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The study investigates the effect of carbon tax adoption on foreign direct investment in Africa. We set up the Dynamic Stochastic General Equilibrium (DSGE) model and estimate it with the differenced GMM techniques. The data span from 1995 to 2019 and covers 43 Sub-Saharan African countries. Data is sourced from the World Bank's World Development Indicators. The findings show that the unmitigated effect of the carbon tax on FDI is repressive. However, if the revenue from the carbon tax is recycled into the economy, the carbon tax will have a significant positive effect on FDI. Hence, the findings corroborate the double dividend theory. The results further suggest that a carbon tax of around US\\ 8.5 per tonne is reasonable to enhance inward FDI but a carbon tax either above US\\ 25 per tonne or below US\\ 3 per tonne will be detrimental to the African region. Also, the entrenched negative relationship between FDI and taxes is worsened if the additional carbon tax is levied among high tax regimes countries than their counterparts. This study opens the frontiers to the discussions on the policy implications of carbon tax introduction on the free movement of international capital. Being among the few studies to examine the effect of the carbon tax on FDI, the study makes a significant contribution to the sparse literature in the African context. The use of a stepwise approach to estimate data based on reasonable assumptions can form the basis for future research to venture into areas where data is constrained. The policy implications are that (i) carbon tax per tonne below US\\ 3 or above US$ 25 is detrimental to FDI, and (ii) the negative effect of the carbon tax on FDI can be overturned by efficiently reinvesting the carbon tax revenue in the economy.

The paper models evolution in pecunia—in the realm of finance. Financial markets are explored as evolving biological systems. Diverse investment strategies compete for the market capital invested in long-lived dividend-paying assets. Some strategies survive and some become extinct. The basis of our paper is that dividends are not exogenous but increase with the wealth invested in an asset, as is the case in a production economy. This might create a positive feedback loop in which more investment in some asset leads to higher dividends which in turn lead to higher investments. Nevertheless, we are able to identify a unique evolutionary stable investment strategy. The problem is studied in a framework combining stochastic dynamics and evolutionary game theory. The model proposed employs only objectively observable market data, in contrast with traditional settings relying upon unobservable investors’ characteristics (utilities and beliefs). Our method is analytical and based on mathematical reasoning. A numerical illustration of the main result is provided.

1. How climatic changes affect migratory birds remains difficult to predict because birds use multiple sites in a highly interdependent manner. A better understanding of how conditions along the flyway affect migration and ultimately fitness is of paramount interest. 2. Therefore, we developed a stochastic dynamic model to generate spatially and temporally explicit predictions of stop-over site use. For each site, we varied energy expenditure, onset of spring, intake rate and day-to-day stochasticity independently. We parameterized the model for the migration of pink-footed goose Anser brachyrhynchus from its wintering grounds in Western Europe to its breeding grounds on Arctic Svalbard. 3. Model results suggested that the birds follow a risk-averse strategy by avoiding sites with comparatively high energy expenditure or stochasticity levels in favour of sites with highly predictable food supply and low expenditure. Furthermore, the onset of spring on the stop-over sites had the most pronounced effect on staging times while intake rates had surprisingly little effect. 4. Subsequently, using empirical data, we tested whether observed changes in the onset of spring along the flyway explain the observed changes in migration schedules of pink-footed geese from 1990 to 2004. Model predictions generally agreed well with empirically observed migration patterns, with geese leaving the wintering grounds earlier while considerably extending their staging times in Norway.

Learning-by-doing and organizational forgetting are empirically important in a variety of industrial settings. This paper provides a general model of dynamic competition that accounts for these fundamentals and shows how they shape industry structure and dynamics. We show that forgetting does not simply negate learning. Rather, they are distinct economic forces that interact in subtle ways to produce a great variety of pricing behaviors and industry dynamics. In particular, a model with learning and forgetting can give rise to aggressive pricing behavior, varying degrees of long-run industry concentration ranging from moderate leadership to absolute dominance, and multiple equilibria.

This paper demonstrates an energy management method using traffic information for commuter hybrid electric vehicles. A control strategy based on stochastic dynamic programming (SDP) is developed, which minimizes on average the equivalent fuel consumption, while satisfying the battery charge-sustaining constraints and the overall vehicle power demand for drivability. First, according to the sample information of the traffic speed profiles, the regular route is divided into several segments and the statistic characteristics in the different segments are constructed from gathered data on the averaged vehicle speeds. And then, the energy management problem is formulated as a stochastic nonlinear and constrained optimal control problem and a modified policy iteration algorithm is utilized to generate a time-invariant state-dependent power split strategy. Finally, simulation results over some driving cycles are presented to demonstrate the effectiveness of the proposed energy management strategy.

An optimal switching control formalism combined with the stochastic dynamic programming is, for the first time, applied to modelling life cycle of migrating population dynamics with non-overlapping generations. The migration behaviour between habitats is efficiently described as impulsive switching based on stochastic differential equations, which is a new standpoint for modelling the biological phenomenon. The population dynamics is assumed to occur so that the reproductive success is maximized under an expectation. Finding the optimal migration strategy ultimately reduces to solving an optimality equation of the quasi-variational type. We show an effective linkage between our optimality equation and the basic reproduction number. Our model is applied to numerical computation of optimal migration strategy and basic reproduction number of an amphidromous fish Plecoglossus altivelis altivelis in Japan as a target species.

Energy storage and renewable sources play a unique role in the future advances of smart grids. In this article, the optimal scheduling of the energy storage system in a hybrid microgrid is presented considering the uncertainties of the renewable generations and the load. The optimisation problem in this article is non‐linear and non‐convex, therefore conventional optimisation methods such as linear programming (LP) are unable to solve this problem. On the other hand, because of parameters uncertainty, special considerations are required to simulate these parameters. In this regard, a new optimisation algorithm that can solve the non‐linearity and non‐convexity of the objective function is proposed based on the Stochastic Quasi‐Gradient optimisation Method (SQGM). Moreover, the uncertainties of the wind, PV generation, and the load are modelled. Different optimisation algorithms: the conventional Stochastic Dynamic Programming (SDP), the Stochastic Dual Dynamic Programming (SDDP) and the proposed SQGM are compared. A 9‐bus benchmark system with distributed generation units is used to evaluate the optimisation strategies.

This paper is concerned with optimal maintenance decision making in the presence of model misspecification. Specifically, we are interested in the situation where the decision maker fears that a nominal Bayesian model may be miss-specified or unrealistic, and would like to find policies that work well even when the underlying model is flawed. To this end, we formulate a robust dynamic optimization model for condition-based maintenance in which the decision maker explicitly accounts for distrust in the nominal Bayesian model by solving a worst-case problem against a second agent, “nature,” who has the ability to alter the underlying model distributions in an adversarial manner. The primary focus of our analysis is on establishing structural properties and insights that hold in the face of model miss-specification. In particular, we prove (i) an explicit characterization of nature’s optimal response through an analysis of the robust dynamic programming equation, (ii) convexity results for both the robust value function and the optimal robust stopping region, (iii) a general robustness result for the preventive maintenance paradigm, and (iv) the optimality of a robust control limit policy for the important subclass of Bayesian change point detection problems. We illustrate our theoretical result on a real-world application from the mining industry.

We analyze a class of stochastic dynamic games among teams with asymmetric information, where members of a team share their observations internally with a delay of d . Each team is associated with a controlled Markov Chain, whose dynamics are coupled through the players’ actions. These games exhibit challenges in both theory and practice due to the presence of signaling and the increasing domain of information over time. We develop a general approach to characterize a subset of Nash equilibria where the agents can use a compressed version of their information, instead of the full information, to choose their actions. We identify two subclasses of strategies: sufficient private information-Based (SPIB) strategies, which only compress private information, and compressed information-based (CIB) strategies, which compress both common and private information. We show that SPIB-strategy-based equilibria exist and the set of payoff profiles of such equilibria is the same as that of all Nash equilibria. On the other hand, we show that CIB-strategy-based equilibria may not exist. We develop a backward inductive sequential procedure, whose solution (if it exists) provides a CIB strategy-based equilibrium. We identify some instances where we can guarantee the existence of a solution to the above procedure. Our results highlight the tension among compression of information, ability of compression-based strategies to sustain all or some of the equilibrium payoff profiles, and backward inductive sequential computation of equilibria in stochastic dynamic games with asymmetric information.

The purpose of this paper is to investigate the existence and uniqueness of the square mean weighted pseudo almost periodic solution for a neutral stochastic partial dynamic equations with weighted Stepanov-like pseudo almost periodic terms on time scales. Firstly, we introduce a time scale version of the weighted Stepanov-like pseudo-almost periodic processes. Finally, an example is provided to illustrate our abstract results.