Modeling some long-term implications of CO2fertilization for global forests and forest industries

Joseph Buongiorno



Modeling some long-term implications of CO2fertilization for global forests and forest industries

Joseph Buongiorno

Abstract

Background：This paper explored the long-term，ceteris-paribus effects of potential CO2fertilization on the global forest sector. Based on the findings of Norby et al.（PNAS 2005，102（50））about forest response to elevated［CO2］.

Methods：Forest productivity was increased in the Global Forest Products Model（GFPM）in proportion to the rising ［CO2］projected in the IPCC scenario A1B，A2，and B2. Projections of the forest area and forest stock and of the production，consumption，prices，and trade of products ranging from fuelwood to paper and paperboard were obtained with the GFPM for each scenario，with and without CO2fertilization beginning in 2011 and up to 2065.

Results：CO2 fertilization increased wood supply，leading to lower wood prices which in turn induced modest lower prices of end products and higher global consumption. However，production and value added in industries decreased in some regions due to the relative competitive advantages and to the varying regional effects of CO2fertilization.

Conclusion：The main effect of CO2fertilization was to raise the level of the world forest stock in 2065 by 9 to 10%for scenarios A2 and B2 and by 20%for scenario A1B. The rise in forest stock induced by fertilization was in part counteracted by its stimulation of the wood supply which resulted in lower wood prices and increased harvests.

Keywords：CO2fertilization，Climate change，Prices，Supply，Demand，International trade

Correspondence：jbuongio@wisc.edu

Department of Forest and Wildlife Ecology，University of Wisconsin-Madison，Madison，WI，USA

©2015 Buongiorno. 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.

Background

The CO2content of the atmosphere has been rising steadily，from a global average of approximately 340 ppm（parts per million）in 1980 to 400 ppm in 2013（NOAA-ESRL 2015）. The［CO2］is expected to increase faster in the next century. According to the International Panel on Climate Change，atmospheric［CO2］could reach 600 to 900 ppm in 2100，depending on varying scenarios concerning economic and demographic growth，and mitigation policies （IPCC 2013）. This rise in［CO2］has consequences for climate change as it influences directly global temperature levels（IPCC 2012，Zickfeld et al. 2012）. In this context，forests are a potential source of CO2emissions especially due to deforestation（Woodwell et al. 1983）.

On the other hand，forests also act as carbon sinks accumulating carbon in trees through photosynthesis. This later role means that forests can be part of a negative feedback working against atmospheric CO2accumulation. In this process，net primary productivity（NPP）can be stimulated by the increase in atmospheric CO2. This phenomenon，referred to as“CO2fertilization”，has been incorporated into vegetation models to predict its consequences for climate change and carbon dynamics （Thompson et al. 2004）. Another consequence of CO2fertilization is its impact on forest stock and thus on wood supply and forest industries which is addressed in this study.

The general issue of climate change and forestry has received wide attention（see Kirilenko and Sedjo 2007 for a review）. Several studies have used projections of biological consequences of climate change in economicforest sector models，for countries（McCarl et al. 2000），regions（Solberg et al. 2003），or the world（Perez-Garcia et al. 2002，Sohngen and Sedjo 2005）. Some studies assume higher growth of forests due to elevated CO2concentrations，higher temperatures and longer growing seasons（Nabuurs et al. 2002）. Faster growth in turn leads to increased timber inventories and supply，and hence lower timber prices（Sohngen and Mendelsohn 1998，Perez-Garcia et al. 2002，Solberg et al. 2003，Sohngen and Sedjo 2005）；although this may be limited by the propagation of pests，diseases，and invasive species （Sohngen and Sedjo 2005）.

In view of the difficulty of determining the effect of multi-factor climate change on forests this study concentrated on the partial，ceteris-paribus，effect of CO2fertilization alone. Specifically，it used the findings of Norby et al.（2005）to predict their long-term consequences for the forest sector，other things being held constant including other parameters of climate change such as temperature and precipitation. Special attention was given to the possibility that the impact could vary considerably depending on the context；that is on different scenarios concerning world economic，demographic growth，and policy.

The next section of the paper describes the theory，models，and data used to in the study. This is followed by the results for the main countries and regions，by product group，consumption，production and prices，forest stock，and value added in forest industries. The conclusion summarizes the main results，and their limitations and potential improvements.

Methods

Theory

The market equilibrium principles that underlie the analysis are illustrated in Fig. 1. It describes the demand，supply，trade，and prices for one commodity，wood，with and without CO2fertilization of forests，in a world divided in two regions，at a particular point in time. Without CO2fertilization Region 1，with demand schedule D1 and supply schedule S1，is a net exporter and Region 2，with demand schedule D2 and supply schedule S2 is a net importer. In Fig. 1，the transport cost is ignored，and as a result the price，P，is the same in Region 1 and Region 2. In the exporting Region 1 the price P is higher than the autarky price at the intersection of D1 and S1 that would prevail without trade，while in the importing Region 2 it is lower. With CO2fertilization the forest stock increases in both regions and as a result the supply schedules shift to the right to S1' and S2'. Consequently，the price decreases from P to P'. Other things being equal，the demand is unchanged by the CO2fertilization. However，in accord with the lower price，the quantities consumed increase from C1to C1' in region 1 and from C2to C2' in region 1. The lower price also induces lower production but with the assumption of Fig. 1 this is more than compensated by the supply shifts so that the quantity produced also increases from Q1to Q1' in region 1 and Q2to Q2' in region 2. For trade，in this Figure，the result of the CO2fertilization is a slight decrease in exports from region 1 and correspondingly lower imports in region 2.

However，the magnitude and even the direction of change may differ depending on the demand and supply curves and on the magnitude of the shifts. For example，in response to CO2fertilization，production may decrease in a region if the price effect（movement along the supply curve）exceeds the effect of the supply shift. Also，net trade may increase if the differential in CO2fertilization change the comparative advantage of a region relative to the other. Thus，in a multi-region，multiproducts situation，the adjustment of the global equilibrium due to CO2fertilization is hard to predict from purely theoretical considerations，and requires a more elaborate model of the forest sector.

Global forest products model（GFPM）

The GFPM adapted for this study calculates every year a global equilibrium across countries and products，linked dynamically to past equilibria. The static phase refers to the calculation of the equilibrium in a given year. The dynamic phase simulates the change in equilibrium conditions from year to year. More details concerning the formulation and the computer implementation are available in Buongiorno and Zhu（2014a）. The current model deals with 180 countries，forest area and stock，and 14 wood products.

In the static phase，the spatial global economic equilibrium in a given year is obtained by solving a quadratic programming problem in which the objective function is the social surplus in the global forest sector in a given year，which competitive markets maximize（Samuelson 1952，Takayama and Judge 1971）. This surplus is equal to the value of the products to consumers（area under all the demand curves），minus the cost of supplying the raw materials（area under their supply curves），minus the transformation cost from raw materials，such as industrial roundwood to sawnwood，and minus the transport cost between countries including trade taxes. The main constraints define the material balance for each country and product：The quantity imported plus the domestic supply and the manufactured quantity must equal the domestic demand plus the quantity used in manufacturing other products and exports. Upon solution of this quadratic problem the shadow prices of the material balance constraints give the market-clearing prices for each commodity and country.

The dynamic phase of the GFPM describes the changes in the condition of the global equilibrium from one period to the next. The demand equations shift over time as a function of the GDP periodic growth rate，and the lagged consumption growth rate，in accord with adaptive expectations or imperfect foresight（Johnston，1984 p. 348）. The shifts of roundwood supply are determined by the rate of change of forest stock（endogenous，see below）. There is one equation of this type for industrial roundwood（logs and pulpwood），fuelwood，and other industrial roundwood. The supply shifts of waste paper and other fiber pulp depend on GDP growth.

The changes in national forest area depend on the level of income per capita，according to a Kuznet's curve （Koop and Tole 1999，Buongiorno 2014）. For each country，the curve is calibrated so that in the base year the observed rate of forest area change is equal to the predicted，given the income per capita. The national forest stock evolves over time according to a growth-drain equation：

where I is the forest stock at the beginning of the current period，S−1is the harvest during the previous period and G−1is the change of forest stock，without harvest，during the previous period，such that：

Where gais the forest area growth rate，and guis the periodic rate of forest growth on a given area without harvest. In this application gu* is the relative change of the periodic rate of forest growth due to CO2fertilization. The periodic rate of forest growth，gu，is inversely related to the stock per unit area（Buongiorno 2014）. For each country this relationship is calibrated so that in the base year the observed guis equal to the predicted given the stock per unit area. Equation（1）then gives the periodic rate of change of forest stock net of harvest，which determines the shift of the wood supply curves.

Other dynamic elements include the changes in the input-output coefficients，for example to reflect increasing use of recycled paper in paper manufacturing，and the changes in manufacturing cost（Buongiorno and Zhu，2014b）.

Model calibration

The input-output（I-O）coefficients and manufacturing costs of the GFPM used in this study were determined simultaneously by a calibration procedure based on FAOSTAT data from 2010 to 2012（Buongiorno and Zhu 2014b）. Each I-O coefficient in a year and country is the ratio of the amount of input used in making a product to the amount of output. The GFPM calibration procedure estimates the I-O coefficients while adjusting the production of the input or output if needed based on prior knowledge of manufacturing processes. Together with data on local prices the procedure also gives estimates of the manufacturing costs. With input-output coefficients and manufacturing costs determined in this way for all other countries，and the end-product demand and wood supply equations positioned with theprice and quantity in each country，the solution of the global equilibrium closely replicates the base-year input data，in terms of production，consumption，net trade （exports minus imports），and prices.

The parameters of the dynamic demand equations leading to the elasticities were estimated with panel country-year data from 1992 to 2012，using the fixedeffects method（Wooldridge 2002，p. 265），with the results shown in Table 1. The environmental Kuznets curve for forest area change，and the equation of the growth rate of forest stock were both estimated with data from FAO（2010）as in Buongiorno（2014）. The elasticities of fuelwood and industrial roundwood supply with price and growing stock were from Turner et al. （2006）. The freight cost between countries was estimated as the difference between unit value of imports and exports. Data on import tariff duties came from the World Trade Organization data base（WTO 2013）.

The solution for each year equilibrium is computed with an interior point solver（BPMPD，Mészáros 1999）. The GFPM input and output for calibration and simulation is facilitated by Excel spreadsheets and graphics. A recent version of the complete software，its documentation，and a pre-calibrated data set are available freely for academic research（Buongiorno and Zhu，2014a）.

Table 1　Parameters of demand equations for end products

IPCC scenarios

Three global scenarios，A1B，A2，and B2 were used in the projections from 2011 to 2065. The scenarios are based on the IPPC scenarios（Nakicenovic et al.，2000），extended and modified for the purpose of the United States Forest Service 2010 RPA Assessment（USDA Forest Service 2012）. Each scenario results from a separate IPCC“storyline”about future global social，economic，technical and policy developments. The storylines also reflect different interaction between developing and industrialized countries.

Scenario A1B，which assumes continuing globalization，leads to high income growth and low population growth，and thus the highest income per capita by the year 2065. Scenario A2 assumes a slowdown of globalization，and the rise of more regional interests. This leads to lower income growth than scenario A1B，and higher population growth，and thus lower income per capita. Scenario B2 has economic and demographic assumptions between scenarios A1B and A2.

For the GFPM simulations，the three main exogenous variables taken from these scenarios were the growth of GDP and population，and the growth of atmospheric ［CO2］. GDP growth from the IPCC was available only by region. National GDP growth was deducted from the regional growth in such a way that the regional growth remained the same as in the IPCC and the growth of individual countries converged towards this average regional growth rate（Buongiorno et al. 2012，p. 117）. Table 2 shows the resulting annual growth rates of GDP for each scenario，for selected world regions and countries.

CO2fertilization and forest growth

Norby et al.（2005）find that“the response of net primary productivity（NPP）to elevated CO2is highly conserved across a broad range of productivity，with a median response of 23±2%. They define“elevated CO2”as～550 ppm of atmospheric［CO2］，which is approximately 180 ppm above the atmospheric［CO2］in 2005. Thus，on average NPP increases by 0.13%per ppm increase in atmospheric［CO2］. Here，it was assumed that the percent increase in NPP was equal to the percent increase in the growth of forest stock in the absence of harvest.

Figure 2 shows the implication of this assumption for the growth rate of forest stock，given the predicted evolution of atmospheric［CO2］in each IPCC scenario （Table 3）. The highest impact was for scenario A2 in which the growth rate was 32%higher in 2070 than in 2006. The lowest was for scenario B2（19%）. The effect of scenario A1B was almost the same as that of A2 up to 2050 but somewhat lower in 2070（29%）. Accordingly，in the GFPM the endogenous forest growth rate，gu，inequation（2）changed over time by the relative fraction gu* shown in Fig. 2.

Table 2　Projected annual percent GDP growth rate in selected world regions and countries，by scenario

Results and discussion

Price effects

Table 4shows the world prices，defined by the unit value of imports，in 2011 and projected with the GFPM in 2065 with scenarios A1B，A2 and B2，with or without the cumulative effect of CO2fertilization from 2011 to 2065. In accord with theoretical expectations，the price of all products was lower with CO2fertilization. The fertilization effect varied substantially with the scenario. Under the A1B scenario，the price of fuelwood and industrial roundwood was 19%lower in 2065 due to CO2fertilization. The price of sawwood was 9%lower，and the price of wood-based panels was 3%to 6% lower. The price of mechanical and chemical pulp was 4%and 6%lower，respectively. The price of paper andpaperboard was 3%to 6%lower depending on the product.

For scenario A2 the price reductions were less than for scenario A1B，and similar to the effects under scenario B2. For scenarios A2 and B2，CO2fertilization decreased the price of fuelwood in 2065 by 12%to 13% and the price of industrial roundwood by 9%to 11%. The price of sawnwood was 4%to 5%lower，and the price of wood-based panels was 1%to 3%lower. The price of wood pulp decreased by 2%to 4%with CO2fertilization，and the price of paper and paperboard decreased by 1%to 3%depending on the product.

Table 3　Past and projected atmospheric CO2saturation（ppm），by IPCC scenario

Fuelwood consumption，production，and trade

In accord with the lower world prices induced by the CO2fertilization from 2011 to 2065，fuelwood consumption（production+imports-exports）was higher in 2065 in all regions than without fertilization，under all three scenarios. The largest effect of CO2fertilization occurred with scenario A1B，under which world consumption of fuelwood was 8%higher in 2065，while it was 5%to 6% higher under scenarios A2 and B2（Table 5）. The effect on fuelwood consumption was most noticeable in developing countries，reaching 13%in Africa under scenario A1B. The smallest relative impact was in Europe and the EU-28：2%with scenario A1B and 1%with A2 and B2.

The regional effect of CO2fertilization on fuelwood production tended to mirror the effect on consumption，except for the EU-28 where production was somewhat lower than consumption with CO2fertilization under all scenarios. In that case，the movement down the supply curve due to the lower world price（Table 4）exceeded the shift of the supply curve due to CO2fertilization from 2011 to 2065，and resulted in lower production，which together with the higher consumption suggested a deterioration of net trade.

Industrial roundwood consumption，production，and trade

Industrial roundwood includes logs，pulpwood，and other industrial roundwood（FAO 2014，p. xx）. The supply of industrial roundwood is，like that of fuelwood，directly affected by the shift due to CO2fertilization. However，while the demand for fuelwood，an end product，depends ultimately only on GDP and fuelwood price，the demand for industrial roundwood is more involved. Since industrial roundwood is an input in the manufacture of sawnwood，wood-based panels，and wood pulp，its demand depends on the price of these end products，and on the price of industrial roundwood，the price of other inputs and the techniques of production.

Globally，the positive supply shift of industrial roundwood due to CO2fertilization more than matched the movement down the supply curve due to the price reduction. Consequently，the GFPM-projected world consumption and production of industrial roundwood was 2%to 4%higher，depending on the scenario，with the assumption of CO2fertilization than without it （Table 6）.

其实他们潜意识里早就知道，从许多许多年前开始，他们就注定了是要互相陪伴的人，不然为什么这么多年来谁也没有跟其他人恋爱过，谁也没有遗忘过谁。

However，the complex derived demand for industrial roundwood，coupled with the shifts of industrial roundwood supply induced by the CO2fertilization led to varying regional effects. Consumption of industrial roundwood tended to be higher with CO2fertilization than without it，except in North/Central America and Asia. Under scenario A1B in particular，industrial roundwood consumption was 10%lower in N/C America，while it was 19%higher in South America and 10%higher in Europe and the EU-28. With this scenario，production was 18%higher in Asia in 2065，while consumption hardly changed，so that the CO2fertilization improved markedly Asia's trade balance of industrial roundwood. The regional pattern of the effects was similar for scenarios A2 and B2，but the magnitudes in both cases were much less than for scenario A1B.

Table 4　World prices of forest products in 2011 and projected in 2065 under scenarios A1B，A2 and B2，with and without CO2fertilization from 2011 to 2065

Table 5　Projected differences in fuelwood production and consumption in 2065 due to CO2fertilization from 2011 to 2065，by region and scenario

Sawnwood and panels consumption，production，and trade

In the GFPM，sawnwood and wood-based panels（veneer and plywood，particleboard，fiberboard（FAO 2014）），are end products. Their demand depends on GDP，the product price，and lagged consumption. As indicated above，the price of all products in 2065 was lower with CO2fertilization from 2011 to 2065. Consequently，the consumption of all end products was higher（Table 7）. In particular，under scenario A1B the world consumption of wood-based panels was 1.2%higher in 2065 with CO2fertilization，with the highest relative changes in the range of 2%to 2.5%in Africa and South America. The relative increases in consumption were about half that magnitude under scenarios A2 and B2.

Meanwhile，the production of sawnwood and panels varied much more across regions. For example，under scenario A1B，while production of sawnwood and panels was 21%to 24%in Africa and South America with CO2fertilization，it was 25%lower in N/C America. However，the regional distribution of production varied markedly between scenarios. In particular，while the production of E-28 countries was 4%higher with CO2fertilization under scenario A1B and their trade balance improved as a result，their production decreased slightly with scenarios A2 and B2，implying a slight worsening of net trade.

Table 6　Projected differences in industrial roundwood production and consumption in 2065 due to CO2fertilization from 2011 to 2065，by region and scenario

Wood pulp consumption production and trade

The production and consumption of wood pulp（mechanical +chemical +semi-chemical pulp（FAO 2014）are determined by its demand and supply. Wood pulp is an input in the manufacture of paper and paperboard，so that its demand depends（negatively）on its own price， and（positively）on the price of paper and paperboard. Meanwhile，the supply of wood pulp is a positive function of the price of wood pulp and a negative function of the price of industrial roundwood（an input in making wood pulp）. The CO2fertilization reduces the price of industrial roundwood，and in the process stimulates the demand for wood pulp. As shown in Table 8，under scenario A1B，the world wood pulp consumption（and the matching production）in 2065 was approximately 2%higher with the CO2fertilization than without it. The effect was approximately half that much under scenarios A2 and B2.

Table 8　Projected differences in wood pulp production and consumption in 2065 due to CO2fertilization from 2011 to 2065，by region and scenario

Paper and paperboard consumption production and trade

The products in the paper and paperboard group：newsprint +printing and writing paper + other paper andpaperboard（FAO 2014）are treated as end products in the GFPM. As for fuelwood，and sawnwood and woodbased panels the national demand is a function of GDP，price，and lagged consumption. Consequently，CO2fertilization influences paper and paperboard consumption through the price effect only. As the relative price reduction induced by CO2fertilization was smallest for paper and paper and paperboard，the effects on consumption were correspondingly small（Table 9）. The largest effects，under scenario A1B implied only a 0.5%to 0.8%higher consumption depending on the region. Under the other two scenarios，consumption was practically the same with or without CO2fertilization.

There were however larger regional impacts on production due to the differences in production cost brought about by the different regional shifts of industrial roundwood supply. In particular，under scenario A1B，paper and paperboard production was nearly 4%lower in South America with CO2fertilization than without it，implying a deterioration of net trade，while in Europe，production was nearly 3%higher，with a corresponding increase of net trade.

Value added in forest industries

For the purpose of this paper，value added is defined as the value of the total output of forest industries（sawnwood，wood-based panels，wood pulp，paper and paperboard） minus the cost of the wood and fiber input used by the industries（industrial roundwood，wood pulp，other fiber pulp，and waste paper）. The effects of the CO2fertilization from 2011 to 2065 on the input cost，value of the output and the resulting value added are summarized in Table 10 by region and scenario. The largest impacts occurred under scenario A1B according to which the CO2fertilization decreased the cost of the world industry inputs in 2065 by nearly $ 94x109or 7%. The reduction in input cost occurred in all regions，and was largest in Asia（−$42 x109）. Although the quantity of inputs，for example of industrial roundwood，increased in several regions（Table 6），their lower world prices（Table 4）still led to a reduction in total input cost.

The value of the industries outputs was also lower in several regions（Table 10）as the higher consumption of end products（e.g. sawnwood and panels）did not compensate for the reduced prices. Under scenario A1B，output value in 2065 was nearly 8%lower in N/C America with CO2fertilization，and nearly 4%lower in Asia. At world level，the reduction in output value came close to the reduction in input cost，leaving only a modest increase of value added（$7.6x109，or less than 1%）. The effects were similar in direction for scenarios A2 and B2，but smaller in magnitude so that at world level the CO2fertilization had hardly an impact on value added，although the regional differences were more substantial.

Table 9　Projected differences in paper and paperboard production and consumption in 2065 due to CO2fertilization from 2011 to 2065，by region and scenario

Table 10　Projected differences in wood and fiber input cost，output value and value added in forest industries in 2065 due to CO2fertilization from 2011 to 2065，by region and scenario

Forest stock

CO2fertilization tended to increase the forest stock，but as observed above a rise in stock shifted the wood supply to the right，increasing wood harvest and thus decreasing the forest stock. These opposite tendencies were further affected by the price decrease induced by the increase in wood supply，which stimulated the demand for wood products and thus the derived demand for wood input and the attendant harvest. Table 11 summarizes the results of the GFPM simulation of these complex interactions for the forest stock in different regions and for the three scenarios.

For all scenarios and regions the growing stock was higher in 2065 with CO2fertilization than without it. Thus，the added growth due to CO2fertilization more than compensated for the cumulative effects of the increased fuelwood and industrial roundwood harvests observed above. The largest effects of the CO2fertilization on the level of forest stock in 2065were under scenario A1B. This was in part due to the high level of atmospheric［CO2］and also to the rise in forest area，and thus young growing forests，induced by the higher GDP per capita growth assumed in scenario A1B. For this scenario，the forest stock was 20%higher in 2065 with CO2fertilization than without it. The largest relative impacts were in Africa and Asia. Under scenarios A2 and B2 the effect of CO2fertilization were similar，globally（9%to 10%）and by region，with the largest relative impact still in Africa.

Conclusions

The objective of this paper was to explore the long-term，ceteris-paribus effects of a potential CO2fertilization on the global forest sector. Based on the findings of Norby etal.（2005）about forest response to elevated CO2concentration it was assumed that the effect of CO2fertilization on forest growth would be conserved across a broad range of productivity，with an average stimulation of 23%for a 180 ppm increase of atmospheric［CO2］.

Table 11　Projected differences in growing stock in 2065 due to CO2fertilization from 2011 to 2065，by region and scenario

This increased productivity was applied to national forest growth rates in the Global Forest Products Model in proportion to the rising levels of［CO2］projected by the International Panel for Climate Change scenarios A1B，A2，and B2. In addition to different［CO2］levels the three scenarios projected different growth rates of gross domestic product and population，which influence the future demand for forest products，and the evolution of forest area. Projections of the forest area and forest stock，and of the production，consumption，prices，and trade of different products（ranging from fuelwood to paper and paperboard）were obtained with the GFPM for each scenario，with and without the assumption of CO2fertilization beginning in 2011 and up to 2065.

The results suggested that CO2fertilization would raise the level of the world forest stock by 9 to 10%for scenarios A2 and B2 and by 20%for scenario A1B. The change in forest stock was in part counteracted by the stimulation of the wood supply which resulted in lower wood prices and increased wood harvest. The lower wood prices in turn led to lower prices of end products and increased global consumption. However，production decreased in some regions due to the relative competitive advantages and to the varying regional effects of CO2fertilization.

These findings rely on strong assumptions；in particular that CO2fertilization can be summarized over very different forest types by the simple number suggested in Norby et al.（2005）. Although the homogeneity of response that they observed is striking，and over a wide range of sites，they all are within the temperate zone. It is uncertain that they can be applied to other ecological zones. Hickler et al. （2008）use a dynamic vegetation model which does reproduce the data of Norby et al.（2005），and conclude that the data“might also be representative of forests globally”. But， they also suggest that the NPP enhancement due to CO2fertilization may be much larger in tropical than boreal forests.

A review of several studies by the Center for the Study of Carbon Dioxide and Global Change（2014）does conclude that“CO2fertilization effects strongly increased recent Net Primary Production trends in regional totals”. But，much work is still needed to quantify this effect apart from or in conjunction with changes in temperature and precipitation（Zickfeld et al. 2012）. In addition，the methods used in this study require good estimates of current forest growth rates in different countries. This will mean further improvement of the global forest inventory and harvest statistics，which are currently subject to substantial errors due to infrequent and unequal sampling，and differences in definitions and classifications（FAO 2010）.

Keeping these caveats in mind，the present study showed that the impact of CO2fertilization would depend very much on the economic and demographic context in which it occurred，but in all cases the impact on the global value added in the forest sector would be modest. On the other hand，CO2forest fertilization per se might have substantial effects on the long-term level of forest stock，and less on the harvest. There would thus be a net positive effect of CO2fertilization on the amount of carbon stored in forests，a clearly beneficial effect for overall climate change.

Competing interests

The author declares that he has no competing interests.

Author’s information

Professor Emeritus，University of Wisconsin-Madison，USA，and Foreign Member，French Academy of Agriculture，Paris，France.

Acknowledgments

The research leading to this paper was supported in part by a joint venture agreement with the USDA Forest Service Southern Research Station in

cooperation with project leader Jeff Prestemon.

Received：30 June 2015 Accepted：14 October 2015

References

Buongiorno J（2014）Global modeling to predict timber production and prices：the GFPM approach. Forestry 88：291-303

Buongiorno J，Zhu S，Raunikar R，Prestemon JP（2012）Outlook to 2060 for world forest and forest industries：a technical document supporting the Forest Service 210 RPA assessment. USDA Forest Service Southern Research Station，General Technical Report SRS-151，Asheville，NC 119p.

Buongiorno J，Zhu S（2014a）Using the Global Forest Products Model（GFPM version 2014 with BPMPD）. Staff Paper Series # 80. Department of Forest and Wildlife Ecology，University of Wisconsin-Madison，35p. http：// labs.russell.wisc.edu/buongiorno/ Accessed 19 Jan 2015.

Buongiorno J，Zhu S（2014b）Technical change in forest sector models：The Global Forest Products Model approach. Scandinavian J Forest Res 30：30-48

Center for the Study of Carbon Dioxide and Global Change（2014）Biospheric Productivity in South America. http：//www.co2science.org/subject/g/ summaries/samergreen.php Accessed 15 January 2015.

FAO（2010）Global forest resources assessment 2010，main report. FAO Forestry Paper 163，Food and Agriculture Organization of the United Nations，Rome

FAO（2014）FAO yearbook，forest products. Food and Agriculture Organization of the United Nations，Rome

Hickler T，Smith B，Prentice IC，Mjöfors K，Miller P，Arneth A，Sykes MT（2008）CO2 fertilization in temperate FACE experiments not representative of boreal and tropical forests. Glob Chang Biol 14：1531-1542

IPCC（2012）Meeting report of the Intergovernmental Panel on Climate Change expert meeting on economic analysis，costing methods，and ethics. In：Field CB，Barros V，Edenhofer O，Pichs-Madruga R，Sokona Y，Mastrandrea MD，Mach KJ，von Stechow C（eds）IPCC Working Group II Technical Support Unit. Carnegie Institution，Stanford，California

IPCC（2013）International Panel for Climate Change，working group I：The scientific basis，II.2 Abundances and burdens II.2.1：CO2 abundances（ppm）. http：//www.ipcc.ch/ipccreports/tar/wg1/531.htm Accessed 3 December 2014.

Johnston J（1984）Econometric methods. McGraw-Hill，New York

Kirilenko AP，Sedjo RA（2007）Climate change impacts on forestry. Proc Natl Acad Sci 104：19697-19702

Koop G，Tole L（1999）Is there an environmental Kuznets curve for deforestation？J Dev Econ 58：231-244

McCarl B，Adams DM，Alig RJ，Burton D，Chen CC（2000）Effects of global climate change on the US forest sector：response functions derived from a dynamic resource and market simulator. Climate Res 15：195-205

Mészáros C（1999）The BPMPD interior point solver for convex quadratic problems. Optimization Methods Softw 11&12：431-449

Nabuurs GJ，Prussinen A，Karjalainen T，Herhard M，Kramer K（2002）Stem volume increment changes in European forests due to climate change-a simulation study with the EFISCEN model. Glob Chang Biol 8：304-316

Nakicenovic N，Davidson O，Davis G，Grübler A，Kram T，Lebre La Rovere E，Metz B，Morita T，Pepper W，Pitcher H，Sankovski A，Shukla P，Swart R，Watson R，Zhou D（2000）. Special Report on Emissions Scenarios：A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press，Cambridge，U.K.，599 pp. http：//www.grida.no/ climate/ipcc/emission/index.htm Accessed 3 Dec 2014.

Norby RJ，DeLucia EH，Gielen B，Calfapietra C，Giardina CP，King JS，Ledford J，McCarthy HR，Moore DJP，Ceulemans R，De Angelis P，Finzi AC，Karnovsy DF，Kubiske ME，Lukac M，Pregitzer KS，Scarascia-Mugnozza GE，Schlesinger WH，Oren R（2005）Forest response to elevated CO2 is conserved across a broad range of productivity. Proc Natl Acad Sci U S A 102：18052-18056

NOAA-ESRL（2015）Annual mean carbon dioxide data. ftp：//aftp.cmdl.noaa.gov/ products/trends/co2/co2_annmean_gl.txt Accessed 17 Jan 2015.

Perez-Garcia J，Joyce LA，McGuire AD，Xiao X（2002）Impacts of climate change on the global forest sector. Clim Change 54：439-461

Samuelson PA（1952）Spatial price equilibrium with linear programming. Am Econ Rev 42：283-303

Sohngen B，Mendelsohn R（1998）Valuing the impact of large-scale ecological change in a market：The effect of climate change on U.S. timber. Am Econ Rev 88（4）：689-710

Sohngen B，Sedjo R（2005）Impacts of climate change on forest product markets：Implications for North American producers. The Forestry Chron 81（5）：689-674

Solberg B，Moiseyev A，Kallio AMI（2003）Economic impacts of accelerating forest growth in Europe. Forest Policy Econ 5：157-171

Takayama T，Judge G（1971）Spatial and temporal price and allocation models. North-Holland Publishing Company，Amsterdam.

Thompson SL，Govindasamy B，Mirin A，Caldeira K，Delire C，Milovich J，Wickett M，Erickson D（2004）Quantifying the effects of CO2-fertilized vegetation on future global climate and carbon dynamics. Geophysical Research Letters 31（3）.

Turner J，Buongiorno J，Zhu S（2006）An economic model of international wood supply，forest stock and forest area change. Scandinavian J Forest Res 21：73-86

USDA Forest Service（2012）Future of America’s forest and rangelands：Forest Service 2010 Resources Planning Act Assessment. United States Department of Agriculture，Forest Service，General Technical Report WO-87，Washington DC

Woodwell GM，Hobbie J，Houghton RA，Melillo JM，Moore B，Peterson BJ，Shaver GR（1983）Global deforestation：Contribution to atmospheric carbon dioxide. Science 222：1081-1086

Wooldridge J.（2002）. Econometric analysis of cross section and panel data. MIT Press，Cambridge，Massachusetts. 752p.

WTO 2013. Tariffs data. World trade organization http：//tariffdata.wto.org/ ReportersAndProducts.aspx Accessed July 9，2013.

Zickfeld K，Arora VK，Gillett NP（2012）Is the climate response to CO2emissions path dependent？Geophys Res Lett 39：L05703