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
Fellings, Increment and Net Growth
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
Fellings, Increment and Net Growth
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
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
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
Fig 11.4 Anvndning av biobrnslen, torv m.m. fr energindaml
Use of biofules, peat etc. for energy production
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
Klla: Energimyndigheten: "Energilget i siffor 2004"
Source: Swedish Energy Agency: "Energy in Sweden, Fact and Figures 2004"
2001-2004
0
0
0
0
0
0
Klla: Energimyndigheten, Energilget i siffror 2005Source: Swedish Energy Agency, Energy in Sweden, Facts and figures 2005
Totalt 107 TWhTotal
Anvndning av biobrnslen, torv mm fr energindaml 2004Utilisation of biofuels, peat etc, for energy production year 2004
2001-2003
Fig 11.4 Anvndning av biobrnslen, torv m.m. fr energindaml
Use of biofules, peat etc. for energy production
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
Klla: Energimyndigheten: "Energilget i siffor 2004"
Source: Swedish Energy Agency: "Energy in Sweden, Fact and Figures 2004"
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
Klla: Energimyndigheten: "Energilget i siffor 2005"
Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"
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
Klla: Energimyndigheten: "Energilget i siffor 2005"
Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"
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
Klla: Energimyndigheten: "Energilget i siffor 2005"
Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"
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
Use of different fuels in district heating
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
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
Klla: Energimyndigheten: "Energilget i siffor 2005"
Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"
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
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
Klla: Energimyndigheten: "Energilget i siffor 2005"
Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"
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
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
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
Klla: Energimyndigheten: "Energilget i siffor 2005"
Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"
&F
MBD002090CF.bin
MBD00209245.bin
MBD00208B1C.bin
Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"
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
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
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
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
Klla: Energimyndigheten: "Energilget i siffor 2005"
Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"
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
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
Klla: Energimyndigheten: "Energilget i siffor 2005"
Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"
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
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
Klla: Energimyndigheten: "Energilget i siffor 2005"
Source: Swedish Energy Agency: "Energy in Sweden, Facts and Figures 2005"
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
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
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!
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 (Mm3sk)
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 (Mm3sk)
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
t2(h1) if Vfuture = 2500
Blad3
t2(Vf=3000)
t2(Vf=2500)
h1 (Million M3sk/Year)
t2 (Years)
t2(h1, Vfuture)
Diagram2
1680106
1703108
1718110
1727112
1732114
1735116
1061680
1161800
1261886
1361939
1461973
1561997
TV(Vf=3000)
TV(Vf=2500)
h1 (Million M3sk/Year)
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
t2(h1) if Vfuture = 3000
Blad2
t2 inf
h1
t2
t2(h1) if Vfuture = 2500
Blad3
t2(Vf=3000)
t2(Vf=2500)
h1 (Million M3sk/Year)
t2 (Years)
t2(h1, Vfuture)
TV(Vf=3000)
TV(Vf=2500)
h1 (Million M3sk/Year)
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
h1 (Million M3sk/Year)
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
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
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
t2(h1) if Vfuture = 3000
Blad2
65
35
25
20
17
t2 inf
h1
t2
t2(h1) if Vfuture = 2500
Blad3
t2(Vf=3000)
t2(Vf=2500)
h1 (Million M3sk/Year)
t2 (Years)
t2(h1, Vfuture)
TV(Vf=3000)
TV(Vf=2500)
h1 (Million M3sk/Year)
Total Value
totval
h1 (Million M3sk/Year)
Total Value
t2
h1 (Million M3sk/Year)
t2 Years
t2(h1)
Diagram5
3055
2666
2586
2556
2541
vfuture
h1 (Million M3sk/Year)
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
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
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
t2(h1) if Vfuture = 3000
Blad2
65
35
25
20
17
t2 inf
h1
t2
t2(h1) if Vfuture = 2500
Blad3
t2(Vf=3000)
t2(Vf=2500)
h1 (Million M3sk/Year)
t2 (Years)
t2(h1, Vfuture)
TV(Vf=3000)
TV(Vf=2500)
h1 (Million M3sk/Year)
Total Value
totval
h1 (Million M3sk/Year)
Total Value
t2
h1 (Million M3sk/Year)
t2 Years
t2(h1)
vfuture
h1 (Million M3sk/Year)
Vfuture (Million M3sk)
Vfuture = Stock level at t2
Diagram3
1806
1906
1966
2004
2029
totval
h1 (Million M3sk/Year)
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
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
t2(h1) if Vfuture = 3000
Blad2
65
35
25
20
17
t2 inf
h1
t2
t2(h1) if Vfuture = 2500
Blad3
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)
1680106
1703108
1718110
1727112
1732114
1735116
1061680
1161800
1261886
1361939
1461973
1561997
TV(Vf=3000)
TV(Vf=2500)
h1 (Million M3sk/Year)
Total Value
totval
h1 (Million M3sk/Year)
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
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