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Water Nexus and the Environment
Alban Thomas1
1Toulouse School of Economics (LERNA)Head of Agricultural Economics and Sociology Scientific Division (SAE2),
INRA (French Institute for Agricultural Research)
JRC Workshop, AnkaraFebruary 12-13, 2015
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 1 / 25
Introduction
Introduction
“The objective of this Workshop is to gather evidence on the potential foreconomic growth deriving from the adoption of a Water Nexus approach inthe Mediterranean region”.
Steps to identify opportunities:
Objectives of energy and food securityAvailable biomass and natural resources including waterCompetition and market opportunitiesGovernance and institutional issues
Three examples for three levels of analysis/action:
1) Innovation in one sector (e.g., agrofood industry)2) Multisectoral integration and optimization of transformation processes
(incl. food, energy, water)3) Food and energy for urban population: cities as processing units of
local biomass
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 2 / 25
Introduction
Introduction
Three levels of analysis requested by policy makers and addressed toeconomists:
1) Firm/plant scale: technology-driven prototypes (SME)2) Landscape: optimal allocation of water-using activities3) Global scale: water depletion and public policy (national, World)
Examples in this presentation:
1) Innovation in agrofood industry: durum wheat in Mediterraneancountries
2) Limiting waste and optimising the use of co-products from agriculture3) Local food and energy for cities
Methods:
Water footprint (virtual water) at country levelCompetitivity of local production: sector and country integrationCost-efficiency and cost-benefit analysis
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 3 / 25
Three examples for different levels of analysis Innovation in agrofood industry: Example of durum wheat
Innovation in agrofood industry: Example of durum wheat
Two-thirds of world’s production of durum wheat in Mediterraneancountries
Increased competition from Australia, Russia, the US, hence need toinnovate with new food products
New food items based on semolina (couscous), pasta, burghul, evenFrench bread from durum wheat
A combination of genetic selection, producer organization, andinnovative consumer products
Genetic selection for a better adaptation to climate change (lowerwater requirements)
New cropping systems to mitigate climate change (e.g., rotationswith protein crops) and reduce nitrogen fertilizer applications
Also, innovation in first- and second-transformation processes(reduction of wastes, less water needs, etc.)
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 4 / 25
Three examples for different levels of analysis Limiting waste and optimising transformation processes
Limiting waste and optimising transformation processes
In relation with the challenge of food security
Consistent with European Societal Challenge 2 on Food Securityincludes ISIB (Innovative, Sustainable and Inclusive Bioeconomy)
Sustainable agriculture and forestrySustainable and competitive bio-based industriesCross-cutting actions covering all the activities
Topic SFS-8: Resource-efficient eco-innovative food production andprocessing
Many applications are still below profitability thresholds because ofscale of activity
Uncertainty about regular access to natural resources and guaranteedaccess to biomass and water is a serious issue
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 5 / 25
Three examples for different levels of analysis Limiting waste and optimising transformation processes
Applications to local biorefinery and energy co-generation frombiomass
Some crops devoted to biofuel are less water-intensive (miscanthus,sorghum, etc.)
Advantages: can be grown on marginal soil and can be mixed withbiomass from urban waste ; can use crop residues from crops
Drawback: may have a negative net environmental effect (pesticide,fertilizer, water)
Strategies to reduce crop loss: a way to reduce the ultimate watercontents of final products
But optimization of technology processes also means optimal spatialallocation of plants and logistics
Literature on the economic analysis of bio-economy innovations?
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 6 / 25
Three examples for different levels of analysis Limiting waste and optimising transformation processes
This is the bio-economy, stupid: upscaling small-scale innovations
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 7 / 25
Three examples for different levels of analysis Local food and energy for cities
Local food and energy for cities
Food for the cities:
Redesigning the relationships between municipalities (first circle) andecosystems and agroecosystems outside cities (second circle)
Consider cities acting as
Processing units of local biomassReceiving fresh (blue) water and “producing” wastewater (grey)
Water Nexus in this case is defined spatially around and for cities
Innovative partnerships between municipalities, farmer cooperatives andlocal water authorities (if any)Idea: subsidies (from water authority) for water potabilization andpretreatment may be conditional on contracts for best agricuturalpractices between municipality and farmer cooperativeLocal food systems exploiting local water resources, possibly grey water(depending on legislation)And energy available for agriculture and industry from biomass waste“produced” in cities
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 8 / 25
Methods Methods: Virtual Water and Water Footprint
Methods: Virtual Water and Water Footprint
Using Water Footprint to identify competitive products for localconsumption and/or exports.
Variable DefinitionVirtual water Water used for the production of a good or
service, not visible in the final productVirtual water content Volume of fresh water consumed or pollutedof a product for producing a productWater Footprint Multi dimensional indicator of freshwater use
(both direct and indirect) by a consumer or producerBlue water Fresh surface or groundwaterGreen water Precipitation on land that does not run off or recharge
the groundwater but is stored in the soilor temporarily stays on top of the soil or vegetation
Grey water Volume of polluted water flow, aquifers and rivers
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 9 / 25
Methods Methods: Virtual Water and Water Footprint
Water and Land Heterogeneity across Regions
Table: Water resources (m3 per capita) and Agricultural Land (m2 per capita)
Country Surface Ground Precipitation Agriculturalwater water land
Malta 1.22 122.23 438.08 220.04Bahrain 4.32 120.98 67.19 84.35UAE 27.75 22.19 1206.35 1076.19Suriname 23,9151.9 156,820.9 748,623.9 1435.35Guyana 321,304.2 137,320.9 684,071.3 22,346.28Iceland 532,792.4 77,030.23 641,276.6 73,729.38
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 10 / 25
Methods Methods: Virtual Water and Water Footprint
Table: Hidden water use (virtual water) in domestic goods
Commodity Water consumed (litre)
1 litre of beer 71 litre of gasoline 101 cola 70A single bath 2001 kg of paper 3201 kg of bread 1,0001 kg of potatoes 1,000Television set 1,0001 kg of meat 4,000 to 10,000One pair of jeans 8,000
Source: UNEP (2004).
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 11 / 25
Methods Methods: Virtual Water and Water Footprint
Virtual water balance per country related to trade in agricultural andindustrial products, 1996-2005. Net exporters are in green, net importersin red.
Source: Mekonnen and Hoekstra (2011).
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 12 / 25
Methods Methods: Virtual Water and Water Footprint
Hotspots of blue water footprints for products exported to France.
Source: WWF (2012).
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 13 / 25
Methods Methods: Virtual Water and Water Footprint
Table: Hotspots of the French blue water footprint for agricultural products
Region % Blue Water Footprint Product(s)
Aral Sea (Uzbekistan) 6.38 CottonGaronne (France) 5.44 Maize, soybeanEscaut (France) 4.46 Maize, potatoLoire (France) 4.43 MaizeIndus (Pakistan) 3.85 Cotton, rice, sugarcaneGuadalquivir (Spain-Portugal) 2.98 Cotton, sunflower, rice, sugarbeetSeine (France) 2.23 Maize, potato, sugarbeetGange (India) 2.19 Rice, sugarcaneGuadiana (Spain-Portugal) 1.79 Grape, sunflower, citrusTiger & Euphrate (Turkey, Syria, Iraq) 1.62 Cotton, ricePo (Italy) 1.59 Rice, animal productsEbro (Spain) 1.39 MaizeSebou (Morocco) 1.39 SugarbeetDouro (Spain-Portugal) 1.29 Maize, sugarbeetTejo (Spain-Portugal) 1.02 grape, maize, animal productsMississippi (US) 0.60 Maize, soybean, rice, cottonKrishna (India) 0.45 Rice, sugarcaneGodavari (India) 0.31 Rice, sugarcaneKizilirmak (Turkey) 0.27 SugarbeetChao Phraya (Thailand) 0.26 Rice, sugarcaneSakarya (Turkey) 0.25 SugarbeetBandama (Cote d’Ivoire) 0.21 Sugarcane, animal productsCauvery (India) 0.19 Rice, sugarcaneYongding He (China) 0.12 Cotton, soybeanLimpopo (SOuth Africa) 0.11 Sugarcane, cottonSacramento (US) 0.10 RiceSan Joaquin (US) 0.10 Cotton, Maize
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 14 / 25
Methods Competitivity of local production: sector and country integration
Competitivity of local production: sector and countryintegration
Investigate price transmission and explore substitutability betweenlocal and imported goods
Main constraint: consider sectors relevant both for local and importedcommodities
Compare Consumer Price, Producer Price, Import Price indices
Application to Lebanon: National accounts (retrospective 1997-2007,and 2008-2009)
10 sectors of the Lebanese economy: Agricultural products, Livestockand fish, Energy, Food industry, Textile & leather, Non-metal ores,Metals, machines & equipment, Wood, rubber & chemicals,Furniture, Other industrial products
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 15 / 25
Methods Competitivity of local production: sector and country integration
Price Transmission
Model for price transmission in first differences:
∆PDkt = µ+ γεkt + θ∆PW
kt + α∆CPIt + ukt ,
where
PDkt : domestice price, sector k and year t
PWkt : world price, sector k and year tεkt = PD
kt − α− βPWkt ,
β: long-term relationship between PDkt and PW
kt ,γ: speed of adjustment (towards long-run equilibrium),θ: intensity of price transmission,α: degree of inflation pass-through (to sectors).
γ = 0 if No convergence
θ = 1 if Law of One Price
θ + α = 1 if Neutral inflation & pass-through
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 16 / 25
Methods Competitivity of local production: sector and country integration
Convergence and pass-through: Test results for Lebanon
Test results, sector by sector (p-values)
Sector γ = 0 θ = 1 θ + α = 1
11. Agricultural products 0.010 0.0735 0.265212. Livestock 0.021 0.0383 0.311514. Food industry 0.110 0.0754 0.717016. Non-metal ores 0.129 0.1682 0.189218. Wood, rubber 0.169 0.0802 0.5125110. Other industrial products 0.035 0.1094 0.0748
Interpretation:
Significant convergence for sectors 11, 12 and 110
Law of One Price in all sectors except 12
Neutral inflation pass-through in all sectors
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 17 / 25
Methods Competitivity of local production: sector and country integration
Import Substitution
Estimation of a simple AIDS-type model, for 2 goods: local (D) orimported (M)
Share of imported commodities, sector k
wMkt = αk + γD,M
k log pDkt + γM,Mk log pMkt + βk log
(Rkt
P∗t
)+ ukt ,
where
Rkt = pDktqDkt + pMkt q
Mkt ,
P∗t =
∑i=D,M w i
kt logP ikt
Computation of substitution elasticities: local vs. imported commodities
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 18 / 25
Methods Competitivity of local production: sector and country integration
Price elasticities, local vs. imported commodities
Sector Own-price Cross-priceelasticity elasticity
11. Agricultural products -0.6553 -1.039112. Livestock & fish -1.0673 -0.468713. Energy -0.8274 0.207914. Food industry -0.3488 -0.878115. Textile, leather -1.2373 -0.234716. Non-metal ores -1.5458 1.551717. Metals, equipment -0.9528 -0.504018. Wood, rubber -1.3890 0.015019. Furniture -0.3899 -1.4419110. Other industrial products -0.3122 0.4405All -0.8933 -0.5214
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 19 / 25
Methods Competitivity of local production: sector and country integration
Interpretation
Strongly elastic import demands: Textile & leather, non-metal ores,metals & equipment, wood & rubber
Poorly elastic import demands: food industry, furniture, otherindustrial products
Almost all local products are complements, not substitutes
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 20 / 25
Methods Cost-efficiency analysis
Methods: Cost-efficiency analysis
Application to water desalinization vs. demand management
Table: Desalinization plant capacity, 2009
Country Capacity (m3/day) Capacity per head (m3/head/day)Saudi Arabia 7 410 460 0.28UAE 5 730 000 1.07Spain 2 500 000 0.053Qatar 1 197 150 0.613Libya 1 000 000 0.178Israel 800 000 0.105Bahrein 518 600 0.415Egypt 431 870 0.005Oman 377 480 0.122Jordan 239 530 0.036Algeria 200 000 0.005Tunisia 100 000 0.009Malta 93 000 0.227
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 21 / 25
Methods Cost-efficiency analysis
Table: Desalinization costs
Technology OI OI MSF MED(grey water) (sea water)
Capacity : 10,000 m3 / dayInvestment (euro/m3/day) 575 1427 3408 2023Marginal cost (euro/m3) 0.27 0.67 1.40 0.83
Capacity: 50,000 m3 / dayInvestment (euro/m3/day) 376 1050 2122 1539Marginal cost (euro/m3) 0.18 0.50 0.87 0.63
Capacity : 275,000 m3 / dayInvestment (euro/m3/day) 241 756 1286 1153Marginal cost (euro/m3) 0.11 0.36 0.53 0.48
Capacity : 500,000 m3 / dayInvestment (euro/m3/day) 206 676 1078 1042Marginal cost (euro/m3) 0.10 0.32 0.44 0.43
OI : Inverse Osmosis ; MSF : Multi-Stage Flash ;MED : Multiple-Effect Distillation
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 22 / 25
Methods Cost-efficiency analysis
Cost-efficiency analysis: example of Algeria
Emergency programme of seawater desalinization adopted in 2002
Objectif towards 2030: 2.2 million m3/day
Cost-efficiency analysis: comparison of two strategies
supply-side management: seawater desalinization (the current plan)demand-side management: promote a more efficient irrigationDuration: 20 years (discount rate 8 %)
Akli, S. and S. Bedrani, Cahiers du CREAD, 96, 2011.
Desalinization Irrigation
• Eight single-block stations • Irrigation (fruit trees), West MitidjaInverse osmosis • Yield drip irrigation: 35 %• Production 4.58 Mm3/year • Same reduction objective• Return 60 % Area considered 1852 ha• Cost: 68.34 DA/m3 • Saving: 8.59 DA/m3
(0.66 euro/m3) (0.08 euro/m3)
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 23 / 25
Concluding remarks
Concluding remarks
Point of view: agricultural and environmental economist, France.
Claim 1. Nexus cannot be explored from agriculture and industryalone: need to design integrated systems with cities
Claim 2. Different ways to look at the water-food-energy-ecosystemnexus
1) A stock of natural resources as a potential for future services2) A combination of ecosystem services from natural resources, biological
processes, and human activity3) A set of process innovations involving many sectors, to reduce waste
and losses.
Three levels of analysis requested by policy makers and addressed toeconomists:
1) Firm/plant scale: technology-driven prototypes (SME)2) Landscape: optimal allocation of water-using activities3) Global scale: water depletion and public policy (national, World)
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 24 / 25
Concluding remarks
Thank you for your attention
Alban Thomas ( Toulouse School of Economics (LERNA) Head of Agricultural Economics and Sociology Scientific Division (SAE2), INRA (French Institute for Agricultural Research))JRC, February 12-13, 2015, Ankara 12/02/2015 25 / 25