Factors determining awareness and knowledge of aquatic invasive species

Public perceptions of invasive species may influence policies and programs initiated by public and private stakeholders. We investigate the determinants of the public's awareness and knowledge of invasive species as few studies have examined this relationship. We focus on aquatic invasive species (AIS) and employ survey data from property owners in a lake district. A major contribution is that we estimate a mixed trivariate binary-ordered probit regression model that accommodates correlations among unobserved characteristics, produces statistically more efficient estimates, and allows a more proper investigation of the probability of knowledge conditional on awareness. Our results provide insights for invasive species education and management programs. We find that individuals are more likely to be aware of AIS if they participate in water-based recreation, visit lakes outside their area, have a boat, belong to a lake association, or are college educated. This has a policy implication: Given high levels of AIS awareness by those most involved in activities around lakes and those with a higher education, it may be beneficial to target informational campaigns at those who do not display these characteristics, so that they can better make informed decisions about whether to support and expend money on invasive species management programs.     

Cattle and Wildlife Competition for Forage: Budget Versus Bioeconomic Analyses of Public Range Improvements in British Columbia

We analyze the gains of public investments in range productivity when cattle compete for forage with wildlife herbivores. Ignoring extra-market values by applying conventional budget analyses leads to higher cattle stocking rates and lower overall social benefits of public range improvements. This is demonstrated numerically for public forage in British Columbia, where privately profitable cattle stocking rates on newly seeded range exceed those that are socially optimal, perhaps by as much as double depending on the assumptions one makes about marginal preservation benefits. This highlights the importance of applying dynamic optimization, bioeconomic models to analyze investments in public range.
Les auteurs analysent quel gain on tire des sommes publiques investies dans la productivité des grands parcours quand les bovins et les herbivores sauvages se livrent concurrence pour les fourrages. Ne pas tenir compte des valeurs extérieures au marché en recourant à des analyses budgétaires ordinaires entraîne une hausse du taux de chargement et une réduction des avantages sociaux globaux issus de la bonification des grands parcours. Il est possible d'en faire la preuve numérique avec les pâturages publics de la Colombie-Britannique, où les parcours nouvellement ensemencés supportent une population de bovins lucrative pour l'éleveur mais supérieure au taux de chargement optimal socialement, parfois même du double, selon les hypothèses qu'on formule sur les avantages d'une préservation marginale. l'étude souligne bien qu'il est important de recourir à une optimisation dynamique, soit d'utiliser les modèles bio-économiques pour analyser les sommes investies dans l'amélioration des pâturages publics.     

Dynamic Programming and Learning Models for Management of a Nonnative Species

Nonnative invasive species result in sizeable economic damages and control costs. Because dynamic optimization models break down if controls depend in complex ways on past controls, nonuniform or scale‐dependent spatial attributes, etc., decision‐support systems that allow learning may be preferred. We compare two models of an invasive weed in California's grazing lands: (i) a stochastic dynamic programming model and (ii) a reinforcement‐based, experience‐weighted attraction (EWA) learning model. We extend the EWA approach by including stochastic forage growth and penalties for repeated application of environmentally harmful controls. Results indicate that EWA learning models offer some promise for managing invasive species. Les espèces non indigènes envahissantes entraînent des dommages économiques et des coûts de lutte considérables. Compte tenu que les modèles d'optimisation dynamique échouent lorsque les moyens de lutte dépendent, de façon complexe, de moyens de lutte antérieurs, d'attributs spatiaux influencés par l'échelle ou non uniformes, etc., l'utilisation de systèmes d'aide à la décision permettant l'apprentissage pourrait être préférable. Nous avons comparé deux modèles dans le cas d'une plante adventice envahissant les pâturages de la Californie: 1. un modèle de programmation dynamique stochastique; 2. un modèle d'apprentissage experience‐weighted attraction (EWA), fondé sur le renforcement. Nous avons élargi le modèle EWA en y incluant la croissance stochastique des fourrages et des pénalités imposées pour l'utilisation répétée de moyens de lutte dommageables pour l'environnement. Selon les résultats obtenus, les modèles d'apprentissage EWA semblent prometteurs pour la gestion des espèces envahissantes.     

A novel Monte Carlo approach to hybrid local volatility models

We present in a Monte Carlo simulation framework, a novel approach for the evaluation of hybrid local volatility [Risk, 1994, 7, 18–20], [Int. J. Theor. Appl. Finance, 1998, 1, 61–110] models. In particular, we consider the stochastic local volatility model—see e.g. Lipton et al. [Quant. Finance, 2014, 14, 1899–1922], Piterbarg [Risk, 2007, April, 84–89], Tataru and Fisher [Quantitative Development Group, Bloomberg Version 1, 2010], Lipton [Risk, 2002, 15, 61–66]—and the local volatility model incorporating stochastic interest rates—see e.g. Atlan [ArXiV preprint math/0604316, 2006], Piterbarg [Risk, 2006, 19, 66–71], Deelstra and Rayée [Appl. Math. Finance, 2012, 1–23], Ren et al. [Risk, 2007, 20, 138–143]. For both model classes a particular (conditional) expectation needs to be evaluated which cannot be extracted from the market and is expensive to compute. We establish accurate and ‘cheap to evaluate’ approximations for the expectations by means of the stochastic collocation method [SIAM J. Numer. Anal., 2007, 45, 1005–1034], [SIAM J. Sci. Comput., 2005, 27, 1118–1139], [Math. Models Methods Appl. Sci., 2012, 22, 1–33], [SIAM J. Numer. Anal., 2008, 46, 2309–2345], [J. Biomech. Eng., 2011, 133, 031001], which was recently applied in the financial context [Available at SSRN 2529691, 2014], [J. Comput. Finance, 2016, 20, 1–19], combined with standard regression techniques. Monte Carlo pricing experiments confirm that our method is highly accurate and fast.     

The Impact of Biofuel Policies on Crop Acreages in Germany and France

A major concern about biofuels is that increasing biofuel feedstock demand reduces availability of crops for food and feed leading to higher food prices. This paper investigates relations between biofuel policies and prices of rapeseed, the major feedstock used for biodiesel production in Europe, and the impact of rapeseed prices on crop acreages in Germany and France. Biodiesel is an important biofuel in Europe, and Germany and France are the largest biodiesel producers in Europe. First, the various biofuel policies in Germany and France are discussed, followed by an analysis of their effects on rapeseed prices. Although theory indicates that such effects exist, we could not find empirical evidence for them. Second, using regional land use panel data from Germany and France we investigate empirically whether crop shares have been affected by rapeseed prices in the period 2000–2015 and whether these price effects changed because of biofuel policy changes. Results show that wheat shares in Germany and France did respond to rapeseed prices, but barley shares did not. Moreover, mandatory blending introduced in Germany in 2007 and production quotas introduced in France in 2005 led to a stronger effect of rapeseed prices on wheat shares, but again did not affect barley shares.     

Determining optimal forest rotation ages and carbon offset credits: Accounting for post-harvest carbon storehouses

Sequestering carbon in forest ecosystems is important for mitigating climate change. A major policy concern is whether forests should be left unharvested to avoid carbon dioxide (CO₂) emissions and store carbon, or harvested to take advantage of potential carbon storage in post-harvest wood product sinks and removal of CO₂ from the atmosphere by new growth. The issue is addressed in this paper by examining carbon rotation ages that consider commercial timber as well as carbon values. A discrete-time optimal rotation age model is developed that employs data on carbon fluxes stored in both living and dead biomass as opposed to carbon as a function of timber growth. Carbon is allocated to several ecosystem and post-harvest product pools that decay over time at different rates. In addition, the timing of carbon fluxes is taken into account by weighting future carbon fluxes as less important than current ones. Using simple formulae for determining optimal rotation ages, we find that: (1) Reducing the price of timber while increasing the price of carbon will increase rotation age, perhaps to infinity (stand remains unharvested). (2) An increase in the rate used to discount physical carbon generally reduces the rotation age, but not in all cases. (3) As a corollary, an increase in the price of carbon increases or reduces rotation age depending on the weight chosen to discount future carbon fluxes. (4) Site characteristics and the mix of species on the site affect conclusions (2) and (3). (5) A large variety of carbon offset credits from forestry activities could be justified, which makes it difficult to accept any.