models of human-environment interaction lecture-11 2015-07-01 · overexploitation of marine...
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Integrated Models of Human-Environment Interaction:Multi-agent, network and spatial models
Jürgen ScheffranCliSAP Research Group Climate Change and Security
Institute of Geography, Universität Hamburg
Models of Human-Environment InteractionLecture 11, July 1, 2015
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Fishery
Technology
Policy
Fishery management in human-environment interaction
Human andSocial Systems
NaturalSystems
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Overexploitation of marine resources and fishery conflicts
70% of fish stocks worldwide heavily overexploited
Some of them collapsed or to be collapsed, e.g. NorthwestAtlantic or North Sea cod
Low quality of management strategies
High levels of subsidies
Collective-action problem in common pool resource (Tragedy of the commons)
Conflicts on scarce fish stocks
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Ecological and economic viability condition
Economic viability condition V = p h − C >= 0 withPrice p = a − b hHarvest h = g x C Source: Scheffran 2000
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Fishery and socio-ecological interaction
g CEffectiveefforts
hHarvest
TechnologyEnvironment
Policy
r (K – x)Fish productivity
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Compatibility of economic and ecological viability
•200 •400 •600 •800 •1000
•-200
•-100
•100
•200
Efforts C
Fish stock x
Ecological viabilityEconomic viability
V<0
V>0
V<0
Source: Bendor/Scheffran 2009
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Viability domains for various parameters
200 400 600 800 1000
-200
-100
100
200
200 400 600 800 1000
-200
-100
100
200
200 400 600 800 1000
-80-60-40-20
204060
200 400 600 800 1000
-200
200
400
600
800
1000
•a=1;=0.001; K=1000; b=1/2000; r=1/K
•a=1;=0.004; K=1000; b=1/2000; r=0.2/K•a=1;=0.004; K=1000; b=1/2000; r=1/K
•a=0.5;=0.001; K=1000; b=1/2000; r=0.2/K
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Multi-player competition for natural resources
Source: Scheffran 2000
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Sustainable competition among multiple actors
Source: Scheffran 2000
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Baseline parameter set
0.2
Source: Scheffran 2000
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Competitive fishery case6 fishing companies, 2 fish species
Source: Scheffran 2000
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Cooperative fishery case
Source: Scheffran 2000
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Co-management of fisheries
ScientificInstitution Fishery
CouncilFishing Firms
(group 1)
Fish Stock
other species
Fishing Firms(group 2)
ManagementAuthority
species interactionestimates, catch dataharvestrecommendationquota
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Watersecurity
Technology
Policy
Water management in human-environment interaction
Human andSocial Systems
NaturalSystems
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Multidimensional conflict constellation:
• increased upstream water consumption limits water availability downstream
• increased industrialization in urbanregions leads to higher water demandwhich increases scarcity for rural(agricultural) water use
• internal conflicts within the rural andurban populations concerningallocation issues
Additional threat to agricultural production in Nile Delta region from sealevel rise in the Eastern Mediterranean
The Nile River water conflict
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Systemic overview of the Nile water conflict
industrial use
agricultural use:irrigation
humanconsumption
agricultural use:irrigation
humanconsumption
industrial use
humanconsumption
water availability upstream
outflow to the ocean
water availability downstream
industrialproduction
agriculturalproduction: yield
agriculturalproduction: yield
industrialproduction
industriallabor
agriculturallabor
agriculturallabor
industriallabor
populationgrowth
migration
climate change
floods
precipitationpatterns
waterpollution
landdegradation
landerosion
sea level rise
salinization
humanwellbeing
humanwellbeing
humanwellbeing
upstream
downstream
ruralpopulation
urbanpopulation
land availability upstream
land availability downstream
conflict between geographic regions
internalconflict
internalconflict
conf
lict b
etw
een
rura
l and
urb
an p
opul
atio
n
negative feedback positive feedback neutral or ambivalent feedback
Source: Scheffran/Link/Schilling 2012
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• Countries attempt to expand their water use through investments
• For each country, the total change in water availability is
• To reach this goal, countries have to make investments. The target investments are
• Each country seeks to adjust its actual investments based on the target investment by
/c hH h C C c
, , ,
1
n
i k i c k i ik
H p h C
,* *
,
c ij ji i
i j c ii
h CC H
h
*, , ,i t i i t i tC C C
Basic features of the simulation model
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Countries invest into water availability to satisfy increased water demand
Change in water use and supply depends on total investments and on unit costs and fractions (priorities) of investments allocated to action paths:
1. National water consumption without exceeding actual water supply. 2. Increase national water supply.3. Invest into and collaborate on water supply in an upstream country to
benefit from increased external supply.4. Threaten or pressure an upstream country not to reduce transboundary
water supply or resist to threats by a downstream country.
Scenarios of climate change (20 year period):• Baseline scenario without climate change • Reduced water availability by 20%• Increased water availability by 20%
Action paths and scenarios applied in the simulation model
Source: Scheffran/Link/Schilling 2012
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Interactions between countries in the Nile river basin
Source: Scheffran/Link/Schilling 2012
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Interactions between countries in the simulation model
Source: P. Michael Link
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Symbol line: baseline; dashed line: reduced water; solid line: increased water
Investments into the expansion of water resources
Source: Scheffran/Link/Schilling 2012
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Symbol line: baseline; dashed line: reduced water; solid line: increased water
Development of water supply
Source: Scheffran/Link/Schilling 2012
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Symbol line: baseline; dashed line: reduced water; solid line: increased water
Development of water consumption
Source: Scheffran/Link/Schilling 2012
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• Distribution of Nile River water has been a contestedissue in the past, shaped by political pressure fromdownstream countries
• Upstream countries increasingly seek to develop and usetheir water potential – risk of conflict increases
• Cooperation of all users in the Nile River Basin isessential for cost-efficient water use and conflictprevention
• Cooperation is still preferred choice of countries• Climate change not only affects supply and demand
but also viability of water development projects
Main conclusions from the Nile model simulations
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Bio-energyFood
Technology
Policy
Food and bioenergy in human-environment interaction
Human andSocial Systems
NaturalSystems
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Spatial-agent model of bioenergy and land use(Scheffan/Bendor 2009)
Spatial modeling: Heterogeneous environmental factors create a non-uniform environment for growing and cultivating crops.
Agent networks: Farmers are responsive to market signals that depend on ability and willingness of other farmers to plant and cultivate biofuel crops.
Multi-agent modeling techniques can explain how actors can adapt to system constraints through learning and negotiation processes.
Local parameterization by GIS data to create a matrix of spatially relevant system dynamics models
Cells act as individual agents and can gain information and material from neighboring cells
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Agents in a spatial modeling environment
GIS Data Layers
Illinois Boundary Map
MM
MM
M MM
M
MM M M
MM
M
MM M M M
GISMaps
SME
M = Models placedin each cell
GIS Data Layers
Illinois Boundary Map
MM
MM
M MM
M
MM M M
MM
M
MM M M M
GISMaps
SME
M = Models placedin each cell
Scheffran/Bendor/Wang/Hannon, A Spatial-Dynamic Model of Renewable Energy Crop Introduction in Illinois, September 2007.
Spatial Boundary Map
Agent models in each cell
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The spatial farmer agent model
Farmer agents in spatial landscape, harvesting crops in cells and selling crops on a common market (no direct interaction among cells)
System dynamics model for each cell, including Crop YieldHarvestingCrop market pricePriority of land for several crop typesInvestment costs for cultivation and harvest Profit Land use dynamics changing crop mix
“Evolutionary game” among competing crops: farmer agents iteratively shift crop priority towards growing profits
Changing distribution of crops in time, depending on adaptation
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Multi-crop multi-agent model
Farmer1
Corn
Soybean
Switchgrass
MiscanthusMarket
Farmer2
Corn
Soybean
Switchgrass
Miscanthus
Physical, economicand political factors
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Miscanthus sector of the bioenergy model
Source: Bendor/Scheffran 2009
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Farmer profits and priorities
hik Harvest of each crop k
pk market price of crop k
Ci invested cost to cultivate crops at per hectare unit cost cik
Si = sk hik : “political” revenue from biofuels subsidies and carbon credits both
of which are assumed to be proportionate to harvest (sk US$ per ton subsidy)
Individual farmer i net profit function (revenue minus cost):
Decision rule: Dynamics of shifting farmer decisions about crop mix given by fraction ri
k of land assigned to crop k (priority) , depending on value gradient:
Source: Bendor/Scheffran 2009
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Spatial extension of ABM model
Equations from system dynamics model into Berkeley Madonna
Model simulates behavior of individual farmer agents and is arrayed in a 37 x 65 grid, with 1568 active grid cells
Scenarios over 50 years: increasing demands ($100 mio./year), policy change, subsidy/carbon credits $25 per ton biomass
Python scripts are used to give every cell a unique identification number within standard, non-spatial database.
Model outputs processed through Python scripts to GIS maps
ArcGIS for representation
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Data collection and processing
Geographically referenced agricultural data for the State of Illinois
Unit of analysis corresponding to the size of one township (6 x 6 mile)
Generate land use map for Illinois using USDA National Agricultural Statistics Service (NASS) Cropland Data Layer with satellite imagery
Layer aggregated to 13 standardized categories of land cover
Classification decisions based on extensive field observations collected during annual NASS June Agricultural Survey.
Aggregate from NASS map (30x30 meters) to county-size (6x6 miles), using ESRI ArcToolbox GIS software
Soybean and corn production data from Illinois Crop Yields Historical NASS Database (1972-2004) with five year yield average (1997-2001)
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Data collection and processing
Sparse data on switchgrass and miscanthus growth and harvesting costs
Potential miscanthus and switchgrass yield estimates, based on soil quality, climate, and other environmental conditions in Dhungana 2007 and Khanna et al. 2005 (2 km x 2 km resolution)
Miscanthus and switchgrass unit cost estimate by John and Watson (2007)
Switchgrass similar geographic production, but 26% yield of miscanthus; data in western Illinois incomplete
Production corn and soybeans: Crop Yields Historical NASS Database (2007)
Average value of production=amount sold multiplied by average price: average $2.604 bill. Soybeans, $4.072 bill. corn per year (2000-2006)
Harvest cost data for corn and soybeans from Illinois Farm Business Farm Management Association through the University of Illinois Farm Decision Outreach Council (FARMDOC 2007)
Cost factors: direct (fertilizer, pesticides, seed, storage, drying, crop insurance)power (machine use/lease/depreciation, utilities, fuel), and overhead (labor, building repair/rent/depreciation, insurance) costs.
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Yield of maize, soy and and miscanthus in Illinois
Madhu Khanna, Basant Dhungana, etal.2007
Based on MISCANMOD model
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Spatial priorities of corn and soybeans with energy crops (t = 50 years)
Corn priority Soybean priority
Source: Bendor/Scheffran 2009
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Simulation of spatial priorities and profits of miscanthus in Illinois
Miscanthus priority
Source: Bendor/Scheffran 2009
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Dynamics of harvest & prices of food and energy crops
Source: Bendor/Scheffran 2009
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Bioenergy infrastructure: Transport, biorefineries, consumption
Refinery PlanningSite Selection based on:• Resources (e.g., water) • Farms• Transp. networks• Storage (optional)
Biorefinery
Objective: Integrate feedstocks, bioprocessing plants and consumer demands into a regional economic model• Logistics optimization• Lifecycle assessment• Emission reductions
Transp. networks (e.g., rail, highway)
Customers (e.g., gasstation)
Harvest
Feedstockfarm
Storage
Transport…
Feedstock Shipping• Transp. mode• Fleet design (e.g., vehicle capacity)
• Dispatch route & schedule• Handling at farm/storage
Fuel Distribution• Transp. mode & fleet• Dispatch route & schedule• Price and demand
uncertainty
Cost Analysis• Transp. • Facility• Inventory• Environmental externalities
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p. 402022
Biomass production and biorefinery localisation in Illinois
Corn
Perennial grass
Kang, Önal, Ouyang, Scheffran, Tursun 2010
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Crop production for Jiangsu province in China
TitelUntertitel Year
20112012
20132014
20152016
20172018
20192020
20212022
20232024
20252026
20272028
20292030
Biomass output (103t)
0
5000
10000
15000
20000
25000
30000Shadow price (CNY/t)
100
150
200
250
300
350
400Wheat Oilseed rape Medium-indiea-rice Non-glutinous-rice Beans Corn Cotton Giantreed on arableland Giant reed on mudflat Biomass shadow price
Crop yield (wheat) Cultivation cost
Source: Kesheng Shu 2014
Cultivation area