sirene 0.0.20

Import data from the 'Sistema de Registro Nacional de Emissões' (SIRENE) from MCTI

Sirene Application

The Sirene application serves four main purposes:

1. Facilitate Access to Raw Greenhouse Gas Emission Data: Provides easy access to raw greenhouse gas (GHG) emission data generated by the MCTI (Ministry of Science, Technology, and Innovation).

2. Access Disaggregated Emission Data: Allows users to access disaggregated emission data using the disaggregation methodology (proposed in future IPEA paper).

3. Estimate emission coefficients: estimate total, direct, and indirect emission coefficients according to the methodology proposed by Luis Masa's paper.

4. Continuous Improvement of Estimations: Facilitates the review, critique, and suggestion of improvements to the methodology used for estimating emission coefficients.

Additionally, the application provides sectoral data on Gross Production Value (VBP) and loan values by sector. With the disaggregated emission data and the VBP, we can estimate total, direct, and indirect emission coefficients according to the methodology proposed by Luis Masa's paper, titled "AN ESTIMATION OF THE CARBON FOOTPRINT IN SPANISH CREDIT INSTITUTIONS’ BUSINESS LENDING PORTFOLIO".

Please note that the data are annual and, according to the latest SIRENE report, are available between the years 1990 and 2020.

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1. Reading Raw Emission Data

The data are aggregated into five sectors: 'agropecuaria', 'energia', 'ippu' (Industrial Processes and Product Use), 'lulucf' (Land Use, Land-Use Change, and Forestry), and 'residuos', along with various subsectors. For each subsector, the MCTI estimates emissions of the main greenhouse gases: 'CO2', 'CH4', and 'N2O'. Moreover, for a more comprehensive view, we can convert these emissions into CO₂ equivalents (CO₂e) using different methodologies: 'CO2e_GWP_SAR', 'CO2e_GWP_AR5', and 'CO2e_GTP_AR5'.

1.1 Retrieving Emission Data of a Specific Sector

#!pip install sirene
from sirene import srn_defl as srn
srn.read('agropecuaria')  # CO₂ emissions between 1990 and 2020 estimated by MCTI (6th edition)
srn.read('energia')
srn.read('ippu')
srn.read('lulucf')
srn.read('residuos').head(2) 
# srn.read('total-brazil-1', 'CO2e_GWP_SAR')

4	1990.0	1991.0	1992.0	1993.0	1994.0	1995.0	1996.0	1997.0	1998.0	1999.0	...	2011.0	2012.0	2013.0	2014.0	2015.0	2016.0	2017.0	2018.0	2019.0	2020.0
setor_nfr
5. Resíduos	533.281271	543.542162	564.801835	535.723279	563.93405	584.509299	681.307133	732.83232	777.821987	906.700852	...	1114.443923	1164.108223	1229.771722	1330.541005	1148.902275	503.660432	654.8382	514.6629	374.4876	234.3123
5.A. Disposição de Resíduos Sólidos	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.00000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0000	0.0000	0.0000	0.0000

2 rows × 31 columns

1.2 Emission Data for Different Gases

srn.read('agropecuaria', 'CO2')          # Annual CO₂ emissions (in Gg) by the 'agriculture' sector
srn.read('agropecuaria', 'CH4')          # Annual CH₄ emissions
srn.read('agropecuaria', 'N2O').head(2)  # Annual N₂O emissions

4	1990.0	1991.0	1992.0	1993.0	1994.0	1995.0	1996.0	1997.0	1998.0	1999.0	...	2011.0	2012.0	2013.0	2014.0	2015.0	2016.0	2017.0	2018.0	2019.0	2020.0
setor_nfr
3. Agropecuária	279.66444	285.562807	294.191788	297.69358	307.340659	312.686904	297.197033	307.411606	315.603113	319.361771	...	473.30052	471.058836	486.479602	495.363993	495.491183	511.261527	465.727887	459.715568	466.367715	493.733508
3.A. Fermentação Entérica	0.00000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

2 rows × 31 columns

1.3 Aggregating Emissions Using Different Methodologies

srn.read('agropecuaria', 'CO2e_GWP_SAR')  # CO₂e emissions using the GWP_SAR methodology
srn.read('agropecuaria', 'CO2e_GWP_AR5')  # CO₂e emissions using the GWP_AR5 methodology
srn.read('agropecuaria', 'CO2e_GTP_AR5')  # CO₂e emissions using the GTP_AR5 methodology
srn.read('total-brasil-1', 'CO2e_GWP_SAR')

4	1990.0	1991.0	1992.0	1993.0	1994.0	1995.0	1996.0	1997.0	1998.0	1999.0	...	2011.0	2012.0	2013.0	2014.0	2015.0	2016.0	2017.0	2018.0	2019.0	2020.0
setor_nfr
Total	1.513050e+06	1.350780e+06	1.473045e+06	1.524658e+06	1.520309e+06	2.658961e+06	1.933597e+06	1.628555e+06	1.906803e+06	1.876171e+06	...	1.330959e+06	1.214650e+06	1.465501e+06	1.354190e+06	1.461633e+06	1.473392e+06	1.363034e+06	1.368710e+06	1.602461e+06	1.675759e+06
1. Energia	1.928096e+05	1.963756e+05	2.015303e+05	2.060034e+05	2.148438e+05	2.309830e+05	2.482115e+05	2.643361e+05	2.718498e+05	2.815097e+05	...	3.879157e+05	4.209064e+05	4.523506e+05	4.788814e+05	4.544386e+05	4.247321e+05	4.295025e+05	4.067935e+05	4.083429e+05	3.894841e+05
2. Processos industriais e uso de produtos (IPPU)	5.692059e+04	6.199831e+04	6.024298e+04	6.320786e+04	6.321482e+04	6.801612e+04	6.739034e+04	7.045198e+04	7.502388e+04	7.437740e+04	...	9.387738e+04	9.520686e+04	9.679476e+04	9.449125e+04	9.542089e+04	9.370133e+04	9.816093e+04	9.657526e+04	1.014626e+05	1.019364e+05
3. Agropecuária	3.296129e+05	3.387376e+05	3.439877e+05	3.492618e+05	3.568910e+05	3.594707e+05	3.388175e+05	3.463707e+05	3.521076e+05	3.566238e+05	...	4.630810e+05	4.620783e+05	4.674368e+05	4.728464e+05	4.762785e+05	4.871702e+05	4.645018e+05	4.641781e+05	4.683711e+05	4.776705e+05
4. Uso da Terra, Mudança do Uso da Terra e Florestas (LULUCF)	9.075130e+05	7.258803e+05	8.385865e+05	8.753576e+05	8.529330e+05	1.966234e+06	1.243209e+06	9.102377e+05	1.169334e+06	1.123254e+06	...	3.285898e+05	1.785544e+05	3.873587e+05	2.460640e+05	3.726926e+05	4.044966e+05	3.064440e+05	3.345022e+05	5.568179e+05	6.370386e+05
5. Resíduos	2.619352e+04	2.778839e+04	2.869725e+04	3.082708e+04	3.242630e+04	3.425745e+04	3.596863e+04	3.715815e+04	3.848735e+04	4.040561e+04	...	5.749480e+04	5.790412e+04	6.156007e+04	6.190650e+04	6.280232e+04	6.329223e+04	6.442457e+04	6.666138e+04	6.746670e+04	6.962985e+04

6 rows × 31 columns

1.4 Identifying Subsectors of a Specific Sector

srn.read('energia').index

Index(['1. Energia',
       '1.A.   Atividades de Queima de Combustíveis (abordagem setorial)',
       '1.A.1.     Indústrias de Energia',
       '1.A.1.a.       Produção de eletricidade e calor como atividade principal',
       '1.A.1.b.       Refino de petróleo',
       '1.A.1.c.       Produção de combustíveis sólidos e outras indústrias de energia',
       '1.A.2.     Indústrias de Transformação e Construção',
       '1.A.2.a.       Ferro e aço', '1.A.2.b.       Metais não ferrosos',
       '1.A.2.c.       Produtos quimicos',
       '1.A.2.d.       Celulose, papel e impressão',
       '1.A.2.e.       Processamento de alimentos, bebidas e tabaco',
       '1.A.2.f.       Minerais não metálicos',
       '1.A.2.g.       Equipamentos de transporte',
       '1.A.2.i.       Mineração (exceto combustíveis) e extração',
       '1.A.2.l.       Têxtil e couro', '1.A.3.     Transporte',
       '1.A.3.a.ii.          Aviação doméstica',
       '1.A.3.b.       Transporte rodoviário',
       '1.A.3.c.       Transporte Ferroviário',
       '1.A.3.d.ii.          Navegação doméstica',
       '1.A.3.e.       Outros transportes', '1.A.4.     Outros Setores',
       '1.A.4.a.       Comercial / institucional',
       '1.A.4.b.       Residencial',
       '1.A.4.c.       Agricultura / silvicultura / pesca / piscicultura',
       '1.A.5.     Não Especificado',
       '1.B.   Emissões Fugitivas a Partir da Produção de Combustíveis',
       '1.B.1.     Combustíveis sólidos', '1.B.2.     Petróleo e Gás Natural ',
       '1.C.   Transporte e armazenamento de CO2'],
      dtype='object', name='setor_nfr')

1.5 Retrieving Data of a Specific Subsector

srn.read('energia').loc['1.A.4.b.       Residencial', :].tail(5)

4
2016.0    18209.105762
2017.0    18348.639738
2018.0    18211.226425
2019.0    18132.857115
2020.0    18854.853915
Name: 1.A.4.b.       Residencial, dtype: float64

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2. Reading Disaggregated Emission Data

Each sector in the MCTI data (referred to as MCTI sectors) has been disaggregated into 68 activities based on the IBGE classification. This allows us to access the emissions of a specific activity within each sector (which could be considered the origin of emissions).

Note that not all years have been disaggregated. Disaggregation requires already disaggregated VBP data, which IBGE does not provide before 20XX.

For data that combine public and private health and education:

srn.co2e_67.head(10)  # Sums of (8591 + 8592) and (8691 + 8692)

	year	atividade_tru68_ibge	energia_Gg_CO2e_GWP_SAR	residuo_Gg_CO2e_GWP_SAR	agropecuaria_Gg_CO2e_GWP_SAR	ippu_Gg_CO2e_GWP_SAR	lulucf_Gg_CO2e_GWP_SAR	total_Gg_CO2e_GWP_SAR
0	1990	0191	0.0	0.0000	0.0	0.000000	118165.392346	118165.392346
1	1990	0192	0.0	569.0895	0.0	0.000000	786620.547583	787189.637083
2	1990	0280	0.0	0.0000	0.0	0.000000	2727.011575	2727.011575
3	1990	1091	0.0	443.0517	0.0	0.000000	0.000000	443.051700
4	1990	1100	0.0	81.8475	0.0	0.000000	0.000000	81.847500
5	1990	1700	0.0	186.8790	0.0	0.000000	0.000000	186.879000
6	1990	2091	0.0	0.0000	0.0	8744.469277	0.000000	8744.469277
7	1990	2300	0.0	0.0000	0.0	15170.640561	0.000000	15170.640561
8	1990	2491	0.0	0.0000	0.0	28232.024448	0.000000	28232.024448
9	1990	2492	0.0	0.0000	0.0	4123.240826	0.000000	4123.240826

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3. Scope of the Decomposition

Disaggregating MCTI emission data into 68 activities is a complex task. Due to this complexity, not all emissions have been assigned to a specific activity. Therefore, when comparing the total emissions of the 68 activities with the total estimated by the MCTI, the former is slightly lower. We have taken care to perform as much disaggregation as possible without significant loss of information to ensure data reliability.

import warnings
import pandas as pd
import matplotlib.pyplot as plt
warnings.simplefilter(action='ignore', category=FutureWarning)


# 1) emission separated (68 activities)
E_total_68 = pd.DataFrame({'year':[],'total68_Gg_CO2e_GWP_SAR':[]})
for t in range(2010,2021):
  result_t = srn.coef(str(t)).result
  footprint = result_t['total_Gg_CO2e_GWP_SAR'].sum()
  df_ = pd.DataFrame({'year':[result_t.ano[0]],'total68_Gg_CO2e_GWP_SAR':[footprint]})
  E_total_68 = pd.concat([E_total_68,df_])

# 2) emission aggregated (official data from MCTI)
E_total = pd.DataFrame(srn.read('total-brasil-1','CO2e_GWP_SAR').loc['Total',:])
E_total.reset_index(inplace=True)
E_total.columns = ['year','total_Gg_CO2e_GWP_SAR']

# 3) plot
E = E_total.merge(E_total_68, on='year')

plt.figure(figsize=(6,4))
plt.plot(E['year'], E['total_Gg_CO2e_GWP_SAR'], label='Total emission (MCTI)', marker='o')
plt.plot(E['year'], E['total68_Gg_CO2e_GWP_SAR'], label='Total emission (MCTI_disaggregated)', marker='o')

plt.title('Year vs. Values')
plt.xlabel('Year')
plt.ylabel('Emission (Gg of CO2e_GWP_SAR)')
plt.legend()
plt.grid(True)
plt.show()

---

4. Emission Coefficients

Using the disaggregated emission data and the deflated VBP (see relevant literature), we can estimate emission coefficients by activity (refer to the methodology proposed by Maza). To estimate these coefficients, we can consider the 'household' sector, which represents households responsible for emissions such as burning waste in rural areas.

Data Field Descriptions

• atividade_tru68_ibge: Represents activity (i) classified under TRU68.
• production_values_mi_brl: Total production value of activity (i) for year (t), expressed in millions of BRL.
• ano: Reference year for the data (complete data available only between 2012 and 2020).
• energia_Gg_CO2e_GWP_SAR: CO2e emissions from the energy sector, measured in Gg and calculated using the CO2e_GWP_SAR methodology.
• residuo_Gg_CO2e_GWP_SAR: CO2e emissions from the waste sector, measured in Gg.
• agropecuaria_Gg_CO2e_GWP_SAR: CO2e emissions from the agricultural sector, measured in Gg.
• ippu_Gg_CO2e_GWP_SAR: CO2e emissions from the IPPU sector, measured in Gg.
• lulucf_Gg_CO2e_GWP_SAR: CO2e emissions from the LULUCF sector, measured in Gg.
• total_Gg_CO2e_GWP_SAR: Total CO2e emissions across all sectors, measured in Gg.
• active_loan_portfolio_mi_brl: Value of active loans by sector, expressed in millions of BRL.
• q_direct: Coefficient representing direct emissions.
• q_total: Coefficient representing total emissions (direct + indirect).
• q_indirect: Coefficient representing indirect emissions.

coef_t = srn.coef(year='2020', emission='total', household=True, ajust_sicor=True, reference_year='2011')
coef_t.reference_year  # Reference year for deflation
# coef_t.adjust_sicor    # Adjust credit data
coef_t.defl_df         # DataFrame with deflation values
coef_t.defl_num        # Deflation scalar
coef_t.emission        # Type of emission: 'total', etc.
coef_t.household             # Boolean indicating inclusion of household sector
coef_t.mLeontiefBarr.shape   # Leontief matrix shape (67, 67)
coef_t.result[['atividade_tru68_ibge', 
               'production_values_mi_brl', 'ano']]   # IBGE data

	ano	production_values_mi_brl	total_Gg_CO2e_GWP_SAR	ano	q_direct	q_total	q_indirect
0191\nAgricultura, inclusive o apoio à agricultura e a pós-colheita	2020	302059.919929	223674.759909	2020	0.740498	1.934712	1.194214
0192\nPecuária, inclusive o apoio à pecuária	2020	112305.027425	942924.465575	2020	8.396102	9.098249	0.702147
0280\nProdução florestal; pesca e aquicultura	2020	23393.557114	-34593.308607	2020	-1.478754	-1.409453	0.069301
0580\nExtração de carvão mineral e de minerais não-metálicos	2020	12780.776306	419.651840	2020	0.032835	0.157375	0.12454
0680\nExtração de petróleo e gás, inclusive as atividades de apoio	2020	131319.134611	2611.296027	2020	0.019885	0.515303	0.495418
...	...	...	...	...	...	...	...
8691 + 8692\nSaúde	2020	309474.34351	173.138934	2020	0.000559	0.004565	0.004006
9080\nAtividades artísticas, criativas e de espetáculos	2020	21619.992666	30.165292	2020	0.001395	0.487969	0.486573
9480\nOrganizações associativas e outros serviços pessoais	2020	94739.917992	91.784941	2020	0.000969	0.219711	0.218742
9700\nServiços domésticos	2020	32717.423064	0.000000	2020	0.0	0.104352	0.104352
\nRESIDENCIAL	2020	1756149.519048	91105.313767	2020	0.051878	5.601229	5.549351

67 rows × 7 columns

coef_t.result[['ano', 'production_values_mi_brl', 
               'total_Gg_CO2e_GWP_SAR', 'ano', 
               'q_direct', 'q_total', 'q_indirect']] # Emission coefficients as per Maza

	ano	production_values_mi_brl	total_Gg_CO2e_GWP_SAR	ano	q_direct	q_total	q_indirect
0191\nAgricultura, inclusive o apoio à agricultura e a pós-colheita	2020	302059.919929	223674.759909	2020	0.740498	1.934712	1.194214
0192\nPecuária, inclusive o apoio à pecuária	2020	112305.027425	942924.465575	2020	8.396102	9.098249	0.702147
0280\nProdução florestal; pesca e aquicultura	2020	23393.557114	-34593.308607	2020	-1.478754	-1.409453	0.069301
0580\nExtração de carvão mineral e de minerais não-metálicos	2020	12780.776306	419.651840	2020	0.032835	0.157375	0.12454
0680\nExtração de petróleo e gás, inclusive as atividades de apoio	2020	131319.134611	2611.296027	2020	0.019885	0.515303	0.495418
...	...	...	...	...	...	...	...
8691 + 8692\nSaúde	2020	309474.34351	173.138934	2020	0.000559	0.004565	0.004006
9080\nAtividades artísticas, criativas e de espetáculos	2020	21619.992666	30.165292	2020	0.001395	0.487969	0.486573
9480\nOrganizações associativas e outros serviços pessoais	2020	94739.917992	91.784941	2020	0.000969	0.219711	0.218742
9700\nServiços domésticos	2020	32717.423064	0.000000	2020	0.0	0.104352	0.104352
\nRESIDENCIAL	2020	1756149.519048	91105.313767	2020	0.051878	5.601229	5.549351

67 rows × 7 columns

---

5. Emissions from the Brazilian Credit Financial System

The application contains credit data by activity extracted from the Credit System maintained by the Central Bank of Brazil (BCB). Knowing the credit volume each sector receives and the emissions each sector produces, we can estimate the emissions per dollar lent by the financial system.

When set to ajust_sicor=True, it incorporates SICOR data when constructing emission coefficients. Otherwise, only SNC data is considered. SNC data already include SICOR data; however, in this case, they are classified as households (CPF). The more accurate approach is to treat this data as corporate entities (CNPJ), given the specific characteristics of the agricultural production system and its financing.

Credit System Data:

loan = coef_t.result[['ano', 'production_values_mi_brl', 'total_Gg_CO2e_GWP_SAR', 'active_loan_portfolio_mi_brl']]  # Loan data

	ano	production_values_mi_brl	total_Gg_CO2e_GWP_SAR	active_loan_portfolio_mi_brl
0191\nAgricultura, inclusive o apoio à agricultura e a pós-colheita	2020	302059.919929	223674.759909	98827.307133
0192\nPecuária, inclusive o apoio à pecuária	2020	112305.027425	942924.465575	61840.483836
0280\nProdução florestal; pesca e aquicultura	2020	23393.557114	-34593.308607	2756.938589
0580\nExtração de carvão mineral e de minerais não-metálicos	2020	12780.776306	419.651840	1040.023262
0680\nExtração de petróleo e gás, inclusive as atividades de apoio	2020	131319.134611	2611.296027	470.467326
...	...	...	...	...
8691 + 8692\nSaúde	2020	309474.34351	173.138934	12731.747667
9080\nAtividades artísticas, criativas e de espetáculos	2020	21619.992666	30.165292	2381.776043
9480\nOrganizações associativas e outros serviços pessoais	2020	94739.917992	91.784941	5932.893967
9700\nServiços domésticos	2020	32717.423064	0.000000	4.419046
\nRESIDENCIAL	2020	1756149.519048	91105.313767	997684.753973

67 rows × 4 columns