Economic and social effectiveness of carbon pricing schemes to meet Brazilian NDC targets

ABSTRACT

Curbing GHG emissions while preserving economic growth is one of the main challenges that developing countries are facing to meet the Paris Agreement commitments. Brazil's NDC target aims to reduce economy-wide absolute levels of GHG emissions by 37% in 2025 and 43% in 2030, compared to 2005 emissions. In this paper, we compare command-and-control and carbon pricing policies to induce the Brazilian economy to meet its NDC targets. We focus on analysing synergies and trade-offs in macroeconomic and social development, captured by economic growth and income distribution while reducing GHG emissions. By integrating a series of sectoral models and a computable general equilibrium (CGE) model, we develop and run different policy scenarios that simulate a set of carbon pricing schemes in Brazil. Our analysis shows that NDC implementation in Brazil under carbon pricing policies allows the country to meet its targets and improve economic and social indicators compared to a command-and-control policy. With about the same GHG emissions up to 2030, important macroeconomic and social co-benefits can be achieved under a carbon pricing policy in Brazil, allowing for reduced welfare losses against business-as-usual trends.

Key policy insights:

• Carbon pricing policies are more cost-effective to meet NDC targets in Brazil up to 2030, resulting in higher GDP and household income, in comparison to other individual policy instruments, including command-and-control and subsidies to investments.

• A carbon price of about 10 USD/tCO2e, combined importantly with deforestation rates under control, would allow Brazil to meet its NDC targets.

• Recycling carbon pricing revenues can help soften the burden on the labour market and protect low-income households from welfare losses.

KEYWORDS:

• Integrated modelling
• carbon revenues
• carbon pricing
• Paris Agreement
• mitigation scenarios
• general equilibrium model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.thepmr.org/.

2 MESSAGE and TIMES are both bottom-up, energy supply models that uses linear-programming to produce a least-cost energy system, optimized according to a number of user constraints, usually over medium to long-term time horizons.

3 IMACLIM exists in a global multi-regional version (Crassous et al., 2006; Sassi et al., 2010) and in a growing number of country versions (Hourcade et al., 2010; Wills, 2013; Schers et al., 2015; Le Treut, 2017; De Lauretis, 2017; Gupta et al., 2019, 2020; Soummane et al., 2020; Le Treut et al., 2021). See http://www.centre-cired.fr/en/imaclim-network/imaclim-network-en/.

4 For the sake of transparency and to facilitate expansion to new economies, IMACLIM, including its Brazilian ‘branch’ IMACLIM-BR, is now open-access and hosted on Github (Le Treut et al., 2019). Additionally, Le Treut (2020) presents the generic equations of country versions of IMACLIM. All specifications therein apply to IMACLIM-BR unless specified otherwise in the following paragraphs.

5 The 19 sectors are: Coal, Oil & oil products excluding diesel, Natural gas, Biofuels, Diesel, Electricity, Forestry, Cattle, Other agriculture, Cement, Iron & Steel, Non-ferrous metals, Chemicals, Dairy and meat products, Other food industries, Pulp and Paper, Other industries, Transports, Other activities. They are aggregated from the 40-sector hybrid matrix published in Grottera et al. (2021).

6 The set of mitigation measures implemented on each scenario is available in the SM.

7 All price variations are relative to the numéraire of the model, that is, the imported composite good. More details can be found in Le Treut (2020).

8 These are the growth rates for the BAU scenario (for details on this scenario please take a look at the SM). The introduction of mitigation measures and emissions pricing schemes are simulated over the BAU scenario and affect final GDP levels of REF, EPS and DS.

Additional information

Funding

The authors would like to thank the Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (National Scientific and Technological Development Council—CNPq) for supporting part of this work through PhD and Post-Doc scholarships.