A Binomial Tree Approach to Valuing Fixed Rotation Forests and Flexible Rotation Forests Under a Mean Reverting Timber Price Process

NPV and LEV are established and common approaches to valuing single rotation and infinite rotation forests respectively, when the rotation age is fixed in advanced. More recently, Real Options approaches have been employed to value single and infinite rotation forests with a flexible harvest age. Under a stochastic timber price process, it has been shown that the valuation of a flexible rotation forest is equal or higher than that of a fixed rotation forest, because a flexible harvest regime delays the harvest if the timber price is not favourable, whereas a fixed harvest regime would proceed to harvest regardless of the price. Often, valuation of fixed and flexible rotation ages are compared using 2 different methods – NPV (or LEV) and Real Options. The latter tends to have higher data requirements, employ different assumptions and is much more complex to estimate. Because of these differences, it may be difficult to isolate the cause of the increased valuation. In this work, we apply a relatively simple Binomial Tree method from Guthrie (2009) to value both fixed rotation and flexible rotation forests. This method uses the same data, with the same assumptions for both valuations. By holding everything equal, the difference in valuation is solely attributable to the fixed versus flexible harvesting decisions. Original results for both single and infinite rotations are presented using New Zealand Radiata Pine data. Under a mean reverting timber price process, the Binomial Tree approach offers useful insights on the increased valuation due to flexible harvest decisions.NPV, LEV, Real Options, Optimal Harvest Decision, Agribusiness, Crop Production/Industries, Environmental Economics and Policy, Land Economics/Use,

Climate Change Mitigation Policy: The Effect of the New Zealand Emissions Trading Scheme on New Radiata Pine Forest Plantations in New Zealand

Climate change is one of the toughest challenges facing the world today. Putting a price on carbon emissions is an important step towards climate change mitigation. A cap and trade system is one of the ways to create a carbon price. The New Zealand Emissions Trading Scheme (NZETS) is the world’s first economy-wide cap and trade system that covers all sectors and all 6 greenhouse gases. Forestry is a major part of the NZETS, allowing foresters to earn carbon credits for new forests planted on and after 1st January 1990 (afforestation and reforestation). At the same time, the NZETS also makes foresters liable for harvesting new forests planted on and after 1st January 1990, and deforesting forests existing on and before 31st December 1989. In this paper, we perform an economic analysis of how a carbon price will likely affect the returns and forestry management behaviour in new forests in New Zealand. Previous works have used the NPV/LEV (fixed harvesting) analysis where the forest is assumed to be harvested (in future) at the estimated optimal rotation age regardless of timber prices at that time. Other works have employed the Real Options approaches (flexible harvesting) where sophisticated models such as Partial Differential Equations and simulations analyse the effects of bringing forward the harvest decision if timber prices are favourable, and deferring the harvest decision if timber prices are unfavourable. Often, these methods tend to have higher data requirements, employ different assumptions and are much more complex to estimate. Because of these differences, it may be difficult to compare the results of NPV/LEV analysis with Real Options. Our work here applies the binomial tree method, which is a relatively simple method that can generate both LEV (fixed harvesting) and Real Options (flexible harvesting) results on a common model with the same data requirements and assumptions. This allows for better comparability of forestry management behaviour and effects of carbon price. The forestry valuations are analysed under a stochastic timber price and a constant carbon price. This paper concludes with some implications on policy in New Zealand.Environmental Economics and Policy,

Real Options Analysis of Carbon Forestry Under the New Zealand Emissions Trading Scheme

In 2008, the New Zealand government passed climate change legislation called the New Zealand Emissions Trading Scheme (NZETS), designed to create a carbon price in the economy. Under the NZETS, new forests planted on and after 1st January 1990 (known as post-1989 forests) are eligible to earn carbon credits and sell them domestically and internationally, with a condition that the credits will have to be repaid back upon harvest of the forests. The amount of credits that have to be surrendered is proportionate to the extent that carbon stocks decrease in the forest land. This research explores the effects of the NZETS on new post-1989 forests. The NPV/LEV and the Real Options valuation methods are respectively employed to analyze fixed harvest and flexible harvest forest management decisions. This approach is applied to study the cases of timber-only forestry (i.e. no NZETS) and carbon forestry (i.e. with NZETS). The major advance of this research is the development of a double Random Variable Real Options methodology that incorporates both stochastic timber and stochastic carbon prices into the calculation of the bareland forestry investment opportunity under the NZETS. Through the work of this thesis, it is shown that the NZETS increases the valuation of bareland on which radiata pine is to be planted with a single rotation or a perpetual series of rotations, especially for the case of flexible harvest forest management. The NZETS will very likely lengthen the rotation age of forests and increase forest carbon sequestration, which contributes positively towards climate change mitigation in New Zealand. The Real Options valuation method can generate optimal harvest price thresholds that help forest owners to decide when to harvest. This thesis concludes with a scenario analysis of potential implications of lengthening the forest rotation age on carbon stock management in New Zealand

Mechanistic Target of Rapamycin (mTOR) in the Cancer Setting

This special issue on mammalian target of rapamycin (mTOR) explores the importance of mTOR in cell growth control and cancer. Cancer cells often exploit mTOR as a mechanism to enhance their capacity to grow. While protein synthesis is by far the best-characterized mTOR-driven process, this special issue also describes a wider array of mTOR-driven biological processes that cancer cells benefit from, including autophagy, cell cycle control, metabolic transformation, angiogenic signaling, and anabolic processes such as nucleotide biosynthesis and ribosomal biogenesis. Other areas of mTOR signaling covered in these reviews delve into cell migration, inflammation, and regulation of transcription factors linked to cancer progression

Oncogenic Signalling through Mechanistic Target of Rapamycin (mTOR): A Driver of Metabolic Transformation and Cancer Progression

Throughout the years, research into signalling pathways involved in cancer progression has led to many discoveries of which mechanistic target of rapamycin (mTOR) is a key player. mTOR is a master regulator of cell growth control. mTOR is historically known to promote cell growth by enhancing the efficiency of protein translation. Research in the last decade has revealed that mTOR’s role in promoting cell growth is much more multifaceted. While mTOR is necessary for normal human physiology, cancer cells take advantage of mTOR signalling to drive their neoplastic growth and progression. Oncogenic signal transduction through mTOR is a common occurrence in cancer, leading to metabolic transformation, enhanced proliferative drive and increased metastatic potential through neovascularisation. This review focuses on the downstream mTOR-regulated processes that are implicated in the “hallmarks” of cancer with focus on mTOR’s involvement in proliferative signalling, metabolic reprogramming, angiogenesis and metastasis

Mathematical study of the effects of applied stress, T-stress and back stress in photoelastic fringe patterns

This work is an attempt at developing a novel mathematical model to describe the stresses near the crack tip, taking into consideration the effects of plasticity. The focus is on describing how the applied stress normal to the crack, herein referred to as the K-stress, Tstress and ‘back stress’ induced by plasticity along the crack flank and in the crack tip plastic zone influence the crack tip elastic stress fields. The important features emerging from this study are that the sign and magnitude of each term can substantially alter the crack tip stress fields, and hence influence the photoelastic fringe patterns. To validate the mathematical model, polycarbonate compact tension specimens have been used and observed in a transmission polariscope in order to study the single effect of a pure ‘back stress’ (acting as an interfacial shear stress at the elastic-plastic boundary) and combination effects of K-stress, Tstress and ‘back stress’. It is observed that the fringe patterns obtained through experiment show good agreement with those derived by mathematical modelling

Crowdsourcing as a novel technique for retinal fundus photography classification: analysis of images in the EPIC Norfolk cohort on behalf of the UK Biobank Eye and Vision Consortium.

AIM: Crowdsourcing is the process of outsourcing numerous tasks to many untrained individuals. Our aim was to assess the performance and repeatability of crowdsourcing for the classification of retinal fundus photography. METHODS: One hundred retinal fundus photograph images with pre-determined disease criteria were selected by experts from a large cohort study. After reading brief instructions and an example classification, we requested that knowledge workers (KWs) from a crowdsourcing platform classified each image as normal or abnormal with grades of severity. Each image was classified 20 times by different KWs. Four study designs were examined to assess the effect of varying incentive and KW experience in classification accuracy. All study designs were conducted twice to examine repeatability. Performance was assessed by comparing the sensitivity, specificity and area under the receiver operating characteristic curve (AUC). RESULTS: Without restriction on eligible participants, two thousand classifications of 100 images were received in under 24 hours at minimal cost. In trial 1 all study designs had an AUC (95%CI) of 0.701(0.680-0.721) or greater for classification of normal/abnormal. In trial 1, the highest AUC (95%CI) for normal/abnormal classification was 0.757 (0.738-0.776) for KWs with moderate experience. Comparable results were observed in trial 2. In trial 1, between 64-86% of any abnormal image was correctly classified by over half of all KWs. In trial 2, this ranged between 74-97%. Sensitivity was ≥ 96% for normal versus severely abnormal detections across all trials. Sensitivity for normal versus mildly abnormal varied between 61-79% across trials. CONCLUSIONS: With minimal training, crowdsourcing represents an accurate, rapid and cost-effective method of retinal image analysis which demonstrates good repeatability. Larger studies with more comprehensive participant training are needed to explore the utility of this compelling technique in large scale medical image analysis