A developing country like Ethiopia suffers a lot from the effects of climate change due to its limited economic capability to build irrigation projects to combat climate change's impact on crop production. This study evaluates climate change's impact on rainfed maize production in the Southern part of Ethiopia. AquaCrop, developed by FAO that simulates the crop yield response to water deficit conditions, is employed to assess potential rainfed maize production in the study area with and without climate change. The Stochastic weather generators model LARS-WG of the latest version is used to simulate local-scale level climate variables based on low-resolution GCM outputs. The expected monthly percentage change of rainfall during these two-time horizons (2040 and 2060) ranges from -23.18 to 20.23% and -14.8 to 36.66 respectively. Moreover, the monthly mean of the minimum and maximum temperature are estimated to increase in the range of 1.296 0C to 2.192 0C and 0.98 0C to 1.84 0C for the first time horizon (2031-2050) and from 1.860C to 3.40C and 1.560C to 3.180C in the second time horizon (2051-2070), respectively. Maize yields are expected to increase with the range of 4.13–7% and 6.36–9.32% for the respective time horizon in the study area provided that all other parameters were kept the same. In conclusion, the study results suggest that rainfed maize yield responds positively to climate change if all field management, soil fertility, and crop variety improve were kept the same to baseline; but since there is intermodal rainfall variability among the seasons planting date should be scheduled well to combat water stress on crops. The authors believe that this study is very likely important for regional development agents (DA) and policymakers to cope up with the climate change phenomenon and take some mitigation and adaptation strategies.
AbstractWe examine the dynamics of liquidity connectedness in the cryptocurrency market. We use the connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271–296, 2018) on a sample of six major cryptocurrencies, namely, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Monero (XMR), and Dash. Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies, whereas BTC and LTC play a significant role in connectedness magnitude. A distinct liquidity cluster is observed for BTC, LTC, and XRP, and ETH, XMR, and Dash also form another distinct liquidity cluster. The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium- and long-run time horizons. In the short run, BTC, LTC, and XRP are the leading contributor to liquidity shocks, whereas, in the long run, ETH assumes this role. Compared with the medium term, a tight liquidity clustering is found in the short and long terms. The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time, pointing to the possible effect of rising demand and higher acceptability for this unique asset. Furthermore, more pronounced liquidity connectedness patterns are observed over the short and long run, reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time–frequency connectedness.
We consider the joint design and control of discrete-time stochastic dynamical systems over a finite time horizon. We formulate the problem as a multi-step optimization problem under uncertainty seeking to identify a system design and a control policy that jointly maximize the expected sum of rewards collected over the time horizon considered. The transition function, the reward function and the policy are all parametrized, assumed known and differentiable with respect to their parameters. We then introduce a deep reinforcement learning algorithm combining policy gradient methods with model-based optimization techniques to solve this problem. In essence, our algorithm iteratively approximates the gradient of the expected return via Monte-Carlo sampling and automatic differentiation and takes projected gradient ascent steps in the space of environment and policy parameters. This algorithm is referred to as Direct Environment and Policy Search (DEPS). We assess the performance of our algorithm in three environments concerned with the design and control of a mass-spring-damper system, a small-scale off-grid power system and a drone, respectively. In addition, our algorithm is benchmarked against a state-of-the-art deep reinforcement learning algorithm used to tackle joint design and control problems. We show that DEPS performs at least as well or better in all three environments, consistently yielding solutions with higher returns in fewer iterations. Finally, solutions produced by our algorithm are also compared with solutions produced by an algorithm that does not jointly optimize environment and policy parameters, highlighting the fact that higher returns can be achieved when joint optimization is performed.
Hydrofracturing, used for shale gas exploitation, may induce felt, even damaging earthquakes. On 15 June 2019, an Mw2.8 earthquake occurred, spatially correlated with the location of earlier exploratory hydrofracturing operations for shale gas in Wysin in Poland. However, this earthquake was atypical. Hydrofracturing-triggered seismicity mainly occurs during stimulation; occasionally, it continues a few months after completion of the stimulation. In Wysin, there were only two weaker events during two-month hydrofracturing and then 35 months of seismic silence until the mentioned earthquake occurred. The Wysin site is in Gdańsk Pomerania broader region, located on the very weakly seismically active Precambrian Platform. The historical documents, covering 1000 years, report no natural earthquakes in Gdańsk Pomerania. We conclude, therefore, that despite the never observed before that long lag time after stimulation, the Mw2.8 earthquake was triggered by hydrofracturing. It is possible that its unusually late occurrence in relation to the time of its triggering technological activity was caused by changes in stresses due to time-dependent deformation of reservoir shales. The Wysin earthquake determines a new time horizon for the effect of HF on the stress state, which can lead to triggering earthquakes. Time-dependent deformation and its induced stress changes should be considered in shall gas reservoir exploitation plans.
The purpose of this research is to carry out an in-depth exploration of the causes of the family firm's success over short and long term, analysing which capabilities are the most valuable sources of sustainable competitive advantage in every time horizon. The results confirm only functional capabilities have a positive and significant effect on short-term economic performance, whereas dynamic capabilities are the only ones that have a positive and significant impact on long-term economic performance.
We investigate the effect of subway system openings on urban air pollution. On average, particulate concentrations are unchanged by subway openings. For cities with higher initial pollution levels, subway openings reduce particulates by 4 percent in the area surrounding a city center. The effect decays with distance to city center and persists over the longest time horizon that we can measure with our data, about four years. For highly polluted cities, we estimate that a new subway system provides an external mortality benefit of about $1 billion per year. For less polluted cities, the effect is indistinguishable from zero. Back of the envelope cost estimates suggest that reduced mortality due to lower air pollution offsets a substantial share of the construction costs of subways. (JEL I12, L92, O13, O18, Q51, Q53, R41)
The article describes an approach to the operational and supervisory control of a gas transmission system for large industrial zones using a model predictive control, as well as analytical and simulation methods. The operational and supervisory control of the gas transportation system covers the time horizon from several hours to several days and involves performing several cyclically repeated actions. The authors propose a time series predictive model of the gas consumption parameter considering temperature weather conditions, which is extended based on accounting for the correlation relationships between the consumption volumes of each consumer. The control methods used today, reacting to the current deviations from the planned regime, a priori do not allow achieving the best results. A significant increase in the stability of control and a reduction in the cost of fuel and energy resources can be achieved by using the control method based on predictive models. In this case, the control object model is used to predict its behavior within the selected time horizon, and optimal control actions are selected on this basis. The process of predicting and selecting control actions is periodically repeated, constantly changing the time horizon boundaries. The described method of changing the flow diagram consists either in changing all the flows at the same time or in a preemptive and smooth transition based on the introduction of a weighted flow diagram for various stationary modes, provided that their mismatch is minimized at neighboring time intervals corresponding to the intervals of constancy of consumption requests.
This paper is concerned with a stochastic linear-quadratic (LQ) optimal control problem on infinite time horizon, with regime switching, random coefficients, and cone control constraint. To tackle the problem, two new extended stochastic Riccati equations (ESREs) on infinite time horizon are introduced. The existence of the nonnegative solutions, in both standard and singular cases, is proved through a sequence of ESREs on finite time horizon. Based on this result and some approximation techniques, we obtain the optimal state feedback control and optimal value for the stochastic LQ problem explicitly. Finally, we apply these results to solve a lifetime portfolio selection problem of tracking a given wealth level with regime switching and portfolio constraint.
We consider the long-time behavior of an explicit tamed exponential Euler scheme applied to a class of parabolic semilinear stochastic partial differential equations driven by additive noise, under a one-sided Lipschitz continuity condition. The setting encompasses nonlinearities with polynomial growth. First, we prove that moment bounds for the numerical scheme hold, with at most polynomial dependence with respect to the time horizon. Second, we apply this result to obtain error estimates, in the weak sense, in terms of the time-step size and of the time horizon, to quantify the error to approximate averages with respect to the invariant distribution of the continuous-time process. We justify the efficiency of using the explicit tamed exponential Euler scheme to approximate the invariant distribution, since the computational cost does not suffer from the at most polynomial growth of the moment bounds. To the best of our knowledge, this is the first result in the literature concerning the approximation of the invariant distribution for SPDEs with non-globally Lipschitz coefficients using an explicit tamed scheme.