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Industry, employment, economics and trade theory with focus on the forest sector

Industry, employment, economics and trade theory with focus on the forest sector. Peter Lohmander www.Lohmander.com. Industry Employment Economics Trade theory The forest sector.

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Industry, employment, economics and trade theory with focus on the forest sector

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  1. Industry, employment, economics and trade theorywith focus on the forest sector Peter Lohmander www.Lohmander.com

  2. Industry Employment Economics Trade theory The forest sector

  3. The optimal combination of forest industry investments, forest industry production and forest production may be determined.

  4. The employment is strongly dependent on the activities in the forest sector. Such dependences may be expressed via constraints in a forest sector model.

  5. The economic result of all activities in the forest sector may be optimized.

  6. Trade theory Is one way to investigate how the activitites in different countries are linked and influence each other.

  7. Trade theory As presented by: Colin Danby. http://faculty.uwb.edu/danby/bls324/trade/tradintr.html

  8. The first purpose of trade theory is to explain observed trade.  That is, we would like to be able to start with information about the characteristics of trading countries, and from those characteristics deduce what they actually trade, and be right. That’s why we have a variety of models that postulate different kinds of characteristics as the reasons for trade. Colin Danby. http://faculty.uwb.edu/danby/bls324/trade/tradintr.html

  9. Secondly, it would be nice to know about the effects of trade on the domestic economy.

  10. A third purpose is to evaluate different kinds of policy.  Here it is good to remember that most trade theory is based on neoclassical microeconomics, which assumes a world of atomistic individual consumers and firms.  The consumers pursue happiness (“maximizing utility”) and the firms maximize profits, with the usual assumptions of perfect information, perfect competition, and so on.  In this world choice is good, and restrictions on the choices of consumers or firms always reduce their abilities to optimize.  This is essentially why this theory tends to favor freer trade.

  11. Here you may study trade theory and the international forest sector:NA0061 The Forest Sector from an International View, 7.5 ECTSSkogssektorn i ett internationellt perspektivhttp://www.slu.se/?id=371&Kurskod=NA0061&engelska=true&

  12. Industry, employment, economics, trade and the forest sector Can we optimize all of these things?

  13. Observation: The raw material stock has been increasing for a very long time. Central Question: What is the optimal stock level when we consider the total present value of the forest sector, employment and the environment ?

  14. Already in 1981 • World Bank Model” to study the Swedish forest sector. (Nilsson, S.) • In the model, timber, pulp wood and fuel wood could be produced and harvested in all regions. • The energy industry was considered as an option in all regions. It was possible too burn wood, not only fuel wood but also “pulp wood”.

  15. Capacity investments • The existing capacity in the saw mills, pulp mills and paper mills was investigated and used in the model. It was possible to invest in more capacity of different kinds in the different regions.

  16. Structure in 1981 • The forest sector of Sweden was modelled as a linear programming problem. • The total economic result of all activities in the forest sector of Sweden was maximized. • The wood based part of the energy sector was considered as a part of this forest sector.

  17. The same method applied to a smaller problem of the same type: • Såg och Massabolaget: • Ett praktikfall i Skogsindustriell Ekonomi • http://www.lohmander.com/SkogIndEk1/SI1.html • Peter Lohmander 2003-12-11 • www.Lohmander.com

  18. ! SMB2; ! Peter Lohmander 2003-10-15; Max = TProf; TProf = - InkK - IntKostn + ForsI; InkK = PKTi*KTimmer + PKMav*KMav + PKFlis*KFlis + PReturpL*KReturpl + PReturpI*KReturpI; IntKostn = AvvK*Avv + TPKostTI*ETimmer + TPKostMA*EMav + CSV*ProdSV + CLiner*ProdLin; ForsI = PSV*ProdSV + PLiner*ProdLin + PSTi*STimmer + PSMav*SMav + PSFlis*SFlis; !Marknadspriser för råvaror samt ev. råvarurestriktioner -------------------------------------------------------; PKTi = 380; PSTi = 330; PKMav = 200; PSMav = 120; PKFlis = 250; PSFlis = 150; PReturpL = 50; PReturpI = 730; [LRetP] KReturpL <= 100;

  19. !SMBs egen skog och avverkning -----------------------------; AvvK = 70; AvvKap = 570; TimAndel = .5; [KapAvv] Avv <= AvvKap; !SMAs egen virkestransport -------------------------; TPKostTI = 60; TPKostMa = 70; !SMBs eget sågverk -----------------; PSV = 1500; CSV = 300; SVKap = 80; TTimmer = ETimmer + KTimmer; ProdSV = .5*TTimmer; ProdFl = .8*ProdSV; ProdSp = .2*ProdSV; [KapSV] ProdSV <= SVKap;

  20. !SMBs råvarubalanser gällande egna producerade råvaror och halvfabrikat -----------------------------------------------------------------; EMav = (1-TimAndel)* Avv - SMav; ETimmer = Timandel*Avv - STimmer; EFlis = ProdFl - SFlis; !SMBs egen linerfabrik ---------------------; PLiner = 4900; CLiner = 1200; LinerKap = 400; TRetP = KReturpL + KReturpI; TFiber = EMav + EFlis + KMav + KFlis; ProdLin = .25*TFiber + .95*TRetP; [FFiberK] TFiber/TRetP >= 4; [KapLiner] ProdLin <= LinerKap; end

  21. Local optimal solution found at step: 10 Objective value: 1373354. Variable Value Reduced Cost TPROF 1373354. 0.0000000 INKK 236846.2 0.0000000 INTKOSTN 563850.0 0.0000000 FORSI 2174050. 0.0000000 PKTI 380.0000 0.0000000 KTIMMER 160.0000 0.0000000 PKMAV 200.0000 0.0000000 KMAV 471.5128 0.0000000 PKFLIS 250.0000 0.0000000 KFLIS 0.0000000 50.00000 PRETURPL 50.00000 0.0000000 KRETURPL 100.0000 0.0000000 PRETURPI 730.0000 0.0000000 KRETURPI 105.1282 0.0000000 AVVK 70.00000 0.0000000 AVV 570.0000 0.0000000

  22. TPKOSTTI 60.00000 0.0000000 ETIMMER 0.0000000 10.00000 TPKOSTMA 70.00000 0.0000000 EMAV 285.0000 0.0000000 CSV 300.0000 0.0000000 PRODSV 80.00000 0.0000000 CLINER 1200.000 0.0000000 PRODLIN 400.0000 0.0000000 PSV 1500.000 0.0000000 PLINER 4900.000 0.0000000 PSTI 330.0000 0.0000000 STIMMER 285.0000 0.0000000 PSMAV 120.0000 0.0000000 SMAV 0.0000000 10.00000 PSFLIS 150.0000 0.0000000 SFLIS 0.0000000 50.00000 AVVKAP 570.0000 0.0000000 TIMANDEL 0.5000000 0.0000000 SVKAP 80.00000 0.0000000

  23. TTIMMER 160.0000 0.0000000 PRODFL 64.00000 0.0000000 PRODSP 16.00000 0.0000000 EFLIS 64.00000 0.0000000 LINERKAP 400.0000 0.0000000 TRETP 205.1282 0.0000000 TFIBER 820.5128 0.0000000

  24. Row Slack or SurplusDual Price LRETP 0.0000000 680.0000 KAPAVV 0.0000000 160.0000 KAPSV 0.0000000 600.0000 FFIBERK 0.6043397E-09 -788.9547 KAPLINER 0.0000000 2915.385

  25. Wood for energy in 1981 • Among these results, it was found that a large proportion of the “pulpwood” should be used to produce energy. • This was particularly the case in the north, at large distances from the coast.

  26. Surprise? Not really! • The cost of transporting pulpwood large distances is very high. • If energy can be produced from pulpwood, far away from the coast and the pulp industry, it is not surprising that this may be the most profitable alternative.

  27. Relevant model in 1981? • Of course, linear programming models are only models of reality. This is true with all models. • Of course, linear programming models do not capture all nonlinear and other “real” properties of the real world such as risk and integer constraints. • Better options exist today to handle nonlinearities, risk, integer constraints and all kinds of other properties of the real world.

  28. Relevant result from 1981? • The general finding that it may be optimal to use some of the wood for energy, still remains!

  29. SVENSKA SKOGS- OCH MASSABOLAGET, SSM, 2000 - 2009 Praktikfallsuppgift i Kostnads - Intäktsanalys med Optimering Peter Lohmander

  30. SVENSKA SKOGS- OCH MASSABOLAGET, SSM, 2000 - 2009 Praktikfallsuppgift i Kostnads - Intäktsanalys med Optimering Peter Lohmander Företaget står i begrepp att utforma en 10- årsbudget innefattande hela verksamheten inklusive avverkning, rundvirkestransport, massa- och pappersproduktion samt investeringar. Man har för avsikt att ekonomiskt optimera två femårsbudgetar simultant via lineär programmering. http://www-sekon.slu.se/~PLO/ki99/SSM99/SSM994.htm

  31. http://www-sekon.slu.se/~PLO/ki99/SSM99/SSM994.htm

  32. Questions today (#1): • Can we combine the forest sector and the energy sector in one modern optimization model for both sectors? The model should include relevant data for the heating and electricity plants and for all types of forest industry mills.

  33. Necessary Model Properties: • The model should be dynamic and include the options to invest in new production capacity. Such new capacity could, when it comes to investments in energy plants, have different properties with respect to technological choices, possible fuels and degrees of flexibility.

  34. Why flexibility? • Prices and the availability of different fuels are impossible to predict over horizons of the economic life time of a heating plant. That is why flexibility is valuable. In the old type of optimization models, such things could not be analyzed at all. Now,economic optimization of flexibility is possible.

  35. Dynamic options • In the model from 1981, one period was analysed. In a new dynamic model, the use of the forest resources can also be optimized over time. • In the model from 1981, the capacities of different mills were constant. In the dynamic model, the capacity investments can be optimized over time.

  36. The Option • A new generation of optimization models is possible to construct. • We should not hesitate to develop this generation!

  37. Detailed long term forest planning is not optimal. Why should we make a detailed long term plan based on future prices and other conditions?Such things can not be perfectly predicted!

  38. Stochastic Price

  39. Stochastic Price

  40. Economic Risk Management in Forestry and Forest Industry and Environmental Effects in a Turbulent World Economy http://www-sekon.slu.se/~plo/erm/ermtot8.htm

  41. Low Correlation between Energy Prices and Pulp Prices (Source: Statistics Sweden)

  42. Low Correlation between Energy Prices and Pulp Prices

  43. Joint probability density function with correlation 0.25 (which corresponds to the prices of electricity and kraft paper)

  44. Low Correlation between Energy Prices and Pulp Prices • It has been proved that the expected marginal capacity value of a production plant increases with price variation when different products are produced with the same type of raw material and the correlation between product prices is less than 1. (Lohmander 1989)

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