Land-use and biodiversity footprints of palm oil embodied in final product consumption

Transcription

1 Land-use and biodiversity footprints of palm oil embodied in final product consumption Johannes Többen *a, Kirsten S. Wiebe a, Francesca Verones a, Daniel Moran a, Richard Wood a and Martin Bruckner b a Norwegian University of Science and Technology (NTNU), Industrial Ecology Programme (Indecol), Trondheim, Norway b WU Vienna, Institute for Ecological Economics, Vienna, Austria * Johannes.tobben@ntnu.no

2 Agenda Footprints 2.0 Project Land use and Biodiversity Impacts of palm oil and other vegetable oils Step 1: Final demand and Land use footprints Drivers: specifying final demand Mixed Unit Multiregional Input-Output Model: Accounting indirect land use impacts MRIO Construction: A Maximum Entropy Approach Step 2: Spatial disaggregation of land-use impacts Step 3:Estimation of biodiversity impacts Conclusion 2

3 Footprints 2.0 Project CONTEXT

4 Footprints 2.0 Project Objective: Connect environmental earth observation data with MRIO to estimate Spatially explicit Biodiversity Footprints Figure 1: Species threat hotspots of Europe's final demand People: Daniel Moran, Francesca Verones, Richard Wood, Johannes Többen Collaboration: Martin Bruckner (WU Vienna) D. Moran, K. Kanemoto. Identifying Species Threat Hotspots from Global Supply Chains Nature Ecology & Evolution, 1(1), 0023, 2017

5 Footprints 2.0 Project MOTIVATION: PALM OIL

6 Motivation Palm oil is the most important vegetable oil today used in a great variety of globally traded products, ranging from food and cosmetics to biofuels. Environmental consequences of landuse changes (e.g. clearing tropical forests) Objectives: Quantify indirect impacts of changes in trade and consumption pattern and technology elsewhere in the world on land-use (changes) and biodiversity loss

7 Work Flow Final Consumption Global Supply Chains Spatialized Land use footprints Biodiversity impacts Step 1 Step 2 Step 2

8 Final Consumption Current specification: Final demand of households from EXIOBASE Detailed breakdown into 200 products by country Example of spatially disaggregated footprints of households in EU regions Future: detailed analysis of socio-economic drivers consumer surveys geodemographic data Ivanova, Diana; Vita, Gibran; Steen-Olsen, Kjartan; Stadler, Konstantin; Melo, Patricia C; Wood, Richard; Hertwich, Edgar G.. (2017) Mapping the carbon footprint of EU regions. Environmental Research Letters. vol. 12 (5).

9 Accounting for land use impacts of vegetable oil Connection between Crop Maps and Supply Chain Information Shortcomings of pure monetary and pure physical accounting approaches Objective: Construct a time series of hybrid MRIOs to be linked with crop maps Main Data Sources: Monetary layer: EXIOBASE3 Physical layer: FAOSTAT Commodity Balances (CBS) 5 oil crops (Oil palm, Rapeseed, Soybean, Sunflower and Other oil corps) 10 processed commodities (Oil and oil cakes each) Bilateral trade data (BACI) Hybrid MRIO Crop Maps

10 Mixed-Unit MRIO Model Demand driven Multiregional Input-Output model in mixed units Total land requirements due to change in final demand Leontief Inverse intermediate inputs per unit of output Change in Final Demand hectares per unit of output

11 A Maximum Entropy Approach HYBRID MRIO CONSTRUCTION

12 Objectives and Challanges Objectives: Develop generic approach to disaggregate and hybridize specific MRIO sectors (here oil crops and vegetable oils) Further development of Többen (2017, Economic Systems Research) Challenges: Partial & possibly conflicting data Many more unknowns to estimate than data points Multiple units of measurement ( and tons) Different levels of aggregation and mismatching classifications

13 Maximum Entropy principle Select the distribution [of commodity flows] that is closest to some prior, reflecting assumptions or theoretical hypothesis, while ensuring that its marginals match empirical data (Shannon, 1948; Jaynes, 1957; Golan et al., 1996) Primal min s.t. Dual max λ where Ω λ ln λ lnω λ exp λ Unknown [MRIO element] to be estimated Prior (initial estimate) of unknown RHS value of data constraint λ Lagrangian Multiplier of constraint Element of concordance matrix relating unknowns to constraints Dual version Computationally more efficient, since number of Lagrangians is typically much lower than the number of unknowns Equivalent to empirical Maximum Likelihood estimation

14 A Maximum Entropy Approach INITIAL ESTIMATE

15 Priors: Initial Estimate Assumption based first approximation of unknown MRIO elements Often proportional allocation Here: 1. Split CBS uses into countries of origin of products using BACI import shares 2. Split CBS use categories into MRIO production and final demand sectors using shares from EXIOBASE

16 A Maximum Entropy Approach DATA CONSTRAINTS

17 Data constraints (1): EXIOBASE Intermediate and final consumption of disaggregated oil crops and vegetable oils must add up to more aggregated EXIOBASE elements Indices and = Countries = disaggregated commodities = aggregated commodities and = production and final demand sectors Parameters and Variables and = MRIO elements = element of correspondence table relating to = disaggregated target elements

18 Data Constraints(2): FAOSTAT Commodity Balance Sheets = element of correspondence table with = CBS use-category = inverse price (tons per ) for transition between monetary and physical units Supply Domestic production Imports Stock changes Use Food Feed Food processing Seed & waste Non food use Exports

19 Data Constraints(3): FAOSTAT technical conversion factors

20 Data Constraints(4): Bilateral Trade Data

21 A Maximum Entropy Approach CONFLICTING DATA AND MIXED UNITS

22 Generalisation: Conflicting data Split each data point into a signal and a noise component Signal Express error as linear combination of Supports : defining lower bounds, upper bounds and mean of errors Noise Maximum Entropy with two weights,0, Weights, where 1 22

23 Attribute Space One and same flow is recorded in several data sets measured in different units. Monetary 1 Mass =2 =3 =0 2 2 Energy 23

24 Attribute Space Express as a linear combination of attribute combinations and weights that add up to 1,1 Example: 1 Initial Estimate ( ) 1 Lower bound 1 Upper bound 1 Monetary Mass Marginal must adhere to monetary data constraints Marginal must adhere to mass data constraints 1 =2 =3 =0 2 2 Energy 24 Marginal must adhere to energy data constraints

25 Estimation model (dual) max λ λ ln Ω λ ln Ψ λ Where Data point Signal component Noise component Ω λ exp λ λ and Ψ λ exp λ 25

26 Estimation model (dual) max λ λ ln Ω λ ln Ψ λ Where Data point Signal component Noise component Ω λ exp λ λ and Ψ λ Initial Estimates and bounds (, tons) exp λ 26

27 Estimation model (dual) max λ λ ln Ω λ ln Ψ λ Where Data point Signal component Noise component Ω λ exp λ λ and Ψ λ Lagrangians of data constraints in monetary and physical units exp λ 27

28 Work Flow Final Consumption Global Supply Chains Spatialized Land use footprints Biodiversity impacts Step 1 Step 2 Step 2

29 Spatialized land-use footprints Approach: Spatial disaggregation of land-use impacts based on MAPSPAM production statistics harmonized with FAOSTAT data at 5 arc-minute resolution You, L., S. Wood, U. Wood-Sichra, and W. Wu (2014) Generating global crop distribution maps: From census to grid. Agricultural Systems, Volume 127, 2014,

30 Tracking spatial land-use changes Land-Use Harmonization (LUH2) project Provides harmonized long time series data about land cover transitions at high spatial resolution LUH1 provided harmonized land-use data for the years , at 0.5 x 0.5 resolution for the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).

31 Work Flow Final Consumption Global Supply Chains Spatialized Land use footprints Biodiversity impacts Step 1 Step 2 Step 3

32 From land use pressure to biodiversity impact Approach: LC-Impact (Verones et al., 2017) Impact on biodiversity measured as global fractions of potential species extinctions (PDF) for 10 different impact categories Characterization Factors for different animal and plant taxa and impact categories Biodiversity Impact of land use change Spatialized Land use footprint + = 32

33 THANKS FOR YOUR ATTENTION

34 References Moran, Daniel; Kanemoto, Keiichiro. (2017) Identifying the Species Threat Hotspots from Global Supply Chains. Nature: Ecology & Evolution. Verones, Francesca; Moran, Daniel; Stadler, Konstantin; Kanemoto, Keiichiro; Wood, Richard. (2017) Resource footprints and their ecosystem consequences. Scientific Reports. Verones, Francesca; Pfister, Stephan; Van Zelm, Rosalie; Hellweg, Stefanie. (2017) Biodiversity impacts from water consumption on a global scale for use in life cycle assessment. The International Journal of Life Cycle Assessment. vol. 22 (8). Ivanova, Diana; Vita, Gibran; Steen-Olsen, Kjartan; Stadler, Konstantin; Melo, Patricia C; Wood, Richard; Hertwich, Edgar G.. (2017) Mapping the carbon footprint of EU regions. Environmental Research Letters. vol. 12 (5). 34