Linking Biophysical and Economic Models for Environmental Policy Analysis

Transcription

1 Linking Biophysical and Economic Models for Environmental Policy Analysis Catherine L. Kling Iowa State University March, 2011 Michigan State University This talk draws heavily from the work of Sergey Rabotyagov, Philip Gassman, Manoj Jha, Todd Campbell, Silvia Secchi, Luba Kurkalova, Keith Schilling, Adriana Valcu and Calvin Wolters, Jinhau Zhao, among others

2 Charge from the Boss (JZ) how land use models have been linked with SWAT, CENTURY, etc., challenges in the linkage how the linked models have helped CARD in promoting its prominence in policy circles, in obtaining grants, in publishing papers, etc., how the model has become connectors that have attracted teams in other disciplines both at ISU and beyond, and whatever else you might want to talk 2

3 Overview of Talk Overview land use decision models and integrated modeling Policy Analysis using integrated modeling Econometric Approaches vs. Simple Budgets and profit maximization assumption Examples Econometric land use model integrated with EPIC using NRI data --- State of Iowa Simple budget based land use model integrated with SWAT at large scale --- Upper Mississippi River Basin Simple budget based land use model integrated with SWAT using field scale data --- Boone River Watershed Future model development/projects 3

4 Using Linked Models to inform Policy Using linked biophysical and economic models to run scenarios Posit changes in prices or policies (taxes, cap and trade, etc.) Predict changes in land use (shift crop choices, implement conservation practices, retire land from production, plant biofuel crops) Evaluate environmental effects of a changed land use configuration (C sequestration, water quality, erosions etc.) Report costs and environmental consequences 4

5 Economic Models Econometric models of land use change Statistically describe probability of adopting a given land use on a given parcel, conditional on other attributes Examples: reduced tillage, crop rotation, CRP, etc. Profit Maximization assumption combined with simple budgets Assume land use that maximizes simple profits is chosen (may miss other drivers of decisions) Can be location specific 5

6 Example of Econometric Estimation Green Subsidies in Agriculture: Estimating the Adoption Costs of Conservation Tillage from Observed Behavior, Canadian Journal of Agricultural Economics 54(2006): (L. Kurkalova, C. Kling and J. Zhao). What does it take for farmers to adopt conservation tillage practices? Profit loss from switching Reluctance due to uncertainty, risk Economic Model posits Adopt if compensated for profit loss and extra stuff Looks at actual adoption decisions to fit parameters

7 Model of conservation tillage adoption Pr Pr adopt 1 0 P Pr x 0 profit DATA: Random sub-sample (1,339 observations) of Iowa 1992 NRI data (soil and tillage) supplemented with Census of Ag. (farmer characteristics) and climate data of NCDA 63% of farmers already use conservation till

8 Results (standard errors in parenthesis) Net returns to conservation tillage 41 I SLOPE 0.63 PM 73 AWC 2.57 TMAX 1 corn (11) (0.012) (0.31) (29) (0.68) 2.48 TMIN 76 PRECIP 194 TENANT (0.72) (69) (92) Premium (corn producers) P corn OFFFARM precip (411) (0.11) (47) TENANT 5.1 AGE 763 MALE (274) (1.8) (302)

9 Results Average required subsidy and decomposition for current non-adopters Average/Current non-adopters Profit loss Premium Subsidy Corn ($/acre)

10 Policy Simulations Data: 13,000 NRI points located in Iowa EPIC: Run on each NRI point for different levels reduced till Use land use model to predict adoption under various subsidy levels Evaluate environmental targeting

11 How many conservation services can Iowa provide? Baseline Soil loss due to erosion, tons/ac/year

12 How many conservation services can Iowa provide? Green payments of $3.25/ac

13 How many conservation services can Iowa provide? Green payments of $10.4/ac

14 Practice versus Performance targeting Getting most conservation tillage for a given budget = rank producers by adoption subsidy ($/acre) from low to high, offer payments to those at the top of the list until the budget is exhausted Getting most CARBON = rank producers by carbon sequestered/($/acre) and select until budget exhausted

15 Alternative targeting 220 target cons. tillage Carbon, 799,029 tons/year Erosion reduction, 45,984,271 tons/year N runoff reduction, 6,136,973 tons/year Area in CT, 3,181,800 acres Budget = $5.7 M 220 target carbon Budget = $5.7 M

16 Upper Mississippi River Basin Model The Water Quality Effects of Corn Expansion in the Midwest, Ecological Applications special issue (2011): forthcoming (with Silvia Secchi, Philip Gassman, Manoj Jha, and Luba Kurkalova). Least Cost Control of Agricultural Nutrient Contributions to the Gulf of Mexico Hypoxic Zone," Ecological Applications 20 (2010): (with Sergey Rabotyagov, T Campbell, M Jha, P Gassman, S Secchi, L Kurkalova, H Feng and J Arnold). SWAT AND EPIC based NRI unit of Analysis, moving to GIS, cropland data layers Largely budget based land use models 16

17 UMRB 189,000 square miles in seven states, dominated by agriculture: 67% of total area, > 1200 stream segments and lakes on EPAs impaired waters list, 17

18 Gulf Hypoxia Over 400 hypoxic areas worldwide, combined affected area of 245,000 km2 (Diaz and Rosenberg, 2008) Naturally occurring, but significantly enhanced by anthropogenic sources of nutrients 43% N and 41 % P reaching hypoxic zone originates from UMRB (USGS) Gulf of Mexico effects still poorly documented, brown shrimp fishery effects, recreational fishing 18

19 UMRB Watershed Model Sub Basin 20 NRI points Average size NRI point (Acres) HRU Count , , , , , , , , , ,

20 Watershed Schematic Grafton

21 Policy Question: The Effect of Corn Expansion on Water Quality The Water Quality Effects of Corn Expansion in the Midwest, Ecological Applications special issue (2011):forthcoming (with Silvia Secchi, Philip Gassman, Manoj Jha, and Luba Kurkalova). Motivated by first generation biofuels concerns Used simple budget analysis NRI unit of analysis SWAT to predict water quality 22

22 Corn Price Scenarios: UMRB Scenario Baseline Corn price bushel Soybean price bushel Alfalfa price ton 23 Soybean/ corn price ratio Corn area 119, , , , , , ,118

23 Corn Acreage: Baseline vs. Scenario 6 24

24 Nitrate Load: Baseline vs. Scenario 6 25

25 Water Quality at UMRB Exit Avg Flow Out Avg Sediment Out (,000 tons) N Out (,000 kgs) Avg Organic P Out (,000 kgs) Avg Mineral P Out (,000 kgs) Baseline 3,480 24, ,030 13,044 12, ,485 26, ,187 13,873 11, ,474 26, ,063 13,721 12, ,462 25, ,705 13,769 12, ,447 25, ,675 13,715 12, ,427 24, ,441 13,611 13, ,416 24, ,750 13,786 14,380 26

26 Boone River Watershed Iowa ~586,000 acres tile drained, 90% corn and soybeans 128 CAFOs (~480,000 head swine)

27 Natural Environment: Boone Some of the highest N loads in Iowa TNC priority area biodiversity Iowa DNR Protected Water Area

28 Common Land Unit Boundaries 16,430 distinct CLUs Detailed data related to: land use, farming practices, production costs, slope, soils, CSRs, etc. Weather station data

29 Least Cost Problem What is the optimal placement of conservation practices? Brute force strategy: Using water quality/hydrology model, analyze all the feasible scenarios, picking cost-efficient solutions But, if there are N abatement possibilities for each field and there are F fields, this implies a total of possible NF configurations to compare 30 fields, 2 options over 1 billion possible scenarios!

30 Strength Pareto Evolutionary Algorithm Search technique to approximate pareto optimal frontier (Zitzler, Laumanns, and Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm, TIK-Report 103, May 2001, Errata added September, 2001) Integrate Evolutionary Algorithm with water quality model Search for a frontier of cost-efficient nutrient pollution reductions

31 The Land use/abatement Set For each CLU Current practice Land retirement No tillage Reduced fertilizer (20%) Cover crops Sensible combinations

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33 Gains from Optimal Placement Practice Allocation (%) Cost ($1000 dollars) % N % P NT NT, RF CC, RF CC NT RF Other Cover Crops, Red. Fert 15, Same N reductions 2, <1 <1 3 Same Cost 15, <1 64 5

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35 Per acre average costs of abatement actions needed to achieve equal percent reductions in N and P

36 Funding/Projects Generated by Model Development 1. Three US EPA Targeted Watershed Grants: Market Feasibility Assessments for Water Quality Trading and Reverse Auctions , $200,000 each Walnut Creek Watershed, Boone River Watershed, and Raccoon River Watershed Evolutionary algorithm being used to find the most cost effective set of options 37

37 2. National Science Foundation Northern Gulf of Mexico Hypoxia and Land Use in the Watershed: Feedback and Scale Interactions, , $650,000 for ISU, total of $1.3 million Nancy Rabalais, Gene Turner, Sergey Rabotyagov, Monika Moskal, Jeff Arnold, Raghavan Srinivasan, Manoj Jha, Phil Gassman Link UMRB, Ohio-Tennessee and route to Gulf model! 38

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39 3. USDA CAP US Department of Agriculture, Climate and Corn-Based Cropping Systems Coordinate Agriculture Program, National Institute of Food and Agriculture (co-pi), , $640,000. Our Role, modeling What is the least cost placement of cover crops and drainage management to achieve nutrient reduction goals in individual watersheds in the UMRB and in the entire watershed? How quickly does the cost rise as the nutrient reduction goals increase? What are the GHG effects of the cover crop and drainage management strategies? How does the optimal placement and cost change when crop prices increase? How do they change if/when there is a substantive market for corn stover? 40

40 4. USDA Economic Research Service USDA, Economic Research Service, The Supply of Greenhouse Gas Offsets from Agriculture and their Water Quality Effects in the Upper Mississippi River Basin, , $100,000. USDA, Economic Research Service, Evaluating the Integrity of Agricultural GHG Offsets: The Costs and Consequences of Alternative Baselines and Program Options, , $120,000. Both related to design and evaluation of GHG Offset markets 41