Come on feel the noise: Ecological foundations in stochastic bioeconomic models

Abstract

Bioeconomic approaches to resource management treat renewable resources as naturally reproducing assets, or natural capital, whose conservation generates an economic rate of return. Stochasticity in resource growth produces stochasticity in these returns, affecting both the economic efficiency and ecological sustainability of resource use. Traditional bioeconomic models find that the rate of return to conservation is increased by a risk premium that may lower conservation incentives for more random populations. But these prescriptions depend on how the stochasticity of resource growth is modeled. Whereas the ecological literature adopts stochastic processes based on ecological considerations, bioeconomic analyses generally rely on ad hoc specifications chosen for convenience. Using analytical and numerical applications to the efficient management of fisheries and forests, we find that risk premium calculations based on traditional bioeconomic approaches versus ecological approaches may differ both quantitatively and qualitatively so that not all sources of ecological stochasticity imply conservation is risky. Recommendations for Resource Managers Bioeconomic approaches to resource management link economic and ecological models to evaluate economic–ecological tradeoffs arising from different management choices. The bioeconomic literature has focused very little on the implications of stochastic resource growth, and the specification of stochasticity in these analyses is generally ad hoc and not ecologically justifiable. We bridge this gap by adopting specifications promoted by ecologists and demonstrate as follows: Not all sources of ecological stochasticity imply conservation is risky. This result contrasts with traditional bioeconomic findings, driven by ad hoc modeling assumptions, that natural capital is always a risky investment. Whether a particular source of stochasticity implies conservation is risky may be state dependent. Even when models based on ecological notions of stochasticity do imply that conservation is risky, the magnitude of these risks and the ability of conservation efforts to manage risks may differ greatly from traditional bioeconomic specifications.

The Probability Distribution of Worldwide Forest Areas

“…Third, our findings regarding the random rates of change of forest areas give support to models of stochastic forest growth [53]. Previous research on forest management under uncertainty focuses on the influence of stochasticity from different sources [60]; among them, we can highlight climate change [61].…”

“…In ecology and bioeconomics the common approach is also to treat changes in th resource population as a random variable [53]. The bioeconomics literature typically as sumes that the standard deviation is proportional to the resource population and com bines this with a mean growth component following a Brownian motion that can be geo metric [54] or logistic [55].…”

Mathematical and numerical analyses of a stochastic impulse control model with imperfect interventions

“…Our model was possibly the simplest one, and can be extended to generalized models. Population dynamics may be subject to nonlinear [67,68] and jump disturbances [69,70]. A more general form of uncertainty in the interventions can be considered.…”

Optimal Harvest with Multiple Fishing Zones, Endogenous Price and Global Uncertainty

“…Note that when the {σ 1 , σ 2 , ..., σ N } are equal to 0, the correlation is equal to 1 and when the {φ 1 , φ 2 , ..., φ N } are equal to zero the correlation is 0. As in Kvamsdal et al (2016), Sims et al (2018 and Pizarro and Schwartz (2021), the growth rate of the biomass is assumed to follow a logistic function:…”