Climate change mitigation strategies in the forest sector: biophysical impacts and economic implications in British Columbia, Canada

  • Zhen Xu
  • Carolyn E. Smyth
  • Tony C. Lemprière
  • Greg J. Rampley
  • Werner A. Kurz
Open Access
Article

DOI: 10.1007/s11027-016-9735-7

Cite this article as:
Xu, Z., Smyth, C.E., Lemprière, T.C. et al. Mitig Adapt Strateg Glob Change (2017). doi:10.1007/s11027-016-9735-7

Abstract

Managing forests to increase carbon sequestration or reduce carbon emissions and using wood products and bioenergy to store carbon and substitute for other emission-intensive products and fossil fuel energy have been considered effective ways to tackle climate change in many countries and regions. The objective of this study is to examine the climate change mitigation potential of the forest sector by developing and assessing potential mitigation strategies and portfolios with various goals in British Columbia (BC), Canada. From a systems perspective, mitigation potentials of five individual strategies and their combinations were examined with regionally differentiated implementations of changes. We also calculated cost curves for the strategies and explored socio-economic impacts using an input-output model. Our results showed a wide range of mitigation potentials and that both the magnitude and the timing of mitigation varied across strategies. The greatest mitigation potential was achieved by improving the harvest utilization, shifting the commodity mix to longer-lived wood products, and using harvest residues for bioenergy. The highest cumulative mitigation of 421 MtCO2e for BC was estimated when employing the strategy portfolio that maximized domestic mitigation during 2017–2050, and this would contribute 35% of BC’s greenhouse gas emission reduction target by 2050 at less than $100/tCO2e and provide additional socio-economic benefits. This case study demonstrated the application of an integrated systems approach that tracks carbon stock changes and emissions in forest ecosystems, harvested wood products (HWPs), and the avoidance of emissions through the use of HWPs and is therefore applicable to other countries and regions.

Keywords

Climate change mitigation strategy Forest sector Mitigation cost Socio-economic impact CBM-CFS3 

1 Introduction

Forests are essential for global climate change mitigation, because they can contribute carbon sinks. Forests not affected by land-use change remove about 8.8 GtCO2e/year from the atmosphere (Pan et al. 2011). The Intergovernmental Panel on Climate Change (IPCC) reported that the forest sector has a mitigation potential of 0.2–13.8 GtCO2e/year in 2030 with a cost up to US$100/tCO2e (Smith et al. 2014). At the global scale, among various mitigation strategies, the use of wood products and wood energy represents high mitigation potential, as well as afforestation and reforestation options (IPCC 2014). Many studies have examined the potential of the forest sector for climate change mitigation at both global and regional scales (Kurz and Apps 1995; Bourque et al. 2007; Nabuurs et al. 2007; Lippke et al. 2011). FAO (2016) highlighted that, globally, forest activities can provide economic mitigation potential ranging from 1.9 to 5.5 GtCO2e/year in 2040 at costs less than US$20/tCO2e. Two background studies for FAO (2016) indicated that wood products, especially panels, also play an important role in climate change mitigation because of substitution effects. Seidl et al. (2007) examined mitigation impacts of alternative silviculture strategies in a forest management unit in Austria. Lundmark et al. (2014) compared two forest management and wood use strategies to a “do-nothing” scenario from a systems perspective in Sweden. Werner et al. (2010) investigated various forest and wood strategies using an integral model-based approach in Switzerland. In Canada, Smyth et al. (2014) conducted the first comprehensive study examining the mitigation potential of Canada’s managed forest and harvested wood products (HWPs). However, there are few studies that provide quantitative analyses of the mitigation potential of the forest sector in response to a series of possible mitigation strategies at national or provincial scale with considerable spatial detail. In fact, a thorough assessment of the impacts of mitigation strategies is complex due to the interaction between the forest sector and energy and other industrial product sectors, and a systems perspective is required to consider the carbon flow within and among forest ecosystems, wood products, and displacement effects when substituting wood-based products and energy for emission-intensive products (e.g., concrete, steel, plastic, etc.) and fossil fuel energy (Nabuurs et al. 2007; Lemprière et al. 2013; Smyth et al. 2014; Kurz et al. 2016).

The economically feasible mitigation benefits are expected to be lower than the biophysical mitigation potential, because the implementation of mitigation strategies has technical and economic constraints (the society’s resources that can be devoted to climate change mitigation are limited). It is therefore necessary to analyze the cost of mitigation strategies (Boyland 2006; Nabuurs et al. 2007; Strengers et al. 2008; van Minnen et al. 2008; Lemprière et al. 2013). The economics of forest carbon mitigation strategies including costs have been examined in Canada for activities such as afforestation (McKenney et al. 2004; Yemshanov et al. 2005; Yemshanov et al. 2015), harvest reduction (Man et al. 2015), bioenergy (Stennes and McBeath 2006; Ralevic et al. 2010; Zhang et al. 2010), intensive forest management (Insley et al. 2002), and for multiple strategies (van Kooten et al. 1992; Krcmar and van Kooten 2005; Kennedy et al. 2007). Based on the results of Smyth et al. (2014), Lemprière et al. (2017) conducted the first in-depth national examination of the economics of mitigation strategies from a systems perspective. Although at a coarse spatial resolution, they found that some strategies are likely to provide substantial cost-effective mitigation over the medium and long term to meet Canada’s emission reduction targets, provided that mitigation actions are initiated soon.

In this study, we further refined the methodology of Smyth et al. (2014) and Lemprière et al. (2017) with a much finer spatial resolution and applied it to the forest sector in the Canadian province of British Columbia to demonstrate the biophysical and economic mitigation potentials in response to a range of regionally differentiated strategies and portfolios. Socio-economic impacts of mitigation strategies were incorporated in the economic analysis to capture implications on the British Columbia’s (BC’s) economy and social welfare. The primary objective of this study was to quantify and compare the biophysical, economic, and socio-economic impacts of various mitigation strategies for BC to a business-as-usual scenario. BC’s forests and forest sector are among the most significant in Canada, and its forests are managed to achieve a balance across multiple goals including habitat, sustainable harvesting, employment, and others (Hoberg et al. 2016): contributing to climate change mitigation could become another goal to include in the balance. This study does not consider these other goals.

2 Methods

2.1 Analytical framework

BC’s forests cover about 55 million hectares, of which 95% are owned by the provincial government. BC is the largest exporter of softwood lumber in the world and the largest bioenergy producer in North America. It is estimated that about 6–7 billion tonnes of carbon are stored in the aboveground biomass (95% of which are certified by third-party certification) (BCMoFLNRO 2013), with an average net carbon removal from the atmosphere of 62.8 MtCO2e/year during the last 25 years (Government of British Columbia 2016), which is equivalent to the total annual CO2e emissions from all other sectors in BC. Harvesting of BC’s forests transfers roughly the same amount of carbon (66.2 MtCO2e/year) to wood products. These characteristics suggest that the mitigation potential in BC’s forest sector could be substantial if appropriate strategies are implemented.

In line with the IPCC’s definition of mitigation (IPCC 2014), our analysis considered mitigation potential as reduced greenhouse gas (GHG) emission or enhanced carbon sequestration that would result from implementation of a mitigation option, relative to a baseline. Such an approach canceled out all factors that were assumed to be unchanged between the baseline and a mitigation scenario, including variables with uncertainties like GHG emissions from wildfires. A systems perspective was employed to include potential mitigation resulting from changes in forest management, use of longer-lived products (LLPs) and bioenergy, as well as avoided emissions in other sectors due to displacement effects (Fig. 1). We defined forest sector mitigation based on carbon stock changes in BC’s forest ecosystems and in harvested wood products manufactured from wood that was harvested in BC regardless of where in the world these products reside—the IPCC production approach for estimation of HWP C balances (IPCC 2013). Domestic mitigation is the sum of forest sector mitigation plus displacement effects in BC resulting from the use of BC harvested wood products. Global mitigation is the sum of domestic mitigation plus the displacement effects that occur outside BC as a consequence of the use of HWP manufactured from wood harvested in BC. All displacement factors assumed that concrete and steel would be used as an alternative to BC wood, rather than wood from elsewhere. At all scales, we did not consider possible leakage effects due to, for example, displacement from imported wood products, or imperfect substitution due to market interactions—we focused on the mitigation that BC’s forest sector may contribute rather than the net mitigation benefit for the global atmosphere. The exclusion of leakage effects in this analysis is justified by the scale of our analysis. Leakages from shifting harvest and land-use change need to be considered at the scale of individual offset projects but are expected to be minimal at the provincial scale, because changes resulting from mitigation strategies were small compared to the size of BC’s forest sector.
Fig. 1

A systems perspective that includes multiple sectors. The solid arrows refer to carbon flows within the forest sector, and the dashed arrows represent substitution effects between biofuel and fossil fuel and between long-lived products and concrete/steel (adapted from IPCC 2007, Fig. 9.3)

Our analysis was conducted at a spatial resolution with 74 spatial units based on the forest management units (FMUs) identified in Canada’s 2014 National GHG Inventory Report (Environment Canada 2016). These FMUs in BC were defined by the boundaries of Timber Supply Areas (TSA) and Tree Farm Licences (TFL) and categorized in five ecozones and three forest regions (Fig. 2). The mitigation potential in each FMU was examined for the period from 2017 to 2050, within which three periods were of particular interest in terms of BC’s and Canada’s GHG emission reduction targets: 2017–2020 (short term), 2017–2030 (medium term), and 2017–2050 (long term).
Fig. 2

Forest management units categorized by ecozones (colors) and forest regions (thick lines)

In this study, eleven different strategies were assessed relative to the baseline, including five individual strategies and six combinations of the individual strategies (Table 1). The mitigation strategies were developed in consultation with experts and professionals from the BC Ministry of Forests, Lands, and Natural Resource Operations (BCMoFLNRO), the BC Ministry of Environment, and other organizations. Although some of the strategies are nominally similar to those in the national study of Smyth et al. (2014), all strategies in this study were adjusted based on the biophysical and economic characteristics of BC’s forest sector, as well as the province’s mitigation needs (see “Appendix” for further details of the five individual strategies). Some strategies were implemented with a ramp-up period of 2017–2020 as it was expected that time would be needed to scale-up efforts.
Table 1

Individual mitigation strategies and their combinations

Individual strategy

Implementation (relative to baseline assumptions)

Higher utilization

• Increase merchantable utilization

 - 2017–2020: from 85 to 86.5%

 - 2021–2050: from 86.5 to 90%

•Increase salvage harvesting

 - 2017–2020: from 6 to 7%

 - 2021–2050: from 7 to 10%

Harvest less

•Reduce harvest volume by 2% from 2017 to 2050

Harvest residue for bioenergy

•Collect harvest residues for local bioenergy production

 - 2017–2020: reduce the proportion of slashburning from 50% of total harvest area to 40% and transfer 10% harvest residue carbon to bioenergy

 - 2021–2050: reduce the proportion of slashburning from 40% of total harvest area to 25% and transfer 25% harvest residue carbon to bioenergy

Restricted harvest

•Restrict harvest to stands less than 250 years old from 2017 to 2050

More longer-lived products

•Shift the commodity mix from pulp and paper to panels

 - 2017–2020: reduce harvest used for pulp and paper by 1.6% and increase harvest used for panels by 1.6%

 - 2021–2050: reduce harvest used for pulp and paper by 4% and increase harvest used for panels by 4%

Strategy combination

Additivitya

Higher utilization + harvest residue for bioenergy

Not additive

Higher utilization + more LLP

Additive

Harvest less + more LLP

Not additive

Harvest residue for bioenergy + more LLP

Additive

Restricted harvest + more LLP

Not additive

Higher utilization + harvest Residue for bioenergy + more LLP

Not additive

aAdditivity indicates whether the individual strategies in a combination interact with each other. If additive, effects of individual strategies can be added together to determine the effect of the strategy combination. If not, the combination was modeled as a combination strategy

The baseline was defined as the forest management activities and use of HWP that would occur in the absence of mitigation activities. Modeling assumptions for the historical period (1990–2012) in the baseline were based on Canada’s GHG National Inventory Report (Environment Canada 2016). We included harvest and wildfire projections for each FMU for the future time period (2013–2050) based on a forecast of future harvest levels and historical average annual area burned during 1990–2012, respectively.

Mitigation effects for each strategy are expected to vary across FMUs depending on various factors, such as size of the spatial unit, forest characteristics, harvest levels, potential for substitution of fossil fuels, and logging and transportation costs. Implementation of the strategies was modeled for each FMU independently, i.e., there were no interactions between FMUs. We recognized that there is no “best-for-all” strategy for the province; rather, a portfolio of strategies with FMU-specific strategy selections would generate greater mitigation and/or require lower cost. Therefore, we first estimated attributes of interest (e.g., global, domestic or forest sector mitigation, or mitigation cost) for all strategies in each FMU and then constructed a specific portfolio by selecting the strategy to best meet the portfolio goal in each FMU over a specific time period. Among others, four portfolios for the long term (2017–2050) were of particular interest: (1) portfolio that maximizes global mitigation (PORT1), (2) portfolio that maximizes domestic mitigation (PORT2), (3) portfolio that maximizes forest sector mitigation (PORT3), and (4) portfolio that minimizes domestic mitigation cost (PORT4) (see Fig. 5 for all portfolios).

2.2 Mitigation impact of strategies

Forest ecosystem carbon dynamics in this study were estimated using the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) (Kurz et al. 2009) with historical datasets for BC from the National Forest Carbon Monitoring, Accounting and Reporting System (Kurz and Apps 2006; Stinson et al. 2011). Carbon transferred from forest ecosystems to HWP and bioenergy were tracked through manufacturing, use/export, and end-of-life use by the Carbon Budget Modeling Framework for Harvested Wood Products (Smyth et al. 2014). More details about the carbon models are described in the “Appendix”.

Following Smyth et al. (2014), we defined mitigation as the difference in GHG emissions between the baseline and a scenario with mitigation actions:

$$ M={E}_{\mathrm{base}}-{E}_{\mathrm{strategy}} $$

where M is the mitigation and Ebase and Estrategy are the net emissions in the baseline scenario and the mitigation scenario, respectively. In both scenarios, net emissions included three components—the forest ecosystem emissions, the HWP emissions including bioenergy, and emissions as a result of displacement. Displacement impacts for products included the emissions associated with the extraction and transportation of raw materials and the manufacturing of products, based on aggregated studies in the literature (Smyth et al. 2016).

To obtain the total cumulative global mitigation impact of a strategy, we aggregated FMU results for the strategy to the provincial level. Emissions associated with exported HWPs were taken into account in order to be consistent with the IPCC Production Approach in accordance with internationally agreed guidance (IPCC 2013). Displacement effects that occurred abroad were included in the global mitigation impact of a strategy but not in the domestic mitigation impact which was used for economic analyses, as mitigation resulting from displacement occurring abroad is not included in Canada’s national or provincial GHG inventories (Environment Canada 2016). We also assumed that bioenergy from harvest residues was produced and consumed only within BC.

Two displacement effects were considered in all individual strategies and associated combinations involving a change in HWP or bioenergy production: substitution between solid wood products (panels) and emission-intensive materials (concrete and steel) in housing construction and substitution between bioenergy from harvest residues and fossil fuel energy in providing power and heat. Emissions related to displacement were calculated by employing displacement factors for sawnwood, panels, and bioenergy. Displacement factors are commonly used to indicate how many tonnes of carbon emissions from alternatives can be avoided per tonne of carbon in wood-based products used (Sathre and O’Connor 2010). In this study, we applied national average displacement factors of 2.1 tC/tC for sawnwood and 2.2 tC/tC for panels derived from Smyth et al. (2016) for the entire period by considering three end-use products: single-family home, multi-family home, and multi-use building. For avoided emissions for bioenergy in BC, we estimated displacement factors using a linear programming (LP) model that maximized avoided emissions in each FMU by selecting different bioenergy facilities to substitute for the most emission-intensive fuel sources that would have been used for baseline electricity and heat generation. Smyth et al. (2016) provided a detailed description of the LP model, and details of the bioenergy facilities are summarized in Table 2.
Table 2

Bioenergy facility types and characteristics adapted from Smyth et al. (2016)

Type

Scale

Description

Biomass demand (kodt/year)

Electrical conversion rate (MWh/odt)

Thermal conversion rate (GJ/odt)

Assumed electrical efficiency (%)

Assumed thermal efficiency (%)

Implied overall efficiency (%)

Production costf ($/MWh)

Heat

Small

0.4 MWth boiler for district heatinga

0.783

15.0

75

75

13.28

Medium

2.3 MWth boiler for district heatinga

3.97

17.0

85

85

9.99

Large

6.62 MWth process heat via syngasb

11.58

16.8

84

84

7.12

Power

Small

0.2 MWe gas turbinec

1.60

1.02

18

18

173.09

Medium

5 MWe steam cycleb

34.97

1.17

21

21

34.70

Large

10 MWe steam cycleb

63.86

1.28

23

23

30.15

CHP

Small

0.2 Mwe, 0.98 MWth organic rankine cycled

2.09

0.78

14.0

14

70

84

123.17

Medium

1.8 MWe,4.5MWth steam turbinee

10.58

1.39

10.8

25

54

79

63.36

Large

8 MWe CHP steam turbineb

46.87

1.39

5.88

25

29

54

56.43

aRETScreen International (2015)

bBiopathways (FPAC and FPInnovations 2011)

cArena et al. (2010)

dWood and Rowley (2011)

ePröll et al. (2011)

fProduction costs (2008 dollars) include fiber costs described in Table 7

2.3 Mitigation costs of strategies

The domestic mitigation costs (Canadian dollars) were estimated using the Model for Economic Analysis of Forest Carbon Management (MEA-FCM) which was originally designed and employed by Lemprière et al. (2017). We only considered domestic mitigation rather than global mitigation, because mitigation costs associated with BC’s forest sector and related local industries were of interest in this study. We defined mitigation cost (TC) as the total cost to implement a mitigation strategy which equals the change between the baseline and a mitigation scenario in the present values of the total net revenues (NR) of both the forest sector and the other industries/sectors affected by displacement:

$$ TC=\varDelta {NR}_{\mathrm{forest}}+\varDelta {NR}_{\mathrm{dis}} $$

where ΔNRforest is the total net revenue change in the forest sector, and ΔNRdis is the total net revenue change in other industries/sectors. The total net revenues of the forest sector (NRforest) in either the baseline or a mitigation scenario can be further broken down as follows:

$$ {NR}_{\mathrm{forest}}={R}_{\mathrm{fm}}-{C}_{\mathrm{fm}}+{R}_{\mathrm{hwp}}-{C}_{\mathrm{hwp}} $$

where Rfm refers to total revenue in forest management via harvesting, and Rhwp refers to total revenue from wood product manufacturing and bioenergy production; and Cfm and Chwp are the associated total costs. The total revenue change in the forest sector was calculated by taking the differences in all components between the baseline and a mitigation scenario.

For the total net revenue change in other industries/sectors, we considered concrete and steel industries and part of the energy sector that generates electricity and heat using fossil fuels:

$$ \varDelta {NR}_{\mathrm{dis}}=\left({p}_{\mathrm{c}}-{c}_{\mathrm{c}}\right)\times \varDelta {Q}_{\mathrm{panel}}\times {u}_{\mathrm{c}}+\left({p}_{\mathrm{s}}-{c}_{\mathrm{s}}\right)\times \varDelta {Q}_{\mathrm{panel}}\times {u}_{\mathrm{s}}+\left({p}_{\mathrm{e}}-{c}_{\mathrm{e}}\right)\times \varDelta {Q}_{\mathrm{residue}}\times {u}_{\mathrm{e}} $$

where pc, ps, and pe and cc, cs, and ce refer to the per unit prices and costs of concrete and steel products, as well as fossil fuel energy, respectively; uc and us are parameters that indicate how many tonnes of concrete and steel can be substituted per cubic meter of panels, respectively, and ue is the parameter that indicates the amount of bioenergy (MWh) that can be produced per cubic meter of captured harvest residues. Bioenergy displaced power and heat for residential and industrial uses, and for simplicity, we measured both energy types using MWh. ΔQpanel and ΔQresidue are the volume changes in panel production and harvest residues between the baseline and the strategies. Note that uc and us are constants while ue varied across FMUs as each had a different fuel mix that was displaced by harvest residues. To be consistent with the calculation of displacement factors, we assumed that all concrete and steel products are domestically produced in BC—we included net revenue changes in those two industries in the mitigation cost, though we realized that most steel used in BC is imported and thus the profit changes in the steel industry actually occur outside of BC.

The cost per tonne ($/tCO2e) of domestic mitigation in each FMU for each strategy was then calculated by dividing mitigation cost by domestic mitigation impact over the time period:

$$ {MC}_{ni}=\frac{PTC_{ni}}{PDE{}_{ni}} $$

where MCni is the cost per tonne in FMU i for strategy n, PTCni is the present value (2016 as the base year) of the total mitigation cost during 2017–2050 with a 3% discount rate, and PDEni is the present value of the change in total domestic emissions between the baseline and the strategy scenario with a 1% discount rate. The 3% real social discount rate was selected based on the Canadian Cost-Benefit Analysis Guide (TBS 2007). The 1% discount rate for carbon emissions was derived from the social discount rate adjusted for the marginal social cost of damages resulting from emissions—we assumed that physical carbon emissions are a proxy for the social cost of the damages, and the marginal damage of emissions will grow at an annual rate of 2% (Greenstone et al. 2013). Therefore, a 3% social discount rate for monetary value and a 2% rate for marginal damage of emissions imply a 1% discount rate for carbon emissions. By discounting both mitigation cost and quantity, cost per tonne of mitigation was considered a measure of cost effectiveness of a mitigation strategy over the entire period of 2017–2050. We kept the 2% difference between the discount rates for monetary values and the carbon emissions in the sensitivity analysis to keep the marginal damage of emission unchanged over time.

For the prices and costs for harvesting and products, we used annual averages to reflect long-term trends and assumed that they did not change over time. Detailed assumptions were developed in consultation with BCMoFLNRO and FPInnovations and summarized in Tables 3, 4, 5, and 6. Softwood/hardwood log costs in the baseline were estimates derived from log cost surveys. The log cost includes tree-to-truck cost, hauling cost, cost of stumpage, and costs for forest planning and administration, road development and management, and silviculture. Prices and costs for salvage logging and other industrial roundwood were assumed to be the same as those for regular harvest and sawnwood, respectively. The higher utilization strategy was assumed to slightly decrease the log cost per cubic meter, because the harvest volume was kept unchanged but the harvested area was smaller. The harvest less strategy reduced the harvest volume and was assumed to increase the logging cost since cut blocks were assumed to be more dispersed in order to match the same timber characteristics as in the baseline. A similar assumption was made for the restricted harvest strategy as more young stands would be harvested to meet the target harvest level. No price/cost changes in harvesting were assumed in the harvest residue for bioenergy (hereafter bioenergy) strategy.
Table 3

Harvest cost and price assumptions for individual strategies ($/m3 in 2014 dollars)

Scenario

Forest region

Softwood log pricea

Hardwood log pricea

Salvage log price

Softwood log costb

Hardwood log costb

Salvage log cost

Base case

Northern interior

53

43

43

48

38

38

Southern interior

57

46

46

52

41

41

Coast

86

76

76

81

71

71

Higher utilization

Northern interior

No change

$0.2/m3 decreasec

No change

Southern interior

Coast

$0.25/m3 decreasec

Harvest less

Northern interior

No change

$0.37/m3 increasec

No change

Southern interior

Coast

Harvest residue for bioenergy

Northern interior

No change

Southern interior

Coast

Restricted harvest

Northern interior

No change

$0.45/m3 increasec

Southern interior

$0.48/m3 increasec

Coast

$1.82/m3 increasec

aLog market reports (2008–2015 averages), BCMoFLNRO. http://www2.gov.bc.ca/gov/content/industry/forestry/competitive-forest-industry/timber-pricing

bBased on personal communication with BCMoFLNRO (Ryan Midgley, January 8, 2016)

cBased on personal communications with FPInnovations (Denis Cormier and Jean Favreau, July 25, 2011)

Table 4

Price assumptions for individual strategies for harvested wood products and bioenergy (2014 dollars)

Scenario

Forest region

Sawnwood price ($/m3)a

Panel price ($/m3)b

Other industrial roundwood price ($/m3)

Pulp price ($/odt)c

Bioenergy price ($/MWh)

Base case

Northern interior

130

238

130

854

Southern interior

130

238

130

854

Coast

130

238

130

854

Harvest residue for bioenergy

Northern interior

No change

Varies spatially

Southern interior

Coast

More longer-lived products

Northern interior

No change

Southern interior

Coast

aLumber average sales price for BC Interior during 2005–2014, Forest Economic Advisors LLC

bOSB (7/16 in.) average sales price for US Western Canada during 2005–2014, Forest Economic Advisors LLC

cAverage price of Northern Bleached Softwood Kraft (NBSK) delivered to China during 2005–2014, Brian McClay & Associates Inc.

Table 5

Cost assumptions for individual strategies for harvested wood products and energy (2014 dollars)

Scenario

Forest region

Sawnwood cost ($/m3)a

Panel cost ($/m3)b

Other industrial roundwood cost ($/m3)

Pulp cost ($/odt)c

Bioenergy cost ($/MWh)

Base case

Northern interior

110

223

110

660

Southern interior

110

223

110

615

Coast

110

223

110

645

Harvest residue for bioenergy

Northern interior

No change

Varies spatially

Southern interior

Coast

More longer-lived products

Northern interior

No change

2% decrease

No change

2% increase

Southern interior

Coast

aAverage manufacturing cost for lumber in BC during 2005–2014, Forest Economic Advisors LLC

bAverage manufacturing cost for OSB (3/8 in.) in Western Canada during 2005–2014, Forest Economic Advisors LLC

cAverage cost (manufacturing plus transportation) for NBSK in 2012 and 2014, FisherSolve, Fisher International Inc.

Table 6

Cost and price assumptions for substituted products in strategies involving displacement effects (2014 dollars)

Scenario

Concrete price ($/tonne)a

Steel price ($/tonne)b

Fossil fuel energy price ($/MWh)

Concrete cost ($/tonne)a

Steel cost ($/tonne)c

Fossil fuel energy cost ($/MWh)

Harvest less/restricted harvest/more longer-lived products

65

834

64

791

Harvest residue for bioenergy

Varies spatially

 

Varies spatially

aNRMCA (2012)

bMEPS (2014)

cBased on the steel price and the ratio between annual averages of the total revenue and total cost of the steel industry between 2004 and 2012 provided by Statistics Canada (CANSIM Table 301–0006)

Harvests were used to produce generic HWP commodities: sawnwood, other industrial roundwood, panels, and pulp and paper products (Tables 4 and 5). In the more LLP strategy, we assumed a 2% increase in the pulp and paper manufacturing cost and a 2% decrease in the panel production cost due to economies of scale in existing mills. Prices and costs for bioenergy were determined by the LP model for each FMU. Since we assumed there was no bioenergy production from harvest residues in the baseline, no baseline prices and costs were needed. In the bioenergy strategy, the price of bioenergy in each FMU was calculated based on generic electricity price ($120/MWh) and heat price ($8/GJ) weighted by proportions of power and heat that bioenergy generated. The bioenergy production cost in the bioenergy strategy was estimated by the LP model based on the selected facilities and associated production costs (Table 2). The bioenergy production cost also included costs for processing harvest residues and transporting to facilities, as well as the avoided cost of reduced slashburning (Table 7). No cost was assumed for extracting harvest residue from cut blocks to roadside, because the full-tree harvesting approach was assumed to be employed in BC.
Table 11

Average annual domestic mitigation and associated economic and socio-economic impacts (in 2016 Canadian dollars), 2017–2050

Strategy

Average annual domestic mitigationa (MtCO2e/year)

Average annual domestic mitigation cost ($M/year)

Average domestic mitigation cost per tonne ($/tCO2e)

Direct employment impact (full-time equivalent)

Total employment impact (full-time equivalent)

Average annual direct GDP impact ($M/year)

Average annual total GDP impact ($M/year)

Average annual total impact on government revenue ($M/year)

Higher utilization

5.0

−7

−2

0

0

−1

−2

0

Harvest less

1.9

33

21

−431

−909

−45

−91

−6

Bioenergy

4.0

248

79

1143

1862

352

583

65

restricted harvest

2.3

81

36

−1322

−2789

−130

−262

−18

More LLP

1.8

147

97

265

495

−45

−96

−8

Higher utilization + bioenergy

8.5

309

48

1401

2282

299

495

55

Higher utilization + more LLP

6.8

140

25

265

495

−46

−97

−8

Harvest less + more LLP

3.7

179

59

−182

−448

−86

−178

−13

Bioenergy + more LLP

5.2

415

103

1408

2357

307

487

57

Restricted harvest + more LLP

3.5

171

63

−1155

−2475

−159

−323

−23

Higher utilization + bioenergy + More LLP

10.2

457

57

1665

2776

254

399

47

PORT2

12.4

610

43

176

−162

66

52

17

PORT2 w/o restricted harvest

10.8

559

46

1289

2166

184

284

34

aDomestic mitigation values here were calculated based on the FMUs with total domestic mitigation greater than 0.01 MtCO2e

Economic assumptions for displacement included prices and costs for concrete and steel products and fossil fuel energy. The prices for fossil fuel energy in the bioenergy strategy were the same as the prices for bioenergy, while the unit costs were calculated by dividing the total energy production cost (including fuel cost and production cost) from all fuel sources being substituted by the total bioenergy production (Table 8).
Table 7

Cost assumptions for the supply of harvest residues (in 2008 dollars)

Forest region

Processing ($/odt)

Other cost ($/odt)c

Avoided cost ($/odt)d

Transportation cost

Fixed cost ($/odt)e

≤50 km ($/odt/km)

>50 km ($/odt/km)

Northern/southern interior

21a

8

−5

8

0.31f

0.16f

Coast

19b

8

−5

8

0.36b

0.2b

aFriesen (2013)

bMacDonald et al. (2012)

cAverage of Ralevic et al. (2010), Ralevic (2013), Ryans and Cormier (2009), and Reynolds et al. (2012)

dBased on a weighted average cost for slashburning from Baxter (2010)

eAverage of Ralevic (2013), MacDonald et al. (2012), Gautam et al. (2010), and Kumar et al. (2008)

fRalevic (2013), including grinding costs with pre-piling

2.4 Socio-economic impacts of strategies

The socio-economic impacts of mitigation strategies were analyzed using multipliers from the national input-output (I/O) model (Statistics Canada 2014). The value of a multiplier refers to the increase/decrease in an indicator (e.g., gross domestic product (GDP)) if the demand for the output of a given industry increases/decreases by $1 (or $1 million for employment). In this study, multipliers were used to assess impacts on employment, GDP, and government revenues in BC’s economy in response to changes in the forest sector resulting from the implementation of mitigation strategies. We considered both direct effects and indirect effects on those indicators, where the former refers to the impacts directly induced from a change in an industry’s output, and the latter measures the impacts of further output changes due to interactions among industries within BC in response to the initial changes in the directly affected industry. Given that induced effects may cause double counting (Horne 2008), we did not consider induced effects in our analysis. We also only focused on the socio-economic impacts in response to changes in BC’s forest sector—no impacts resulting from displacement effects in other industries/sectors were estimated.

Five different industries in the forest sector as defined in the North American Industry Classification System (NAICS) (Table 9) were used to estimate socio-economic impacts. Mitigation actions with harvest-related activities were assigned to “forestry and logging”; strategies involving HWP were linked to two manufacturing industries—“wood products manufacture” and “pulp, paper, and paperboard mills”; for the bioenergy strategy, multipliers from the “electric power generation, transmission and distribution” were employed for bioenergy generation, and averages of multipliers for forestry and logging and “truck transportation” were used to represent impacts of harvest residue extraction for bioenergy since this activity is not specified in NAICS. For strategies involving multiple industries, total impacts were estimated as the sum of impacts on all of the relevant industries.
Table 8

Cost assumptions for power and heat production using fossil fuels (2008 dollars)

Energy type

Fuel type

Total cost ($/MWh)

Heat

Natural gas

22a

Electricity

42b

Fuel oil

101c

Waste fuels

5d

Power

Diesel

259e

Natural gas

35f

aNEB (2014), Manitoba Hydro (2015), EPA (2013), and IEA (2010)

bNEB (2014), Manitoba Hydro (2015), USDC (2011), and Hamilton Home Products (2015)

cNRCan (2015), EPA (2013), and IEA (2010)

dWaste fuels refer to fuels recovered from industrial processes, such as coke, coke oven gas, petroleum coke, and distilled gas. The total cost was estimated based on EIA (2013b), EPA (2013), and IEA (2010)

eNRCan (2015), Dunn (2011), and Osler (2011)

fNEB (2014), Manitoba Hydro (2015), and EIA (2013a)

Our analysis estimated the direct and indirect effects using multipliers shown in Table 9. For each mitigation strategy, the initial change in an industry was estimated as the change in total revenues of that industry between the baseline and a mitigation scenario. Following the initial change, total direct/indirect socio-economic impacts on GDP (SEGDP) for the entire period (2017–2050) were estimated using the corresponding multipliers:

$$ {SE}_{\mathrm{GDP}}=\sum_j^J\sum_t^T\frac{GDP_{j t}}{{\left(1+ r\right)}^t}=\sum_j^J\sum_t^T\frac{\Delta { T R}_{j t}\times {m}_j^{GDP}}{{\left(1+ r\right)}^t} $$

where GDPjt is the GDP impact from industry j at time t, ΔTRjt is the total revenue change estimated in MEA-FCM and \( {m}_j^{GDP} \) is the multiplier for direct/indirect GDP impacts of industry i, and r is the discount rate.

Because there are no multipliers specified for impacts on government revenue in the national I/O model, we used the sum of the multipliers for the government revenue-related components that are used for GDP calculations (personal and business income taxes were not included). We did not separately estimate impacts on provincial government revenue in BC since no associated multipliers were available; rather, we estimated the impact on total government revenue for all federal, provincial, and municipal governments:

$$ {SE}_{\mathrm{revenue}}=\sum_j^J\sum_t^T\frac{GR_{j t}}{{\left(1+ r\right)}^t}=\sum_j^J\sum_t^T\frac{\Delta { T R}_{j t}\times \left({m}_j^{T p}+{m}_j^{T n}-{m}_j^{Sp}-{m}_j^{Sn}\right)}{{\left(1+ r\right)}^t} $$

where GRjt is the total government revenue impact from industry j at time t; \( {m}_j^{Tp} \) and \( {m}_j^{Tn} \) are multipliers for taxes on products and production of industry j, respectively; and \( {m}_j^{Sp} \) and \( {m}_j^{Sn} \) are multipliers for subsidies on products and production of industry j, respectively.

The direct impacts on employment were not examined using employment multipliers because most mitigation actions involved in the strategies were so specific that the available multipliers were too general and cannot reflect their impacts on employment appropriately. Instead, we estimated the direct impacts on jobs (\( {SE}_{\mathrm{direct}}^{\mathrm{Job}} \)) by multiplying a labor intensity parameter (e.g., person-year (PY)/m3, see Table 10) for each industry with the corresponding biophysical change that occurred in that industry when a given mitigation strategy was implemented:
Table 9

Industries identified for the forest (including bioenergy) sector and associated multipliers

Industry (NAICS code)

Direct jobs

Indirect jobs

DirectGDP

Indirect GDP

Total govt. revenue

Forestry and logging (113)

3.74

3.21

0.38

0.29

0.05

Harvest residue extractiona

4.84

2.89

Wood products manufacture (3212)

3.30

4.06

0.31

0.38

0.04

Pulp, paper, and paperboard mills (3221)

1.84

3.07

0.27

0.32

0.04

Electric power generation, transmission and distribution (2211)

2.46

1.77

0.81

0.11

0.05

aThis industry is not specified in NAICS and was artificially made by using averages of the multipliers for “Forestry and Logging” and “Truck Transportation” industries. Only impacts of harvest residue extraction on employment were considered in the socio-economic impacts of the Bioenergy strategy, because costs for extracting harvest residues were included in bioenergy generation, as were the associated socio-economic impacts

$$ {SE}_{\mathrm{direct}}^{\mathrm{Job}}=\sum_j^J\sum_t^T{PY}_{j t}=\sum_j^J\sum_t^T\Delta {Q}_{j t}\times {k}_j $$

where PYjt refers to the direct change in person-year (the amount of work done by one person in a year) required in industry j at time t, ΔQjt represents the biophysical changes (e.g., harvest volume, volume of harvest residue for bioenergy, or HWP or bioenergy production) due to strategy implementation, and kj is the labor intensity parameter for industry j.

Specifically, the labor intensity required for every cubic meter of fiber harvested or manufactured was estimated for each of the forestry and logging, wood products manufacture, and pulp, paper, and paperboard mills’ industries by dividing the annual average of the total number of employees in each industry by the annual average of total industrial roundwood harvest in BC during 2009–2013. The data were derived from Statistics Canada (CANSIM 2015) and the National Forestry Database (NFD 2015), respectively. We used estimates in FPInnovations (2010) as the labor intensity for harvest residue extraction, and the labor requirement for an 8 MWe combined heat and power (CHP) steam turbine (FPAC and FPInnovations 2011) to estimate the labor intensity for bioenergy generation.

Although we did not use multipliers to calculate the direct effects on employment, we assumed that the ratio between multipliers for direct and indirect effects on employment is accurate. We then used the ratio and the estimated direct effects described previously to calculate the indirect impacts on jobs (\( {SE}_{\mathrm{indirect}}^{\mathrm{Job}} \)):

$$ {SE}_{\mathrm{indirect}}^{\mathrm{Job}}=\sum_j^J\sum_t^T{PY}_{j t}^{\hbox{'}}=\sum_j^J\sum_t^T{PY}_{j t}\times \frac{m_j^{\hbox{'}}}{m_j} $$

where \( {PY}_{jt}^{\hbox{'}} \) is the indirect change in PY required in industry j at time t and mjand \( {m}_j^{\hbox{'}} \)are multipliers for direct and indirect effects on employment, respectively.

3 Results

3.1 Global mitigation impacts

The biophysical impacts of mitigation strategies and portfolios are shown as cumulative global mitigation during the entire time period in Figs. 3 and 4. The initial impacts of the restricted harvest strategy (2017–2035) and the harvest less strategy (2017–2021) were negative (i.e., the strategies did not achieve positive cumulative mitigation until a number of years after initial implementation). Cumulative mitigation in all individual strategies changed nonlinearly over time, except the more LLP strategy (which was linear with two different slopes before and after 2020 due to the initial ramp-up period), and the magnitudes of the cumulative mitigation in 2050 ranged from a low of 24.3 MtCO2e (restricted harvest) to a high of 169.9 MtCO2e (higher utilization) (Fig. 3). The cumulative global mitigation resulting from strategy combinations and portfolios was significantly higher, at 449.1 MtCO2e for PORT1 (Fig. 4) with a regionally differentiated strategy mix (Fig. 5). This is about 38% of the estimate of 1180 MtCO2e for global mitigation resulting from strategies implemented across Canada provided by Smyth et al. (2014).
Fig. 3

Cumulative global mitigation impacts of individual strategies, 2017–2050, relative to the baseline. Values in the brackets are the numbers of FMUs with global mitigation impacts greater than 0.01 MtCO2e–only these FMUs are included in the impacts shown

Fig. 4

Global mitigation impacts of combined strategies and the portfolio that maximizes global mitigation (PORT1), 2017–2050. Values in the brackets are the numbers of FMUs with global mitigation impacts greater than 0.01 MtCO2e—only these FMUs are included in the impacts shown

Fig. 5

Distribution of the strategy mix in portfolios

3.2 Economic and socio-economic impacts

We summarize average annual domestic mitigation and associated costs, as well as socio-economic impacts, over the whole period for all strategies and PORT2 in Table 11. In terms of individual strategies, the higher utilization strategy had the highest annual average domestic mitigation (5.0 MtCO2e/year) and the lowest mitigation cost but it had minimal socio-economic impacts. The bioenergy strategy had the second highest mitigation (4.0 MtCO2e/year) and the greatest socio-economic contributions, although the annual mitigation cost was also the highest ($248 M/year). The more LLP strategy resulted in the least domestic mitigation (1.8 MtCO2e/year) with the highest cost per tonne value ($97/tCO2e). For combined strategies, “higher utilization” plus “bioenergy” plus “more LLP” had the highest average domestic mitigation of 10.2 MtCO2e/year with a cost of $57/tCO2e. It also considerably increased socio-economic benefits. higher utilization plus bioenergy achieved the second highest domestic mitigation of 8.5 MtCO2e/year and had positive socio-economic impacts, especially for GDP and government revenue. Since the restricted harvest strategy provided relatively little domestic mitigation but had significant negative socio-economic impacts, we included two versions of PORT2 (with/without the restricted harvest strategy) for comparison (Table 11). Clearly, both versions of PORT2 generated large mitigation impacts with relatively low cost per tonne values (with little of the mitigation arising from the restricted harvest strategy), but there were considerable socio-economic benefits in the version without the restricted harvest strategy.
Table 10

Industry distribution among strategies and labor intensity assumptions

Strategy

Logging (person-year/million m3)

Harvest residue extraction (person-year/million m3)

Wood manufacture (person-year/million m3)

Pulp and paper manufacture (person-year/million odt)

Bioenergy generation (person-year/million m3)

Higher utilization

170

    

Harvest less/restricted harvest

170

 

390

270

 

Bioenergy

 

90

  

30

More LLP

  

390

270

 

Higher utilization + bioenergy

170

90

  

30

Higher utilization + more LLP

170

 

390

270

 

Harvest less + more LLP/restricted harvest + more LLP

170

 

390

270

 

Bioenergy + more LLP

 

90

390

270

30

Higher utilization + bioenergy + more LLP

170

90

390

270

30

Cost curves showing the spatial variation of cost per tonne values across FMUs for combined strategies and portfolios are provided in Fig. 6 (see Fig. 7 for individual strategies). They were constructed by ranking cost per tonne values for all FMUs from the lowest to the highest and plotting against cumulative annual domestic mitigation. Because neither cost per tonne nor annual mitigation were continuous across FMUs, cost curves were discrete with each horizontal line segment representing the annual domestic mitigation in a single FMU at the estimated cost. For each strategy, the curve can be used to estimate the implementation costs of a strategy if specific annual mitigation targets need to be met in the period or to estimate how much mitigation can be achieved by a strategy with a given budget. By comparing cost curves across strategies, we can interpret the cost-effectiveness of a strategy relative to others at a certain mitigation level. For example, in panel a of Fig. 6, higher utilization plus bioenergy was more cost-effective than higher utilization plus bioenergy plus more LLP if the annual domestic mitigation was less than 3 MtCO2e/year, but the former became less cost-effective if more mitigation was required. Significant increases in mitigation potential and decreases in cost per tonne can be seen in Fig. 6 if longer time periods were considered (panels a, b, and c). On the right panels, the cumulative annual domestic mitigation and cost levels were similar when FMUs were selected to maximize global and domestic mitigation (PORT1 and PORT2). However, only two thirds of the amount could be achieved if the goal was to maximize mitigation in the forest sector (PORT3), and the mitigation was more expensive. Even less (nearly half of the total level achieved by PORT2) can be achieved with net economic gains when domestic mitigation cost was minimized (PORT4). More mitigation can be accomplished at a lower cost level in PORT1 and PORT2 than any strategies during the same time period (e.g., panels a and d). Finally, a portfolio that maximizes global mitigation (PORT1) achieves slightly less domestic mitigation than a portfolio that focuses on maximizing domestic mitigation (PORT2).
Fig. 6

Cost curves for strategies and portfolios in various time periods. The left three panels are cost curves for combined strategies during 2017–2050 (a), 2017–2030 (b), and 2017–2020 (c); the right three panels are cost curves for domestic mitigation for the four portfolios during 2017–2050 (d), 2017–2030 (e), and 2017–2020 (f). Some extreme values have been eliminated for display purposes

To examine the economic potential of mitigation relevant to BC’s emission reduction targets, we calculated the annual averages of domestic mitigation in PORT2 with and without the restricted harvest strategy at various cost levels in Tables 12 and 13, respectively. We found that, in both cases, the economic potential of mitigation impacts increased substantially in later decades or if higher costs were tolerated, which was consistent with the nonlinear curves in Figs. 3 and 4. However, significant reductions in the forest sector mitigation were found when restricted harvest plus more LLP was removed from the portfolio (Table 13), indicating that excluding such a strategy reduced mitigation potential in the forest sector if no displacement effects were considered. BC’s emissions targets are a 33% reduction in 2020 and an 80% reduction in 2050 relative to the 2007 level (Government of British Columbia 2007). Under PORT2, including the restricted harvest strategy, we calculated that 1 MtCO2e would be mitigated under $100/tCO2e in 2017–2020, which is only about 1.5% of the province’s 2007 emissions of 66 MtCO2e based on BC’s latest GHG inventory (Government of British Columbia 2016), while the contribution of PORT2 under $100/tCO2e in 2050 is 18.2 MtCO2e, equivalent to 28% of 2007 emissions (or 35% of the 2050 target).
Table 12

Annual averages of mitigation potentials in PORT2 at various cost levels for different periods, MtCO2e/year

Period

Scale

<$30/tCO2e

<$50/tCO2e

<$100/tCO2e

All costs

2017–2020

Forest sector

−0.6

−0.6

−0.4

−0.8

Domestic

0.7

0.7

1.0

2.5

2021–2030

Forest sector

0.5

2.4

3.3

3.1

Domestic

2.9

5.0

7.4

9.8

2031–2040

Forest sector

5.4

6.9

7.1

8.2

Domestic

7.9

10.8

12.7

14.4

2041–2050

Forest sector

6.3

7.5

9.6

10.3

Domestic

8.5

10.1

15.2

16.8

2017–2050

Forest sector

3.5

4.9

5.8

6.2

Domestic

5.8

7.7

10.5

12.4

Table 13

Annual averages of mitigation potentials in PORT2 without the “Restricted Harvest” strategy at various cost levels, by period

Period

Scale

<$30/tCO2e

<$50/tCO2e

<$100/tCO2e

All costs

2017–2020

Forest sector

−0.6

−0.6

−0.5

−1.0

Domestic

0.7

0.7

1.0

2.4

2021–2030

Forest sector

−0.1

1.3

1.4

1.3

Domestic

2.7

4.4

6.5

9.0

2031–2040

Forest sector

3.4

3.4

3.8

4.9

Domestic

6.5

8.6

10.7

12.4

2041–2050

Forest sector

4.0

3.6

5.5

6.4

Domestic

7.1

7.9

12.6

14.6

2017–2050

Forest sector

2.1

2.4

3.1

3.6

Domestic

4.9

6.2

8.9

10.8

4 Discussion

Our results indicated that implementing two or more strategies simultaneously in a FMU would achieve more mitigation than having only one individual strategy. However, combined strategies may not necessarily be more cost-effective or have more socio-economic benefits. For example, the combination of the bioenergy strategy and the more LLP strategy increased the average domestic mitigation from 4.0 MtCO2e in the bioenergy strategy alone to 5.2 MtCO2e in the long term but resulted in increased mitigation cost and less GDP and government revenue (Table 11). Strategies may or may not affect each other when combined (i.e., they are additive or not). In particular, the higher utilization strategy and the bioenergy strategy were not additive because higher utilization in harvest caused fewer harvest residues left on site, which resulted in a smaller supply for local bioenergy. In contrast, the more LLP strategy and the bioenergy strategy were additive, as shifting the wood product mix for a given harvest volume did not affect the amount of harvest residues. Therefore, whether a combined strategy was preferred varied, depending on the interaction between mitigation actions and policy goals. Further, our results also suggested that a portfolio would be superior to any single strategy applied across all FMUs. For instance, if the goal is to maximize domestic mitigation in the long run, PORT2 (panel d, Fig. 6) achieved more domestic mitigation than any individual strategy during the same period (panel a, Fig. 6). Finally, similar to what was found by Smyth et al. (2014), there was a trade-off in portfolios between long-term mitigation and short-term mitigation—less mitigation would be achieved in the long term to 2050 if the best short-term portfolio for 2020 was applied. This is because most of the strategies’ mitigation contributions are nonlinear over time (Fig. 3), and different strategies would be selected in the portfolios depending on the time scale of mitigation goals.

The higher utilization strategy assumed that the same amount of wood was harvested in a smaller area with a higher utilization level and that the same product mix could be achieved, i.e., incremental harvest from a stand had the same quality as the regular harvest from the stand, although this may not be the case in practice as the incremental harvest could be of lower quality and produce less valuable wood products. However, changes in the shares among different harvest products (softwood, hardwood, salvage harvest, and harvest residues) were captured in our analysis. This strategy resulted in the highest cumulative mitigation, but most mitigation was achieved in later years (Fig. 3) because fewer residues were left to decay and more carbon was sequestrated in unharvested stands, relative to the baseline. This strategy had a very flat cost curve that was close to the horizontal axis because unchanged harvest levels and a small logging cost reduction caused a small increase in total net revenue. The unchanged harvest level also meant that no new jobs were created and that there was insignificant contribution to other socio-economic indicators.

The harvest less strategy in our analysis assumed a reduced harvest level and increased costs because more dispersed cut blocks were needed to keep the same harvest characteristics (e.g., diameters, tree species, etc.). The mitigation impact of this strategy was limited because reduced emissions and increased carbon density resulting from harvesting less were offset over time by a lower carbon uptake rate due to less post-harvest regeneration and negative displacement effects since more emission-intensive products (e.g., concrete and steel) were assumed to be used to meet society’s demands. Also, because of higher logging costs and lower log production, respectively, less net profit was generated and negative socio-economic impacts were found. Therefore, this strategy highlights trade-offs between carbon density and carbon uptake rate based on the society’s demand for biomass (Lemprière et al. 2013), even though it had higher forest sector mitigation potential in the short term (Fig. 5).

In the bioenergy scenario, how much mitigation could be achieved at what cost varied greatly across FMUs because different numbers and types of bioenergy facilities were selected to substitute different fossil fuels by the LP model based on local energy demands, harvest residue availability within each FMU, transportation distances (simplified), and production costs for both bioenergy and fossil fuel energy being displaced. In this study, we assumed that in most FMUs, nearly 80% of the electricity was hydro-electricity and about 35% of heat was produced from biomass, which had no displacement benefits from bioenergy. Most mitigation benefits came from substituting bioenergy for heat and power generated using natural gas and fuel oil/diesel, and the percentage of these fossil fuels in total energy consumption varied across FMUs. Consequently, only 36 FMUs showed positive mitigation in this strategy. Among those, considerable cumulative mitigation was estimated in some areas and significant variation in cost per tonne was also observed (panel a, Fig. 7). High mitigation from this strategy was mostly from FMUs with large populations due to higher energy demands, but the strategy also incurred relatively high costs because in populated areas, a large proportion of fossil fuel energy is from natural gas which is generally much cheaper than bioenergy. In some other FMUs, especially those in the northern interior of BC, some remote communities are not connected to the power grid and produce energy using diesel generators with higher costs, indicating that lower energy costs would be possible in those FMUs if they converted to bioenergy, if the transportation costs for harvest residues were competitive. However, we did not directly estimate these effects.
Fig. 7

Cost curves for individual strategies in a long term (2017–2050), b mid term (2017–2030), and c short term (2017–2020). Some extreme values were eliminated for display purposes

Because there was no bioenergy from harvest residues assumed in the baseline and no socio-economic impacts were estimated for displaced energy facilities, harvest residue-based bioenergy production was a new industry in this mitigation scenario and thus substantial revenue was created, generating large socio-economic benefits.

The restricted harvest strategy resulted in two outcomes compared to baseline harvest. In about three quarters of FMUs, restricting harvest to stands less than 250 years old while not also lowering the harvest target forced the model to harvest larger areas within a FMU to achieve the baseline harvest level because volume per hectare harvested is greater in older stands. In the remaining quarter of FMUs, however, the model was unable to find a sufficient amount of stands eligible for harvest within the FMU and the harvest targets were not achieved. Over time, the total area of harvest declined, resulting in decreasing total revenue in the forest sector and negative socio-economic impacts. Given that there are few stands over 250 years in BC’s interior region, harvest levels could be maintained in most FMUs by shifting harvest to younger stands based on the model assumptions. Negative mitigation impacts were found in those FMUs even if only the forest sector mitigation was considered (no displacement effects), indicating that the additional carbon preserved in old stands would be offset by the increased carbon losses resulting from increased harvest of younger stands. Without considering socio-economic impacts and values for other ecosystem services, most mitigation benefits generated by this strategy would be limited to the central and north coast of BC (panels a and b, Fig. 8) where harvest levels were significantly lowered because old growth represents a greater proportion of the baseline harvest in those areas and there are insufficient younger stands to meet harvest targets. In fact, those areas covered most of the Great Bear Rainforest protection area where ecosystem-based management is implemented and logging is largely prohibited (BCMoFLNRO 2016).
Fig. 8

Spatial distribution of the strategy mix in portfolios with short-term (2017–2020) and long-term (2017–2050) goals. FMUs without color refer to spatial units that were not included in this study. Different strategy mixes were selected for the goal of maximizing forest sector mitigation versus the goal of maximizing domestic mitigation. For forest sector mitigation (b, d), Higher Utilization + More LLP and Harvest Less + More LLP were the dominant strategies in the long term and short term, respectively, because of their significant mitigation potentials in the forest sector. When domestic displacement effects were considered (a, c), the Bioenergy strategy was included in the best strategies in many FMUs except areas where large amounts of harvest residues were available, but local heat and electricity demand was relatively low. Compared to ( a), Harvest Less + More LLP was selected in ( c) in some coastal areas because large proportion of the harvest in those areas are from mature or old-growth forest stands with a high carbon density; therefore, less harvest provided greater gains in the carbon stock initially. In ( b), Higher Utilization + More LLP was the best for the interior of the province because all mitigation from this strategy was gained within the forest sector, and the Restricted Harvest strategy was preferred for the coast because of the great proportion of old-growth harvest there. In ( d), the Harvest Less + More LLP strategy almost completely replaced other strategies in all FMUs because in the short term a reduction in the harvest level always resulted in the highest initial forest mitigation

Note that the restricted harvest strategy was developed based on simplified assumptions and do not reflect actual harvest practices and policies currently employed in BC (e.g., a stand age of 250 years as a criterion for restricting harvesting of old-growth forest would be too high for the interior area, and an average utilization rate of 85% may overestimate utilization in coastal old-growth stands). Furthermore, BC’s government policy on removing any land from what is harvestable results in an a priori reduction in the allowed harvestable volume, so the harvest rate on younger stands would remain the same. Further research is needed to refine utilization levels for different logging practices (e.g., helicopter logging that can be used in BC coastal forests typically has lower utilization standards and higher costs) to review and, where necessary, update yield curves and estimates of carbon sequestration in old-growth stands and to refine assumptions about compensation costs and changes in harvest targets that may be associated with the implementation of an age-restricted harvest strategy. Thus, we caution that this analysis of a restricted harvest strategy should not be used for policy development in the province without further assessment.

The only strategy regarding wood use in this study, the more LLP strategy, had linear cumulative mitigation over time because a constant 4% shift was assumed in the annual production from pulp and paper products to panels. This strategy greatly increased the carbon storage in HWP by transferring carbon from pulp and paper products (2 years half-life) to panels (25 years half-life) (IPCC 2006). In addition, more panel products further enhanced the mitigation impact via displacement effects. Consequently, this strategy demonstrated the highest cumulative global mitigation until 2040 when it was surpassed by the higher utilization strategy. Because of the fixed shift in product commodities, this strategy had a flat cost curve (Fig. 7) with only three different costs per tonne values recognizing the different cost and price assumptions we used for the three forest regions in the province. The cost per tonne values was high, because the pulp and paper industry is relatively more capital intensive—pulp and paper mills usually have larger proportion of capital investments in equipment and facilities than sawmills. Therefore, an equal proportional change in the production would cause more revenue loss in pulp and paper industry than would be gained in the panel industry. This is also the reason why shifting total production to panels would cause small negative GDP and government revenue effects (Table 11).

Although the strategy mixes selected by FMU across portfolios varied, higher utilization plus more LLP and higher utilization plus bioenergy plus more LLP were chosen in most FMUs in portfolios considering displacement effects (Fig. 5), indicating that those two strategies are among the best examined here when considering mitigation efforts of BC’s forest sector from a systems perspective. For the forest sector alone (ignoring displacement effects), however, the bioenergy strategy was not selected in portfolios for either long-term or short-term mitigation goals, because burning harvest residues for bioenergy created limited mitigation benefits relative to other strategies (panel b, Fig. 8). When the goal was to minimize domestic mitigation cost regardless of GHG emissions reductions, completely different strategies were chosen for the portfolios.

A sensitivity analysis of discount rates was conducted for PORT2 to examine possible changes in cost per tonne values if higher monetary and carbon discount rates were applied. Considering 3 and 1% discount rates to be the most conservative assumptions, we re-estimated the cost curve for PORT2 based on a moderate assumption of 5 and 3 and also a high assumption of 8 and 6%, respectively (Fig. 9). The results suggested that the moderate assumption would increase the cost curve by up to 13% (from $106/tCO2e to $119/tCO2e) to achieve up to 12 MtCO2e per year, and the ambitious assumption would shift the curve upwards by up to 25% (from $106/tCO2e to $149/tCO2e) to achieve the same annual mitigation.
Fig. 9

Sensitivity analysis of the cost of the portfolio that maximizes domestic mitigation during 2017–2050 (PORT2) based on three different assumptions about discount rates

The biophysical mitigation potential in this study is by no means the upper limit of what can be achieved if society is ambitious in implementing changes to reduce GHG emissions. Other mitigation options involving forests have not been assessed here, such as reductions in deforestation rates, rehabilitation of stands affected by natural disturbances that have not successfully regenerated, afforestation including short-rotation bioenergy plantations (Amichev et al. 2012), and intensive forest management including nutrient management (Lemprière et al. 2013). Potential impacts of climate change on mitigation potentials were not considered here but need to be addressed in ongoing research. For the economic and socio-economic analyses, future work could consider dynamic price and cost assumptions.

5 Conclusions

In this study, we examined how much BC’s forest sector may contribute to climate change mitigation by developing several potential mitigation strategies and portfolios and examining their biophysical impacts, costs, and socio-economic impacts on BC’s economy. The results indicated that significant mitigation with positive socio-economic benefits would be possible if long-term regionally differentiated strategies were implemented soon. Our analysis estimated that regionally differentiated portfolios provided the highest cumulative global mitigation (PORT1) and the highest cumulative domestic mitigation (PORT2) by 2050 with similar strategy mixes that consisted of combinations of the higher utilization, bioenergy, restricted harvest, and more LLP strategies. By implementing PORT2 starting in 2017, BC’s forest sector could contribute 35% of the province’s emission reduction target in 2050 at a cost of less than $100/tCO2e, although implementation of such a forest sector mitigation portfolio would also cost $610 million Canadian dollars every year on average between 2017 and 2050.

Consideration of mitigation strategies would need to balance the multiple objectives that exist for BC’s forests and forest sector. This study is the first regionally differentiated forest-related mitigation study for the entire province of BC, incorporating biophysical, economic, and socio-economic impacts using rigorous quantitative analyses from a systems perspective. Moreover, our findings indicate that mitigation benefits from strategy implementation in one country could be extended to other countries via international trade if exported wood products are used to displace emission-intensive materials.

Activities related to higher utilization and increased use of longer-lived wood products and bioenergy from waste wood have been recognized in this study and in several other countries (Werner et al. 2010; Lundmark et al. 2014; Nordström et al. 2016; FAO 2016) as effective options to mitigate climate change. Based on these analyses, we recommend further exploration of potential forest sector-based mitigation activities globally. The analyses conducted in this study contribute to the global understanding of forest sector mitigation options by clearly outlining economic aspects of the mitigation under various goals and over various time periods and by synthesizing the methods, tools, and datasets needed to quantify mitigation activities. An understanding of economically feasible and socio-economically attractive mitigation strategies or portfolios helps decision makers with long-term planning for land sector contributions to GHG emission reduction efforts, and with efforts to obtain the social license to implement mitigation strategies, which will require dialogue with various stakeholders.

Acknowledgements

This study would not have been possible without close cooperation and active participation of the BC Ministry of Forests, Lands, and Natural Resource Operations (Ryan Midgley, Caren Dymond, Dennis Paradine, and James Sandland). We thank the Canadian Forest Service colleagues Max Fellows and Michael Magnan in the Carbon Accounting Team for their contributions to the carbon modeling. We also thank Mark Boyland and Minet Prinsloo in the Economic Analysis Division, Canadian Forest Service, for providing economic data and advice, and all of our collaborators for their support in developing model inputs. Finally, we thank Dan McKenney, Thomas White, James Sandland, Dennis Paradine, Caren Dymond, and three anonymous reviewers for reviewing earlier versions of this paper. Funding for this study was provided by the Pacific Institute for Climate Solutions and Natural Resources Canada. However, the authors accept full responsibility for the assumptions made in this analysis. The views expressed in this study do not necessarily reflect the positions of the Government of British Columbia or the Government of Canada.

Copyright information

© The Author(s) 2017

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Zhen Xu
    • 1
  • Carolyn E. Smyth
    • 2
  • Tony C. Lemprière
    • 3
  • Greg J. Rampley
    • 4
  • Werner A. Kurz
    • 2
  1. 1.Faculty of Forestry, Department of Forest Resources Management, 2045–2424 Main MallUniversity of British ColumbiaVancouverCanada
  2. 2.Natural Resources CanadaCanadian Forest ServiceVictoriaCanada
  3. 3.Natural Resources CanadaCanadian Forest ServiceTorontoCanada
  4. 4.Natural Resources CanadaCanadian Forest ServiceOttawaCanada

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