Misinterpreting the Internal Rate of Return in Sustainable Forest Management Planning and Economic Analysis

Wagner, John E.

doi:10.1080/10549811.2011.582830

Cited by 5 publications

(4 citation statements)

References 56 publications

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“…However, these findings are remarkable given that HCBs were policy instruments designed to protect habitats providing financial incentives to landowners with less or no thought on economic returns. Also, with the increasing RoR, NPV increased for most HCBs exhibiting the possibility of multiple internal rates of returns [34], indicating uncertainty in their financial feasibility over time and an underdeveloped imperfect market for these habitat conservation credits. Large payments at the early stage of the project contributed to such counterintuitive performance.…”

Section: Discussionmentioning

confidence: 99%

“…Contrary to the normal understating of NPV calculations, with the increase of the RoR from 4% to 8%, only 7 HCBs exhibited loss (negative NPV). This kind of reverse NPV curve is observed when there are large cash flows (revenues in our analysis) at the early stages with smaller cashflows (annual costs) spread out through the project timeline [33,34]. In such a case, multiple internal rates of return can be observed [34,35].…”

Section: Financial Analysis Of Habitat Conservation Banks In Californiamentioning

confidence: 92%

“…This kind of reverse NPV curve is observed when there are large cash flows (revenues in our analysis) at the early stages with smaller cashflows (annual costs) spread out through the project timeline [33,34]. In such a case, multiple internal rates of return can be observed [34,35]. Therefore, caution must be taken while comparing NPVs at different RoR for HCBs, and a decision must be made on RoR observing the markets before doing financial analysis.…”

Section: Financial Analysis Of Habitat Conservation Banks In Californiamentioning

confidence: 99%

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Financial Analysis of Habitat Conservation Banking in California

Poudel

Pokharel

2021

Sustainability

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Habitat conservation banking is a policy instrument for conserving endangered species by providing financial incentives for the landowners in the United States. This policy instrument aims to protect habitat, but little or no thought has been given to its financial performance. A financial analysis of habitat conservation banks (HCB) informs policymakers and conservation biologists of the long-term success of this policy and the future of HCBs. This paper evaluates 26 habitat conservation banks (HCB) in California by calculating their Net Present Values (NPV). We do so by compiling the cost and revenue data for habitat conservation banks. The average annual cost of operating HCBs was $42.78/acre (median: $22.58/acre), and the average credit price or revenue from credit sale was $6014.72/acre (median: $553.65/acre). The average NPV for 26 HCBs was $4205.90/acre at a 4% rate of return, indicating an overall positive return from such an easement instrument. However, only 14 HCBs out of 26 produced a positive return. With the inclusion of land acquisition costs, three of eight HCBs performed financially well. On the brighter side, the number of HCBs has increased with time. But there is not enough evidence to ascertain financial certainty from their revenues. A right selection of space (land acquisition costs can make or break finances for HCB) and species could encourage landowners to establish HCBs. This could build confidence on those who may have been discouraged from lack of knowledge and fear of losing revenue due to regulatory compliance to conserve endangered species habitat in their land. The findings are helpful in identifying lands and prioritizing investments to generate conservation credits.

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Section: Discussionmentioning

confidence: 99%

Section: Financial Analysis Of Habitat Conservation Banks In Californiamentioning

confidence: 92%

Section: Financial Analysis Of Habitat Conservation Banks In Californiamentioning

confidence: 99%

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Financial Analysis of Habitat Conservation Banking in California

Poudel

Pokharel

2021

Sustainability

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“…The manipulation and reuse of this information promote its use in the management development itself and helps in the identification of opportunities that can be foreseen. However, a mechanized timber harvesting operation planning scope necessarily requires a quantitative, robust, and reliable quantitative database [7,8].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods to Estimate Productivity of Harvesters: Mechanized Timber Harvesting in Brazil

Munis

Almeida

Camargo

et al. 2022

Forests

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The correct capture of forest operations information carried out in forest plantations can help in the management of mechanized harvesting timber. Proper management must be able to dimension resources and tools necessary for the fulfillment of operations and helping in strategic, tactical, and operational planning. In order to facilitate the decision making of forest managers, this work aimed to analyze the performance of machine learning algorithms in estimating the productivity of timber harvesters. As predictors of productivity, we used the availability of hours of machine use, individual mean volumes of trees, and terrain slopes. The dataset was composed of 144,973 records, carried out over a period of 28 months. We tested the predictive performance of 24 machine learning algorithms in default mode. In addition, we tested the performance of blending and stacking joint learning methods. We evaluated the model’s fit using the root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient. After cleaning the initial database, we used only 1.12% to build the model. Learning by blending ensemble stood out with a determination coefficient of 0.71 and a mean absolute percentage error of 15%. From the use of data from machine learning algorithms, it became possible to predict the productivity of timber harvesters. Testing a variety of machine learning algorithms with different dynamics contributed to the machine learning technique that helped us reach our goal: maximizing the model’s performance by conducting experimentation.

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