Using an Input–Output framework, this analysis investigates how shifts in production technology and final demand have impacted the Forest Sector’s economic contributions over time. The total output change was decomposed into three factors production technology of the Forest Sector, production technology of other sectors and final demand of the economy— allowing us to determine the primary factors responsible for a change in output over time. Further, diagonalizing the final demand vector allows us to examine the trends along its supply and value chains. The study revealed a significant decline in New York State’s (NYS’s) Forest Sector output, attributable to reduced demand and external economic pressures, including the impacts of the 2008–2009 Great Recession and the COVID-19 pandemic. Further, we found that despite a 3.3

Despite comprising a small portion of US wood consumption, tropical hardwoods like Keruing and Meranti are highly valued for their aesthetic and physical properties. However, their sustainability is threatened by illegal logging and over-harvesting, compounded by the use of generic names that obscure species identities, complicating trade monitoring and regulation. Enacted in May 2008, the Lacey Act Amendment (LAA) aims to ensure the legality of plant and plant product sourcing in the US. This study evaluates the LAA’s impact on the import of these tropical hardwoods from Indonesia and Malaysia, hypothesizing that the amendment has curtailed illegal imports, thereby reducing import volumes and increasing prices. Using data from 1990 to 2023, we employed (i) intervention multiple regression analysis with autoregressive error and (ii) intervention auto-regressive integrated moving average models with step transfer function to analyze changes in import trends. Findings indicate that while LAA has significantly impacted import trends as anticipated, the effects are complex and evolving over time, highlighting the need for ongoing regulation analysis and enforcement monitoring. This research underscores the critical role of targeted legal frameworks in promoting sustainable trade practices and conservation, offering valuable insights for policymakers aiming to combat exploitative and illegal logging globally.

The forest products industries play a vital role in the economic, social, and environmental well-being of the Lake States in the United States. While various economic contribution analyses of forest products industries have been conducted to highlight the importance of such industries to regional economies, little effort has yet been made to parse out the contribution of activities in the value chain. The value chain is a series of steps involved in producing goods or services. This study used a matrix decomposition approach to estimate the economic contribution along the value chain through multiple pathways of four forest resource-based industries using wood as inputs: biomass power generation, sawmills, paper mills, and the construction of new single-family residential structures in the Lake States. The direct and indirect economic output values in 2017 resulting from the construction of new single-family residential structures were $19.1 billion, sawmills were $2.5 billion, paper mills were $17.6 billion, and the biomass power generation industry was $759 million. Of the direct and indirect economic output contributed by each industry, the highest percentage of output attributable to the logging industry was observed from the sawmills industry (12%), followed by biomass power generation (9%), paper mills (1.4%), and the construction of new single-family residential structures (<1%), respectively. The percentage of total economic output attributable to the stumpage industry in the region followed a similar trend as commercial logging for all value-chain industries. The relative economic contribution of the value-chain industries to the total economic contribution of the final industry varied based on whether the industry was a primary or secondary forest products industry and the pathways used for sourcing wood inputs.

Stratified random sampling is often used to obtain reference data for assessing the accuracy of land cover maps created from remotely sensed data and for estimating area of land cover and land cover change. The sample size allocation to strata determines the precision of estimates of user's accuracy, producer's accuracy, and proportion of area. Different choices of nh (the sample size in stratum h) may favor precision of one estimate at the expense of a larger standard error for another estimate. Here we address the question of optimally allocating a sample of size n when multiple estimates are of interest, focusing on applications in which the target class is rare (<= 10% of the study region). We limit attention to the case of stratified random sampling with two strata, stratum 1 being the mapped area of the target class with sample size n1 and stratum 2 including all other area with sample size n2 = n - n1. We investigate how n1 changes depending on which estimates are targeted by the optimal allocation. For example, the optimal n1 would differ when the estimated proportion of area is optimized versus when estimates of user's and producer's accuracies are optimized. We compare the standard errors resulting from these different optimal allocations for a diverse set of 80 populations created by all possible combinations of five proportions of area of the rare class (p = 0.001, 0.005, 0.01, 0.05, and 0.10), four user's accuracies (60%, 75%, 85%, and 95%), and four producer's accuracies (60%, 75%, 85%, and 95%). The results indicate that estimating accuracy exerts a stronger impact on the optimal n1 than estimating proportion of area. Larger n1 is advantageous for precise estimation of user's accuracy, but conflicts with the smaller n1 that is optimal for estimating producer's accuracy and proportion of area. The trade-offs among the standard errors of the three estimates resulting from different n1 are magnified as the target class becomes rarer (i.e., p decreases). When deciding between an allocation optimizing estimation of accuracy versus an allocation optimizing estimation of proportion of area, it is precision of estimated user's accuracy that is most strongly impacted by the choice. Conversely, precision of estimated producer's accuracy and precision of estimated proportion of area are relatively insensitive to the decision between these two allocation options. Choosing a sample allocation when multiple estimates are of interest is complex because of the precision trade-offs among the estimates. This article provides quantitative evidence to guide sample allocation decisions given the estimation objectives specified for a particular application.

United States Army trucks and trailers use an estimated one million board feet (2381 cubic meters) of a critically endangered tropical hardwood, apitong (Dipterocarpus spp.), from southeast Asian rainforests, for wood decking annually. However, their purchasing specifications require the use of domestic hardwoods for decking, floorboards, and platforms. Several US hardwood species, including northern red oak (Quercus rubra), white oak (Quercus alba), hickory (Carya spp.), black locust (Robinia pseudoacacia), and sugar maple (Acer saccharum) could serve as viable substitutes. They have comparable strength properties to apitong, and there is an abundant and sustainable feedstock based on the United States Forest Service Forest Inventory Analysis (USFS FIA) database. The economic impact in New York State of manufacturing the decking panels in Onondaga County from three selected species: hickory, white oak, and black locust, was estimated using IMPLAN. The economic impact could be as high as $27 million, creating 128 full-time equivalent (FTE) jobs. Equally important to providing local and regional economic benefits, domestically sourced decking panels also contributes to the preservation of tropical rainforests, particularly when the entire decking market is considered (beyond the US Army), which includes wood decking consumption by other government agencies at various levels and the private sector.

Forestry Economics introduces students and practitioners to the economics of managing forests and forest enterprises. The book adopts the approach of managerial economics textbooks and applies this to the unique problems and production processes faced by managers of forests and forest enterprises. What many future forest and natural resource managers need is to understand what economic information is and how to use it to make better business and management decisions. John E. Wagner draws on his 30 years of experience teaching and working in the field of forest resource economics to present students with an accessible understanding of the unique production processes and problems faced by forest and other natural resource managers. The second edition has been updated to include: Expanded discussion of compounding, discounting, and capital budgeting, as well as an expanded discussion of when to replace a capital asset that has (i) costs but no direct revenue stream such as a machine; (ii) costs and a direct annual revenue stream such as a solar array; or (iii) costs and a periodic revenue stream illustrated by the forest rotation problem. New practical examples to provide students with applications of the concepts being discussed in the text, most notably on New Zealand and a Radiata Pine (Pinus radiata) Plantation. A brand-new chapter that develops business plans for for-profit businesses to illustrate how a business plan is derived from the economic information contained within the Architectural Plan for Profit and how it can be used to make business decisions about continuing to operate a business or to start a new business. This textbook is an invaluable source of clear and accessible information on forestry economics and management not only for economics students, but also for students of other disciplines and those already working in forestry and natural resources.

There has been increasing interest among foresters and landowners in modifying existing even-aged forest structures to multi-aged or uneven-aged structures. Maintaining a continuous forest structure often provides a wider array of forest values over the long term. The conversion process is challenging in regions of the Northeast United States characterized by forests composed of dense diseased beech thickets and low-vigor deformed overstory trees. Abundance of noncommercial beech may result in negative cash flows during the conversion process to achieve a desired balanced uneven-aged structure. The Forest Vegetation Simulator was used to model growth and yield. Given the possibility of negative cash flows, a least-cost dynamic program with a penalty function was used to determine least-cost time paths for two management scenarios, one characterized by the successful removal of beech and the other simulating the continued presence of beech. Incorporating a penalty function allowed creating a continuum of least-cost paths from a zero penalty with the greatest weight given to net revenue goals to the largest penalty with the greatest weight given to ecological goals. Sensitivity analyses revealed least-cost paths were more stable given changes in prices and wages when greater emphasis was placed on the ecological goals associated with the target structure. Study Implications: There has been increasing interest in modifying existing even-aged forest structures to multi-aged or uneven-aged structures. However, in cases where the initial forest is dominated by undesirable growing stocks and might not provide sufficient revenue to cover management costs, a least-cost optimization would be more a suitable approach. The proposed method creates a continuum of least-cost paths from a zero penalty with the greatest weight given to net revenue goals to the largest penalty with the greatest weight given to ecological goals. This also allows identifying the opportunity cost of choosing one least-cost management regime over another. Finally, the landowner's choice of the least-cost path most consistent with their management goals is the revealed optimal solution to maximize their welfare.

The classic wealth maximization modeling of forest landowners may not be useful when examining the behavior of family forest landowners in particular. My challenge to the forestry community is to think more broadly with respect to the economic modeling of management decisions by these landowners. I would propose that forest structure (e.g., trees per unit area versus diameter class/distribution) versus time, as opposed to volume versus time, is a superior and practical approach to model forest dynamics given these landowners' well-published preferences. I would also propose that a cost-minimization/least-cost model is also more consistent with their well-published preferences. These proposals, however, are not without their advantages and disadvantages that are examined briefly. Nonetheless, my conclusions are that scholarship based on a least-cost approach could provide insights that the classical wealth maximization modeling may not, given landownership trends.

Riparian Management Zone (RMZ) allocations can place a burden on landowners due to restrictions (sometimes prohibitions) on harvesting. The opportunity cost for the landowner may be minimized by shifting the primary management objective in RMZs from timber production to compensation for above-ground carbon. Our primary objective was to compare long-term net revenue generating potential of RMZs under three scenarios: (I) compensation for carbon credits without harvesting; (II) partial harvesting using Best Management Practices (BMP) guidelines without carbon credits; (III) partial harvesting combined with carbon credits as per the California Compliance Offset Protocol. Basic stand data on trees of 2.5 cm and higher were collected in riparian forest plots along headwater streams within two experimental forests in the northeast US. The USFS Forest Vegetation Simulator was used to simulate growth and yield and schedule management activities over 20-year cutting cycles. Timber volumes and registry offset credits along with their market values were calculated for the respective scenarios and a Net Present Value (NPV) and Equal Annual Equivalent (EAE) analysis was performed under assumptions of constant prices and costs. The initial aboveground carbon stocks at both locations were 32% and 140% higher than the average value for their assessment areas. Having above-average carbon stocks and basal areas between 30 and 33 m(2)/ha, all scenarios returned positive NPVs and EAEs. The hardwood riparian forest had a higher NPV and EAE by not participating in the carbon markets and pursuing partial harvesting as per BMP guidelines (Scenario II) at lower discount rates but had higher NPVs and EAEs under carbon markets at higher discount rates (Scenario I and III). The conifer/mixed species riparian forest provided greater positive net revenue flows by participating in the carbon markets either in a no harvesting scenario or under partial harvesting as per guidelines in the Protocol (Scenarios I and III). Our results indicate that a protocol for compensating landowners with large forest holdings for riparian carbon offsets provides an opportunity to generate positive net revenues in scenarios in which state BMP guidelines may restrict harvesting in RMZs. Given the high density of ecologically critical headwater streams in the Northeast and potential RMZ restrictions, the carbon offset option provides landowners with the opportunity to remain economically viable.

Stumpage price is one of the key economic inputs in developing sustainable forest-management plans and analyzing forest-management decisions and investments. A robust method to forecast stumpage prices and prediction intervals would help timberland owners and consultants bound uncertainty and improve the chance that future management actions agree with specified objectives. We evaluated three forecasting methods: simple moving average, linear weighted moving average, and exponential weighted moving average. Real annual stumpage prices for New York's six most common species were used to evaluate the robustness of the three forecasting methods. We concluded that, while being more complex computationally, the exponential weighted moving average was preferred to the simple and linear weighted moving average.

The economic feasibility of Biomass District Heating (BDH) networks in rural villages is largely unknown. A cost-effective evaluation tool is developed to examine the feasibility of BDH in rural communities using secondary data sources. The approach is unique in that it accounts for all the major capital expenses: energy center, distribution network, and energy transfer stations, as well as biomass procurement. BDH would deliver heat below #2 fuel oil in eight of the ten rural study villages examined, saving nearly $500,000 per year in heating expenses while demanding less than 5% of the forest residues sustainably available regionally. Capital costs comprised over 80% of total costs, illuminating the importance of reaching a sufficient heat density. Reducing capital costs by 1% lowers total cost by $93,000 per year. Extending capital payment period length five years or lowering interest rates has the next highest influence decreasing delivered heat price 0.49% and 0.35% for each 1% change, respectively. This highlights that specific building heat is a strong determinant of feasibility given the relative influence of high-demanding users on the overall village heat-density. Finally, we use a stochastic analysis projecting future #2 fuel oil prices, incorporating historical variability, to determine the probability of future BDH feasibility. Although future oil prices drop below the BDH feasibility threshold, the villages retain a 22-53% probability of feasibility after 20 years as a result of high #2 fuel oil price variability. (C) 2015 Elsevier Ltd. All rights reserved.

Although the economic benefits of biomass heating in rural regions have been widely asserted, few studies have conducted a thorough economic impact analysis within the United States. This study proposes biomass district heating (BDH) as a means to stimulate the rural economy of the Tug Hill region of New York State by establishing a local industry and providing lower cost heat compared to the local alternative, #2 fuel oil, and examines the associated economic impacts. Since there are no BDH networks endogenous to the region, an expenditure pattern approach to input–output analysis is employed. The $11.4 million spent annually over the 20 year project payment period on the construction, biomass procurement, and production of heat with BDH would generate $18.7 million in local economic activity and create 143 jobs throughout the three county model region; a significant impact if concentrated around the rural study villages. These impacts are comparable, but less than, those modeled by other studies in countries with more established BDH networks. However, the precision of the impacts generated by the model are tempered by the significant discrepancy between the study region and the three county model region which included several larger urban areas. Ultimately, the limitations associated with the model scale and the absence of established expenditure patterns inhibit major conclusions in regards to the discreet impacts of BDH to the study villages, although a significant portion of the annual impacts estimated can be expected to occur in the villages given the location of the BDH networks.

The purpose of this article is to provide a thorough review of the hedonic studies that investigate environmental amenity values of urban open spaces. Open space amenities have a unique feature than structural characteristics of residential properties; that is, an individual's preference for them may depend on their location relative to a residential property. Twenty-six empirical hedonic studies that estimate values of environmental amenities from open spaces published between 1995 and 2015 were reviewed. We paid special attention to the spatial hedonic literature that used spatial econometric methods to deal with the spatial effects which are common issues in hedonic studies. To our best knowledge, this study is the first study that devoted special attention to the hedonic studies on urban open space using spatial econometric methods.

Eastern white pine (Pinus strobus L.) is capable of impressive yields on a wide range of sites, but that volume is often skewed to low grade because of black knots. In this case study, we examined log and lumber yields from two contrasting sites in the Adirondacks of New York. A subsample of butt logs was isolated from each site to quantify the benefits of pruning. Lumber grade distribution was the same for both sites, with 60

The results of a map accuracy assessment are often summarized by reporting user's accuracy and producer's accuracy for each class in the map legend. Additionally estimating the proportion of area of each class based on the best assessment of ground condition (i.e., the reference classification) of the locations selected in the sample is often of interest for monitoring status and change in land cover. Stratified random sampling is a commonly used sampling design for accuracy assessment, and an important decision for this design is the allocation of sample size to the strata. In this article, the allocation that minimizes the sum of the variances of the estimators of user's accuracy, producer's accuracy, and area of a single targeted class for a fixed total sample size is derived for stratified random sampling. For example, the targeted class might be a rare land-cover type such as wetland or in the case of a land-cover change assessment forest loss. An Excel sample allocation calculator implements the optimization and two examples illustrate the application. Practitioners can apply these optimization results to guide sample size allocation decisions when using a stratified random sampling design for accuracy assessment and area estimation.

We propose an approach to develop economic-based yields for even-and uneven-aged stands that could be compared with yields generated by using silvicultural treatments. Economic-based yields are derived from economic parameters that describe markets and the landowner's ownership goals and objectives. This study highlights five conclusions. First, economic-based yields define a lower bound on silvicultural-based yields required to just satisfy these economic parameters and provide a metric of confidence that a silvicultural prescription would increase (or decrease) the landowner's wealth. Second, a main driver of the economic-based yields is the opportunity costs of the reserve growing stock or regeneration costs and the land. Third, the economic-based yields followed a similar pattern regardless of whether the stand was defined as even-or uneven-aged. Fourth, the economic-based yields illustrate the physical impacts that recreational leases, taxes, or the sale of nontimber forest ecosystem goods and services have on this lower bound. Finally, if the economic-based yields are greater than the silvicultural-based yields and if physical output estimates could be derived for the suite of nontimber forest ecosystem goods and services resulting from the forest structure, then implied economic values for this suite of goods and services could be derived using the models presented.

Absolute resource scarcity is all too real for many students, especially those from noneconomic disciplines. They have a Malthusian economic worldview. We describe a pedagogical model using “The Bet” between Paul Ehrlich and Julian Simon as its focus. The model includes a “Bet” – the students taking the Ehrlich position; a directed discussion providing students with an approach to determine who wins the Bet; and a written assignment, oral presentations, and reflection. The learning outcomes are: 1) students examine their Malthusian beliefs through developing hypotheses, analyzing and testing them, and writing and presenting their conclusions orally; 2) that market's incorporate changes in substitutes, technology, recycling, and discovery; 3) that market prices rise and fall in the short term but it is the long term that illustrates how markets respond to changes in information; and 4) students reflect critically on their initial absolute scarcity assertion.

Sustainable forest management planning includes accounting for revenues and costs that accrue throughout time. While debate continues as to the how to account for these cash flows, the most used techniques are net present value, benefit cost ratios, and internal rate of return (irr). Managing forests sustainably depends critically on interpreting the results and management implications of these techniques accurately. It is appealing to equate the irr with a market-derived rate of return given its definition. Unfortunately, its mathematical derivation does not support this interpretation and past use of irr often illustrates this confusion and misinterpretation. The irr only reflects the amount and timing of the net cash flows for a given venture and does not include any social, economic, or other external factors found in market-derived discount rates. Therefore, the irr does not reflect an appropriate rate of return or opportunity cost of capital for sustainable forest management. My purpose is to provide a theoretical argument that can be used to help correct this misinterpretation and stimulate discussions on the economics of sustainable forest management.

The traditional hedonic model uses market purchases to estimate implicit prices. Hedonic models composed of only public land purchases violate key assumptions of hedonic model theory. The resulting implicit prices cannot be interpreted as the purchasing agency's maximum willingness to pay. The problems are illustrated using a hedonic model of public land purchases in the Town of Brookhaven, on Long Island, New York, USA. The model reveals negative elasticities for attributes for which the agency has stated positive preferences. For example, the presence of unique glacial landforms (a positive attribute) was associated with a 97% increase in property cost. However, if purchasing the open space property prevented development that is incompatible with existing land uses (also a positive attribute), the property cost decreased by 69%. The results confirm that elasticities and implicit prices derived from open space “public hedonic models” should be interpreted in the context of the broader market for land, not as the agency's willingness to pay. The work has implications for open space preservation policies in urbanizing regions.

Cities are increasingly promoting policies that increase and conserve urban forests based largely on biophysical and land use-cover metrics. This study demonstrates how socioeconomic factors need to be considered in geospatial analyses when formulating urban greening policies. Using remote sensing, geographical information systems, spatial field and census data, and policy analyses, we analyzed the effectiveness of urban forest cover policies that included socioeconomic factors when quantifying urban forest cover. We found that urban forest cover was heterogeneous across the study area and non-white residents younger than 19 and greater than 45 years old living in rentals were more likely to reside in areas with less urban forest cover than any other age cohort. Our analyses also indicated that urban forest cover was temporally variable and demographic factors unique to Miami-Dade County bring to light the complexity of establishing homogenous, county-wide "tree canopy" and urban greening policy goals. We present a localized socioeconomic and ecologically based geospatial approach for formulating urban forest cover goals.