1 Economic forest production with consideration of the forest- and energy- industries Presentation...

Economic forest production with consideration of the forest- and energy- industries

Presentation at the E.On Conference in Malm, Sweden, 2008-10-30

Peter Lohmander Professor of Forest Management and Economic OptimizationSLU, Swedish University of Agricultural SciencesUmea, Swedenhttp://www.Lohmander.com

Structure of the presentation:#01. Objectives#02. Key questions #03. Recent developments in the world#04. The present state#05. Forests, CO2, CCS and risk management#06. The forest harvest level and industrial expansion#07. Integrated regional study and risk management#08. Conclusions and plans for the future #09. Thanks to E.On#10. References

#01. ObjectivesTo describe the analyzed system.

To describe the most important problems.

To describe and motivate selected solution approaches and obtained results. To serve as a starting point in an extended discussion of important problems, solutions approaches and future cooperation.

#02. Key questionsHow should we define the system to be analyzed?Spatial definition? Time horizon?Included organizations?Uncertainty, risk or certainty?Objective function?

How should we manage the analyzed system in order to optimize the total result?

#03. Recent developments in the world

Recent developments in the world with very strong impacts on the key questionsA. The Financial Crisis:Extreme risk and uncertainty in the general global economic system.B. The Global Warming:The CO2 emission level has become the dominating environmental concern in the world. C. The CCS Technology: - Extremely promising method that can handle the global warming problem. Strong support from E.C., British Gov. and several large energy coorporations.

#04. The present state

Sweden is a country that is dominated by the forests.

The Initial Physical StateThe information from the Swedish Board of Forestry (Yearbook of Forest Statistics and Internet) clearly shows that the stock of wood in the Swedish forest has increased very much since 1920. This is true for pine, spruce and birch.

Source:The Swedish Board of Forestry 2007-10-26:http://www.svo.se/episerver4/templates/SFileListing.aspx?id=16583

Source: www.svo.se 2008-01-02

Diagram2

57.870.403571412.6035714

58.371.167142812.8671428

54.771.930714217.2307142

55.372.694285617.3942856

51.273.45785722.257857

49.574.221428424.7214284

48.174.984999826.8849998

47.375.748571228.4485712

47.176.512142629.4121426

49.177.27571428.175714

49.978.039285428.1392854

49.878.802856829.0028568

51.48230.6

50.38231.7

50.98332.1

51.68331.4

538330

53.88228.2

57.18224.9

58.88122.2

608020

62.18017.9

63.37915.7

63.87814.2

66.17811.9

69.8777.2

70.280.310.1

72.882.39.5

75816

74.181.57.4

71.581.510

68.584.215.7

6587.522.5

61.289.228

58.992.133.2

57.893.535.7

58.997.438.5

60.799.138.4

62.3100.237.9

63.295.532.3

63.797.834.1

64.296.432.2

64.3101.136.8

64.7101.436.7

65.2100.335.1

65.4102.136.7

65.8100.634.8

66.198.732.6

66.5104.738.2

68.910233.1

7010030

71.799.227.5

73.1100.927.8

73.710430.3

73.9104.730.8

75.6109.934.3

77.1111.234.1

79123.844.8

81.9120.238.3

90.6111.220.6

Gross Felling

Increment

Net Growth

Year

Million M3sk

Fellings, Increment and Net Growth

Diagram1

194457.870.403571412.6035714

194558.371.167142812.8671428

194654.771.930714217.2307142

194755.372.694285617.3942856

194851.273.45785722.257857

194949.574.221428424.7214284

195048.174.984999826.8849998

195147.375.748571228.4485712

195247.176.512142629.4121426

195349.177.27571428.175714

195449.978.039285428.1392854

195549.878.802856829.0028568

195651.48230.6

195750.38231.7

195850.98332.1

195951.68331.4

1960538330

196153.88228.2

196257.18224.9

196358.88122.2

1964608020

196562.18017.9

196663.37915.7

196763.87814.2

196866.17811.9

196969.8777.2

197070.280.310.1

197172.882.39.5

197275816

197374.181.57.4

197471.581.510

197568.584.215.7

19766587.522.5

197761.289.228

197858.992.133.2

197957.893.535.7

198058.997.438.5

198160.799.138.4

198262.3100.237.9

198363.295.532.3

198463.797.834.1

198564.296.432.2

198664.3101.136.8

198764.7101.436.7

198865.2100.335.1

198965.4102.136.7

199065.8100.634.8

199166.198.732.6

199266.5104.738.2

199368.910233.1

19947010030

199571.799.227.5

199673.1100.927.8

199773.710430.3

199873.9104.730.8

199975.6109.934.3

200077.1111.234.1

200179123.844.8

200281.9120.238.3

200390.6111.220.6

Gross Felling

Increment

Net Growth

Year

Million M3sk


data

Berknad rlig tillvxt sedan 1926 och bruttoavverkning sedan 1853

Calculated annual increment since 1926 and gross fellings since 1853

Avverkningen fr 1944 och framt r glidande femrsmedeltal

Fellings since 1944 are 5-year averages

185326.33

185828.18

186331.02

186835.35

187337.82

187837.82

188340.17

188842.52

189344.87

189848.2

190349.07

190849.19

191352.28

191855.25

192348.95

192652.858

192855.3759.37

193352.0461.42

193854.8864.84

194355.7469.64

194853.5473.46

195352.0477.27

194457.870.4035714

194558.371.1671428

194654.771.9307142

194755.372.6942856

194851.273.457857

194949.574.2214284

195048.174.9849998

195147.375.7485712

195247.176.5121426

195349.177.275714

195449.978.0392854

195549.878.8028568

195651.482

195750.382

195850.983

195951.683

19605383

196153.882

196257.182

196358.881

19646080

196562.180

196663.379

196763.878

196866.178

196969.877

197070.280.3

197172.882.3

19727581

197374.181.5

197471.581.5

197568.584.2

19766587.5

197761.289.2

197858.992.1

197957.893.5

198058.997.4

198160.799.1

198262.3100.2

198363.295.5

198463.797.8

198564.296.4

198664.3101.1

198764.7101.4

198865.2100.3

198965.4102.1

199065.8100.6

199166.198.7

199266.5104.7

199368.9102

199470100

199571.799.2

199673.1100.9

199773.7104

199873.9104.7

199975.6109.9

200077.1111.2

200179123.8

200281.9120.2

200390.6111.2

YearGross FellingIncrementNet Growth

194457.870.403571412.6035714

194558.371.167142812.8671428

194654.771.930714217.2307142

194755.372.694285617.3942856

194851.273.45785722.257857

194949.574.221428424.7214284

195048.174.984999826.8849998

195147.375.748571228.4485712

195247.176.512142629.4121426

195349.177.27571428.175714

195449.978.039285428.1392854

195549.878.802856829.0028568

195651.48230.6

195750.38231.7

195850.98332.1

195951.68331.4

1960538330

196153.88228.2

196257.18224.9

196358.88122.2

1964608020

196562.18017.9

196663.37915.7

196763.87814.2

196866.17811.9

196969.8777.2

197070.280.310.1

197172.882.39.5

197275816

197374.181.57.4

197471.581.510

197568.584.215.7

19766587.522.5

197761.289.228

197858.992.133.2

197957.893.535.7

198058.997.438.5

198160.799.138.4

198262.3100.237.9

198363.295.532.3

198463.797.834.1

198564.296.432.2

198664.3101.136.8

198764.7101.436.7

198865.2100.335.1

198965.4102.136.7

199065.8100.634.8

199166.198.732.6

199266.5104.738.2

199368.910233.1

19947010030

199571.799.227.5

199673.1100.927.8

199773.710430.3

199873.9104.730.8

199975.6109.934.3

200077.1111.234.1

200179123.844.8

200281.9120.238.3

200390.6111.220.6

&F

Sida &P

data

Gross Felling

Increment

Net Growth

Year

Million M3sk


Age distribution in the county of Gvleborg (2001-2005).Thousands of hectares in different age classes (years).

From the forest to the energy plants and forest industry mills

A harvester

A forwarder

A harvester in action

After harvesting and before forwarding

GROT prepared for energy production

A liner mill consuming wood of low dimensions. SCA.

A saw mill consuming wood of larger dimensions and high quality. SCA.

A flexible combined heat and power plant consuming wood, GROT, peat and other raw materials. E.ON Sweden.

Energy in Sweden

Bioenergy in Sweden

Biomass flows in Sweden

Distribution of the forest harvest with respect to forest industry, energi industry, stock level changes and others

Total Energy Supply, Sweden (2006)Bio Energy incl. Peat, 116 TWhNuclear Power, 194 TWhOil, 201 TWhHydro Energy, 62 TWh

Diagram1

10.1

37.7

21.1

15

13

13.3

Klla: Energimyndigheten, Energilget i siffror 2006Source: Swedish Energy Agency, Energy in Sweden, Facts and figures 2006

Totalt 110 TWhTotal

Anvndning av biobrnslen, torv mm fr energindaml 2005Utilisation of biofuels, peat etc, for energy production year 2005

Returlutar i massaind och fjrrvrmeverk. Spent liquors in pulp industry and district heating plants34%

Biobrnslen i bostadssektorn och vrigt Biofuels in residence sector and other12%

Biobrnslen fr elproduktion Biofules for electricity production9%

Trdbrnslsen i skogs- och trindustri. Wood fuels in forest and wood industry12%

Avfall, torv mm huvudsakligen i fjrrvrmeverk. Waste material, peat etc. mainly in district heating plants14%

Trdbrnslen i fjrrvrmeverk. Wood fuels in district heating plants19%

2005

Fig 11.4 Anvndning av biobrnslen, torv m.m. fr energindaml

Use of biofules, peat etc. for energy production

2005%9%34%19%14%12%12%100%

TWh10.137.721.115.013.013.3110.2

Klla: Energimyndigheten: "Energilget i siffor 2006"

Source: Swedish Energy Agency: "Energy in Sweden, Fact and Figures 2006"

2005


Totalt 110 TWhTotal


Trdbrnslen i fjrrvrmeverk. Wood fuels in district heating plants19%

Avfall, torv mm huvudsakligen i fjrrvrmeverk. Waste material, peat etc. mainly in district heating plants14%

Trdbrnslsen i skogs- och trindustri. Wood fuels in forest and wood industry12%

Biobrnslen fr elproduktion Biofules for electricity production9%

Biobrnslen i bostadssektorn och vrigt Biofuels in residence sector and other12%

Returlutar i massaind och fjrrvrmeverk. Spent liquors in pulp industry and district heating plants34%

2001-2004



2004%10%39%10%15%19%13%105%

TWh10.339.410.415.419.112.8107.4

2003%6%38%10%15%19%12%100%

TWh5.738.710.515.619.212.4102.1

2002%5%36%9%20%18%12%100%

TWh5.235.88.92017.91299.8

2001%5%40%9%17%19%11%100%

TWh4.836.88.215.717.31092.8



2001-2004

0

0

0

0

0

0


Totalt 107 TWhTotal


2001-2003



2003%6%38%10%15%19%12%100%

TWh5.738.710.515.619.212.4102.1

2002%5%36%9%20%18%12%100%

TWh5.235.88.92017.91299.8

2001%5%40%9%17%19%11%100%

TWh4.836.88.215.717.31092.8



2001-2003

0

0

0

0

0

0

Klla: Enegerimyndigheten, Energilget i siffror 2004Source: Swedish Energy Agency, Energy in Sweden, Facts and figures 2004

Totalt 103 TWhTotal

Anvndning av biobrnslen, torv mm fr energindaml 2003

1999-2001

Anvndning av biobrnslen, torv m.m. fr energindaml.

Use of biofuels, peat etc. for energy production

2002%

TWh

2001%8.5%9.2%20.6%10.3%38.3%18.3%100

TWh8.18.719.69.836.417.495

2000%7.1%10.3%15.4%10.8%40.3%16.0%100

TWh6.91014.910.53915.596.8

1999%7.210.519.512.838.611.4100

TWh6.89.918.312.036.310.794

1999-2001

00

00

00

00

00

00

Avfall, torv m.m. Refuse, peat etc. 17%

Trdbrnslen i massa- och pappersindustrin Wood fuels in pulp and paper industry8%

Trdbrnslen i sgverk Wood fuels in sawmills9%

Ved i smhus Wood fuel in dwellings 10%

Returlutar inom massaindustrin Black liquor in pulp industry36%

Klla: Energimyndigheten Source: Swedish Energy Agency

Totalt 95 TWh

Trdbrnslen i fjrrvrmeverk Wood fuels in district heating plants 20%

Anvndning av biobrnslen, torv m.m. fr energindaml under 2001Use of biofuel, peat etc. for energy purposes, 2001

Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"

Diagram5

9.81.636.948.3

11.6237.551.1

11.32.833.948

11.93.74055.6

134.943.861.7

13.46.745.265.3

12.4845.866.2

12.99.246.768.8

9.99.148.167.1

9.69.345.864.7

12.210.645.268

11.112.846.870.7

10.913.546.771.1

11.815.848.375.9

10.618.449.678.6

11.321.451.484.1

11.22450.785.9

1125.35490.3

10.826.454.291.4

1125.254.290.4

10.525.355.190.9

9.329.453.492.1

9.930.657.297.7

1232.760.4105.1

1138.557.4106.9

Small houses

District heating

Industry

Total

Year

TWh

Use of Bio Energy (office heating etc. not included)

Totalt

Anvndning av biobrnsle, torv m.m. fr energindaml (inkl elproduktion), TWh. Anvndingen i lokaler ingr ej.

Use of biofuel, peat etc. for energy purpose incl. electricity prodktion, TWh. The use in officies, servcie promise etc premises is not included

rYearSmhusOne- or two dwelling housesFjrrvrmeDistrict heatingIndustrinIndustryTotaltTotal

TWh

197012.1......

19718.8......

19727.6......

19736.7......

19746.8......

19756.0......

19766.3......

19776.9......

19787.8......

19798.8......

19809.81.636.948.3

198111.62.037.551.1

198211.32.833.948.0

198311.93.740.055.6

198413.04.943.861.7

198513.46.745.265.3

198612.48.045.866.2

198712.99.246.768.8

19889.99.148.167.1

19899.69.345.864.7

199012.210.645.268.0

199111.112.846.870.7

199210.913.546.771.1

199311.815.848.375.9

199410.618.449.678.6

199511.321.451.484.1

199611.224.050.785.9

199711.025.354.090.3

199810.826.454.291.4

199911.025.254.290.4

200010.525.355.190.9

20019.329.453.492.1

20029.930.657.297.7

2003 112.032.760.4105.1

200411.038.557.4106.9

1 Vissa siffror korrigerade

1 Some figures are corrected



YearSmall housesDistrict heatingIndustryTotal

19809.81.636.948.3

198111.62.037.551.1

198211.32.833.948.0

198311.93.740.055.6

198413.04.943.861.7

198513.46.745.265.3

198612.48.045.866.2

198712.99.246.768.8

19889.99.148.167.1

19899.69.345.864.7

199012.210.645.268.0

199111.112.846.870.7

199210.913.546.771.1

199311.815.848.375.9

199410.618.449.678.6

199511.321.451.484.1

199611.224.050.785.9

199711.025.354.090.3

199810.826.454.291.4

199911.025.254.290.4

200010.525.355.190.9

20019.329.453.492.1

20029.930.657.297.7

200312.032.760.4105.1

200411.038.557.4106.9

Totalt

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

Small houses

District heating

Industry

Total

Year

TWh

Use of biofuels for energy including electricity (office heating etc. not included)

Fjrrvrme

Anvndning av biobrnsle, torv m.m. fr energindaml inom fjrrvrmesektorn, TWh

Use of biofuel, peat etc. for energy purpose in the district heating sector, TWh

rYearAvfallRefuseTrdbrnsleWood fuelReturlutar och rtallolja Black liquor, crude tall oilTorvPeatBiobrnslen fr elproduktion Biofuels for electricity prod.vriga brnslenOther fuelsSummaSum

TWh

19801.30.31.6

19811.60.42.0

19822.00.80.02.8

19832.41.30.03.7

19843.01.60.34.9

19853.32.70.76.7

19863.83.11.18.0

19874.23.31.70.19.2

19883.93.61.50.19.1

19893.63.32.10.29.3

19904.03.62.60.30.110.6

19914.24.83.10.40.212.8

19924.15.43.30.30.313.5

19934.27.00.73.10.50.415.8

19944.39.11.32.80.40.518.4

19954.510.31.43.71.00.621.4

19964.512.41.63.51.00.924.0

19974.813.71.43.01.41.025.3

19985.113.72.03.81.50.326.4

19994.714.02.22.81.50.025.2

20005.614.31.52.41.50.025.3

20015.517.31.92.72.00.029.4

20025.217.91.83.72.00.030.6

2003 16.517.71.73.60.23.032.7

20047.219.11.23.22.25.638.5

1 Siffror fr o m 1997 r reviderade

1 Figures from 1997 onwards are revised



YearReuseWood fuelBlack licourPeatBio for electricityOther fuels

19801.30.3

19811.60.4

19822.00.80.0

19832.41.30.0

19843.01.60.3

19853.32.70.7

19863.83.11.1

19874.23.31.70.1

19883.93.61.50.1

19893.63.32.10.2

19904.03.62.60.30.1

19914.24.83.10.40.2

19924.15.43.30.30.3

19934.27.00.73.10.50.4

19944.39.11.32.80.40.5

19954.510.31.43.71.00.6

19964.512.41.63.51.00.9

19974.813.71.43.01.41.0

19985.113.72.03.81.50.3

19994.714.02.22.81.50.0

20005.614.31.52.41.50.0

20015.517.31.92.72.00.0

20025.217.91.83.72.00.0

20036.517.71.73.60.23.0

20047.219.11.23.22.25.6

Fjrrvrme

1.30.31980198019801980

1.60.41981198119811981

20.81982019821982

2.41.31983019831983

31.619840.319841984

3.32.719850.719851985

3.83.119861.119861986

4.23.319871.70.11987

3.93.619881.50.11988

3.63.319892.10.21989

43.619902.60.30.1

4.24.819913.10.40.2

4.15.419923.30.30.3

4.270.73.10.50.4

4.39.11.32.80.40.5

4.510.31.43.710.6

4.512.41.63.510.9

4.813.71.431.41

5.113.723.81.50.3

4.7142.22.81.50

5.614.31.52.41.50

5.517.31.92.720

5.217.91.83.720

6.517.71.73.60.23

7.219.11.23.22.25.6

Reuse

Wood fuel

Black licour

Peat

Bio for electricity

Other fuels

Year

TWh

Use of different fuels in district heating

Industri

Anvndning av biobrnsle, torv m.m. fr energindaml inom industrin, TWh

Use of biofuel, peat etc. for energy purpose in the industry, TWh

rYearMassaindustrins returlutarCellulose industry, black liquorMassaindustrins vriga biprodukterCellulose industry, other byproductsSgverksindustrins biprodukterSawmill industry byproductsBiobrnslen fr elproduktion i industrinBiofuels for electricity productionvriga branscherOther sectorsSummaSum

TWh

198026.04.64.80.7-36.9

198125.66.84.10.80.237.5

198222.46.34.11.00.333.9

198326.27.45.21.10.140.0

198428.78.25.42.40.143.8

198528.19.05.82.10.245.2

198628.39.16.12.40.145.8

198728.69.36.22.50.146.7

198829.010.06.42.60.148.1

198929.07.56.52.50.345.8

199027.68.26.42.20.845.2

199128.68.47.02.20.646.8

199228.38.37.12.40.646.7

199329.78.67.32.20.548.3

199429.88.18.02.11.549.6

199531.47.68.42.31.751.4

199630.96.98.92.11.950.7

199733.26.99.72.51.754.0

199833.06.99.82.52.054.2

199933.96.79.82.01.854.2

200036.88.65.43.40.955.1

200134.97.74.32.83.753.4

200234.06.94.93.28.257.2

2003 135.37.55.03.78.960.4

200439.47.54.84.70.957.3

1 Vissa vrden korrigerade

1 Some figures are corrected



YearCel ind Black liqourCel ind other byprodSaw ind byprodBiofuels for electrOther sectors

198026.04.64.80.736.9

198125.66.84.10.80.237.5

198222.46.34.11.00.333.9

198326.27.45.21.10.140.0

198428.78.25.42.40.143.8

198528.19.05.82.10.245.2

198628.39.16.12.40.145.8

198728.69.36.22.50.146.7

198829.010.06.42.60.148.1

198929.07.56.52.50.345.8

199027.68.26.42.20.845.2

199128.68.47.02.20.646.8

199228.38.37.12.40.646.7

199329.78.67.32.20.548.3

199429.88.18.02.11.549.6

199531.47.68.42.31.751.4

199630.96.98.92.11.950.7

199733.26.99.72.51.754.0

199833.06.99.82.52.054.2

199933.96.79.82.01.854.2

200036.88.65.43.40.955.1

200134.97.74.32.83.753.4

200234.06.94.93.28.257.2

200335.37.55.03.78.960.4

200439.47.54.84.70.957.3

&F

Industri

264.64.80.71980

25.66.84.10.80.2

22.46.34.110.3

26.27.45.21.10.1

28.78.25.42.40.1

28.195.82.10.2

28.39.16.12.40.1

28.69.36.22.50.1

29106.42.60.1

297.56.52.50.3

27.68.26.42.20.8

28.68.472.20.6

28.38.37.12.40.6

29.78.67.32.20.5

29.88.182.11.5

31.47.68.42.31.7

30.96.98.92.11.9

33.26.99.72.51.7

336.99.82.52

33.96.79.821.8

36.88.65.43.40.9

34.97.74.32.83.7

346.94.93.28.2

35.37.553.78.9

39.47.54.84.70.9

Cel ind Black liqour

Cel ind other byprod

Saw ind byprod

Biofuels for electr

Other sectors

Year

TWh

Use of fuels for bioenergy in industry

MBD002090CF.bin

MBD00209245.bin

MBD00208B1C.bin

Diagram2

1.30.31980198019801980

1.60.41981198119811981

20.81982019821982

2.41.31983019831983

31.619840.319841984

3.32.719850.719851985

3.83.119861.119861986

4.23.319871.70.11987

3.93.619881.50.11988

3.63.319892.10.21989

43.619902.60.30.1

4.24.819913.10.40.2

4.15.419923.30.30.3

4.270.73.10.50.4

4.39.11.32.80.40.5

4.510.31.43.710.6

4.512.41.63.510.9

4.813.71.431.41

5.113.723.81.50.3

4.7142.22.81.50

5.614.31.52.41.50

5.517.31.92.720

5.217.91.83.720

6.517.71.73.60.23

7.219.11.23.22.25.6

Reuse

Wood fuel

Black licour

Peat

Bio for electricity

Other fuels

Year

TWh


Totalt



TWh

197012.1......

19718.8......

19727.6......

19736.7......

19746.8......

19756.0......

19766.3......

19776.9......

19787.8......

19798.8......

19809.81.636.948.3

198111.62.037.551.1

198211.32.833.948.0

198311.93.740.055.6

198413.04.943.861.7

198513.46.745.265.3

198612.48.045.866.2

198712.99.246.768.8

19889.99.148.167.1

19899.69.345.864.7

199012.210.645.268.0

199111.112.846.870.7

199210.913.546.771.1

199311.815.848.375.9

199410.618.449.678.6

199511.321.451.484.1

199611.224.050.785.9

199711.025.354.090.3

199810.826.454.291.4

199911.025.254.290.4

200010.525.355.190.9

20019.329.453.492.1

20029.930.657.297.7

12.032.760.4105.1

200411.038.557.4106.9



Fjrrvrme



TWh

19801.30.31.6

19811.60.42.0

19822.00.80.02.8

19832.41.30.03.7

19843.01.60.34.9

19853.32.70.76.7

19863.83.11.18.0

19874.23.31.70.19.2

19883.93.61.50.19.1

19893.63.32.10.29.3

19904.03.62.60.30.110.6

19914.24.83.10.40.212.8

19924.15.43.30.30.313.5

19934.27.00.73.10.50.415.8

19944.39.11.32.80.40.518.4

19954.510.31.43.71.00.621.4

19964.512.41.63.51.00.924.0

19974.813.71.43.01.41.025.3

19985.113.72.03.81.50.326.4

19994.714.02.22.81.50.025.2

20005.614.31.52.41.50.025.3

20015.517.31.92.72.00.029.4

20025.217.91.83.72.00.030.6

6.517.71.73.60.23.032.7

20047.219.11.23.22.25.638.5




19801.30.3

19811.60.4

19822.00.80.0

19832.41.30.0

19843.01.60.3

19853.32.70.7

19863.83.11.1

19874.23.31.70.1

19883.93.61.50.1

19893.63.32.10.2

19904.03.62.60.30.1

19914.24.83.10.40.2

19924.15.43.30.30.3

19934.27.00.73.10.50.4

19944.39.11.32.80.40.5

19954.510.31.43.71.00.6

19964.512.41.63.51.00.9

19974.813.71.43.01.41.0

19985.113.72.03.81.50.3

19994.714.02.22.81.50.0

20005.614.31.52.41.50.0

20015.517.31.92.72.00.0

20025.217.91.83.72.00.0

20036.517.71.73.60.23.0

20047.219.11.23.22.25.6

Fjrrvrme

Reuse

Wood fuel

Black licour

Peat

Bio for electricity

Other fuels

Year

TWh


Industri



TWh

198026.04.64.80.7-36.9

198125.66.84.10.80.237.5

198222.46.34.11.00.333.9

198326.27.45.21.10.140.0

198428.78.25.42.40.143.8

198528.19.05.82.10.245.2

198628.39.16.12.40.145.8

198728.69.36.22.50.146.7

198829.010.06.42.60.148.1

198929.07.56.52.50.345.8

199027.68.26.42.20.845.2

199128.68.47.02.20.646.8

199228.38.37.12.40.646.7

199329.78.67.32.20.548.3

199429.88.18.02.11.549.6

199531.47.68.42.31.751.4

199630.96.98.92.11.950.7

199733.26.99.72.51.754.0

199833.06.99.82.52.054.2

199933.96.79.82.01.854.2

200036.88.65.43.40.955.1

200134.97.74.32.83.753.4

200234.06.94.93.28.257.2

35.37.55.03.78.960.4

200439.47.54.84.70.957.3



&F

MBD002090CF.bin

MBD00209245.bin

MBD00208B1C.bin


Diagram4

264.64.80.71980

25.66.84.10.80.2

22.46.34.110.3

26.27.45.21.10.1

28.78.25.42.40.1

28.195.82.10.2

28.39.16.12.40.1

28.69.36.22.50.1

29106.42.60.1

297.56.52.50.3

27.68.26.42.20.8

28.68.472.20.6

28.38.37.12.40.6

29.78.67.32.20.5

29.88.182.11.5

31.47.68.42.31.7

30.96.98.92.11.9

33.26.99.72.51.7

336.99.82.52

33.96.79.821.8

36.88.65.43.40.9

34.97.74.32.83.7

346.94.93.28.2

35.37.553.78.9

39.47.54.84.70.9



Saw ind byprod

Biofuels for electr

Other sectors

Year

TWh


Totalt



TWh

197012.1......

19718.8......

19727.6......

19736.7......

19746.8......

19756.0......

19766.3......

19776.9......

19787.8......

19798.8......

19809.81.636.948.3

198111.62.037.551.1

198211.32.833.948.0

198311.93.740.055.6

198413.04.943.861.7

198513.46.745.265.3

198612.48.045.866.2

198712.99.246.768.8

19889.99.148.167.1

19899.69.345.864.7

199012.210.645.268.0

199111.112.846.870.7

199210.913.546.771.1

199311.815.848.375.9

199410.618.449.678.6

199511.321.451.484.1

199611.224.050.785.9

199711.025.354.090.3

199810.826.454.291.4

199911.025.254.290.4

200010.525.355.190.9

20019.329.453.492.1

20029.930.657.297.7

12.032.760.4105.1

200411.038.557.4106.9



Fjrrvrme



TWh

19801.30.31.6

19811.60.42.0

19822.00.80.02.8

19832.41.30.03.7

19843.01.60.34.9

19853.32.70.76.7

19863.83.11.18.0

19874.23.31.70.19.2

19883.93.61.50.19.1

19893.63.32.10.29.3

19904.03.62.60.30.110.6

19914.24.83.10.40.212.8

19924.15.43.30.30.313.5

19934.27.00.73.10.50.415.8

19944.39.11.32.80.40.518.4

19954.510.31.43.71.00.621.4

19964.512.41.63.51.00.924.0

19974.813.71.43.01.41.025.3

19985.113.72.03.81.50.326.4

19994.714.02.22.81.50.025.2

20005.614.31.52.41.50.025.3

20015.517.31.92.72.00.029.4

20025.217.91.83.72.00.030.6

6.517.71.73.60.23.032.7

20047.219.11.23.22.25.638.5




19801.30.3

19811.60.4

19822.00.80.0

19832.41.30.0

19843.01.60.3

19853.32.70.7

19863.83.11.1

19874.23.31.70.1

19883.93.61.50.1

19893.63.32.10.2

19904.03.62.60.30.1

19914.24.83.10.40.2

19924.15.43.30.30.3

19934.27.00.73.10.50.4

19944.39.11.32.80.40.5

19954.510.31.43.71.00.6

19964.512.41.63.51.00.9

19974.813.71.43.01.41.0

19985.113.72.03.81.50.3

19994.714.02.22.81.50.0

20005.614.31.52.41.50.0

20015.517.31.92.72.00.0

20025.217.91.83.72.00.0

20036.517.71.73.60.23.0

20047.219.11.23.22.25.6

Fjrrvrme

1.30.31980198019801980

1.60.41981198119811981

20.81982019821982

2.41.31983019831983

31.619840.319841984

3.32.719850.719851985

3.83.119861.119861986

4.23.319871.70.11987

3.93.619881.50.11988

3.63.319892.10.21989

43.619902.60.30.1

4.24.819913.10.40.2

4.15.419923.30.30.3

4.270.73.10.50.4

4.39.11.32.80.40.5

4.510.31.43.710.6

4.512.41.63.510.9

4.813.71.431.41

5.113.723.81.50.3

4.7142.22.81.50

5.614.31.52.41.50

5.517.31.92.720

5.217.91.83.720

6.517.71.73.60.23

7.219.11.23.22.25.6

Reuse

Wood fuel

Black licour

Peat

Bio for electricity

Other fuels

Year

TWh


Industri



TWh

198026.04.64.80.7-36.9

198125.66.84.10.80.237.5

198222.46.34.11.00.333.9

198326.27.45.21.10.140.0

198428.78.25.42.40.143.8

198528.19.05.82.10.245.2

198628.39.16.12.40.145.8

198728.69.36.22.50.146.7

198829.010.06.42.60.148.1

198929.07.56.52.50.345.8

199027.68.26.42.20.845.2

199128.68.47.02.20.646.8

199228.38.37.12.40.646.7

199329.78.67.32.20.548.3

199429.88.18.02.11.549.6

199531.47.68.42.31.751.4

199630.96.98.92.11.950.7

199733.26.99.72.51.754.0

199833.06.99.82.52.054.2

199933.96.79.82.01.854.2

200036.88.65.43.40.955.1

200134.97.74.32.83.753.4

200234.06.94.93.28.257.2

35.37.55.03.78.960.4

200439.47.54.84.70.957.3



YearCel ind Black liqourCel ind other byprodSaw ind byprodBiofuels for electrOther sectors

198026.04.64.80.736.9

198125.66.84.10.80.237.5

198222.46.34.11.00.333.9

198326.27.45.21.10.140.0

198428.78.25.42.40.143.8

198528.19.05.82.10.245.2

198628.39.16.12.40.145.8

198728.69.36.22.50.146.7

198829.010.06.42.60.148.1

198929.07.56.52.50.345.8

199027.68.26.42.20.845.2

199128.68.47.02.20.646.8

199228.38.37.12.40.646.7

199329.78.67.32.20.548.3

199429.88.18.02.11.549.6

199531.47.68.42.31.751.4

199630.96.98.92.11.950.7

199733.26.99.72.51.754.0

199833.06.99.82.52.054.2

199933.96.79.82.01.854.2

200036.88.65.43.40.955.1

200134.97.74.32.83.753.4

200234.06.94.93.28.257.2

200335.37.55.03.78.960.4

200439.47.54.84.70.957.3

&F

Industri



Saw ind byprod

Biofuels for electr

Other sectors

Year

TWh


MBD002090CF.bin

MBD00209245.bin

MBD00208B1C.bin

http://www.svo.se/episerver4/dokument/sks/Statistik/dokumenten/Produktion/Tradbransle/ProjTradbr/Biomassaflden%20i%20svensk%20skogsnring%202004-2(frf%20P-O%20Nilsson,%20prof%20emer).pdf

Biomass flows in the Swedish Forest Sector 2004 (translated)

Increase of the forest stockEnergyForestIndustryProductsRaw materialleft in the forest

Increase of the forest stockEnergyRaw materialleft in the forestForestIndustryProducts

Final fellingsIncrease of the forest stockCommercialThinningsPartial harvest

#05. Forests, CO2, CCS and risk management

Optimal dynamic control of the forest resource with changing energy demand functions and valuation of CO2 storage

Presentation at the Conference:

The European Forest-based Sector: Bio-Responses to Address New Climate and Energy Challenges?Nancy, France, November 6-8, 2008Peter Lohmander Professor of Forest Management and Economic OptimizationSLU, Swedish University of Agricultural SciencesUmea, Swedenhttp://www.Lohmander.com

Structure of the presentation:#1. Introduction to rational use of the forest when we consider CO2 and energy production

#2. Optimal dynamic control of the forest resource with changing energy demand functions and valuation of CO2 storage

#3. Optimal CCS, Carbon Capture and Storage, Under Risk

#4. Conclusions

#1. Introduction to rational use of the forest when we consider CO2 and energy production

The role of the forest?The best way to reduce the CO2 in the atmosphere may be to increase harvesting of the presently existing forests (!), to produce energy with CCS and to increase forest production in the new forest generations.

We capture and store more CO2!

Permanent storage of CO2Coal mineOil fieldNatural gasCCS, Carbon Capture and Storage, has alreadybecome the main future emissionreduction method of the fossile fuel energy industry Energy plant with CO2 capture and separation

BBC World News 2008-10-17:The British government declares that the CO2 emissions will be reduced by 80% by 2050!CCS is the method to be used in combination with fossile fuels such as coal.

Reference to CCS in the energy industry and EU policy2nd Annual EMISSIONS REDUCTION FORUM: - Establishing Effective CO2, NOx, SOx Mitigation Strategies for the Power Industry, CD, Marcus Evans Ltd, Madrid, Spain, 29th & 30th September 2008

The CD (above) includes presentations where several dominating European energy companies show how they develop and use CCS and where the European Commission gives the general European emission and energy policy perspective.

Conference programme:

http://www.lohmander.com/Madrid08/MadridProg08.pdf

Lohmander, P., Guidelines for Economically Rational and Coordinated Dynamic Development of the Forest and Bio Energy Sectors with CO2 constraints, Proceedings from the 16th European Biomass Conference and Exhibition, Valencia, Spain, 02-06 June, 2008 (In the version in the link, below, an earlier misprint has been corrected. ) http://www.Lohmander.com/Valencia2008.pdf

Lohmander, P., Economically Optimal Joint Strategy for Sustainable Bioenergy and Forest Sectors with CO2 Constraints, European Biomass Forum, Exploring Future Markets, Financing and Technology for Power Generation, CD, Marcus Evans Ltd, Amsterdam, 16th-17th June, 2008 http://www.Lohmander.com/Amsterdam2008.ppt

Lohmander, P., Tools for optimal coordination of CCS, power industry capacity expansion and bio energy raw material production and harvesting, 2nd Annual EMISSIONS REDUCTION FORUM: - Establishing Effective CO2, NOx, SOx Mitigation Strategies for the Power Industry, CD, Marcus Evans Ltd, Madrid, Spain, 29th & 30th September 2008http://www.lohmander.com/Madrid08/Madrid_2008_Lohmander.ppt

Lohmander, P., Optimal CCS, Carbon Capture and Storage, Under Risk, International Seminars in Life Sciences, UPV, Universidad Politcnica de Valencia, Thursday 2008-10-16http://www.Lohmander.com/OptCCS/OptCCS.ppt

CO2Permanent storage of CO2How to reduce the CO2 level in the atmosphere,

not only to decrease the emission of CO2 Energy plant with CO2 capture and separation

The role of the forest in the CO2 and energy systemThe following six pictures show that it is necessary to intensify the use of the forest for energy production in combination with CCS in order to reduce the CO2 in atmosphere! All figures and graphs have been simplified as much as possible, keeping the big picture correct, in order to make the main point obvious. In all cases, we keep the total energy production constant.

CO2Permanent storage of CO2Coal, oil, gasThe present situation.41501CO2 increase in the atmosphere:5-1 = 4

CO2Permanent storage of CO2Coal, oil, gasIf we do not use the forest for energy production but use it as a carbon sink. Before the forest has reached equilibrium, this happens:5501CO2 increase in the atmosphere:5-1 = 4

CO2Permanent storage of CO2Coal, oil, gasIf we do not use the forest for energy production but use it as a carbon sink. When the forest has reached equilibrium, this happens:51501CO2 increase in the atmosphere:5+1-1 = 5

CO2Permanent storage of CO2Coal, oil, gasIf we use CCS with 80% efficiency and let the forest grow until it reaches equilibrium.51141CO2 increase in the atmosphere:1+1-1 = 1

CO2Permanent storage of CO2Coal, oil, gasIf we use CCS with 80% efficiency and use the forest with traditional low intensity harvesting and silviculture. 41141CO2 increase in the atmosphere:1-1 = 0

CO2Permanent storage of CO2Coal, oil, gasIf we use CCS with 80% efficiency and use the forest with increased harvesting and high intensity silviculture. 32142CO2 increase in the atmosphere:1-2 = -1

General conclusions:The best way to reduce the CO2 in the atmosphere may be to increase harvesting of the presently existing forests (!), to produce energy with CCS and to increase forest production in the new forest generations.

We capture and store more CO2!

#2. Optimal dynamic control of the forest resource with changing energy demand functions and valuation of CO2 storage

The optimal control derivations and the software are found here:Lohmander, P., Optimal resource control model & General continuous time optimal control model of a forest resource, comparative dynamics and CO2 consideration effects, Seminar at SLU, Umea, Sweden, 2008-09-18 http://www.lohmander.com/CM/CMLohmander.ppt

Software:http://www.lohmander.com/CM/CM.htm

The Total EconomicResult (Present Value)The Stock LevelThe Control LevelEconomic valuation of CO2 storage in the natural resourceEconomic Valuation of the Production of Energy and Other Industrial Products

Initial stock levelTerminal stock levelThe change of the stock level during a marginaltime interval

V0Time0StockThe forest stock level has increased very much in Sweden during 80 years!

If the forest owner gets paid for the CO2 stored in the forest, it becomes optimal for the forest owner to harvest less and increase the stock level. Still, it may be even better for society to harvest more, decrease the wood stock and use CCS to store the CO2. The stored CO2 is rewarded.

The stored CO2 is not rewarded.

Diagram2

310031003100

296429203008

283727612913

272126282814

262325332712

254624852607

250025002500

x_f1=5

x_f1=0

x_f1=10

Time (Years)

Optimal Stock (Mm3sk)

Optimal Stock Path

Blad1

CM results 080905 f

Peter Lohmander

CASE 1CASE 2CASE 3

t1t2r

CASE 1

txuLambdaJ

f1f2031001341981433.4404787432

52964131166

102837128141

k1k2k3152721124121

202623119105

25254611392

g0g1g230250010682

x1x2CASE 2

txuLambdaJ

031001431391216.3440051854

52920138132

J1433.4404787432J1216.3440051854J1655.0435182073102761132126

152628124120

txuLambdatxuLambdatxuLambda202533115114

031001341980310014313903100124256252485104108

52964131166529201381325300812420030250090103

102837128141102761132126102913124157

152721124121152628124120152814123123

20262311910520253311511420271212397CASE 3

2525461139225248510410825260712276txuLambdaJ

302500106823025009010330250012260031001242561655.0435182073

53008124200

102913124157

152814123123

20271212397

25260712276

30250012260

tx_f1=0x_f1=5x_f1=10

0310031003100

5292029643008

10276128372913

15262827212814

20253326232712

25248525462607

30250025002500

tu_f1=0u_f1=5u_f1=10

0143134124

5138131124

10132128124

15124124123

20115119123

25104113122

3090106122

tSP_f1=0SP_f1=5SP_f1=10

0139198256

5132166200

10126141157

15120121123

2011410597

CASE 2251089276

301038260

J_f1=0J_f1=5J_f1=10

VALUE1216.34400518541433.44047874321655.0435182073

CASE 2

CASE 2

Blad1

x

Time (Years)


Optimal Stock Path

Blad2

u

Time (Years)

Optimal Control (Mm3sk)

Optimal Control Path

Blad3

Lambda

Time (Years)

Optimal Shadow Price (Relevant Currency)

Optimal Shadow Price Path

VALUE

Alternative

Objective Value (Relevant Currency)

Optimal Objective Function Values

x_f1=5

x_f1=0

x_f1=10

Time (Years)


Optimal Stock Path

u_f1=5

u_f1=0

u_f1=10

Time (Years)

Optimal Control (Mm3sk)

Optimal Control Path

SP_f1=5

SP_f1=0

SP_f1=10

Time (Years)

Shadow Price (Relevant Currency)

Optimal Shadow Price Path

MBD00000632.unknown

MBD00000CF8.unknown

MBD0000106C.unknown

MBD0000123C.unknown

MBD0006EA07.unknown

MBD000012B4.unknown

MBD0000132C.unknown

MBD00001154.unknown

MBD000011CA.unknown

MBD000010E0.unknown

MBD00000EC6.unknown

MBD00000F82.unknown

MBD00000FFA.unknown

MBD00000F38.unknown

MBD00000DDE.unknown

MBD00000E52.unknown

MBD00000D6A.unknown

MBD0000097C.unknown

MBD00000B4C.unknown

MBD00000C36.unknown

MBD00000CAE.unknown

MBD00000BBE.unknown

MBD00000A62.unknown

MBD00000AD6.unknown

MBD000009EE.unknown

MBD000007D6.unknown

MBD000008BA.unknown

MBD00000904.unknown

MBD00000848.unknown

MBD000006EE.unknown

MBD00000762.unknown

MBD0000067C.unknown

MBD00000288.unknown

MBD0000045A.unknown

MBD00000542.unknown

MBD000005BA.unknown

MBD000004D0.unknown

MBD00000372.unknown

MBD000003E6.unknown

MBD00000300.unknown

MBD000000E6.unknown

MBD000001CC.unknown

MBD0000023E.unknown

MBD0000015A.unknown

MBD00000072.unknown

#3. Optimal CCS, Carbon Capture and Storage, Under Risk

The stochastic optimal control derivations of CCS are found here:Lohmander, P., Optimal CCS, Carbon Capture and Storage, Under Risk, International Seminars in Life Sciences, Universidad Politcnica de Valencia, Thursday 2008-10-16http://www.Lohmander.com/OptCCS/OptCCS.ppt

Optimal CCS, Carbon Capture and Storage, Under RiskThe objective function is the total present value of CO2 storage minus CCS costs.Discountingfactoru = control = CCS levelx = The total storage level of CO2

The controlled storageA stochastic differential equation:Change of theCO2 storage level.Control =CCS level.Expected CO2 leakage. The CO2 storage level is to some extent affected by stochastic leakage and other stochastic events. Z = standard Wiener process.

The optimal CCS objective function for different risk levels. The details are found in the reference.V(x,t)xt

#4. Conclusions

Optimal Forest management conclusions:If the forest owner gets paid for the CO2 stored in the forest, it becomes optimal for the forest owner to harvest less and increase the stock level. Still, it may be even better for society to harvest more, decrease the present wood stock and use CCS to store the CO2.The best way to reduce the CO2 in the atmosphere may be to increase harvesting of the presently existing forests (!), to produce energy with CCS and to increase forest production in the new forest generations.

Optimal CCS Conclusions:A mathematical approach to optimal CCS control has been developed that can handle risk.Possible leakage is an important issue that has to be carefully investigated in the future.It is important that the future management decisions are based on a decision model consistent with the structure of this model and that the parameter values are carefully estimated before practical management decisions are calculated.

#06. The forest harvest level and industrial expansion

Operations Research with Economic Optimization:

Raw material PerspectiveTotal Perspective ITotal Perspective II

Raw Material PerspectiveThe present value as a function of the time of the final felling, t:

Discounting factorValue of the forest standValue of the bare landPresent valueof the stand and the land

Figure 1. The Present ValueEXP(- 0.03t)(20000 + 1000t + 2000)Present Value (SEK/Hectare)Number of Years from the Present

The Raw Material Perspective and OptimizationYou may instantly calculate the economically optimal decisions, from a raw material perspective, using software available from the Internet:

http://www.lohmander.com/program/Faust_Slut/InFaust3.html

http://www.lohmander.com/program/Stump02/InStump022.html

= Stock level= Growth= Net Price = Net Price Growth= Land Value= Interest Rate(%)Optimize!Web Software for Economic Optimization from a Raw Material Perspective

Optimal ResultsOptimal Harvest YearOptimal Present Value

Harvest Year Present Value Present Value Difference

Web Software for Economic Optimization from a Raw Material Perspective = Stock levelWeb Software for Economic Optimization from a Raw Material Perspective = Growth= Net Price = Net Price Growth= Land Value= Interest Rate(%)Optimize!

Optimal ResultsOptimal Harvest YearOptimal Present Value

Harvest Year Present Value Present Value Difference

ObservationsFrom a pure raw material perspective, you may show that a very large part of the Swedish forest should be instantly harvested, even if the real rate of interest is not higher than 3%. If the real rate of interest exceeds 3%, you should if possible harvest even more. If the growth rate of the next forest generation increases, you should also harvest the present forest earlier.

Age distribution in the county of Gvleborg (2001-2005).Thousands of hectares in different age classes (years). A large part of the forest is much older than the optimal harvest age

Total perspective I

V0Time0Stock

h0 < gh1 > gh2 = g

Appendix 1: General proof that the total economic value is a strictly increasing function of the production (and capacity) level in the next industry generation (as as long as the time when the capacity will be utilized is not sufficiently short to give extraordinary capacity costs.)

This approach represents Total Perspective I.

Derivations and parameters (I)

http://www.lohmander.com/EF2008/EF2008.htm Web software for Total Perspective I

vfutureh1t2totval3000106 inf168030001085517033000110301718300011221,61727300011417,517323000116151735

ObservationsEven if we do not accept to decrease the stock level below the very high level of today, we should strongly increase harvesting during a considerable time interval.In this first derivation, the improved growth rate in new plantations has not been considered.

vfutureh1t2totval2500106 inf168025001166518002500126351886250013625193925001462019732500156171997

ObservationsIf we are prepared to adjust the stock level to the stock level of the year 1985, (approximately 2 500 Mm3sk), we should increase harvesting very much during a long time period.Then, the total economic value strongly improves.In this derivation, the improved growth rate in new plantations has not been considered.

Diagram1

106106

55108

30110

21.6112

17.5114

15116

106106

11665

12635

13625

14620

15617

t2(Vf=3000)

t2(Vf=2500)

h1 (Million M3sk/Year)

t2 (Years)

t2(h1, Vfuture)

Blad1

Model_1

20080103

Peter Lohmander

r0.06

g106

t15

v03000

h086

z01

z11

z21

vfutureh1t2totval

3000106inf1680

3000108551703

3000110301718

300011221.61727

300011417.51732

3000116151735

vfutureh1t2totval

2500106inf1680

2500116651800

2500126351886

2500136251939

2500146201973

2500156171997

h1t2(Vf=3000)t2(Vf=2500)

106

10855

11030

11221.6

11417.5

11615

106

11665

12635

13625

14620

15617

Blad1

t2 inf

h1

t2

t2(h1) if Vfuture = 3000

Blad2

t2 inf

h1

t2


Blad3

t2(Vf=3000)

t2(Vf=2500)


t2 (Years)

t2(h1, Vfuture)

Diagram2

1680106

1703108

1718110

1727112

1732114

1735116

1061680

1161800

1261886

1361939

1461973

1561997

TV(Vf=3000)

TV(Vf=2500)


Total Value

Blad1

Model_1

20080103

Peter Lohmander

r0.06

g106

t15

v03000

h086

z01

z11

z21

vfutureh1t2totval

3000106inf1680

3000108551703

3000110301718

300011221.61727

300011417.51732

3000116151735

vfutureh1t2totval

2500106inf1680

2500116651800

2500126351886

2500136251939

2500146201973

2500156171997

h1t2(Vf=3000)t2(Vf=2500)TV(Vf=3000)TV(Vf=2500)

1061680

108551703

110301718

11221.61727

11417.51732

116151735

1061680

116651800

126351886

136251939

146201973

156171997

Blad1

t2 inf

h1

t2


Blad2

t2 inf

h1

t2


Blad3

t2(Vf=3000)

t2(Vf=2500)


t2 (Years)

t2(h1, Vfuture)

TV(Vf=3000)

TV(Vf=2500)


Total Value

Total perspective II

Appendix 2: Derivations of exlicit functions for the stock levels at different points in time under the influence of changing harvest levels and production in dynamically introduced new plantations.

This approach represents Total Perspective II.

How rapidly will new forests grow?Example: Pine:Director of silviculture Dr. Per Persson, SCA says: Pinus contorta on average grows 40% faster than Scots pine.

Source: Jgmstarnas frenings hstexkursion, Skogsakademikern, rgng 21, Nr 4, 2007

How rapidly will new forests grow?Example: Spruce: Dr. Bo Karlsson, Skogforsk: Improved spruce plants already today give 15% better growth than naturally regenerated plants, but the potential is even higher. It should not be impossible to obtain up to 40% growth improvements.

Source: Skogsvrdsstyrelsens seminarium "Granen i fokus" i Bors,Tidningen Skogsvrden, Nr 4, 2005 http://www.skogssallskapet.se/skogsvarden/2005_4/sv13.php

How rapidly will new forests grow?

Example: Intensive plantations:

- Treat forestry seriously! Start with intensive forest management! These are the words of Fredrik Klang, district manager at Sveaskog, Vstra Gtaland. He says that a production increase of 20 procent is easy to obtain if you really want to. With fertilization, the production could even increase by 150%.

- Perhaps we can use 2-5% of the land for more intensive production. If 10% of the forest land is used for intensive production (that is the size of the area today set aside for environmental purposes), this would improve the national forest production by 15%. - This, in turn, would improve employment, the environment and the growth. Source: Skogsvrdsstyrelsens seminarium "Granen i fokus" i Bors,Tidningen Skogsvrden, Nr 4, 2005 http://www.skogssallskapet.se/skogsvarden/2005_4/sv13.php

If harvested areas are replanted with more rapidly growing seedlings, the stock path becomes strictly convex (during time periods with constant harvesting)

Derivations and parameters (II)

http://www.lohmander.com/EF2008/EFchange2008.htm Web software for Total Perspective II

h1t2totvalvfuture11665180630551263519062666136251966258614620200425561561720292541

Diagram4

65

35

25

20

17

t2


t2 Years

t2(h1)

Blad1

Model_1

20080103

Peter Lohmander

r0.06

g106

t15

v03000

h086

z01

z11

z21

vfutureh1t2totval

3000106inf1680

3000108551703

3000110301718

300011221.61727

300011417.51732

3000116151735

vfutureh1t2totval

2500106inf1680

2500116651800

2500126351886

2500136251939

2500146201973

2500156171997


1061680

108551703

110301718

11221.61727

11417.51732

116151735

1061680

116651800

126351886

136251939

146201973

156171997

Model_2

20080103

Peter Lohmander

r0.06

g0106

g1126

t15

v03000

h086

z01

z11

z21

ATKvot80

vfutureh1t2totval

106

10855

11030

11221.6

11417.5

11615

vfutureh1t2totval

3055116651806

2666126351906

2586136251966

2556146202004

2541156172029

Blad1

55

30

21.6

17.5

15

t2 inf

h1

t2


Blad2

65

35

25

20

17

t2 inf

h1

t2


Blad3

t2(Vf=3000)

t2(Vf=2500)


t2 (Years)

t2(h1, Vfuture)

TV(Vf=3000)

TV(Vf=2500)


Total Value

totval


Total Value

t2


t2 Years

t2(h1)

Diagram5

3055

2666

2586

2556

2541

vfuture


Vfuture (Million M3sk)

Vfuture = Stock level at t2

Blad1

Model_1

20080103

Peter Lohmander

r0.06

g106

t15

v03000

h086

z01

z11

z21

vfutureh1t2totval

3000106inf1680

3000108551703

3000110301718

300011221.61727

300011417.51732

3000116151735

vfutureh1t2totval

2500106inf1680

2500116651800

2500126351886

2500136251939

2500146201973

2500156171997


1061680

108551703

110301718

11221.61727

11417.51732

116151735

1061680

116651800

126351886

136251939

146201973

156171997

Model_2

20080103

Peter Lohmander

r0.06

g0106

g1126

t15

v03000

h086

z01

z11

z21

ATKvot80

vfutureh1t2totval

106

10855

11030

11221.6

11417.5

11615

vfutureh1t2totvalh1vfuture

30551166518061163055

26661263519061262666

25861362519661362586

25561462020041462556

25411561720291562541

Blad1

55

30

21.6

17.5

15

t2 inf

h1

t2


Blad2

65

35

25

20

17

t2 inf

h1

t2


Blad3

t2(Vf=3000)

t2(Vf=2500)


t2 (Years)

t2(h1, Vfuture)

TV(Vf=3000)

TV(Vf=2500)


Total Value

totval


Total Value

t2


t2 Years

t2(h1)

vfuture


Vfuture (Million M3sk)

Vfuture = Stock level at t2

Diagram3

1806

1906

1966

2004

2029

totval


Total Value

Blad1

Model_1

20080103

Peter Lohmander

r0.06

g106

t15

v03000

h086

z01

z11

z21

vfutureh1t2totval

3000106inf1680

3000108551703

3000110301718

300011221.61727

300011417.51732

3000116151735

vfutureh1t2totval

2500106inf1680

2500116651800

2500126351886

2500136251939

2500146201973

2500156171997


1061680

108551703

110301718

11221.61727

11417.51732

116151735

1061680

116651800

126351886

136251939

146201973

156171997

Model_2

20080103

Peter Lohmander

r0.06

g0106

g1126

t15

v03000

h086

z01

z11

z21

ATKvot80

vfutureh1t2totval

106

10855

11030

11221.6

11417.5

11615

vfutureh1t2totval

3055116651806

2666126351906

2586136251966

2556146202004

2541156172029

Blad1

55

30

21.6

17.5

15

t2 inf

h1

t2


Blad2

65

35

25

20

17

t2 inf

h1

t2


Blad3

106106

55108

30110

21.6112

17.5114

15116

106106

11665

12635

13625

14620

15617

t2(Vf=3000)

t2(Vf=2500)


t2 (Years)

t2(h1, Vfuture)

1680106

1703108

1718110

1727112

1732114

1735116

1061680

1161800

1261886

1361939

1461973

1561997

TV(Vf=3000)

TV(Vf=2500)


Total Value

totval


Total Value

ObservationsIn this derivation, a growth improvement of 19% in future forest generations has been assumed. (126/106 -1 = 19%). The maximum potential future growth, 40% or much more with intensive management, has not at all been utilized or assumed. Still, we should strongly increase harvesting during a long period.

For instance, we may harvest 136 Miljon m3sk per year during 20 years. This period starts in five years. 25 years from now, we will have 2.6 billion cubic metres in the forest (which is the same as the stock level in 1985). Harvesting increases by 58%!!!

The total economic value strongly improves.

Industrial capacity of different kinds that utilize forest raw material should be very much expanded.

The employment improves for a long time.

New observationsThe forest policy and regulations are not optimally chosen with respect to the economy of Sweden, employment and the environment.

If we want to get the best possible forest sector and energy policy, coordinated activities of a new kind are necessary.

Eon investsts billions in Sverige2008-01-02|07:41

The new director of E.ON Nordic, Hkan Buskhe, informs about large investments in Sweden during the next years.

Between 2007 and 2013, the investment plans represent almost 50 billion SEK (=5 billion Euro) (Dagens Industri). Between 2007 and 2010, we are talking about 37 billion SEK, Buskhe says. With the investments in nuclear power, where Eon partly owns all ten Swedish reactors, a CCP power station in Malm, wind power and four bioenergy power stations, the new investments will give 8,5 terawatt hours. This roughly corresponds to the two nuclear reactors that have been shut down in Barsebck.

www.realtid.se

General observationsThe harvest level in Sweden is absolutely not too high! Sweden would, in every way, benefit if the harvest level strongly increased during a long time period. We do not need the expensive roundwood import from Russia.We should not shut down the pulp mills. The unemployment in the Gvle region is quite unneccesary.The forest industry and the energy industry utilizing raw material from the forest should be strongly expanded.

Sources:Lohmander, P., Ekonomiskt rationell utveckling fr skogs- och energisektorn i Sverige, Nordisk Papper och Massa, Nr 3, 40-41, 2008, http://www.Lohmander.com/ERD2008/ERD2008.pdfLohmander, P., Lgg inte ned Svensk skogsindustri p grund av virkesbrist, Krnika, Nordisk Papper och Massa 8/2007 http://www.Lohmander.com/kronika_NPM07.pdf

Suggestions for the futureWe need a special commission with this task:Create a coordinated development plan for the forest-, energy- and car- industry sectors in Sweden that is rational with respect to total economics, employment and the environment. The comission should report directly to the government, once a year, 2009 2011, and have a budget of 50 MSEK (5 M EURO). Organization: Peter Lohmander (Chair), The Forest Sector, The Energy Sector, The Car Industry and the Department of the Environment.

#07. Integrated regional study and risk management

The optimal joint management strategy of the forests, the energy plants and the forest industry mills will be determined in a region.

Three coorporations are involved:E.ON Sweden, Holmen and Sveaskog.

Integrated regional study and risk management

Preliminary map of the locations of the main energy plants (red filled circles) and forest industry mills (black filled squares) that will be included in the total optimization.

Coorporations: E.ON Sweden, Holmen and Sveaskog.

Risk is an important property of the real world!

Where do we have risk?Future market prices of energy and raw materials.The properties of the capital market.Future environental regulations.Technological options and future costs.

Consequences for optimal strategies: The management strategy must be optimal when we consider risk.

Flexible strategies must be defined and optimized!

Long term predictions and detailed long term plans are not relevant in a world influenced by risk.

Adaptive optimization, stochastic dynamic programming and stochastic optimal control are the only relevant approaches.

Stochastic Dynamic Optimization of Forest Industry Company Management

INFORMSInternational Meeting 2007 Puerto RicoPeter LohmanderProfessor SLU Umea, SE-901 83, Sweden, http://www.Lohmander.comVersion 2007-06-21

AbstractForest industry production, capacity and harvest levels are optimized. Adaptive full system optimization is necessary for consistent results. The stochastic dynamic programming problem of a complete forest industry company is solved. The raw material stock level and the main product prices are state variables. In each state and at each stage, a linear programming profit maximization problem of the forest company is solved. Parameters from the Swedish forest industry are used as illustration.

QuestionHow should these activities in a typical forest industry company be optimized and coordinated in the presence of stochastic markets? *Pulp, paper and liner production and sales,*Sawn wood production and sales,*Raw material procurement and sales,*Harvest operations*Transport

Optimal stock and purchase policy with stochastic external deliveries in different markets

12th Symposium for Systems Analysis in Forest Resources, Burlington, Vermont, USA, September 5-8, 2006

Peter Lohmander

Professor of Forest Management and Economic Optimization, Swedish University of Agricultural Sciences, Faculty of Forestry, Dept. of Forest Economics, 901 83 Umea, Sweden, http://www.lohmander.com/

Version 060830

Optimally controlled stochastic stock path under monopsony when the entering stock level state is 6.

Optimally controlled stochastic stock path under perfect raw material market when the entering stock level state is 6.

#08. Conclusions and plans for the future

International Project Development: Title (prel.):Rational European Forest Management with Increasing Bioenergy Demand and Risk

Coordination:Sweden (Peter Lohmander)

Cooperators (prel.):France, Germany, Spain, Sweden, Switzerland

Future discussions:

Peter Lohmander is organizing the conference stream Optimal Forest Management with Increasing Bioenergy Demand within The 23rd European Conference on Operational Research (EURO XXIII), July 5-8, 2009, Bonn, Germany. http://www.lohmander.com/Bonn2009/Bonn2009.pdf

Let us continue our discussions and meet there!

#09. Thanks to E.On

My warmest Thanks to E.ON Sweden for economic support to the project Economic forest production with consideration of the forest- and energy- industries!Peter Lohmander Professor of Forest Management and Economic Optimization, Swedish University of Agricultural Sciences

http://[email protected]

#10. Referenceshttp://www.lohmander.com/Information/Ref.htm

More options:Go to http://www.Lohmander.com Click on:Economic Optimization Software,Courses and Conferences,or Information

Some of the latest referencesKrr, P (Interview with Peter Lohmander): Skogsprofessor tonar ned Skogsstyrelsens larm: "Det r inget katastroflge", Vsterbottningen - Jord och Skog, 7 Juni, 2007, http://www.lohmander.com/information/Vasterbottningen070607.doc Lohmander, P., Stochastic Dynamic Optimization of Forest Industry Company Management, INFORMSInternational Meeting 2007, Puerto Rico, Power Point Presentation, http://www.Lohmander.com/SDO.ppt Lohmander, P., A Stochastic Differential (Difference) Game Model With an LP Subroutine for Mixed and Pure Strategy Optimization, INFORMSInternational Meeting 2007, Puerto Rico, Power Point Presentation, http://www.Lohmander.com/SDG.ppt

Lohmander, P., Adaptive Optimization of Forest Management in a Stochastic World, in Weintraub A. et al (Editors), Handbook of Operations Research in Natural Resources, Springer, Springer Science, International Series in Operations Research and Management Science, New York, USA, pp 525-544, 2007 http://www.amazon.ca/gp/reader/0387718141/ref=sib_dp_pt/701-0734992-1741115#reader-link

Lohmander, P., Fatta beslut med hjlp av spelteori, Hemvrnet - Nationella Skyddsstyrkorna, 2007-10-26 http://tidningenhemvarnet.se/ http://webnews.textalk.com/se/article.php?id=281997

Mohammadi, L.S., Lohmander, P., Stumpage Prices in the Iranian Caspian Forests, Asian Journal of Plant Sciences, 6 (7): 1027-1036, 2007, ISSN 1682-3974, 2007 Asian Network for Scientific Information, http://ansijournals.com/ajps/2007/1027-1036.pdf http://www.Lohmander.com/MoLo2007.pdf

Ekman, S-O., (Interview with Peter Lohmander): Fabriken lggs ner helt i ondan, Gefle Dagblad, 2007-10-30 http://www.gd.se/start.jsp http://www.gd.se/Article.jsp?article=116927

Lohmander, P., Skapa inte arbetslshet nr industrikapaciteten borde expanderas! (SVT Nyheter, 2007-10-30, 19.10) http://svt.se/svt/play/video.jsp?a=379740 Lohmander, P., kad avverkning skulle kunna rdda Norrsundet, Nordic Paper Journal, 2007-10-30 http://www.papernet.se/iuware.aspx?pageid=395&ssoid=69620

Lohmander, P., Fabriken lggs ned helt i ondan, Skogsindustrierna, 2007-10-31 http://www.skogsindustrierna.org/litiuminformation/site/page.asp?Page=10&IncPage=578&Destination=227&IncPage2=236&Destination2=226&PKNews=5935

Lohmander, P., Norrsundet lggs ner helt i ondan, Nordisk Papper och Massa, 2007-11-01 http://www.branschnyheter.se/article11497.php Lohmander, P., Lgg inte ned Svensk skogsindustri p grund av virkesbrist, Krnika, Nordisk Papper och Massa 8/2007 http://www.Lohmander.com/kronika_NPM07.pdf

Lohmander, P,. Energy Forum, Stockholm, 6-7 February 2008, Conference program with links to report and software by Peter Lohmander: http://www.energyforum.com/events/conferences/2008/c802/program.php http://www.lohmander.com/EF2008/EF2008Lohmander.htm

Lohmander, P., Ekonomiskt rationell dynamisk utveckling fr skogen, skogsindustrin och energiindustrin i Sverige (Manuscript 2008-03-03) http://www.Lohmander.com/ERD2008/ERD2008.pdf

Lohmander, P., Ekonomiskt rationell utveckling fr skogs- och energisektorn i Sverige, Nordisk Papper och Massa, Nr 3, 2008

Lohmander, P., Mohammadi, S., Optimal Continuous Cover Forest Management in an Uneven-Aged Forest in the North of Iran, Journal of Applied Sciences 8(11), 2008 http://ansijournals.com/jas/2008/1995-2007.pdf http://www.Lohmander.com/LoMoOCC.pdf Mohammadi, L.S., Lohmander, P., A game theory approach to the Iranian forest industry raw material market, Caspian Journal of Environmental Sciences, Vol 6, No1, pp. 59-71, 2008 http://research.guilan.ac.ir/cjes/.papers/969.pdf http://www.Lohmander.com/MoLoAGTA.pdf Lohmander, P., (Eng: Peter Lohmander (in white jacket and black tie) explains that the forest growth strongly exceeds the harvest. Lohmander motivates increased harvesting and increased capacity expansion in bioenergy plants and the forest products industry), Swe: Skogsavverkningen kan kas enligt forskare! (Swedish Television, News, 2008-05-29, 19.15) http://svt.se/svt/play/video.jsp?a=1158529

Lohmander, P., Guidelines for Economically Rational and Coordinated Dynamic Development of the Forest and Bio Energy Sectors with CO2 constraints, Proceedings from the 16th European Biomass Conference and Exhibition, Valencia, Spain, 02-06 June, 2008 (In the version in the link, below, an earlier misprint has been corrected. ) http://www.Lohmander.com/Valencia2008.pdf Lohmander, P., Economically Optimal Joint Strategy for Sustainable Bioenergy and Forest Sectors with CO2 Constraints, European Biomass Forum, Exploring Future Markets, Financing and Technology for Power Generation, CD, Marcus Evans Ltd, Amsterdam, 16th-17th June, 2008 http://www.Lohmander.com/Amsterdam2008.ppt Lohmander, P., Ekonomiskt rationell utveckling fr skogs- och energisektorn, Nordisk Energi, Nr. 4, 2008

Lohmander, P., Tools for optimal coordination of CCS, power industry capacity expansion and bio energy raw material production and harvesting, 2nd Annual EMISSIONS REDUCTION FORUM: - Establishing Effective CO2, NOx, SOx Mitigation Strategies for the Power Industry, CD, Marcus Evans Ltd, Madrid, Spain, 29th & 30th September 2008 http://www.lohmander.com/Madrid08/Madrid_2008_Lohmander.ppt Lohmander, P., Optimal CCS, Carbon Capture and Storage, Under Risk, International Seminars in Life Sciences, UPV, Universidad Politcnica de Valencia, Thursday 2008-10-16http://www.Lohmander.com/OptCCS/OptCCS.ppt

Thank you for listening!Here you may reach me in the future:Peter LohmanderProfessor of Forest Management and Economic Optimization,SLU, Swedish University of Agricultural Sciences, Faculty of Forest Sciences, Dept. of Forest Economics, SE-901 83 Umea, Sweden

http://www.Lohmander.com

[email protected] [email protected]

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1 Economic forest production with consideration of the forest- and energy- industries Presentation...

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