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Article

# Biophysical and Economic Analysis of Black Spruce Regeneration in Eastern Canada Using Global Climate Model Productivity Outputs

by
Jung Lee
1,
Daniel W. McKenney
2,
John H. Pedlar
2 and
M. Altaf Arain
1,*
1
School of Geography & Earth Sciences and McMaster Centre for Climate Change, McMaster University, Hamilton, ON L8S 4K1, Canada
2
*
Author to whom correspondence should be addressed.
Forests 2017, 8(4), 106; https://doi.org/10.3390/f8040106
Submission received: 11 October 2016 / Revised: 22 March 2017 / Accepted: 27 March 2017 / Published: 31 March 2017

## Abstract

:

#### 2.4.2. Wood and Carbon Sequestration Calculations

Hartman [22] provided a formulation for economic assessments of the optimal harvest age of a forest stand when both timber value and flows of other services (e.g., carbon sequestration) are considered:
$P V c = P cs ∫ 0 T F ′ ( t ) β c e − r t d t − P cs F ( T ) e − r T$
where PVC is the present value of sequestered carbon, Pcs is the carbon price ($·ton−1·CO2), F(t) is the carbon sequestration function, $F ′ ( t )$ is the integral of F(t), $β c$ is the conversion factor (set to 0.3 for the current work) to reduce the total carbon mass to biomass volume which is consistent with an estimated carbon content of wood of approximately 200 kg·m−3 [40], r is the discount rate (%), and t is the age of the forest stand. The term to the left of the minus sign in Equation (6) represents the NPV of carbon stored over the period of the standing trees, whereas the term to the right represents the carbon value loss when the stand is harvested, as forest biomass is expected to be set to zero at the time of harvest [15]. The mass of carbon in standing trees (biomass: ton·ha−1) as a function of stand age, is given by: $F ( t ) = v 1 ( 1 − e − v 2 t ) v 3$ where v1, v2, and v3 are fitted parameters with values of 189.6, 0.0268, and 2.56, respectively. This equation was parameterized by fitting GCM-simulated NEP data over the 1901 to 2100 period. Inserting Equation (6) into the Faustmann model (Equation (5)) gives the NPV for both timber production and carbon sequestration: $NPV = [ P V ( t ) − C ] e − r t + P cs ∫ 0 t F ′ ( t ) β c e − r t d t − P cs F ( t ) e − r t 1 − e − r t$ We reiterate that the optimal rotation age is that at which NPV is maximized. Importantly, Hartman showed that the “optimal” decision may be to leave a stand unharvested, depending on details such as growth rates, prices of the values under consideration, and the relevant discount rate. #### 2.5. Sensitivity Analysis Sensitivity analyses are a useful tool to examine possible outcomes when uncertainty is inherent in the problem formulation. In this case, we have used them to explore the response of NPV to parameter uncertainty. For each model parameter, simulations were repeated with the parameter adjusted to represent known uncertainty in these values, while all other parameters were maintained at their original values (Table 1). The zero establishment cost is intended to represent the possible example of natural regeneration occurring with no additional stand management activities after an area is harvested. Ultimately, regeneration costs will vary according to a managers objectives and a stand’s condition after harvest. ## 3. Results and Discussion #### 3.1. Climate, NPP, and Growth Projections Figure 2 provides temporal trends in GCM-simulated temperature, precipitation and NPP for each grid cell across the study area over the course of the current century. An increasing temperature trend was projected by all three models, although the rate of temperature increase varied slightly among the GCMs. Over the study area, the average rate of temperature increase was 0.76, 1.03 and 1.16 °C per decade as simulated by CanESM2, MIROC and MPI models, respectively (p-values < 0.05). Temperature projections by the MPI model were slightly lower than those of the other two models. There was also a slight increasing trend in the precipitation projections, though this is largely masked by significant spatial and temporal variability in the precipitation estimates. The average rate of precipitation increase was 0.33, 1.20 and 0.63 mm per decade as simulated by CanESM2, MIROC and MPI models. CanESM2 and MPI exhibited gradual increases in NPP over time of 1.53 and 2.17 g·C·m−2 per decade, respectively (p-values = 0.18 and 0.05), while MIROC-simulated NPP showed a curvilinear relationship over the course of the century (Figure 2f). Liu et al. [41] reported an average NPP in eastern Canada of approximately 259 g·C·m−2 year−1; the GCM-simulated NPP values reported here are smaller in comparison, reaching a maximum of ~170 g·C·m−2·year−1. This issue may arise because the GCMs use single class representations of land cover to describe large grid cells, which can lead to prediction errors [42]. There was a clear spatial gradient in NPP across the study area, with higher average NPP values (82, 101 and 123 g·C·m−2·year−1 for CanESM2, MICRO and MPI, respectively) in the southeast and lower values (43, 60 and 94 g·C·m−2·year−1, respectively) in the northwest (Figure 3). Pearson’s linear correlation analysis indicated a positive correlation between temperature and NPP as well as between precipitation and NPP as reported in previous studies [43,44,45]. ANOVA indicated that 43% of the variability in NPP is explained by temperature and precipitation (29.7% and 12.7% respectively). Experimental studies have also reported that carbon assimilation by black spruce may increase due to climate warming [46,47], although it is possible that such a response may be inhibited by other warming-induced processes affecting plant growth, such as drought-related impacts on photosynthesis [48,49,50]. Similar to NPP, projections of merchantable wood volume were highest in the southern part of the study area and lowest in the north (Figure 4). The projected average yield volumes in 2100 for CanESM2, MIROC, and MPI in southern Ontario were 270, 277, and 253 m3·ha−1, respectively. In contrast, the projected average yield volumes in 2100 for CanESM2, MIROC, and MPI in northern Ontario were 170, 170, and 156 m3·ha−1, respectively. Again, this north-south productivity gradient is likely driven by a combination of higher NPP, mean annual temperature, and annual precipitation in the south [51,52,53]. Our yield estimates, are comparable to (though slightly higher than) published yield values for Ontario, which generally range between 120 and 250 m3·ha−1 at 80–100 years of age [54,55,56]. #### 3.2. Economic Benefits Based on wood value alone, black spruce regeneration was not economically attractive across the study area under our baseline assumptions (Figure 5, Table 2). At the optimal economic harvest ages, the average net present values for CanESM2 in the northwestern, northeastern, and southern regions of the study were −$136, −$265, and −$51 ha−1, respectively. Similarly, NPVs for these regions using MIROC were −$137, −$269, and −$39 ha−1; and using MPI were −$226, −$263, and −$90 ha−1, respectively. The optimal harvest ages for CanESM2, MIROC, and MPI were 39, 38, and 38 years, respectively, for the baseline scenario. These rotation ages are well below the traditional rotation ages for this species, which are typically determined using the maximum sustained yield (MSY) approach. This disparity reflects the fact that the time value of money is not considered in the MSY approach, only biophysical yields (see discussion in Yang et al. [57]). Interestingly, despite the decline in projected NPP in the second half of the century by the MIROC model (see Figure 3) both optimal harvest age and NPV did not vary much from the other two GCMs. This is due to the discount rate, which reduces the impact of the NPP modifiers—particularly toward the end of the century when the modifiers are the largest.
When wood and carbon values were considered, NPVs were positive across the study area (Figure 6; Table 2). Average NPVs at their optimal harvest age for the baseline scenario for CanESM2 in northwestern, northeastern, and southern Ontario were $430,$305, and $522 ha−1, respectively. NPVs for these regions using MIROC were$424, $304, and$529 ha−1, respectively, and using MPI were $341,$306, and $485 ha−1, respectively. A price of$5 ton−1 CO2 appears to be a minimum threshold at which black spruce regeneration investments start to become attractive (see also Yemshanov et al. [8]). This suggests that the overall investment results rely heavily on the carbon price assumptions/expectations. We note the maps should only be used as general indicators of relative potential value due to the coarse scale of the spatial model inputs used in the study. Optimal harvest ages ranged from 42–44 years for the three GCMs, which were slightly longer than those identified in the wood-only analysis. In comparison, Yemshanov et al. [8] reported an optimal rotation age of 49 years for coniferous forests in eastern Canada inclusive of carbon benefits.
We note that a number of studies have indicated that water limitations and heat stress may limit the ability of black spruce to grow and sequester carbon, but there is no general consensus on the impacts of climate warming on the productivity of black spruce and forests in general [49,58,59,60]. Girardin et al. [45,50] looked at the impacts of climate warming, drying, and increasing CO2 concentrations on the productivity of black spruce forests and reported that inter-annual variability in black spruce productivity is significantly driven by soil water availability across broad areas of the western to eastern Canadian boreal forest and by autotrophic respiration in warm southern boreal regions. Thus, it is plausible that pending climate change could result in a decrease in carbon uptake and affect actual outcomes in a manner not captured by the GCM-based NPP adjustments utilized here. The general issue of predicting forest productivity under a rapidly changing climate remains an important research topic.

#### 3.3. Sensitivity Analyses

Sensitivity analyses were performed for both the wood-only and wood + carbon scenarios to determine the effect of economic uncertainty on model results. Table 3 summarizes the sensitivity analyses of the wood-only scenario for a range of r, C, and Stumpage Price (P) values. When r was increased from 4% to 8%, the optimal rotation period was shortened by about 6 years for all three GCMs. Furthermore, because of this, NPV was significantly lower at the new adjusted optimal harvest ages. The largest relative drop in NPV was observed in the southern region of the study area, whereas the smallest reduction was seen in the northeastern region. Conversely, when r was decreased from 4% to 2%, rotation periods lengthened by 7–8 years and NPVs increased by up to $1086/ha across the three GCMs. Again, NPV changes were greatest in southern Ontario, reflecting the higher growth potential in this region. Establishment costs (C) were adjusted from$500 to $0,$200 and $1000 per hectare. When C was reduced to$0, the rotation periods were shorter: from 39 years to 33 years for CanESM2, from 38 years to 34 years for MIROC, and from 38 years to 33 years for MPI. Furthermore, NPVs increased by approximately $300. When C was reduced to$200, a similar pattern emerged, with rotations periods shortening by two to four years depending on the GCM and increased NPVs. Conversely, when C was increased to $1000, the rotation periods lengthened by 7 to 8 years and NPVs declined making the overall investment questionable for all three GCMs. This has been shown in other studies as well, where the economic attractiveness of plantations increases in areas where opportunity costs (e.g., land values) are low [9]. Finally, when P was adjusted from$20 to $50, the result was a shorter rotation period by 2 to 5 years depending on the GCM. Increasing P turns NPV from negative to positive, making the investment more attractive. Although the increases in NPV vary by region, the highest values occur primarily in the southern portion of the study area, with the lowest values in northeastern Ontario. While perhaps counterintuitive, these shortened rotation periods are due to the increased value of future rotations, hence causing the rotation ages to decline to capture these increases in value. Table 4 illustrates the results of the sensitivity analyses of the wood + carbon scenario, where r, C, P and Pcs were adjusted to observe the effects of parameter uncertainty on the resulting NPVs. Decreasing r from 4% to 2% resulted in longer rotation periods by 3 to 7 years depending on the GCM; NPV also increased by as much as$1,000 in the southern region of the study area. When r was increased from 4% to 8%, shorter rotation periods were identified for all three GCMs, and NPVs were reduced. When C was adjusted, the results were similar to those described above for the wood-only scenario: as C increases, the rotation periods become longer and NPVs decrease. When P was adjusted from $20 to$50, the rotation periods shortened by 4 to 9 years depending on the GCM; however, NPVs increased by almost 3 fold in southern Ontario, making the investment significantly more attractive. The results highlight the importance of future price and yield expectations and establishment costs in evaluating forest investments. Forest managers will have to make judgements about their ability to adjust costs and still attain desirable future yields. In this study, we have not adjusted yields based on establishment costs given the lack of empirical information on this issue. This will be a subject of future research.
The optimal harvest age and related NPVs increased with increasing carbon price. In comparison to the wood-only results, the positive NPVs associated with the range of Pcs values, indicate the significance of including C uptake benefits in this investment. These results also provide some evidence for the potential cost-effectiveness of black spruce regeneration efforts, as these values could be compared to other possible carbon sequestration activities. The results from the sensitivity analyses also suggest that the discount rate is a critical factor in determining the optimal harvest age and value. To properly compare carbon sequestration costs, the discount rate should be consistently applied across the options being examined. Because northern temperate forest regeneration efforts require long-term investments, perceptions of the time value of money are very important [61,62].

## 4. Conclusions and Summary

Our findings suggest that investments in black spruce regeneration for timber production in Ontario are relatively unattractive without introducing financial benefits related to carbon sequestration. This underlines the importance of appropriate carbon pricing policies for making slow-growing tree species a more attractive economically renewable resource [63]. The current work contributes to the ongoing effort to identify the effectiveness of particular carbon sequestration prices and projects in forest management.
Our results are portrayed spatially, which is often overlooked in economic analyses. Non-spatial analyses often apply average values over large areas, thus ignoring significant geographic variation of key biological and financial factors. This can be problematic because climate change is likely to affect ecosystems at multiple spatial scales—from local to regional to global. Our use of GCM-derived NPP values and climate-driven yield equations allowed us to present results spatially—albeit at a relatively low resolution—and thus we were able to distinguish regions where black spruce regeneration investment should be most attractive.
A significant aspect of this study is that it incorporates inter-annual future climate variability into the growth and yield model, which in principle allows for more realistic growth estimates. Past studies have utilized generalized forest growth and yield curves where seasonal and inter-annual climate variability are ignored [6,7,8]. Interestingly, our findings suggest that the range of black spruce growth and yield estimates for the end of the current century using annual future climate inputs do not vary substantially from those obtained using standard curves [64]. We do note, however, the issue of predicting future forest productivity under a changing climate is complex and requires further research, and is the subject of significant ongoing research.
Further research is required to quantify additional risk factors associated with investments in forest regeneration. In our study, risk factors such as wildfire, disease outbreaks, or drought were not explicitly included in the economic calculus (which was done at the stand level). However, while the inclusion of such factors would help to improve future estimates of the economic values associated with forest regeneration, the general result of shortening rotations with increased risk is well known. Martell [65] reported that, when probabilistic fire occurrence was incorporated into a stochastic forest stand rotation model, it led to shorter rotation intervals. Daigneault et al. [66] also reported a reduction in rotation length with increasing fire probability, but noted that this reduction could be offset by the introduction of a carbon pricing system. Inclusion of climate change-modified forest fire risk would be a useful addition, because fire represents a major disturbance in the Canadian boreal forest that could impact large-scale patterns in biodiversity, carbon, vegetation, and forest management strategies [66,67].
There are many uncertainties involved in this work. Our sensitivity analyses have attempted to explore some of the implications of this uncertainty as it relates to the underlying economic parameters; however, there is likely to remain significant uncertainty regarding the future growth of forests in general—with black spruce being a particularly important boreal species. Our use of GCM-based NPP-modifiers in combination with empirical yield equations represents a novel approach for incorporating climate change into future estimates of forest productivity. Ongoing efforts should continue to incorporate new insights into modelling future growth and yield and related economic values as they become available.

## Acknowledgments

We acknowledge funding provided by the National Sciences and Engineering Research Council (NSERC) of Canada through Discovery and Strategic grants and Ontario Ministry of Environment and Climate Change (MOECC). The Canadian Centre for Climate Modelling and Analysis (CCCma), the Earth System Grid Federation (ESGF) and the Max-Planck-Institute (MPI) are acknowledged for providing global climate model (CanESM2, MIROC, and MPI) data sets. Gridded meteorological data and support from Natural Resources Canada and Environment Canada are also acknowledged. We thank all the scientists involved in observed and simulated flux and meteorological datasets.

## Author Contributions

M.A.A. and D.W.M conceived and designed the experiments; J.L performed the experiments and analyzed the data; J.H.P contributed to analysis. J.L wrote the paper with significant contributions from all other co-authors.

## Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of study are in North America and the Province of Ontario in Canada. Grid sizes are based on the resolution of global climate model outputs for (a) CanESM2 and MIROC (~2.8°) and (b) MPI-ESM (~2.5°).
Figure 1. Location of study are in North America and the Province of Ontario in Canada. Grid sizes are based on the resolution of global climate model outputs for (a) CanESM2 and MIROC (~2.8°) and (b) MPI-ESM (~2.5°).
Figure 2. Time series of simulated temperature, precipitation and Net Primary Production (NPP) from three global climate models; (ac) CanESM2 (Canadian); (df) MIROC (Japanese); and (gi) MPI (European) for IPCC future climate scenario RCP 8.5 for each grid (~10 km × 10 km) over the study area. Each line represents a grid-specific value from 2006 to 2100. Fitted average lines are also shown.
Figure 2. Time series of simulated temperature, precipitation and Net Primary Production (NPP) from three global climate models; (ac) CanESM2 (Canadian); (df) MIROC (Japanese); and (gi) MPI (European) for IPCC future climate scenario RCP 8.5 for each grid (~10 km × 10 km) over the study area. Each line represents a grid-specific value from 2006 to 2100. Fitted average lines are also shown.
Figure 3. Spatial distribution of projections of annual Net Primary Production (NPP) over time as simulated by the Global Climate Models: (a) CanESM2; (b) MIROC; and (c) MPI.
Figure 3. Spatial distribution of projections of annual Net Primary Production (NPP) over time as simulated by the Global Climate Models: (a) CanESM2; (b) MIROC; and (c) MPI.
Figure 4. Spatial distribution of gross merchantable wood volume (m3·ha−1) projections modified by (a) CanESM2-; (b) MIROC-; and (c) MPI-simulated annual Net Primary Productivity (NPP) ratio (see text for details).
Figure 4. Spatial distribution of gross merchantable wood volume (m3·ha−1) projections modified by (a) CanESM2-; (b) MIROC-; and (c) MPI-simulated annual Net Primary Productivity (NPP) ratio (see text for details).
Figure 5. Spatial distribution of Net Present Values (NPVs) based on wood value only for the (a) CanESM2; (b) MIROC; and (c) MPI models.
Figure 5. Spatial distribution of Net Present Values (NPVs) based on wood value only for the (a) CanESM2; (b) MIROC; and (c) MPI models.
Figure 6. Spatial distribution of Net Present Values (NPVs) based on wood and carbon sequestration benefits for (a) CanEMS2; (b) MIROC; and (c) MPI model.
Figure 6. Spatial distribution of Net Present Values (NPVs) based on wood and carbon sequestration benefits for (a) CanEMS2; (b) MIROC; and (c) MPI model.
Table 1. Parameter values used in the baseline scenario and sensitivity analyses.
Table 1. Parameter values used in the baseline scenario and sensitivity analyses.
ParameterValue
Discount rate (%)4, (2, 8) *
Establishment costs ($·ha−1)500, (0, 200, 1000) * Timber price ($·m−3)20, (50) *
Price for carbon ($·ton−1·CO2)5, (10, 20) * * Values in parentheses used in the sensitivity analyses. Parameter values were only changed one at a time. Table 2. Net Present Values (NPV,$·ha−1) of timber and carbon by region for the baseline scenarios.
Table 2. Net Present Values (NPV, $·ha−1) of timber and carbon by region for the baseline scenarios. ModelScenarioOHA *Northwest OntarioNortheast OntarioSouthern Ontario MeanMax.Min.Std.MeanMax.Min.Std.MeanMax.Min.Std. Can-ESM2W *2049−13633−22199−265−220−30033−5129−12867 W + C *20524305033451003053472723152260744570 MIROCW2048−13738−228103−269−226−30733−3918−13068 W + C2054424592337963043452742947160044368 MPIW2048−226−105−29761−263−208−33039−9057−258120 W + C20523414592826030635324835485628321118 * OHA represents optimal harvest year; W represents wood only scenario; W + C represents both wood and carbon value scenario. Table 3. Sensitivity analyses of wood-only scenario, showing variation in NPV ($·ha−1) with varying discount rate (r), establishment cost (C), and stumpage value (P).
Table 3. Sensitivity analyses of wood-only scenario, showing variation in NPV ($·ha−1) with varying discount rate (r), establishment cost (C), and stumpage value (P). CanESM2MIROCMPI Scenariosr = 2% (46)r = 4% (39)r = 8% (33)r = 2% (45)r = 4% (38)r = 8% (33)r = 2% (46)r = 4% (38)r = 8% (32) NW666−136−430661−137−424391−226−444 NE267−265−456261−269−454277−263−453 S1012−51−4161047−39−409900−90−419 ScenariosC =$0 (33)C = $200 (35)C =$500 (39)C = $1000 (46)C =$0 (34)C = $200 (36)C =$500 (38)C = $1000 (45)C =$0 (33)C = $200 (36)C =$500 (38)C = $1000 (46) NW534262−136−762334257−137−759−51−132−226−841 NE398125−265−876196122−269−876−84−171−263−873 S604332−51−663410346−39−65392−2−90−696 ScenariosP =$20 (39)P = $50 (34)P =$20 (38)P = $50 (36)P =$20 (38)P = $50 (36) NW−136660−137643−226418 NE−265316−269306−263322 S−51824−39864−90744 Values in parentheses represent optimal harvest age. For the analysis, each variable was varied while the remaining variables were held at baseline values of r = 4%, C =$500 ha−1, and P = $20 m−3. Table 4. Sensitivity analyses of wood plus carbon scenario, showing variation in NPV ($·ha−1) with varying discount rate (r), establishment cost (C), stumpage value (P), and carbon price (Pcs).
Table 4. Sensitivity analyses of wood plus carbon scenario, showing variation in NPV ($·ha−1) with varying discount rate (r), establishment cost (C), stumpage value (P), and carbon price (Pcs). CanESM2MIROCMPI Scenariosr = 2% (49)r = 4% (42)r = 8% (35)r = 2% (47)r = 4% (44)r = 8% (35)r = 2% (49)r = 4% (42)r = 8% (35) NW12764301081285424109100034190 NE880305818803048188730682 S163152212216625291251518485115 ScenariosC =$0 (35)C = $200 (39)C =$500 (42)C = $1000 (49)C =$0 (35)C = $200 (38)C =$500 (44)C = $1000 (49)C =$0 (36)C = $200 (38)C =$500 (42)C = $1000 (51) NW885811430−177886812424−175789723341−251 NE748682305−285748679304−286755686306−280 S955896522−80969909529−68943858485−111 Scenarios P =$20 (42)P = $50 (38)P =$20 (44)P = $50 (35)P =$20 (42)P = $50 (36) NW43011844241216341978 NE305851304871306882 S522139152914244851304 ScenariosPcs =$5 (42)Pcs = $10 (42)Pcs =$20 (51)Pcs = $5 (44)Pcs =$10 (45)Pcs = $20 (56)Pcs =$5 (42)Pcs = $10 (46)Pcs =$20 (60)
NW430504675424502680341420624
NE305379573304384589306387601
S522596773529608783485565745
Values in parentheses represent optimal harvest age. For the analysis, each variable was varied while the remaining variables were held at baseline values of r = 4%, C = $500 ha−1, P =$20 m−3, and Pcs = \$5 ton−1·CO2.

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MDPI and ACS Style

Lee, J.; McKenney, D.W.; Pedlar, J.H.; Arain, M.A. Biophysical and Economic Analysis of Black Spruce Regeneration in Eastern Canada Using Global Climate Model Productivity Outputs. Forests 2017, 8, 106. https://doi.org/10.3390/f8040106

AMA Style

Lee J, McKenney DW, Pedlar JH, Arain MA. Biophysical and Economic Analysis of Black Spruce Regeneration in Eastern Canada Using Global Climate Model Productivity Outputs. Forests. 2017; 8(4):106. https://doi.org/10.3390/f8040106

Chicago/Turabian Style

Lee, Jung, Daniel W. McKenney, John H. Pedlar, and M. Altaf Arain. 2017. "Biophysical and Economic Analysis of Black Spruce Regeneration in Eastern Canada Using Global Climate Model Productivity Outputs" Forests 8, no. 4: 106. https://doi.org/10.3390/f8040106

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