Godar Javier

8/13/2019 Godar Javier

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Javier Godar, Stockholm Environment [email protected]

More spatially-explicit trade analyses:the development of a pixel-to-consumer model for

Brazilian farming production


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(Land) footprint analyses lack a detailed spatial connection between

production regions and final consumers.

Distortion of causal links between consumers choices and the environment

Errors in footprint calculations because env. impacts are site-specific (spatialvariability) while we use global or national averages (yields, C, WF index)

Impacts in specific regions remain invisible to consumers

This hampers our capacity to:

Allocate consumer responsibilities, accountability of governments/producersInclude externalities in the prize of traded goods

Understand mechanisms for improved efficiency

Inform policies

BACKGROUND


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WHITOUT A SPATIAL DIMENSION? Geography matters!

WHAT LAND FOOTPRINT REALLY MEANS?....

For agricultural land: Europe is net importing large amounts of embodiedland Brazil (19 million ha) (Friends of the Earth Europe, 2013)

A cup of coffee has a land footprint of 4.3 m2

SERI (2011) for Friends of the Earth


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A BRAZILIAN EXAMPLE

Municipality Yield (Tn/ha)GENERAL SALGADO 6.60

NOVA CRIXS 6.51FELIZ 1.00

JANGADA 0.90

Brazil av. soy yield 2011= 3.11 Tn/ha

Brazilian soy production per biome (Tn)


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ATLANTICFOREST

PAMPA

AMAZON

CERRADO

CAATINGA

PANTANAL


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A PIXEL TO CONSUMER MODEL (1)


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DATA:

Farming data at (sub)municipal scales

Multi-temporal LULC maps

Trade data from exporting facilities

National transportation networkFAO bilateral trade matrices

Review of socio-environmental data to calculate footprints


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1- Modeling of environmental impacts at municipal scale

1.a Caused by land transformation:LULC change (pixel)

Ecosystem services mapping (INVEST)

1.b Caused by land occupation:

Conversion factors applied to crops for:WF

Agrochemicals

Nutrients

Soil degradation

Biodiversity assessments


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Brazilian network with costweights (minutes)

Origin destination cost matrix(GIS)

Linear programming minimize

total cost of transportation(optimization)

National consumptioncompetes with exports in theallocation.

2- Modeling transport allocation


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1.a Apparent consumption, re-exports: Products consumed in a country originate

in proportional shares from the country's imports and domestic production (Kastneret al, 2011). The exporting municipalities are treated as countries.

1.b Multirregional Input Output Analysis (MRIO) ???

3- Modeling trade allocation

0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

Netherlands Norway Spain Finland Sweden Japan Iran

FAO, soy imports fromBrazil (1000 Tn)

Apparent consumption fromBrazil


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4- Modeling consumption

SEI tool: Resources and Energy Analysis Programme (REAP). REAP generatesecological, carbon and greenhouse gas at municipal, regional and national scales.

Data on emissions and land/resources required for production are allocated fromthe generating sector to the goods or services produced through the MRIO.


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Origin of Brazilian soy consumed in China2010 (minimum radius=0-20 th. Tn)

Origin of Brazilian soy consumed in the EU2010 (minimum radius=0-20 th. Tn)

EXAMPLE 1


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Procedence of consumed soy2010, in percentage per biome.

8,6 10,118,3

42,6

60,0 48,3

43,5

23,9 32,8

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CHINA EU RUSSIA

PANTANAL

PAMPA

MATA ATLANTICA

CERRADO

CAATINGA

AMAZONIA

CHINA EU RUSSIABRAZIL 45.3 32.8 0.9

AMAZONIA 42.8 36.3 1.9CAATINGA 0.9 80.1 0.0CERRADO 38.6 39.3 0.9

MATAATLANTICA 56.5 22.5 0.9

PAMPA 40.1 31.6 0.1

PANTANAL 8.5 46.7 0.7

Country share of soy producedper biome (2010)

(Per unit of soy consumed), the EU has imported a 17% moresoy from the Amazon and 41% more from the Cerrado thanChinamoratorium or ILUC??

The majority of soy China consumes comes from the alreadydeforested Mata Atlantica.


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EXAMPLE 2

CHINA

EU

AFRICA

OTHERS

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

Soy consumption from theEnawene Nawe municipalities in2010, in Tonnes.


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FINAL REMARKS

The model is in the process of further automatization for easy replicability(GRASS+R).

At the end this is about integrating well-consolidated disciplines:

The study of farming production dynamics

Environmental impact assessments

Trade analysisConsumption footprinting

Exports data at fine scales not generally available in most countries, but there isgood tracking-traceability of goods for tax/health purposes We need to demand

better trade data and provide resources to increase data standards.


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THANKS!

[email protected]


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CURRENT STATUS AND FUTURE DIRECTIONS

-Trade model just finished for all crops, code implemented in R andGRASS.

-Ongoing calculation of environmental impacts.

-Analyses and link to policies in 2014

-Several decisions to take:

-Which amortization time?-Crop substitution-Socio-economic impacts?-Integration with REAP, how?-Move to other countries.

-What can MRIO add to the methodology?-Automatization of several steps (LP)-Help!


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1.b MRIO: Analysis of global supply chain. Goods and services,economic structure, inter-industry and inter-regional

transactions. Monetary data represent the flows of physicalcommodities. Per economic sector as well. Few dates!!!!

Demand of Brazilian soybean in the UK (1000 Tn, 2007, West et al., 2013)FAO data Apparent consumptionMRIO (to satisfy demand for all goods and services)

861 1,096 1,417

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