Optimizing rotation periods of forest plantations: The effects of carbon accounting regimes
Introduction
Climate change has become a global concern, and scientists agree that the high concentration of greenhouse gases contributes to it. However, forests can offset the accumulation of atmospheric carbon dioxide (IPCC, 2013; van der Gaast et al., 2018). Globally, 2.4 ± 0.4 109 Mg of carbon emissions can be sequestered by forestland annually. In 2018, this summed up to 68.6% of the terrestrial carbon dioxide (CO2) sink (Pan et al., 2011; Friedlingstein et al., 2019). Forest carbon sinks, as a cost-efficient option for climate policy, can contribute to reducing the overall costs of climate change mitigation (Tavoni et al., 2007; Cho et al., 2018).
Forests have high capacity of both producing timber and storing carbon. However, the growth rate and the capacity of additional carbon uptake decline as forests grow older (Zhu et al., 2019). This characteristic of trees indicates that there is an optimal rotation period that maximizes carbon uptake (Zhou and Gao, 2016).
Faustmann's model, developed in 1849, is the most influential for estimating the optimal rotation period of forests with a focus on timber harvest. However, this model does not address the additional services forests provide, such as biodiversity conservation (Crouzeilles et al., 2016), recreation (Müller et al., 2019), and carbon sequestration (Hou et al., 2019b). Hartman's (1976) research is recognized as the first to estimate the optimal rotation period incorporating these additional values.
During the last two decades, Hartman's model has been widely applied to account for the additional services provided by forests. Zhou and Gao (2016) examined the optimal rotation period of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) stands in Southern China considering both timber and carbon revenues. West et al. (2019) adapted the model and found that including the value of the carbon sequestered by the vegetation would justify extending the rotation period of fast-growing plantations. Yemshanov et al. (2005) used a modified Hartman model to estimate the price of carbon at which reforestation would be an attractive investment. Olschewski and Benítez (2010) addressed the issue of optimizing the joint production of timber and carbon sequestration and found that including carbon in the analysis doubles the rotation period compared to focusing on timber production alone. Lastly, Yu et al. (2014) used the model to incorporate carbon revenues into the value of land to calculate the optimal rotation period for newly planted trees to maximize social benefits. In conclusion, recent research suggests that the optimal rotation period is postponed when the value of carbon sequestration services is factored into modeling.
Due to the huge potential of forests to store carbon, there have been international endeavors to raise financial funding for reforestation or forest conservation efforts through schemes such as the Clean Development Mechanism (CDM) and Reduced Emissions from Deforestation and Degradation (REDD+). Since the Bali Action Plan was adopted by the Conference of Parties in 2007 (COP13), REDD+ has evolved as a performance-based payment mechanism to sequester and store carbon in forests (Pandit, 2018). In both schemes, forest owners can obtain payments based on the amount of carbon sequestered (West et al., 2018).
Within the CDM framework, there are two types of carbon credits for non-permanent carbon sequestration: temporary certified emission reduction (tCER) and long-term certified emission reduction (lCER), each representing one ton of carbon dioxide equivalent (tCO2e) (UNFCCC, 2013). The amount of carbon sequestered by the forest is verified at least once every 5 years, for both tCERs and lCERs. While the verification could also take place annually (up to the proponent to decide), we choose a 5-yearly verification period since transaction costs would be lower than with annual verification. In the case of tCER, the first credits are generated 5 years after commencement of the project, for the carbon that was sequestered in the forest from year 0 to year 5. These credits expire at the end of the commitment period following the one they were issued. Considering the second commitment period comes to an end in 2020, it is not clear how the following commitment periods will be defined. To simplify our calculation, we assume the commitment period of tCERs to be consistent with the verification and certification period, which is usually 5 years. Therefore, the first credits generated in year 5 expire in year 10. However, in year 10 new tCERs can be issued for the total amount of carbon that is stored in the forest (i.e. the amount of carbon that has accumulated from year 0 to year 10). In year 15 these credits in turn expire, and can be reissued again for another 5 years for the total amount of carbon stored in the forest during the last 15 years. As long as the forest is not harvested, the same process can continue for a maximum of 30 years (Fig. 1a).
As an alternative, lCER credits can be used. lCERs may be issued for either 20 years (renewable twice, for a total length of 60 years), or 30 years (non-renewable). In our paper we use the case of non-renewable lCERs that expire after 30 years. As with tCERs, the amount of carbon sequestered is verified every 5 years (in year 5, 10, 15, 20 and 25). However, while tCERs expire every 5 years and new tCERs are issued for the total amount of carbon stored in forests, lCERs do not expire until the end of the project. In consequence, further credits are only issued for the additional carbon since the previous verification. For example, the first verification will take place 5 years after the trees are planted. The second verification will take place in year 10, and the landowners receive credits for the additional carbon stored in the forest from year 5 to year 10. The third verification takes place in year 15, and the farmer receives credits for the additional carbon that was stored from year 10 to year 15, and so on until year 25. Since the CERs issued in year 25 have a lifetime of another 5 years (farmers have to keep these credits valid during year 25 to year 30), there are no credits issued for the carbon sequestered from year 25 to year 30 for the fixed crediting period of 30 years.
To summarise, there are two important differences between tCERs and lCERs. First, while tCERs expire at the end of the commitment period following the one in which they were issued (i.e. every 5 years), lCERs expire at the end of a project crediting period, provided that the carbon stocks are still in place. Second, the amount of tCERs issued every 5 years equals the total amount of CO2e stored in the soil and tree biomass that has accumulated since the first tCERs were issued (Fig. 1a). On the other hand, the amount of lCERs issued every 5 years corresponds to the additional amount of carbon sequestered since the last verification (Fig. 1b).
These different types of accounting procedures can affect the amount of CERs ascribed to afforestation projects and the price of each CER, therefore impacting the carbon payment to landowners. Olschewski and Benítez (2010) determined the optimal rotation period of a fast-growing species considering tCER accounting and found that the joint production of timber and carbon can double the optimal rotation period compared to timber production only. Galinato and Uchida (2011) further examined the effect of tCERs on the landowner's harvesting decisions, land use allocation, and carbon supply in forest plantations, and found that rotation periods and carbon credit supply increase slightly when revenues from carbon sequestration are included. Galinato et al. (2011) were the first to investigate the effect of lCERs on rotation intervals and carbon credit supplied to forestry projects. They applied the same constant price to lCERs and tCERs for different crediting periods. However, certificates issued under different accounting regimes expire at different times, and since lCERs have a longer duration, their price can expected to be higher (Olschewski and Benitez, 2005). To optimize forest management decisions with regard to the accounting system to be implemented, it is necessary to comprehensively compare the carbon accounting rules and their impact on the optimal rotation period.
In this paper we determine and compare the influence of tCER and lCER carbon accounting on the optimal rotation period of plantations, using China as a case study. China has undertaken large afforestation programs in most provinces during the last three decades. In particular, in 1999 China introduced the Grain for Green (GfG), the largest reforestation program in the world, which pays farmers to reforest their marginal farmland (Delang and Yuan, 2016). However, farmers' payments come from the government, and therefore are limited in both time and space. If there are additional sources of funding, more farmers can participate in afforestation activities, and can possibly receive funding for a longer period of time. Carbon sequestration by forests could also be incorporated into China's planned nation-scale Emission Trading Scheme (ETS) (Zhou and Gao, 2016; Gu et al., 2019). This emission trading market would likely generate considerable funds to support reforestation.
An important precondition for calculating the optimum rotation is the availability of (a) reliable biomass and growth models for the tree species in question, and (b) representative market data. While timber harvesting costs and revenues can be derived from local market prices, the prices of carbon certificates are determined by international markets. We assume that China's ETS would be linked to the international markets and use the same system of tCER and lCER. This study addresses three research questions: (i) How does the value of the carbon sequestered by forests affect the optimal rotation period? (ii) How do different carbon accounting methods (tCER and lCER) affect the optimal rotation period across different tree species and regions? (iii) How do changes in discount rates and carbon prices influence the optimal rotation period of different plant species?
Section snippets
Carbon data
The data on soil carbon sequestration come from a meta-analysis of 90 articles (see Hou et al., 2019a). We focus on data about the top 60 cm soil layer, since it contains 70–80% of the SOC stocks in the first meter of soil and is the layer most heavily affected by land-use change (Jacobs et al., 2009; Kukal and Bawa, 2014). The forest floor is not considered in this study because, due to management practices, there is usually little forest litter on the ground. We analyze three tree species in
Total carbon sequestration
Fig. 2 shows the total carbon sequestered by tree biomass and soil after afforestation for the three species considered. Eucalyptus has the highest growth rate and carbon sequestration. Chinese fir has a regular increase in carbon sequestration, similar to that of Eucalyptus, though on a much lower level. On the other hand, Poplar shows considerable regional variation, sequestering more carbon in the E, S, and SW regions. The accumulation in these regions peaks at year 20, and drops
Conclusions
Forests have huge potential of both generating timber and storing carbon in a joint production process. Thus, countries have widely adopted afforestation projects as a means to achieve poverty alleviation and environmental protection goals, particularly in reducing atmospheric carbon dioxide.
We determine the optimal rotation periods of three tree species—Eucalyptus, Chinese fir, and Poplar—using data from various Chinese regions. Applying a modified Hartman rotation model, our results show that
Author contributions
All authors contributed to the design and development of this manuscript. G.H., X.L. and C.O.D. carried out the fieldwork, G.H. analyzed the data and prepared the first draft of the manuscript with R.O., C.O.D. and R.O. revised and edited the manuscript.
Declaration of Competing Interest
The authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.
Acknowledgments
The work described in this paper has been fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (project No. 12305116).
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