Why are the Uses Multiple By Peter Berck University of California, Berkeley

(c) 1998 by Peter Berck 1

Goals

 This is the story of shifting goals and the effect they have on multiple use planning.

 Planning history in PNW  Politics and Planning  Implications for planning  Stochastic  Mapping 2

Multiple Use is Unavoidable

 Water quantity insensitive to management  but quality can be affected by management  Recreationalists can’t be excluded  but can be encouraged with facilities  Wildlife lives there anyway  but clearcuts favor game; no cuts favor owls 3

Multiple Use: Which Use Shall Be Master

 American Politics drives multiple use management in the forests of the West.  There are three distinct political and management regimes: Pre, During, and Post Owl  Management tools adapt to the politics  Management plans adapt to the politics: Does the planner matter?

4

Postwar and Pre-Owl

 Political agreement on timber  Informal tools--discretion 5

Planning: Old Style

 Planner  professional forester  knowledge of resource  Owner  preferences over uses  supplies capital  Planning job  determine preferences  determine budget  find best plan among feasible plans  easily amenable to programming formulation, but there was no need to do so!

6

The Catch

 The catch was that there needed to be an owner. A close substitute would be wide consensus on the appropriate goals and a political willingness to let the planner determine the goals within that consensus.

 Before ~1970, management of the Forests was not so contentious. 7

Multiple Use Sustained Yield Act of 1960

 Multiple Uses  recreation  range  timber   watershed  wildlife  fish (later wilderness is added)  No one use is to predominate  “High level annual … output  without impairment of the productivity of the land” 8

Agency Freedom

 The USFS had ample latitude to operate forests as it wished under MUSY of 1960.

 The act codified what USFS was doing anyway.

 The Agency was trusted and political consensus was pretty high.

 This was easy because there were substantial areas untouched by cutting.

9

Old Stated Objectives

 Community Stability: JOBS  coincident with mill profits  Supply of Fiber (that’s wood)  Recreation  Game and Fish  Scenic Drives  Hiking  Went together:  More wood is more jobs is  more open forest  is more game 10

Wilderness Act (1964)

 FS had designated wilderness on its own and was now constrained by law on those areas.  Forced to study additional lands for inclusion.

 Large single purpose reserves went against the Multiple Use grain.  The Planner would not decide which lands to reserve 11

Politics and Formalized Planning

 Oddly played out through acts thought to innocuous or planning acts  National Environmental Policy Act  Endangered Species Act  Resource Planning Act 12

NEPA

 Before a major federal action can be taken, the agency must  Get public comment on issues to be considered  Make a plan (Environmental Impact Statement) and several alternative plans  Get public comment on the plans  Choose a preferred alternative  This was not thought to be radical legislation.

13

Resource Planning Act (‘74)

 Resource assessment at the National level  Targets for Regions and Forests  Plans to meet those targets  This act was a way for the FS to get long term agreement by Congress on goals and for the Industry to get a clear mandate to produce wood.

14

RPA Didn’t work

 Environmentalists wanted more wildland than the FS was planning for.

 Monongahela Decision: Resurrected language in 100 year old law that made it necessary to consider each tree before cutting.

 Clear need for new legislation 15

National Forest Management Act (1976)

 Political compromise  Non-declining flow  meant to preserve oldgrowth  would only delay cut out  CMAI  meant to put teeth into sustained yield  ecologically meaningless: trees still too small 16

Endangered Species Act

 Can’t take animal, even on private land  Take includes remove habitat  Must list habitat to be protected  Leads to legal question: when does regulation become confiscation of property?

 Current answer is when no economic use possible 17

Participation

 RPA  Interdisciplinary Teams (Regs. Restored supervisors power)  Public comment  Full written disclosure to public  ESA  Public right to sue to protect animals  Public could see and could sue 18

Formal Planning

 Under NFMA and RPA, formal planning for multiple use was carried out by linear programming.

 The basic idea was to maximize present value of timber, subject to CMAI, non declining flow, and other constraints.

 The Spotted Owl became the most celebrated constraint 19

SimpleForest Planning

 Type of Site, j  Many “birthdays”  h j (t,s)  t is calendar time  s is birthday of stand  h is acres harvested  D j (t-s) is volume per acre  v(t) is cut at t  v=  j  s D j ( t-s ) h j (  Max present value t, s )  of P times V  s.t. biology  v(t+1)  v(t)  t-s > CMAI or h = 0 20

Biology

 Initial Acres = Cut over all time  A j (s) =  t h j (t,s)  W is what is left standing  w j (s,t) = A j (s)  or =  s h j (t,s)  t  a h j (t,s) h j (a,t)  Cut acres regrow and are recut   s h j (t,s) =  a h j (a,t) This is Johnson and Scheurman, Model II.

21

More meaning to the model

 Types of sites, j  different species  site classes  critical locations  near streams  visual buffers  More Constraints  Don’t cut type j  Keep N% of forest at age, t-s, > 100  constraint on w  More treatments  commercial thin  pre-commercial thin 22

Traditional Problems with Planning

 Find the Cut  Plans were not spatial  Foresters still had to designate specific parcels to be cut  Hard to see cumulative effect of decisions because of mapping technology  The problem (Hrubes)  The cuttable land base was much smaller than the planned land base because of streams, Indian burial grounds, needed habitat, etc.

 Difference only discovered when “finding the cut” 23

Allowable Cut Effect

 To get nondeclining flow  cut oldgrowth now  plan to cut unprofitable trees later  When later comes, make new plan and don’t cut remote trees  Thus cut declines under non-declining constraint.

 Industry likes this. They get more wood  Environmentalists hate this. They see oldgrowth cut down sooner.

 It is an example of “no commitment” 24

Forest Plans Took Forever

 Not innocent: Old plans used while waiting.

 Once it was clear that the plans would call for less timber, industry and Republican administration did not want plans to be final  Environmentalists obliged by obstructing plans for their goals.

 (graphic on how much plans did to cut) 25

Spotted owl habitat Goshawk habitat Visual retention Partial Visual ret.

Bald eagle habitat Semi-primitive WILD AND SCENIC RIVER RECREATION AREA Minimal management Private Land Timber emphasis 26

Owl Lead-up

 FS released draft EIS on owl in August of 1986, 5% cut reduction  Final EIS April 1988, little less than 5% ASQ reduction  But, this wasn’t enough to comply with the law to protect the Owl, which wasn’t even yet officially “threatened” http://www.sweet-home.or.us/forest/owl/index.html

Injunction

 March, 1989. Order restraining the FS from offering 139 planned sales.

 Yaffee (Wisdom of the Spotted Owl) takes this as the pivotal action  There was a FS owl plan before this  Point at which the Owl became primary 28

Listing of the Owl

 June 1989, proposed listing of Owl as threatened in Fed. Register  June 1990 listed, but no critical habitat 29

Congress in the Act

 No stranger control  Non-sustainable ASQ as far back as Carter  1984 Bailout  Because of inflation, companies bid too much for timber; Congress released them from their contracts withou full penalties.  Hatfield-Adams  1989. Prescribed the sale for (fiscal)‘89-’90  9.6 billion bd ft  streamlined appeals- SEIS not subject to judicial  no temp restrain or prelim injunct on fisc ‘90 timber sales  deadlines for judicial review; special masters 30

Interagency Scientific Committee

 Future Chief Thomas, a biologist and others  April 4 1990  Reduce harvest levels in owl area by 30-40% 31

Listing of Habitat May ‘91

 Fish and Wildlife complies with ESA (finally)  Takes ISC report and enshrines it in law  critical habitat 11.6 million acres  of which 3 million were private  Small administration counterattack  1992 G_d Squad exempts small number of sales for BLM 32

FEMAT: Option 9

 “ecosystem management plan,” holistic, adaptive  Option 9 is response to summit in april ‘93  Timber: year 1, 2 b bdf; then 1.7 b bdf then decline to near 1 billion in the long run so it averaged to 1.2 b bdf over 10 years.

 About 90% reduction from the all time highs  adaptive management  local communities and agencies  still protects owls 33

Presidents Forest Plan

 Is Option 9  Less timber  More attention to “ecosystem”  Replaces the planner: Jack Ward Thomas  and now Mike Dombeck  Replacing the planning process: No more Forplan  Maybe no role for programming  GIS is in.

34

Congress Sets Cut Directly (again)

 Salvage Ride (good for two years)r  Response to destructive fires  Response to declining cut  Under the logging provision, the U.S. Forest Service is directed to double the cutting of dead and dying trees in national forests over the next 18 months. The agency would be virtually unhindered by the Endangered Species Act and other laws protecting wildlife, and timber sales would be exempt from court challenge. (Bee, JULY 27, 1995) 35

Murrelets

 The marbled murrelet was listed as threatened on October 1, 1992  It nests in older redwood trees.

 Various species of trout and salmon are also listed as endangered.

 Endangered species also live on private land.

36

The Murrelet lives in the valuable timber. ESA prohibits cutting. A Deal for Headwaters in the the works.

Map Copyright © 1998 California Resources Agency. All rights reserved.

Headwaters Deal

 US and State to buy Headwaters for $250 m (fed) + $130 m (state)  Agree to Habitat Conservation Plan for rest of PL’s holdings.

 Does the HCP enable of hinder PL?

 Headwaters sold for less than market  Environmentalist complaint about Salmon habitat continues 38

Stakeholder Processes

 Get the interested parties into room  Bargaining in shadow of the law  ESA  Political power  Clausowitz: War is the continuation of politics by other means  Republicans and Environmentalists ascendant at same time 39

Quincy Library Group

 Locals (Jobs/Timber/Fire) try to get Congress to accept their view over  National Conservation Organizations (Animals/Oldgrowth)  in planning for N. Sierra Forests  Big Issue is condition: Locals want thinning to reduce fire risk  Is an “adaptive management” experiment 40

Making Sense of the Record

 Explain the outcome with Political Economy  Find the implications for Planning 41

New Emphasis on Stock

 Agency and Administration  Protect Wildlife per se (stock): owls and Fish  Fire (stock): reduce hazard for wood and for communities  Create “healthy,” “natural,” or “diverse” forest (stock)  get back to pre-european conditions 42

Counterpoint

 Republican and Congressional  JOBS (flow)  Timber (flow)  But, Jobs makes much better politics than timber.

43

JOBS

 Does timber produce Jobs?

 Jobs per bd ft, but can only cut once  Not constant: factor demand (Sullivan)  Not constant over time: technical progress  Indirect jobs?

 IMPLAN I/O work says yes  Stewart says little: transfer payments as basic  Regression says no 44

An Economist makes Sense of Politics

 A Game  Median Voter  Money and Votes  Political Business Cycle 45

Environmentalists and Timber Beast

 Timber Beast R = argmax R V(T(R) ,r+E)  where R is rotation age; T is timber quantity  r is interest rate and E is chance of expropriation by regulation  Environmentalist lobbies for reservations of timber,  E = argmax U(E,R, c(E))  where c(E) is the cost of achieving E 46

continued

 In this framework there are two reasons for the environmentalist to exercise restraint in his lobbying: cost and the adverse effect on current management (R).  Chris Costello, unpublished 1998.

47

Median Voter (Shalit)

 Assuming that preferences are single peaked in commodity/amenity space and that voters decide the allocation between these uses, Shalit (unpublished 1976) uses the median voter theorem to find the actual allocation. He notes that the outcome is not pareto optimal and shows how side payments can be used to achieve a PO.

48

Money and Votes (Peltzman, 1976)

 Timber Beast wants gov’t to cut timber. Cutting unpopular with voters.  Timber Beast donates money for political campaigns.  Voters respond to campaigns  Beast gives politicians enough money to overcome voter dislike of cutting.

49

Political Business Cycle (Ken Rogoff)

 Presidents last only four years till re-election. That gives them a short time horizon and induces a political business cycle. Take the downturn at the beginning of term so that the recovery will be well underway by re election. The Senator from Washington (and Idaho) needs rural votes. Shift timber harvest to the present to buy votes even if there is a severe restructuring later President’s plan starts high and ends low. 50

Simple Stochastic View

 Goals for timber, owls  T G , O G  V = min E  t (T- T  r is interest rate G ) 2 + b(O - O G ) 2 (1+r)  b is price of owls relative to timber  s.t. biology, other constraints as before  Clinton’s selection of Option 9:  T G = 1.2 billion, long run; O G high.

-t  Recent History: a shift in the goals 51

Two period Stochastic

 V = min .5 {(T 1 -T G ) 2  s.t. O  O G 2 2 = (O = O G 1 + 1 n + E b(O 2 - T 1 ) d + e - O G 2 ) 2 (1+r) 1 }

T

1 *  1  1 

r r

 d 2

b

  1 d

b



r



O

1 d 

O

1

G

 

T

1

G

 

V

 1 2 

T

1 * 

T G

 2 

b

1 

r

 d 

O

1 

T

1 *  

O

1

G

 2 

b

1 

r Var

( e  n ) 52

Stochastic Lessons

 The randomness in goals contributes to an increase in regret in the same way that randomness in the biological processes do.

 With a vacillation in goals from 1 to 10 billion bd ft., goal uncertainty could be more costly than biological uncertainty.

53

Stochastic: The “simple”

 Fire stochastically assigns acres to new “birthdays” ( Johnson et al in SNEP ) without harvest  Trees don’t always go to the one year older age class  Relation between Owls O and Habitat W has random element and unknown parameter 54

Stochastic: The Horror

 The Goals GO and GT change with the political winds.

 In a linear quadratic system, the control variables would be linear in the targets GO and GT and the state variables. Thus the control variables would exhibit the same sort of random fluctuation as the targets.

55

Implications for Planning

 The planning exercise will be done and redone. No commitment to carrying out the plans. Shift (for a while) with the political winds.

 old-growth dependent species on a one way trip. Once the habitat goes, no later plan can bring it back. They will lose out a piece at a time.

 The Planner will look like a fool.

56

Forward and Backward

 The Biologist rules the roost. They will plan with mapping tools to get the forest condition that they want.  They will do it “by eye”  An optimization will be after the fact and only on lands they do not consider important.

 GIS will drive LP and not vice versa.

57