess the development of the Brazilian energy system (supply and demand) across three GHG emissions pathways of NDC targets achievement with and without emissions pricing schemes. The Reference scenario (REF) achieves NDC goals without carbon pricing, and its mitigation measures are induced mainly by command-and-control instruments. The Emissions Pricing Scenario (EPS) and Sensitive Fuel Exemption scenario (SFE) consider different carbon pricing schemes to induce the mitigation pathways.

We develop a framework to carry out a conceptual experiment regarding the Brazilian development strategy and its NDC targets. Through an integrated modelling approach that combines several bottom-up models representing the energy demand and supply in Brazil, and a macroeconomic framework to ensure consistency across We assess these issues by examining the projected changes in the energy supply system, energy demand, associated GHG emissions and investment requirements in the aforementioned scenarios.

The remainder of this paper is structured as follows. The following section provides a brief literature review, focusing specifically on NDC implementation in developing countries, and convenes the most important contributions for climate policy in Brazil, including exercises simulating carbon pricing mechanisms. Section 3 presents an overview of the Brazilian energy system, including the main energy-demanding sectors, and opportunities for decarbonisation. Section 4 describes the scenario storylines, methodological aspects of energy demand and supply models, and sectoral assumptions. It also explores the integration between the energy and economic frameworks and the main data sources. Section 5 presents and discusses the results from the scenario comparative analysis. Finally, section 6 draws the main conclusions of the study and provides insights on energy policy implications.

Overview of the Brazilian energy system

In Brazil, the energy transformation sector comprises the extraction of petroleum and natural gas, petroleum refining, biofuel production, power generation and coke production. Energy use strongly correlates with GDP, per capita income and urbanisation levels, a pattern experienced worldwide [START_REF] Cornillie | The energy intensity of transition countries[END_REF][START_REF] Ezcurra | Is there cross-country convergence in carbon dioxide emissions?[END_REF][START_REF] Markandya | Energy intensity in transition economies: Is there convergence towards the EU average?[END_REF][START_REF] Mielnik | Converging to a common pattern of energy use in developing and industrialised countries[END_REF][START_REF] Nilsson | Energy intensity trends in 31 industrial and developing countries 1950-1988[END_REF][START_REF] Ribas | Bridging the energy divide and securing higher collective well-being in a climate-constrained world[END_REF].

In the past decade, the consumption of petroleum products, biofuels and electricity increased steadily, namely to meet the demand of transportation and services sectors.

Primary energy continuously increased over the last years due to the exploitation of the pre-salt layer petroleum. Renewable energy supply also increased, both regarding biofuels for transportation and the expansion of intermittent power generation sources such as wind and solar. Despite becoming one of the largest crude oil producers and exporters globally, Brazil remains increasingly dependent on energy imports, namely natural gas and oil products such as diesel, gasoline and naphtha (EPE, 2019a).

In 2017, final energy consumption was distributed as follows: transportation (32. 7%), industry (31. 7%), buildings (14. 8%), energy transformation (11. 2%) and agriculture (4. 1%), in addition to 5. 5% from non-energy final consumption (EPE, 2019a).

In 2017, the Brazilian government launched the Brazilian Biofuels Program called RenovaBio3 . It establishes GHG reduction mandates based on the overall carbon footprint of the road sector. Fuels distributors (obligated parties) are required to reduce their carbon footprint by 10% in 10 years. They will do so by buying decarbonisation credits from biofuels producers called Cbios [START_REF] Carvalho | How likely is Brazil to achieve its NDC commitments in the energy sector? A review on Brazilian low-carbon energy perspectives[END_REF], which correspond to 1t of avoided CO2e. RenovaBio is, therefore, the first public policy in Brazil to put a price on carbon emissions4 . The eligibility criteria of RenovaBio requires producers to demonstrate that the raw material is free of deforestation and in compliance with the Brazilian Forest Code, reducing the risk of land-use emissions 5 .

Literature review

Environmental economic theory tells that carbon pricing is the cost-efficient way to reach a given emissions target. Carbon prices create incentives for markets to use all levers available to reduce emissions, from the pursued activity, the chosen fuel, to the structure and energy intensity of a particular industry or the economy as a whole.

Equalising the marginal abatement costs across different ways of reducing emissions ensures a necessary condition for an efficient transition to a low-carbon economy [START_REF] Stiglitz | Report of the High-Level Commission on Carbon Prices, Carbon Pricing Leadership Coalition, supported by the World Bank Group[END_REF], as opposed to command-and-control measures, which fail to do so.

In practice, carbon pricing can take two forms: a carbon or GHG emissions taxas a Pigouvian tax -or indirectly through an emission cap with trading of emissions allowances. In economic theory, these are the two sides of the same coin. With no market failure, implementing either a price or quantity-based carbon constraint leads to the same marginal abatement costs conditions and same total emission level: with the price-based instrument, all economic agents reduce their emissions until their marginal abatement cost equals the carbon tax. With a quantity-based tool, economic agents will trade emissions allowances until all marginal abatement costs equalise, regardless of the initial allocation for a given emission cap. In both cases, the same total minimum mitigation cost is reached. The difference lies in the distribution of this cost among economic agents. With full carbon taxes, economic agents pay for the remaining emissions beyond mitigation costs. In an Emissions Trading Scheme (ETS), the breakdown of costs depends, naturally, on the initial allocation of allowances (Lefèvre et al., 2018). [START_REF] Goulder | Carbon taxes versus cap and trade: A critical review[END_REF] show that, under aspired circumstances such as symmetric information (e.g., policymakers who know the private mitigation costs of the different economic agents) and the absence of transaction costs, a carbon tax, an ETS or a hybrid instrument with a similar design are equivalent instruments with no inherent difference in mitigation incentive and distributional impacts.

Carbon pricing schemes have been increasingly adopted as an economic instrument to induce economic agents towards the required GHG emission reduction and particularly to achieve NDC targets [START_REF] Stiglitz | Report of the High-Level Commission on Carbon Prices, Carbon Pricing Leadership Coalition, supported by the World Bank Group[END_REF]. A growing number of studies focus on analyses of NDCs in developing countries since they are envisaged to increasingly contribute to future emissions due to the yearning for economic development in these economies [START_REF] Grottera | The role of lifestyle changes in lowemissions development strategies: an economy-wide assessment for Brazil[END_REF]. Scenarios of low-carbon development pathways with the application of different energy system models can support policymakers in the transition to a low-carbon society [START_REF] Rogelj | Paris Agreement climate proposals need a boost to keep warming well below 2 °C[END_REF].

In Dioha e Kumar (2020), scenarios simulations for Nigeria indicate that the implementation of a carbon tax level of US$40-80/tCO2 by 2020 and US$50-100/tCO2 by 2030 promote a 77% energy system decarbonisation by 2050, delivering the most significant primary energy demand reduction and emission mitigation among other scenarios that do not consider carbon pricing policy. However, its modelling framework relies on a bottom-up energy system model that does not provide an economy-wide assessment of the carbon pricing implementation. The authors acknowledge that such a level of a carbon tax is not realistic, given its impacts on Nigerian economic development. [START_REF] Gupta | Macroeconomic assessment of India's development and mitigation pathways[END_REF] highlight that deep decarbonisation pathways are compatible with Indian economic growth despite the high investment costs required in renewables and energy efficiency enhancements. The results show that deviating from oil imports could result in foreign exchange savings of US$1 trillion from 2012 to 2050. However, even relying on an integrated macroeconomic framework, the exercise proposed by the authors does not consider the implementation of carbon pricing policies to induce such pathways, assuming that command-and-control policies would implement underlying technological transformations of low-carbon scenarios.

Le Treut et al. (2021) also consider an energy-macroeconomic modelling framework to contrast an Argentinean NDC scenario with two ambitious decarbonisation scenarios (DD), which differ in the transformation of power generation (natural gas coupled with CCS versus nuclear and hydropower expansion). The results lead to significant jobs and economic value displacements at the scale of the energy sector in DD scenarios, even with limited macroeconomic implications in aggregate terms. However, even considering capital cost requirements in DD energy system transformation, the modelling framework does not include explicit carbon pricing schemes to trigger the technological changes, implicitly assuming economically neutral incentives.

The significance of Brazil in Latin America's climate commitments is incontestable since its decisions drive relevant changes in the regional energy mix [START_REF] Postic | Energy contribution to Latin American INDCs: Analyzing subregional trends with a TIMES model[END_REF]. While there is considerable debate about the best mechanism to reduce emissions under a climate policy [START_REF] Da Silva Freitas | The distributional effects of emissions taxation in Brazil and their implications for climate policy[END_REF], there are only a few studies about carbon pricing schemes in the Brazilian case. A relevant portion of the literature refers to the distributional impacts of carbon pricing in Brazil, even though most studies do not focus on the energy system implications [START_REF] Garaffa | Distributional effects of carbon pricing in Brazil under the Paris Agreement[END_REF][START_REF] Grottera | Impacts of carbon pricing on income inequality in Brazil[END_REF][START_REF] La Rovere | Economic and social implications of low-emission development pathways in Brazil[END_REF][START_REF] Moz-Christofoletti | Winners and losers: the distributional impacts of a carbon tax in Brazil[END_REF][START_REF] Wills | Economic and social effectiveness of carbon pricing schemes to meet Brazilian NDC targets[END_REF].

Regarding the Brazilian NDC specifically, Diniz [START_REF] Oliveira | International market mechanisms under the Paris Agreement: A cooperation between Brazil and Europe[END_REF] implying limited detailing of the energy system transformation. They also rely on global databases, which may take no account of particular features of the Brazilian economy.

Multi-sectoral CGE models are powerful models since they are flexible in exploring how alternative carbon pricing schemes (whether a carbon tax, an ETS or any combination of the two) impact economic agents' decisions on production and consumption choices [START_REF] Timilsina | Where is the carbon tax after thirty years of research?[END_REF]. In CGE models, carbon pricing induces substitutions in production inputs and consumption baskets as embedded in production and demand functions.

Nonetheless, as the sectoral disaggregation corresponds to the level of disaggregation of micro-founded economic behaviours (e.g., each distinguished economic sector or representative agent), it fails to capture specific mechanisms playing at a sub-sector level (Lefèvre et al., 2018).

This paper contributes to a growing body of literature examining low-carbon development pathways with the application of energy system models to discuss sectoral challenges in the search for safe, reliable, low-cost, clean and efficient energy supply and use. More specifically, we intend to investigate the effectiveness of different carbon pricing designs and their implications for transforming the energy sector in Brazil. An integrated methodological framework, combining a hybrid CGE model (IMACLIM-BR)

and sectoral bottom-up models for energy use and production, is proposed to further assign the required technological transformation in the energy system to achieve NDC targets. Furthermore, the sectoral mitigation portfolio is incorporated within a marginal abatement cost curve (MACC), wherefrom the level of abatement and the carbon tax is defined. Finally, the simulation of the carbon pricing scheme in IMACLIM-BR ensures macroeconomic consistency to bottom-up models' estimation of GHG emission pathways.

4 Methodology, scenario assumptions and data sources

Scenario methodology and overview

The definition of the scenarios' assumptions was undertaken through a participatory process involving stakeholders from the Brazilian Forum on Climate Change. This Scenario Building Team (SBT) comprises experts from the government, private sector, academia and civil society, discussed and validated assumptions concerning market trends, the performance of public policies and societal transformation, critically appraised to outline possible development pathways for the Brazilian economy. A more detailed description of such a process can be found in Goes et al. (2020d) (with a particular focus on transportation mitigation measures). From this process, the assumptions were then parameterised to quantify 24 mitigation options required to achieve Brazilian NDC targets. Three scenarios were then developed:

Reference Scenario (REF) -In this scenario, mitigation measures outlined by the SBT are achieved through command-and-control measures implemented by the federal government, with no carbon pricing scheme in force. Mitigation efforts are undertaken in the land-use change and agriculture, industry, transportation and energy supply sectors. This scenario was created to serve as a basis for comparing its results with those obtained by the different carbon pricing packages. In this scenario, as well as in all carbon pricing scenarios, the government actively reduces emissions to meet NDC targets, and deforestation rates are set exogenously to reach 13,700 km 2 /year in 2025 and 12,600 km 2 /year in 2030 and are the same for all scenarios.

Emissions Pricing Scenario (EPS) -This is the basic emissions pricing scenario, in which NDC targets are met through the implementation of carbon pricing schemes (combining a cap-and-trade system in the industry with a carbon tax imposed on fossil fuels used in other sectors). The climate policy is fiscally neutral, using carbon revenues to reduce labour taxes. This aims to decrease potential negative impacts of carbon taxation by favouring employment creation, increasing the Brazilian tax system's efficiency as a whole and thus enhancing national competitiveness, as advocated by [START_REF] Chen | Economic implications of reducing carbon emissions from energy use and industrial processes in Brazil[END_REF].

While emissions from deforestation are out of the scope of the carbon pricing mechanism, an underlying assumption of EPS is that the market structure created by the introduction of carbon pricing creates the necessary conditions for the transaction of offsets from the restoration of native vegetation, which are not available in REF. Offsets are credits related to emission reductions obtained by unregulated sectors, that is, in sectors not covered by the pricing mechanism 6 . The supply of such offsets is estimated at an average price of 6.3 US$/tCO2e and 8.4 US$/tCO2e for the 2021-25 and the 2026-30 periods, respectively. The abatement potential is estimated at 61 MtCO2e and 122

MtCO2e for the same periods (details in Margulis et al. (2019)).

Sensitive Fuels Exemption Scenario (SFE) -This scenario reduces the scope of the carbon pricing scheme by exempting gasoline, diesel and liquefied petroleum gas (LPG) from carbon taxes. The high political cost of charging these fuels could jeopardise carbon pricing viability, as experienced in recent events such as the yellow vests in France and the truckers' strike in Brazil in 2018, both related to rising fuel prices. The exemption could therefore allow buying political support for the regulatory instrument. This scenario seeks to identify the required efforts of other emitting sectors and the system's efficiency as a whole when a sensitive fuel exemption scheme is applied. As in EPS, economic sectors can buy carbon offsets from native vegetation restoration.

Table 2 The GHG emission scenarios are then confronted with the expected abatement potential of the mitigation measures relative to a business-as-usual scenario (BAU), a construct used to determine the necessary increase in ambition and mitigation actions to achieve the Brazilian NDC targets in 2025 and 2030. 7 Mitigation measures are implemented in ascending order according to their cost until the required abatement to achieve NDC targets is reached. Table 3presents the abatement potential (MtCO2e) and 7 The BAU assumptions and methodology are detailed in the Supplementary Material.

cost (USD/tCO2e -avoided) in the 2021-25 (a) and 2026-30 (b) periods, for the 24 mitigation measures.

Table 3 The mitigation cost assessment approach is proposed by [START_REF] Gouvello | Brazil Low-carbon Country Case Study[END_REF] to calculate the total cost of a given mitigation action and its abatement potential 8 .

Measures are ranked by ascending abatement cost, which may vary largely between the two periods due to increasing abatement potential over time and variations in costs assumptions (e. g., decreasing costs for electric vehicles and renewable electricity).

As explained, in the emissions pricing scenarios, carbon offsets from the restoration of native forests are transacted, sparing the implementation of costly mitigation measures (those below the dashed line in Table 3 are not implemented in the EPS and SFE scenarios). The following sections describe the methodology of energy demand and supply estimates.

Energy demand projection methodology and assumptions

The estimates for energy demand in this study are undertaken through sectoral bottom-up models combining the approaches described. The models share the same nature, in the sense that they require similar inputs, such as demographic (population)

and macroeconomic (GDP, sectoral GDP) data, as well as activity levels and energy 8 The cost assessment methodology and parameters are described in the Supplementary Material.

intensity, to provide comparable outputs (e. g., final energy demand and GHG emissions). However, they may differ broadly in terms of sectoral specification, level of detail and data availability.

This section provides a brief description of the main features and assumptions of the energy demand models, which are detailed for transportation and industry, the two sectors for which mitigation measures are simulated in NDC scenarios. We also describe how the demand from the buildings and commercial sector is computed -even though there are no mitigation efforts in these sectors. In section 4.3, we then describe the methodology for estimating the energy supply required to meet the demand. While we put particular emphasis on the sectoral assumptions considered in the scenarios, the full description of the models and the data used to calibrate the scenarios and conduct the cost assessment can be found in the Supplementary Material.

Transport

The 'Transport-Energy-Emissions Multi-Tier Analysis' (TEMA) model calculates the energy use scenarios in the Brazilian transport sector. The model was developed by Gonçalves et al. (2019) and applied in a variety of studies (Goes et al., 2020a(Goes et al., , 2020b ; ;[START_REF] Gonçalves | ENERGY TRANSITION IN BRAZIL: PARIS AGREEMENT COMPATIBLE SCENARIO FOR THE TRANSPORT SECTOR UP TO[END_REF]. Energy-climate scenarios are designed by simulating the application of climate policies, market trends and user behaviour that best represent the transformations of society over the years. In this process, macroeconomic inputs from Table 2 are used as proxy variables to project transport activity and the consequent use of energy and carbon emissions. GDP is the variable that determines freight transport activity (t-km), whereas GDP per capita is used to predict passenger transport activity (pass-km).

TEMA considers 31 technologies from road transport, combining vehicle categories (e.g., cars, buses, trucks) and power trains (e.g., internal combustion engines, ICE, battery-powered electric vehicles, etc.). The rail, air, water, and pipeline transport modes are not specified at the technology level but account for the activity, structure and intensity levels, as well as fuel use.

The narratives common to all scenarios cover general transformations in human settlement, land development, spatial organisation and some aspects of the auto industry. Brazilian society experiences new configurations of mobility in the initial years after the COVID-19 pandemic. Teleactivities gain importance, but the effect on transport activity is minor, as only telework and teleservices significantly reduce travel. Along the period, the densification process in large cities stabilises as they become more compact.

Land-use planning considers the decentralisation of activities, creating important tripattracting zones in different neighbourhoods. However, public transportation still faces predatory competition from individual motorised transport and car ownership rates increase by 2030 [START_REF] Gonçalves | ENERGY TRANSITION IN BRAZIL: PARIS AGREEMENT COMPATIBLE SCENARIO FOR THE TRANSPORT SECTOR UP TO[END_REF].

Transformations that vary across scenarios include infrastructure, consumption patterns, trade, fuel generation and carbon content (Table 4). They are summarised in mitigation measures, selected through the analysis of national commitments, current policies, international trends, and market awareness. The measures implemented in each scenario are defined after conducting the marginal abatement cost curves (MACC) analysis, as explained in section 4.1.

Part of the mitigation measures related to infrastructure is selected from the National Logistics Plan and the Ten-Year Energy Expansion Plan, which project the expansion of railways and waterways [START_REF] Epe | Balanço Energético Nacional[END_REF][START_REF] Epl | Plano Nacional de Logística -PNL[END_REF]. The expansion of subway systems, logistics optimisation and the increased active transportation are also considered, selected from Gonçalves et al. (2019b) and Goes et al. (2020c).

Biofuel-related measures are the increased use of ethanol, biodiesel and biokerosene, in line with sectoral studies from Brazil and Europe, such as EPE (2020), [START_REF] Dafnomilis | Review of solid and liquid biofuel demand and supply in Northwest Europe towards 2030 -A comparison of national and regional projections[END_REF] and [START_REF] Kousoulidou | Biofuels in aviation: Fuel demand and CO2 emissions evolution in Europe toward 2030[END_REF]. Measures related to electromobility comprise incentives to battery-powered electric and plug-in hybrid electric trucks, buses, and cars. They were selected based on the Global EV Outlook (IEA, 2018) and Goes et al. (2020c). The estimation of GHG emissions comprises: (i) emissions from energy consumption and (ii) emissions from industrial process and product use (IPPU) from metals, chemical products and cement and other mineral products. The utilisation of hydrofluorocarbons (HFCs) for refrigeration and air conditioning and SF6 in electricity transmission and distribution equipment are also emitting processes.

The estimated growth of industrial segments varies according to macroeconomic trends, including demand from importing countries and international competition (Table 2). They are defined by the SBT, mainly based on long-term national prospects such as EPE (2015). Mitigation options in the industrial sector include (i) energy efficiency; (ii) increased use of renewable fuels (or natural gas replacing more carbonintense fuels); (iii) research and deployment of low-carbon process routes [START_REF] Fischedick | Industry[END_REF]IEA, 2020).

Energy efficiency in the iron and steel sector focuses on reducing consumption in the main processes of steel production: sintering, coke making, iron making, steelmaking, casting refining and other general measures [START_REF] Carvalho | Sustentabilidade da siderurgia brasileira: eficiência energética e competitividade[END_REF]EPE, 2018a;[START_REF] Guedes | OPÇÕES DE MITIGAÇÃO DE EMISSÕES DE GASES DE EFEITO ESTUFA EM SETORES-CHAVE DO BRASIL[END_REF][START_REF] Hasanbeigi | A bottom-up model to estimate the energy efficiency improvement and CO 2 emission reduction potentials in the Chinese iron and steel industry[END_REF][START_REF] Worrell | Policy scenarios for energy efficiency improvement in industry[END_REF]. Replacing coking coal with charcoal from planted forests can also significantly reduce GHG emissions in the Brazilian iron and steel industry [START_REF] Pinto | CO2 emissions mitigation strategy in the Brazilian iron and steel sector-From structural to intensity effects[END_REF].

Mitigation in the cement sector includes reducing consumption in raw material preparation, clinker production and cement grinding [START_REF] Kajaste | Cement industry greenhouse gas emissions -Management options and abatement cost[END_REF][START_REF] Li | Quantifying the impacts of decarbonisation in China's cement sector: A perspective from an integrated assessment approach[END_REF]. There is also potential for mitigating emissions in cement production by reducing the amount of clinker in its composition [START_REF] Miller | Carbon dioxide reduction potential in the global cement industry by 2050[END_REF].

General energy efficiency in all industrial sectors includes process optimisation, heat and steam recovery, more efficient equipment, process control and integration(EPE, 2018a; [START_REF] Guedes | OPÇÕES DE MITIGAÇÃO DE EMISSÕES DE GASES DE EFEITO ESTUFA EM SETORES-CHAVE DO BRASIL[END_REF]Guedes, 2017a;[START_REF] Henriques | Potential for reduction of CO2 emissions and a lowcarbon scenario for the Brazilian industrial sector[END_REF][START_REF] Pinto | CO2 emissions mitigation strategy in the Brazilian iron and steel sector-From structural to intensity effects[END_REF]. Table 5 describes the energy intensity in the base year (2015) for industrial sectors and the measures implemented in each scenario. The improvements are described (as reduced GJ/t of product for iron and steel and percentage gains (%) for other sectors). The energy demand of other sectors is estimated considering historical trends and different drivers. In the residential sector, energy demand responds to demographic growth and per capita income, while in services and agriculture to sectoral GDP growth.

No mitigation measures were assumed, as no major technological changes are expected.

As a result, differences in energy demand pathways reflect only minor changes in per capita income and GDP shares across scenarios. In all scenarios, firewood for cooking is gradually replaced (LPG) in the residential sector, and there is an increase in the share of electricity compared to firewood and LPG in the services sector. In agriculture, the share of electricity increases slightly while diesel oil and fuelwood remain nearly stable. The main mitigation measures considered are renewable energy for power generation and fugitive emissions control technologies (flaring and venting activities on platforms and fuel distribution). In the electricity sector, wind, photovoltaic, CSP, hydroelectric, nuclear, and bioelectricity technologies are included. However, mitigation only occurs from 2023 on since, up to this year, the sectoral expansion is already defined through the past energy auctions outcomes. For photovoltaic, CSP, and wind power, a linear decrease of 10% in costs is assumed until 2030. For fuel supply, ethanol, biodiesel, biokerosene, and natural gas are taken into account. Given the cost-minimising nature of the MATRIZ model, we highlight that natural gas can be considered a mitigation measure if it displaces energy sources with higher carbon content, such as coal and diesel oil. Table 6 summarises the main scenario assumptions for the projections). Second, the sectoral models also provide the capital requirements (considering the investment in the sectors that adopted mitigation measures), which are summed up to the capital consumption of the BAU scenario. The new level of capital consumption allows estimating the capital cost of each NDC scenario (absolute terms of sectoral capital consumption divided by sectoral production from BU models). The capital cost is then set as an exogenous parameter in IMACLIM. As for the energy, the capital consumption results from the sectoral activities that are endogenously determined by the model.

After macroeconomic equilibrium, the IMACLIM-BR model provides the yearly sectoral economic activity (endogenous sectoral production), feeding the bottom-up models once again. This is the key output that ensures consistency across the energy and economic system, e. g., the endogenous sectoral production is the convergence criteria between IMACLIM-BR and bottom-up models. The GHG emission pathway is then estimated from the second loop of bottom-up runs.

As explained in section 4. The bottom-up modelling process starts from the energy demand models (industry, transport and others). The BLUM model plays a role in translating bioenergy supply constraints from the land-use sector and fuels relative prices to the consumers.

After reconciling this iteration, the aggregated energy demand feeds the MATRIZ model, which optimises the energy system and provides the energy supply profile. Thus, the national energy balances are set to load IMACLIM-BR.

Linking energy models through IMACLIM-BR endogenous sectoral production allows capturing more detailed feedbacks between the energy and economic systems.

Compared to the standalone CGE approach, it ensures the richness of both approaches, simulating endogenous energy system transformations triggered by carbon pricing. On the other hand, sectoral models do not provide economy-wide impacts, so the integration embraces both features, providing better insights for the macroeconomic and sectoral effects in the long term.

5 Results and discussion

Sectoral results

This section presents the results for the two main energy-demanding sectors (transport and industry) and energy supply. Additional results such as energy use by source can be found in the Supplementary Material.

Transport

Table 7 summarises the main results of energy demand and GHG emissions across the scenarios up to 2030.

Table 7

The The shift from internal combustion engine (ICE) to hybrid and electric light vehicles still presents low economic viability in the 2030 horizon (Table 3). This is due to the lower In summary, results up to 2030 point to energy transition pathways based on biofuels as crucial drivers of GHG emissions reduction in the transport sector. Still, electromobility is expected to be more relevant in Brazil over the long run.

Industry

In Food and Beverage, the industrial branch with the largest energy consumption, presents a reduction of 19% in its energy intensity in REF. Iron and Steel, the secondlargest, presents a reduction of 8% (detailed in the SM). This smaller reduction compared to other branches is due to the increase in the share of charcoal in the production process (from 10% in 2015 to 13% in 2030). Albeit a low carbon route, this is also a more energy-intensive one.

Overall, EPS and SFE scenarios present a small reduction in energy intensity compared to REF due to lower penetration of costly mitigation measures. In Iron and Steel, there is even a slight increase in energy intensity. This is due to the combination of increased use of charcoal and not implementing higher cost energy efficiency and heat recovery measures in EPS and SFE scenarios.

Power generation and oil products supply

Table 9 shows the main energy supply and demand indicators for each scenario.

For the country as a whole, energy demand is the lowest in the REF scenario. A slightly higher level of electrification of the economy is also observed, mainly due to the shift from internal combustion engine vehicles (ICE) to hybrid and electric ones in the transport sector. However, in EPS and SFE scenarios, part of the abatement yielded by ambitious yet costly mitigation measures used in REF in the industrial, transport and power supply sectors is replaced by enhanced climate action in land use and agriculture, as explained in section 4.1Smaller efforts to pursue energy efficiency in the EPS and SFE scenarios lead to higher overall energy demand (approximately 312 Mtoe in 2030).

Table 9

The renewables share in the SFE scenario shows that the lack of carbon pricing for sensitive fuels can delay a higher penetration of renewables in the energy mix.

Emissions from energy supply are higher in the SFE scenario than in the EPS scenario due to higher demand for fossil fuels, increasing refineries' energy intensity and GHG emissions (both fugitive and due to energy self-consumption).

Regarding power generation, results are very similar across the scenarios, mainly because the existing system is already highly based on renewables, with a large share of hydropower and increasing shares of wind and solar energy. Besides, the power sector expansion is already defined up to 2023 (derived from the outcomes of past electricity auctions), so there is little margin for significant variations in the short-to-medium terms. Table 10 shows that there is no increase in natural gas power plants in REF because no new capacity of power generation from fossil fuels is allowed, replaced by an increase of solar power. In the absence of this constraint, the natural gas power plants capacity increases in the EPS and SFE scenarios. However, this increase in the use of natural gas for power generation remains to play a complementary role to ensure the dispatching of intermittent (wind and solar) and seasonal (hydro) renewable power sources, matching the needs of the grid operator to proper adjustments to peak demands. It leads, however, to a higher grid emissions factor in these scenarios.

Table 9 also shows the different performances across scenarios for the energy sector indicators included in Brazilian NDC as indicative goals illustrating how Brazil would meet its GHG emissions economy-wide targets. It can be seen that Brazil is on a good track to meet its NDC commitments in the energy supply sector, as the 2030 goals are achieved in all scenarios. Accordingly, GHG emissions from energy supply are very low compared to the world average, as shown in Table 16. Even if oil and gas production increases substantially up to 2030, venting and flaring fugitive emissions from platforms are kept under control since regulations imposed in 2000 by the Oil and Biofuels Regulatory Agency (ANP) established that all new oil and gas fields production platforms should limit flaring or venting to no more than 3% of the associated natural gas.

Table 10

Economic and environmental results

This section focuses on the aggregate, country-wide results. Figure 2 presents total GHG emissions, split between energy and non-energy related.

Figure 2

Non-energy GHG emissions are dominant, mainly from deforestation in the Amazon and savannahs ("Cerrado") and agriculture (due to methane emissions from around 210 million heads of cattle). Nonetheless, the share of emissions from fossil fuels consumption in total emissions steadily grows across scenarios, from 29% in 2015 to a range between 31% (REF) and 36% (SFE) in 2030. This is primarily driven by energy demand growth in transport and industry induced by economic activity increase (while GHG emissions from AFOLU can decrease sharply even with high GDP growth, as experienced in the past).

The adoption of carbon pricing schemes, combining a cap-and-trade system in the industry with a carbon tax imposed on fossil fuels used in other sectors, enables the use of a considerable potential of offsets (from the restoration of native vegetation) at an inexpensive cost (up to 6. 3 US$/tCO2e in 2025 and 8. 4 US$/tCO2e in 2030). This allows meeting NDC targets with limited use of costly mitigation actions implemented through command-and-control tools in the REF scenario (e.g., electromobility in transport, energy efficiency in industry, solar power generation).

The undergoing policy of mandating increasing quotas of renewable fuels to the sales by liquid fuels distribution companies creates an implicit carbon pricing scheme, as quotas can be traded (calculated according to its product carbon footprint). The

RenovaBio program provides the framework for gradually increasing the share of renewable energy (ethanol from sugarcane and biodiesel) in the consumption of liquid fuels. In EPS, the use of biofuels increases compared to the base year, while being lower than in REF, and helps to meet NDC targets, together with other mitigation actions such as energy efficiency improvement and modal shifts. In the SFE scenario, the scope of GHG emissions covered by a carbon pricing scheme is narrower (down to 303 MtCO2e in 2030) than in EPS (519 MtCO2e in 2030). This leads to increased consumption of "politically sensitive fuels" (diesel oil, gasoline and LPG) and of GHG emissions from energy, compared to EPS. Therefore, energy-related GHG emissions are 4% higher in 2030 than in EPS (432 against 416 MtCO2e). The demand for forest offsets is consequently higher in the SFE scenario.

Total investment requirements in EPS and SFE scenarios are, therefore, significantly lower -less than half -than in REF, as shown in Figure 3. This result confirms the effectiveness of carbon pricing and market mechanisms to provide increased flexibility and reduce the cost to meet the same mitigation target, compared to command-and-control policy tools only, when lower-cost mitigation options are available in other sectors (as offsets from AFOLU in the Brazilian case).

Figure 3

From a macroeconomic perspective, the greater flexibility and economic effectiveness made possible by carbon pricing schemes induce higher GDP growth in the Carbon pricing mechanisms play an indispensable role in any cost-effective emission reduction strategy [START_REF] Stiglitz | Report of the High-Level Commission on Carbon Prices, Carbon Pricing Leadership Coalition, supported by the World Bank Group[END_REF]. As an instrument internalising carbon costs into the decisions made by different agents, such a mechanism sheds light on the existing opportunities and strategies to pursue climate objectives. This paper sought to explore the extent to which carbon pricing may improve the effectiveness of achieving the Brazilian Nationally Determined Contribution (NDC). In line with existing literature [START_REF] Gurgel | The impacts of the Brazilian NDC and their contribution to the Paris agreement on climate change[END_REF][START_REF] Rochedo | The threat of political bargaining to climate mitigation in Brazil[END_REF], our analysis demonstrated the enormous potential of complying with official short-term climate commitments at an affordable cost, namely when seizing opportunities in the land-use and agricultural sectors. This is contrasted with fewer opportunities to decarbonise the Brazilian energy mix compared to countries with similar income levels and development goals, since hydroelectricity and biofuels already play an important role in Brazil.

Brazil is well placed to meet its NDC targets up to 2030 even under stringent commitments, thanks to its clean energy mix and the considerable potential of offsets provided by the AFOLU sector. This mitigation capacity can be better tapped by adopting a carbon pricing scheme that allows flexibility to meet domestic abatement targets with low-cost options. On the one hand, a pricing mechanism unravels the enormous offset supply, which would not be available in the absence of such an explicit (or at least indirect) instrument. The pricing can also help dynamise the NDC implementation, considering the implicit costs of command-and-control policies (e.g., institutional capacities and transaction costs). On the other hand, a pricing mechanism may raise concerns regarding macroeconomic and distributional issues, which would entail alternative arrangements such as the transport fuel exemption in the SFE scenario.

Nonetheless, limiting the scope of the carbon pricing scheme adds pressure on other economic sectors to maintain the same economy-wide emission levels. A higher carbon price would be expected to compensate for a stricter scope, but forest offsets are abundant at low prices and served as a buffer. This allowed for an unchanged carbon price to reach the same abatement. However, the use of offsets in this scenario exceeded 20% of emissions reduction, which imposes additional pressure on the land-use sector and fails to ensure that decarbonisation would be pursued in all economic sectors, and mainly to encourage low-carbon investments in industry and transportation.

Moreover, in the long run, the offset potential from AFOLU will only be available at progressively higher costs. In 2020, Brazil submitted its updated NDC to the UNFCCC, as preconised by the Paris Agreement guidelines 13 . During this process, the country issued an indicative commitment to achieving carbon neutrality in 2060 (Brazil, 2020). Previous scenario studies up to 2050 [START_REF] La Rovere | Economic and social implications of low-emission development pathways in Brazil[END_REF][START_REF] Fbmc | Brasil Carbono Zero Em[END_REF][START_REF] Fbmc | Brasil Carbono Zero Em[END_REF] have shown the feasibility of the neutrality goal by using a substantial amount of offsets from AFOLU to compensate for the GHG emissions from the other sectors. However, in the longer run towards the end of the century, given the higher costs of the remaining offsets available, Brazil will need to implement new low-carbon options in other sectors (such 13 Just before the conclusion of this study, the Brazilian government sent to the UNFCCC Secretariat an updated version of its first NDC (Brazil, 2020). As GHG emissions in 2005 (the reference year) were officially revised upwards to 2.8 GtCO2e (according to Brazil, 2016), and Brazil has confirmed the percentage reductions of its commitments (37% in 2025 and 43% in 2030), the new absolute caps for Brazilian economy-wide GHG emissions are now of 1.8 GtCO2e in 2025 and 1.6 GtCO2e in 2030, instead of the previous targets of 1.3 GtCO2e in 2025 and 1.2 GtCO2e in 2030. However, we consider the first indicative targets (as in the version ratified by the Brazilian Congress in 2016). We also highlight that reductions in climate ambitions are not allowed under the scope of the Paris Agreement.

as electric vehicles, new batteries and electric storage technologies, new industrial processes for manufacturing intermediate goods and materials, among others) to ensure the sustainability of its development strategy under the climate neutrality constraint, as in the rest of the world.

The current transition in the Brazilian energy system is the shift from an oilimporting economy to a net oil exporter, thanks to the steady production increase from the pre-salt layer of offshore fields that is bound to continue and accelerate, which would seem inconsistent with the Paris Agreement goals. However, the Brazilian Congress has decided that oil production would be primarily channelled to exports, and the oil rent would fund the much needed public investments in education and health.

Current trends of fossil fuel consumption and governmental scenarios up to 2050 (EPE e MME, 2020) confirm the intention to pursue this policy. In recent years, the country also experienced conflicts between energy and economic policies. The government opted for subsidising domestic prices of transport fuels (diesel and gasoline), an attempt to curb inflation rates (with disastrous economic impacts) from 2010 to 2015. This was subsequently reversed, allowing for reducing the total amount of subsidies recorded, which, however, remain high [START_REF] Inesc | Incentivos e Subsídios aos Combustíveis Fósseis no Brasil em[END_REF].

The Brazilian government also recently approved a new regulatory framework aiming to develop natural gas markets in Brazil. Private-public partnerships will be encouraged to build the infrastructure required to avoid wasting the energy potential of associated natural gas from offshore oil fields. This policy is not necessarily conflicting with the achievement of NDC targets in 2025 and 2030. The crucial condition would be to avoid massive use of natural gas for thermopower generation to meet baseload demand (as this would displace power generation from renewable sources). Instead, if priority is given to channelling natural gas to replace oil and coal products in other markets (in industrial processes such as chemicals and fertiliser production, direct reduction of iron ore for the steel industry, replacement of fuel oil and LPG in the residential, commercial, and services sectors), then it may contribute to a smooth energy transition in Brazil.

Conflicts between energy, climate and economic policies can arise, especially when a myopic perspective prevails. As much as pursuing the lowest-cost mitigation in the short-term can help ensure development and equity objectives, this may also come in the form of deleterious effects in the longer term, with consequences for the energy mix and a higher cost of achieving net-zero emissions in the second half of the century. Only the integration of short and long-term planning efforts will allow maximising synergies and delivering both development and climate goals. Source: the authors

  them, we aim to investigate: (i) how do different carbon pricing schemes fit into climate mitigation strategies embedded in the Brazilian NDC, and what are the energy-related implications of such?; (ii) what are the main features of these mitigation pathways, and what insights do they provide to energy planning?

4. 3

 3 Energy supply methodology and assumptions4.3.1 Power sector and oil refiningThe MATRIZ model[START_REF] Cepel | MATRIZ -Energy Matrix Projection Model[END_REF] represents the Brazilian energy system and details the electricity and refining sectors. MATRIZ is a linear programming bottom-up model for medium to long-term energy system planning, similar to MESSAGE and TIMES(IEA-ETSAP, 2020; IIASA, 2020). Considering exogenous inputs of final energy demand (i.e., the resulting aggregation of all energy demand models) and availability of resources, its objective function minimises the present value of the total cost of investment and system operation for capacity expansion and energy supply on the assessed horizon.Energy chains are represented by linking primary, secondary, final, and useful energy levels. A mix of different technologies represents energy conversions and resource extractions. Four operating subsystems are considered in order to account for the electricity sector's complexity. Each analysis period is detailed into four seasons, each containing two energy demand levels: peak and non-peak. Such a level of specification is essential for energy security, as it ensures the system meets seasonal and horo-seasonal demands, as well as periods of critical hydrology. MATRIZ also computes GHG emissions endogenously. A penalty is simulated in the objective function specifically for fossil-fuel technologies to represent the carbon pricing.

  BLUM uses a simple equation to estimate the production level of biodiesel, adopting the official mandate stipulated by the Brazilian government for the assessed year for the diesel consumption estimated by the TEMA model. BLUM uses the total energy demand for the vehicle fuels (estimated by the TEMA model) to estimate ethanol production levels as a starting point. It then combines equations and databases representing: (i) the total fleet (e. g., number of flex-fuel vehicles); (ii) user behaviour (in particular, the decision to consume either hydrous ethanol or gasoline according to relative prices); (iii) the level of biofuels supply according to the prices received by producers (linked to the land market).4.4 Integrated modelling frameworkThe final step of the GHG emissions pathway simulations is to ensure consistency across the energy and economic systems, namely energy demand, supply and mitigation investment requirements induced in NDC scenarios. The IMACLIM-BR model is used for this purpose.IMACLIM-BR is a hybrid multi-sectoral computable general equilibrium model (CGE) that reconciles an open economy with the energy system. Its calibration to the base year of 2015 relies on harmonising data from the Brazilian national accounting system, market prices statistics, and the national energy balance (Le Treut, 2020) 9 . The model depicts the economic growth with dynamic simulations resulting from assumptions of labour endowment and productivity 10 .The coupling between IMACLIM-BR and bottom-up models consists of exchanging key outputs of one model as exogenous parameters for another. The bottom-up models provide consistent national energy balances and the investment requirements of each scenario. Together with the carbon pricing policy implementation (when applicable), these energy and investment scenarios are used in IMACLIM-BR to constrain the economy-wide projection to 2030 and generate a response from the rest of the economy.First, the energy balances are converted into an input-output format compatible with IMACLIM-BR, detailing the energy consumptions per energy type in volume (ktoe) across the economy. On the one hand, imports and exports are directly used in absolute terms (ktoe) to be set as exogenous parameters in the IMACLIM-BR model. On the other hand, energy intensities (absolute terms divided by sectoral production from BU models) are exogenously informed into IMACLIM-BR for the economic sectors. The energy consumption, in absolute terms, is then resulting from the sectoral activities that are endogenously determined by the model.

1 ,

 1 BAU is initially simulated, tracing the current trend in growth, population, GHG emissions and mitigation policies. The REF scenario is then implemented to represent the achievement of NDC goals in 2025 and 2030 as a result of regulatory enforcement and command-and-control implementation of mitigation measures. While the BAU is the scenario upon which the modelling shock is assimilated, the REF scenario is the baseline to compare the simulation outcomes of the carbon pricing scenarios (EPS and SFE).

  Figure 1 illustrates the integrated modelling framework developed for this study.

Figure 1 -

 1 Figure 1 -Integrated modelling framework

  REF scenario presents the lowest GHG volume emitted in 2030 (144 Mt CO2e), 29% lower than 2015 levels. This result stems from the intense use of biofuels, either by increasing the mandated blend (biodiesel and biokerosene) or by the preference for hydrated ethanol in flex-fuelled vehicles. As a result, this scenario represents the largest share of renewable energy in the transportation sector, reaching 40% in 2030, against 21% in 2015. The greater participation of buses and active transport and a higher share of plug-in electric and hybrid cars reduce the energy consumption of light passenger vehicles, also contributing to mitigation. Lower shares of renewable energy are observed in the SFE scenario, with volumes of biodiesel and hydrous ethanol consumption 29% lower than in the REF scenario, in 2030. This reflects the deliberate choice of exempting both gasoline and diesel from taxation, reducing the attractiveness of hydrous ethanol in flex-fuel vehicles (only 47% of the market share in 2030 in SFE), and hampering the economic viability of increasing the mandated biodiesel blend (10% blend in SFE vs 20% in REF and EPS). Only the REF scenario considers biokerosene, although limited to the minimum requirements of the initial phases of the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) since production costs do not reach competitive prices in the short and medium-term. Even though biokerosene does not have a significant mitigation potential in the 2030 time frame, it can play a crucial role in post-2030 scenarios.

  VKT 11 usually performed by users in Brazil, which reduces the savings on maintenance and operating costs compared to acquisition costs. In the REF scenario, 2.5 million cars and motorcycles, 45 thousand buses and 146 thousand trucks and light commercial vehicles are electric by 2030. This amount is lower than expected by the IEA 12 in 2030(IEA, 2018). In EPS and SFE, electric and hybrid light vehicles are limited to buses and trucks in urban areas and ride-sharing services, namely from 2026 on. The lack of local suppliers and manufacturers in Brazil also contributes to this result.The penetration of electromobility is reflected in the scenarios' energy intensity, which is lowest in REF. A 19% reduction relative to 2015 levels in the energy intensity of passenger transport occurs, also due to higher active and public transportation, including passenger rail expansion (e.g., subways and urban trains). For freight transportation, the reduction in energy intensity amounts to 31%. The energy intensity of passengers reaches 862 kJ/km-pass in 2030, in the EPS and SFE scenarios(-17% compared to 2015). The difference across scenarios is negligible for freight transport, as mitigation measures are more attractive (lower average abatement costs) for freight than for passenger transport and are implemented both in the EPS and SFE scenarios.

  EPS and SFE scenarios, compared to REF, up to 2030, as illustrated in Figure 4. This gain more than compensates for the increased energy demand, and therefore the energy intensity of GDP is lower in EPS and SFE scenarios than in REF. In 2030, decarbonisation of energy supply is also lower in EPS and SFE scenarios than in REF. Carbon pricing and the availability of low-cost offsets from AFOLU can thus make possible a smooth energy transition in Brazil.As much as other elements play an important role in explaining the macroeconomic and environmental results described in this section, we highlight that the main focus of this study was to assess the variations in key features of the energy demand and supply sectors across different pathways.[START_REF] Wills | Economic and social effectiveness of carbon pricing schemes to meet Brazilian NDC targets[END_REF] performed a similar scenario exercise applying a macroeconomic lens. The authors display results for employment, price indices, trade balance, household consumption and income inequality, among others, which are beyond the scope of our paper.

Figure 4 6

 4 Figure4

Figure 3 -

 3 Figure 3 -Additional investments in mitigation relative to business-as-usual, per period (million USD 2015)

Figure 4 -

 4 Figure 4 -Economic, Energy and GHG Emissions Indicators in 2030 relative to 2015 (growth in %)

Table 5 4

 5 .2.3 Energy demand of buildings, agriculture and other sectors

Table 6 4

 6

.3.2 Biofuels and biomass

Finally, the Brazilian Land Use Model (BLUM) is applied to estimate the ethanol and biodiesel production levels. BLUM is a single country, partial, multi-sectoral and multiregional equilibrium model, which has been applied in a variety of studies investigating the climate impacts of land use policies in Brazil

[START_REF] Harfuch | Empirical Findings from Agricultural Expansion and Land Use Change in Brazil[END_REF][START_REF] Moreira | Estratégias para expansão do setor sucroenergético e suas contribuições para a NDC brasileira (Doutor em Planejamento de Sistemas Energéticos)[END_REF][START_REF] Moreira | Socioenvironmental and land-use impacts of double-cropped maise ethanol in Brazil[END_REF]

. It represents the Brazilian agricultural sector through two modules: supply-demand and land use. The model includes 14 products, among them soybean and sugarcane. In terms of land use, these commodities are broadly classified between agricultural land and pasture, while commercial forests are considered exogenous projections. These activities account for approximately 95% of the total Brazilian agricultural area.

  Iron and Steel, Food and Beverage and Chemicals sectors. In these four industrial branches, several mitigation measures are not implemented in EPS and SFE scenarios due to high costs. This reflects in both higher energy demand and intensity levels (Table8).The share of renewables in industrial energy demand does not vary significantly across scenarios and is explained essentially by the use of charcoal in the Iron and Steel sector. In 2030, even though this share is lower in the EPS and SFE scenarios (19. 7%), compared to REF (21. 8%), it induces a higher share of renewables in the overall energy use, explained by the higher participation of the Iron and Steel branch in total energy demand(21% in EPS and SFE, against 19% in REF).

	the transportation sector, total energy demand increases between 2015 and 2030 in
	REF, but only 4. 7%
	Since diesel oil and gasoline consumption in the industrial sector is negligible
	(approximately 1% of total energy use), the tax exemption for these fuels in SFE has little
	impact. Therefore, the EPS and SFE scenarios results are virtually the same and
	correspond to an increase in energy demand of 13% in 2030 compared to 2015. The
	major differences between the EPS and SFE compared to the REF scenario lie in the
	Cement,

the Reference scenario (REF), mitigation measures for the Brazilian industry are implemented and lead to significant decreases in industrial energy intensity. Unlike

Table 8

 8 Industrial GHG emissions in the REF scenario increase 21% in 2030 compared to 2015 levels, from 168 MtCO2e to 204 MtCO2e. In the REF scenario, emissions decrease by 3. 6%compared to 2015 levels, reaching 162 MtCO2e in 2030. The major industrial

	branches leading to this decrease are Cement and Chemicals (-6 MtCO2e and -2 Mt CO2e,
	respectively).

Table 3 -

 3 Abatement potential (MtCO2e) and average abatement cost (USD/tCO2e) for periods 2021-25 (a) and2026- 30 (b)

	2021-2025 (a)			2026-2030 (b)
	Abatement	Average cost		Abatement	Average cost
	potential	(US$/tCO2e)		potential	(US$/tCO2e)
	(MtCO2e)			(MtCO2e)
	Hybrid and electric buses	4. 5	-309. 0	Hybrid and electric	14.	-310. 0
				buses
	Active transportation	1. 6	-271. 7	Active transportation	3.	-274. 8
	Railways	3. 2	-242. 2	Railways	9.	-260. 3
	Ethanol	1. 6	-185. 1	Ethanol	14.	-154. 2
	Food and Beverages	0. 6	-130. 3	Food and Beverages	1.	-111. 6
	(energy efficiency)			(energy efficiency)
	Non-ferrous (energy	0. 7	-69. 5	Non-ferrous (energy	1.	-73. 4
	efficiency)			efficiency)
	Chemicals (energy	0. 6	-61. 3	Non-ferrous (heat	0.	-60. 9
	efficiency)			recovery)
	Non-ferrous (heat	0. 1	-53. 3	Logistics optimization	4.	-56. 2
	recovery)
	Logistics optimization	2. 3	-50. 8	Chemicals (energy	1.	-48. 4
				efficiency)
	Iron and Steel (charcoal)	0. 7	-37. 9	Iron and Steel (charcoal)	1.	-39. 3
	Waterways	5. 3	-4. 4	Rest of industry (heat	4.	-12. 0
				recovery and process
				improvements)
	Cement (additives)	0. 6	0. 4	Waterways	5.	-9. 3
	Rest of industry (heat	2. 2	1. 3	Hybrid and electric	0.	-3. 3
	recovery and process			trucks
	improvements)
	Biodiesel	6. 9	4. 2	Cement (additives)	1.	-0. 4
	*Offsets from native	61. 0	6. 3	Biodiesel	15.	3. 6
	vegetation (EPS and SFE
	only)
	Chemicals (heat recovery)	0. 3	23. 4	*Offsets from native	122.	8. 4
				vegetation (only EPs and
				SFE)
	Cement (energy	1. 2	35. 4	Chemicals (heat	0.	24. 2
	efficiency)			recovery)
	Hybrid and electric trucks	0. 1	53. 3	Cement (energy	2.	30. 4
				efficiency)
	Iron and Steel (energy	2. 0	72. 0	Renewables in power	12.	33. 1
	efficiency)			generation
	Food and Beverages (heat	0. 1	131. 9	Iron and Steel (energy	4.	74. 0
	recovery)			efficiency)
	Renewables in power	0. 4	132. 5	Food and Beverages	0.	138. 4
	generation			(heat recovery)
	Iron and Steel (heat	1. 5	336. 5	Subways	0.	149. 3
	recovery)
	Hybrid and electric cars	0. 3	1, 164. 1	Iron and Steel (heat	3.	347. 6
				recovery)
	Subways	n/a	n/a	Hybrid and electric cars	2.	363. 2
	Biokerosene	n/a	n/a	Biokerosene	0.	671. 4
	Source: the authors
	Observations: Cost values for the 2021-25 period in present value of 2021 and for the 2026-30 period in present
	value of 2026, both using a 8% p. y. discount rate.

Table 4 -

 4 Main scenario assumptions for the transport sector

	Mitigation measures	REF	EPS	SFE
	Logistics optimization	Programs to encourage good practices and labelling in the sector
		4. 6% improvement in energy efficiency of road and rail modes
	Railroad expansion	Increased share of rail transport in the modal split, considering investments
		in progress with no delay
	Waterway expansion	Increased share of water transport in the modal split, considering
		investments in progress with no delay
	Passenger rail expansion	Additional 18% in the	Additional 5% in the	Additional 5% in the
		extension of the rail	extension of the rail	extension of the rail
		network	network	network
	Increased use of active	Incentive to active transportation behaviour.
	transportation	Additional 608 km of cycle paths
	Plug-in electric and hybrid cars	Share of electric and	Share of electric and	Share of electric and
		hybrid cars reaching 4.	hybrid cars reaching 1.	hybrid cars reaching 1.
		3% of the fleet. Electric	3% of the fleet. Electric	3% of the fleet. Electric
		motorcycles reach 1%	motorcycles reach 1%	motorcycles reach 1%
		of the fleet	of the fleet	of the fleet
	Plug-in electric and hybrid buses Widespread in urban	Widespread in urban	Widespread in urban
		areas. Share of electric	areas. Share of electric	areas. Share of electric
		and hybrid buses	and hybrid buses	and hybrid buses
		reaching 11. 2% of	reaching 11. 2% of	reaching 11. 2% of
		national fleet	national fleet	national fleet
	Plug-in electric and hybrid trucks Share of electric and	Share of electric and	Share of electric and
		hybrid trucks reaching	hybrid trucks reaching	hybrid trucks reaching
		2. 4% of national fleet	2. 4% of national fleet	2. 2% of national fleet
	Increased use of biodiesel (B20)	Blend of 20 vol. % of	Blend of 20 vol. % of	Blend of 10 vol. % of
		biodiesel (B20) in diesel	biodiesel (B20) in diesel	biodiesel (B10) in diesel
	Increased use of biokerosene	Blend of 2 vol. % of	-	-
		biokerosene in
		kerosene
	Increased use of ethanol	Blend of 27 vol. % of	Blend of 27 vol. % of	Blend of 27 vol. % of
		anhydrous ethanol	anhydrous ethanol	anhydrous ethanol
		(E27) in gasoline.	(E27) in gasoline.	(E27) in gasoline.
		Annual supply of	Annual supply of	Annual supply of
		ethanol in 23. 6 million	ethanol in 18. 7 million	ethanol in 18 million
		toe	toe	toe
	Source: the authors

Table 7 -

 7 Main results for the transport sector

		2015	2020		2025			2030
				REF	EPS	SFE	REF	EPS	SFE
	Road mode share in the modal split
	Passenger transport	92%	90%	90%	90%	90%	90%	90%	90%
	Freight transport	56%	52%	47%	47%	47%	43%	43%	43%
	Energy intensity
	Passenger intensity (kJ/pass-km)	1, 038 1, 058	941	947	947	838	862	862
	Freight intensity (kJ/t-km)	1, 023	934	801	802	802	708	709	709
	Renewable energy share
	Renewable energy use	21%	23%	31%	29%	26%	40%	32%	27%
	Market share of hydrous ethanol	33%	33%	50%	40%	36%	75%	60%	47%
	Biodiesel blend	7%	10%	15%	15%	10%	20%	20%	10%
	Total energy use (million toe)	84. 5	85. 4	79. 6 81. 0 81. 2 77. 4	81. 6	81. 4
	Plug-in electric vehicle stock
	(thousands)
	Light passenger vehicles	1	3	421	51	51	2, 502	243	243
	Buses	-	0	9	9	9	45	45	45
	Light commercial vehicles and trucks	0	0	23	9	9	146	66	66
	Emissions (Mt CO2e)	204	203	170	178	186	144	171	184
	Source: the authors

Table 8 -

 8 Energy demand per industrial branches across scenarios (million toe)

	Energy demand	2015	2020	REF	2025 EPS	SFE	REF	2030 EPS	SFE
			6. 1	7. 0	7. 0	7. 0	8. 2	8.	8. 1
	Chemical industry	6. 9	7. 0	6. 6	6. 9	6. 9	6. 1	6.	6. 6
	Food and beverage	21. 5	24. 0	22. 3	23. 6	23. 6	20. 7	23.	23. 2
	Textiles	0. 90	0. 91	0. 91	0. 91	0. 91	0. 91	0.	0. 92
	Pulp and paper	11. 7	13. 5	14. 3	14. 3	14. 3	15. 0	15.	15. 1
	Ceramic	4. 6	4. 4	4. 4	4. 5	4. 5	4. 4	4.	4. 5
	Other industries	7. 9	8. 2	8. 3	8. 4	8. 4	8. 4	8.	8. 5
	() Total	85. 1	89. 4	89. 1	92. 7	92. 7	89. 1	96.	96. 2
	Share renewables (total industry)	39. 0%	40. 6%	40. 1%	40. 1%	40. 1%	39. 6%	39. 7%	39. 7%
	Share biomass (Iron and Steel)	17. 9%	14. 7%	17. 9%	17. 1%	17. 1%	21. 8%	19. 7%	19. 7%
	Variation in energy intensity compared to 2015	-	7. 9%	-9. 3%	-6. 5%	-6. 5%	-22. 5%	-18. 1%	-18. 1%

Table 9 -

 9 Main results and indicators for the energy supply sector in simulated scenarios

		2015	2020		2025				2030
				REF	EPS	SFE	REF	EPS	SFE	NDC goals -2030
	Final demand (Mtoe)	261. 4	276. 3	284. 6	289. 9	290. 2	300. 4	312. 5	312. 2	n/a
	Primary energy transformed into electricity (Mtoe)	77. 6	68. 6	77. 7	78. 9	78. 9	90. 9	93. 8	94. 2	n/a
	Other primary energy supply (Mtoe)	222. 0	233. 0	228. 9	234. 0	234. 1	236. 2	247. 6	247. 5	n/a
	Total primary energy supply -TPES (Mtoe)	299. 6	301. 6	306. 6	312. 9	313. 0	327. 1	341. 4	341. 7	n/a
	Sectoral indicators
	Grid emission factor (kgCO2/MWh)	130. 0	50. 5	50. 5	51. 7	51. 8	41. 9	51. 0	54. 8	n/a
	Share of bioenergy 1 () in energy supply (%)	17. 9%	21. 1%	22. 3%	21. 8%	20. 9%	23. 7%	21. 8%	20. 2%	18. 0%
	Share of renewables in energy supply (%)	41. 3%	48. 6%	52. 7%	51. 8%	50. 2%	56. 0%	52. 9%	49. 7%	45. 0%
	Share of renewables in energy supply, except hydropower (%)	30. 0%	35. 4%	38. 2%	37. 6%	36. 0%	42. 1%	39. 5%	36. 3%	28. 0%
	Share of electricity from
	renewables, except hydro (in	11. 5%	19. 4%	21. 0%	21. 1%	21. 0% 29. 3%	27. 7%	26. 8%	23. 0%
	total electricity supply) (%)

Table 10 -

 10 Installed capacity of power generation across scenarios (MW) Figure -Total GHG emissions across scenarios (Mt CO2e)

		2015	2020		2025			2030
				REF	EPS	SFE	REF	EPS	SFE
	Nuclear	1, 990	1, 990	1, 990	1, 990	1, 990	3, 395	3, 395	3, 395
	Coal	3, 389	3, 515	3, 515	3, 515	3, 515	3, 515	3, 515	3, 515
	Natural Gas	12, 743	14, 537	18, 344	18, 344	18, 344	18, 344	22, 828	23,
	Other non-renewables	10, 174	5, 171	4, 139	4, 139	4, 139	1, 836	1, 836	1, 836
	Hydropower	91, 650	108, 437	110, 850 110, 850 110, 850 115, 591 115, 291 115, 291
	Biomass	13, 257	13, 853	14, 189	14, 319	14, 253	18, 687	17, 819	17,
	Wind	7, 633	15, 537	18, 630	18, 630	18, 630	30, 600	31, 363	29,
	Solar	21	4, 529	12, 273	12, 273	12, 273	28, 868	22, 781	22,
	Total	140, 857	167, 569	183, 929 184, 058 183, 992 220, 835 218, 827 217, 329

Source: the authors

Figure 1 -Integrated modelling framework Source: the authors

In its

NDC ratification, Brazil had pledged to reach climate neutrality by 2060, and conditioned reaching this goal by 2050 on the proper functioning of carbon market mechanisms. The new 2050 target (announced in

2021) still needs to be formalized in an updated version of the NDC, and then be submited to the UNFCCC.2According to(Brazil, 2010).

Inspired by theLow Carbon Fuel Standard (2019) and the Renewable Fuels Standard (EPA, 2020).

In its first year of implementation, the Program faced the COVID-19 crisis (which required revising the 2020 GHG emissions target) and a lawsuit from fuel distributors. Nevertheless, the program succeeded in engaging a large number of ethanol and biodiesel producers. As of November 2020, almost 14 million Cbios were emitted, of which

54% is already retired at an average price of 8 USD/Cbio (aassuming an exchange rate of 5. 5 BRL/USD).5 The Supplementary Material of this paper contains a section exploring the specificities of the Brazilian energy demand and supply, providing a brief overview and prospects of each sector and discusses its potential contribution to achieve NDC goals.

To prevent carbon prices from falling too low, undermining the incentive to decarbonise regulated sectors and sectoral technological innovation, and to minimize possible leakage effects, most jurisdictions introduce limits on the use of offsets by a regulated entity or in the aggregate total. This limit varies, but 20% is usually considered a reasonable level in market approaches(Margulis et al., 2019).

IMACLIM-BR first developments are described in[START_REF] Grottera | THE ROLE OF LIFESTYLE CHANGES IN LOW-EMISSIONS DEVELOPMENT STRATEGIES -THE CASE OF BRAZIL[END_REF],[START_REF] Lefèvre | Hybridisation challenges in energy-economy integrated models and representation of the low carbon transition: An application to the Brazilian case[END_REF] and[START_REF] Igo Wills | MODELAGEM DOS EFEITOS DE LONGO PRAZO DE POLÍTICAS DE MITIGAÇÃO DE EMISSÃO DE GASES DE EFEITO ESTUFA NA ECONOMIA DO BRASIL[END_REF], where more details on data requirements and the hybridization procedure can be founded. The calibration of the first version relied on 2005 and counts on a series of applications[START_REF] Grottera | The role of lifestyle changes in lowemissions development strategies: an economy-wide assessment for Brazil[END_REF][START_REF] La Rovere | Economic and social implications of low-emission development pathways in Brazil[END_REF][START_REF] Igo Wills | MODELAGEM DOS EFEITOS DE LONGO PRAZO DE POLÍTICAS DE MITIGAÇÃO DE EMISSÃO DE GASES DE EFEITO ESTUFA NA ECONOMIA DO BRASIL[END_REF][START_REF] Lefèvre | Hybridisation challenges in energy-economy integrated models and representation of the low carbon transition: An application to the Brazilian case[END_REF].

The full set of IMACLIM-BR 2015 equations can be found at[START_REF] Le Treut | Description of the IMACLIM-Country model: A country-scale computable general equilibrium model to assess macroeconomic impacts of climate policies 30[END_REF]. For the sake of transparency, a platform in open-access has been released to support the development of national IMACLIM versions[START_REF] Le Treut | IMACLIM-Country platform : a country-scale computable general equilibrium model[END_REF]. The hybrid IOT built to 2015 calibration can be founded at[START_REF] Grottera | Hybrid Input-Output tables for Brazil at year[END_REF].[START_REF] Wills | Economic and social effectiveness of carbon pricing schemes to meet Brazilian NDC targets[END_REF] is another example of IMACLIM-BR 2015 application.

Considering the category "Rest of the World Scenarios".

Ethanol and biodieselSource: the authors

Acknowledgements

This study was financed by the Partnerships for Market Readiness (PMR) project,

Energy-related measures and targets

• Increasing the share of sustainable biofuels in the Brazilian energy mix to approximately 18% by 2030 (including advanced biofuels) • In the energy sector, achieving 45% of renewables in the energy mix by 2030, including:

-expanding the use of renewable energy sources other than hydropower in the total energy mix to between 28% and 33% by 2030 -expanding the use of non-fossil fuel energy sources, increasing the share of renewables (other than hydropower) in the power supply to at least 23% by 2030 (including by raising the share of wind, biomass and solar) -achieving 10% efficiency gains in the electricity sector by 2030 2019) and expert elicitation 1 These are the estimated growth rates for the business-as-usual scenario. The implementation of mitigation measures and emissions pricing schemes are simulated over this baseline to derive the REF, EPs and SFE scenarios, as explained in the scenarios' description. These shocks may affect the final GDP levels.

2 Consistent with IEA low-oil price scenario (IEA, 2019). *The process of producing steel using charcoal decreases the emissions of GHG. However, it can increase the energy intensity of the sector. An increase from 10% in 2019 to 13. 7% in 2030 was assumed. Source: the authors based on [START_REF] Dnpm | Sumário Mineral Brasileiro -2015[END_REF][START_REF] Epe | Balanço Energético Nacional[END_REF]Guedes, 2017;[START_REF] Iabr | Estatísticas da siderurgia[END_REF]IBGE, 2014;[START_REF] Mme | Anuário estatístico do setor metalúrgico 2019[END_REF] for energy intensity andand [START_REF] Couto | OPÇÕES DE MITIGAÇÃO DE EMISSÕES DE GASES DE EFEITO ESTUFA EM SETORES-CHAVE DO BRASIL[END_REF][START_REF] De Oliveira | OPÇÕES DE MITIGAÇÃO DE EMISSÕES DE GASES DE EFEITO ESTUFA EM SETORES-CHAVE DO BRASIL: SETOR QUÍMICA[END_REF][START_REF] Epe | Balanço Energético Nacional[END_REF]Guedes, 2017) for mitigation measures.