A trade flow analysis of the global softwood log market: implications of Russian log export tax reduction and New Zealand log production restriction

Author

1. Wei-Yew Chang1,* and 
2. Chris Gaston1,2

-Author Affiliations

1. ¹Department of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
2. ²FPInnovations, Vancouver, BC, Canada V6T 1Z4

1. ↵*Corresponding author. Tel.: +1 6048226509; E-mail: changwy@mail.ubc.ca
2. 
   
3. Received March 16, 2015.

Abstract

This study employs a recursive dynamic spatial partial equilibrium model to investigate the trade flow trends in the global softwood log market. A baseline forecast from 2012 to 2021 is first projected and then compared with three alternative scenarios: (1) Russia reduces its ad valorem softwood log export tax to 8 per cent to comply with its World Trade Organization accession agreements; (2) New Zealand experiences no expansion at all of its plantation forest log production due to social and environmental considerations (i.e. increased Māori ownership of forest land and the implementation of emissions trading schemes); and (3) a combination of the proposed policies in Russia and New Zealand mentioned above. The results of the baseline projection demonstrate that softwood log prices will increase in every region globally and that China will continue to be the world's largest softwood log importer. However, softwood log exports from Russia, the US and Canada are expected to drop significantly as a result of current Russian export restrictions, the recovery of the US housing market and mountain pine beetle infestations in western Canada. A comparison of the simulated scenarios with the baseline projection reveals that reducing the Russian softwood log export tax will have a greater impact on softwood log prices and total world trade than restricting log production in New Zealand due to the comparatively large log production capacity in Russia. In any scenario, significant trade flow changes (i.e. trade-offs) in response to the proposed policy changes are observed in China and the major export regions. The results of this study offer insights for forest managers and policy makers to examine the global impacts of potential changes in trade policies and supply constraints in these two important softwood log supply regions in addition to highlighting China's role in the world softwood log market.

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Discussion and conclusions

The objective of this study is to investigate future global softwood log trade flows and also to evaluate the potential effects on the world softwood log market of three what if scenarios involving reduced Russian softwood log export taxes, restricted growth in softwood log production in New Zealand, and a combination of policy changes in Russia and New Zealand. A recursive dynamic world spatial partial equilibrium model was first used to project the baseline softwood log supply, demand, prices, total trade and bilateral trade flows through 2021; the baseline results were then compared with the results from the three alternative scenarios incorporating the proposed policy changes in Russia and/or New Zealand. First, the results of our baseline projection revealed that China would continue to be the largest importer of softwood logs in the world; however, there would be a significant change in the major suppliers of softwood logs for imports over the 2012–2021 period. The model forecast that Canada would completely halt its log exports (representing a 100 per cent decline) based on the effects of the mountain pine beetle infestation on the timber supply in western Canada. Because most of the harvested softwood logs would enter into the domestic wood processing sectors (e.g. lumber) to meet the demand from the recovering American housing market, there would be no excess Canadian logs available for export. This result is consistent with Chang and Gaston (2014), who found that exports of US dimension lumber from Canada to the US would increase over the period of 2011–2021, despite the timber supply constraints imposed by the mountain pine beetle infestation in western Canada.

In addition to the decrease in log exports from Canada, annual exports to China from the US export region and from Russia would also significantly decrease by 47 and 41 per cent, respectively, over the same period due to increases in domestic timber demand. To meet the growing domestic timber demand and to compensate for the significant decline in imports from Russia, Canada and the US, China would source its imports from the European export region (most likely from the Baltic countries – Estonia, Latvia and Lithuania – according to the actual trade flow data from recent years), New Zealand, Chile and the rest of world export region (with annual imports from those regions projected to significantly increase by 149, 49, 597 and 50 per cent, respectively). Although we did not specifically consider the substitutability of logs from different export regions in the model (i.e. cross-price elasticities for softwood logs) and assumed countries/regions to trade homogeneous softwood logs, the above results are consistent with Sun (2014), who used a Rotterdam demand system to assess China's roundwood import demand by supplying source and product type and found that there is little competition among softwood log-supplying countries in the Chinese timber market.

The results of the scenario analysis showed that reducing Russian ad valorem softwood log export taxes would increase Russian log production, exports and prices over the 2012–2021 period. These results are consistent with Turner et al. (2008), Solberg et al. (2010)and van Kooten and Johnston (2014), who found that liberalizing Russian export taxes would increase the Russian log harvest, exports and prices, whereas the opposite effects were observed following the imposition of the log export tax. The results of our bilateral trade flow analysis also suggested that the largest beneficiaries of Russian log trade liberalization would be China (in terms of volume) and the European import regions (in terms of percentage) due to geographic proximity, with projected annual imports from Russia in those regions increasing dramatically by 263 per cent (from 8.1 to 29.5 million m³) and by 640 per cent (from 0.3 to 2.0 million m³) in 2021, respectively. In addition, the significant increases in total exports from Russia and total trade in the world may also support the findings of Mäkelä (2009), who used export data to assess the competitiveness of Russian wood products and observed that the highest revealed comparative advantage of Russian wood products are products that required little processing, such as untreated softwood logs, due to its significant market share with respect to these products.

The simulation results show that reducing the Russian softwood log export tax would lead to only modest reductions in its softwood log consumption over the projection period (e.g. a decline of 5 per cent (4.4 million m³) in 2021) might also imply that imposing the softwood log export tax would not be an effective strategy to achieve the goal of encouraging forest industry development. Indeed, strategies such as improving the investment climate in Russia would have a greater impact on the development of the forest sector than imposing a log export tax as suggested by Solberg et al. (2010).

The simulated results of restricted log production in New Zealand showed that such restrictions would significantly reduce log exports to the world (primarily to China). It is projected that exports to China from New Zealand would drop by 29 per cent (4.8 million m³) in 2021 under this scenario compared with the baseline projection. However, this decline in exports would be largely offset by increased exports from other export regions, such as Russia and the European export region, during the same period. Thus, restricting New Zealand's log production would cause global prices to marginally increase by $0.5/m³ in 2021 and would not substantially affect production, consumption or total trade in other regions of the world.

Overall, our results indicate that reducing Russian softwood log export taxes would cause an increase in global softwood log trade, whereas the restriction of softwood log production in New Zealand would lead to a decrease in global softwood log trade. When further combining the proposed policies in Russia and New Zealand, we found that Russia would export more softwood logs to the world in response to the timber harvest constraint in New Zealand in addition to reduced Russian softwood log export taxes. The impact on total trade and prices as a result of reducing Russian softwood log export taxes would be greater than the impact of restricting New Zealand timber production due to the comparatively large log production capacity in the Russian primary forestry and logging sector, therefore having larger effects on the global softwood log market. In addition, under any scenario, China would significantly adjust its softwood log imports from different export regions in response to these proposed trade policy changes or market shocks in Russia and/or New Zealand. This result is also reflected in the actual trade flow data from recent years that shows that China's softwood log imports have been diversified, transitioning from one region (Russia) dominating the market to various supply regions playing a larger role due to China's strong demand for softwood logs on the world market (Global Trade Information Services Inc., 2014).

Several refinements could be made to our research to improve the accuracy of the results. First, following most spatial partial equilibrium models (e.g. Buongiorno and Zhu (2013); Kallio et al., 2004), we used a single year as the base year, and the year 2011 was the latest year of available trade flow data when we prepared the manuscript. As there were no significant global events in that year (e.g. the global financial crisis in 2008 to 2009) and due to the good calibration of the model for the base year, it is apparent that the model can give plausible projections for scenario analysis. Nevertheless, given that our model is based on recursively dynamic programming for every 5-year period to allow enough time for the global softwood log market to adjust to the proposed policy changes, it would be interesting to investigate whether the impact estimates changed when using 5-year average (e.g. 2006–2011) data for model calibration to minimize the influence of significant events or market fluctuations in any single year on the results and analyses.

Additionally, to more easily distinguish and compare the effects of alternative policy scenarios, supply/demand price elasticities and transportation costs were taken from the literature and assumed to remain constant over the projection period. Discussing the sensitivity of these assumptions is beyond the scope of this study, as they may potentially interfere with (deviate from) the changes brought about by the proposed forest policies of interest and also require more assumptions. However, it is possible that altering these assumptions could have different levels of impact on demand, supply and trade flows in the regions (Kallio et al., 2006). For example, if Chinese importers show more preference for New Zealand softwood logs than Russian softwood logs in the future, which was not internalized in our model via cross-price elasticities, some of the trade flow projections in our study may be too high. Thus, caution must be taken when interpreting our results along with the awareness of the associated assumptions behind them. Future research could investigate these factors more completely by explicitly incorporating uncertainty issues into the analysis. This includes incorporating recursive dynamic models with stochastic modelling (see Kallio, 2010) or further adapting an intertemporal optimization model (see Latta et al., 2013) to fully account for how the actors will react to the proposed policy changes when future supply/demand conditions are considered endogenously.

Finally, another way this study could be improved is by including additional forest products in the trade flow model. For instance, adding hardwood logs to the model and considering the input–output relationships of logs with different categories of lumber (e.g. appearance, construction and utility and economy grades) and biomass (e.g. wood chips and wood pellets), if the necessary data become available, could provide more valuable information. Adding these products and product categories would better help policy makers and forest industries to examine the linkage effects among all related forest products in response to changes in policy or market scenarios, including the analysis of so-called bio-pathways of resource allocation for value chain optimization. Disaggregating softwood logs into two product groups (i.e. sawlogs and pulpwood) was initially undertaken by the authors, but it was discovered that the majority of softwood logs were traded in sawlog form internationally, and no significant price variation was observed between sawlogs and pulpwood (Global Trade Information Services Inc., 2014). Thus, unlike other forest products (e.g. hardwood logs, lumber) that generally show significant price variation among product groups, species or place of production, it is plausible to consider softwood logs as a homogeneous good in a trade flow analysis. Refinements such as these are intended for future research by the authors.

Notwithstanding the above issues, we believe our analysis has provided a valuable application of spatial partial equilibrium models to the forest sector. The findings of this study can help forest managers and policy makers assess the global impact of potential changes in trade policy and supply constraints in these two important softwood log supply regions and also highlight China's role in the world softwood log market.

Conflict of interest statement

None declared.

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Funding

This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Strategic Research Network on Value Chain 

Acknowledgements

We thank Editor Dr Anssi Ahtikoski and one anonymous reviewer for their valuable comments and suggestions that have greatly improved the article.

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Appendix

Appendix C. Changes in net supply (export) function for the ad valoremexport tax

Following Houck (1986), suppose region j (e.g. Russia) has the following domestic demand and supply functions of softwood logs:

qdj=aj−bjpj$$q_j^d{a}_j{b}_j{p}_j$$

(A23)

qsj=cj+djpj$$q_j^s{c}_j{d}_j{p}_j$$

(A24)

The net supply function in region j can be expressed as follows

qsj−qdj=Xj=(cj−aj)+(dj+bj)pj$$q_j^s{q}_j^d{X}_j{c}_j{a}_j{d}_j{b}_j{p}_j$$

(A25)where X_j is the excess supply (export) in region j. The inverse net supply function is then derived as

pj=−(cj−aj)(dj+bj)+1(dj+bj)Xj$$p_j\frac{c_j{a}_j}{d_j{b}_j}\frac{1}{d_j{b}_j}{X}_j$$

(A26)Before imposing the ad valorem export tax, the domestic price in region j is the same as the world price (assuming that the cost of moving logs between regions is zero):

pj=pwj$$p_j{p}_j^w$$

(A27)

After the ad valorem export tax t, which creates a wedge between world and domestic prices, the world price is domestic price plus export tax as shown below:

pwj=pj(1+t)orpj=pwj(1+t)$$p_j^w{p}_{j}1tor{p}_j\frac{p_j^w}{1t}$$

(A28)

By substituting the new world price of equation (A28) into the net supply function of equation (A25), the new net supply and inverse net supply functions after the ad valorem export tax can then be given in equations (A29) and (A30) below:

Xj=(cj−aj)+(dj+bj)pwj(1+t)$$X_j{c}_j{a}_j{d}_j{b}_j\frac{p_j^w}{1t}$$

(A29)

pwj=−(cj−aj)(dj+bj)(1+t)+(1+t)(dj+bj)Xj$$p_j^w\frac{c_j{a}_j}{d_j{b}_j}1t\frac{1t}{d_j{b}_j}{X}_j$$

(A30)

Comparing the intercepts and slopes of inverse net supply functions (Equations A26 and A30) before and after the export tax, both the price intercept and slope of Equation (A30) increase by (1 + t) after the ad valorem tax. However, the quantity intercept remains the same at (c_j − a_j) before and after the ad valorem tax (when P_j = 0), which indicates that imposing an ad valorem tax will pivot the net supply curve inwards from the same point on the horizontal quantity axis.

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