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A major challenge in sustainability science is identifying targets that maximize ecosystem benefits to humanity while minimizing the risk of crossing critical system thresholds. One critical threshold is the biomass at which populations become so depleted that their population growth rates become negative—depensation. Here, we evaluate how the value of monitoring information increases as a natural resource spends more time near the critical threshold. This benefit emerges because higher monitoring precision promotes higher yield and a greater capacity to recover from overharvest. We show that precautionary buffers that trigger increased monitoring precision as resource levels decline may offer a way to minimize monitoring costs and maximize profits. In a world of finite resources, improving our understanding of the trade-off between precision in estimates of population status and the costs of mismanagement will benefit stakeholders that shoulder the burden of these economic and social costs.

AbstractWe have measured the effect of degradation on the I-V characteristics in APFO3:PCBM solar cells. The solar cell devices were subject to ambient air under simulated solar illumination. We found that the degradation resulted in a lowering of the fill factor and short circuit current while the open circuit voltage remained unchanged. In order to gain insight into what has caused the degraded I-V characteristics we have studied the active layer film using various techniques. We found clear spectral changes both in absorption and in photoinduced absorption spectroscopy correlated with increased carrier lifetimes and lowered mobility when comparing the degraded film with a pristine one. The results show a significant degradation of the active layer causing a lower fill-factor and short circuit current.

Abstract Three-dimensional (3D) metal halide perovskite solar cells (PSCs) have a power conversion efficiency that is now comparable with conventional silicon solar cells. For PSC applications to succeed in the market, long-term reliability under open-air conditions is essential. Recent experiments have shown that two-dimensional (2D) perovskites seem to exhibit good stability due to the presence of hydrophobic organic spacers, but 2D PSCs are incapable of generating and transporting a large amount of charge due to their extended optical bandgaps. Mixed dimensional perovskites with dimension lies between 2D and 3D recently became a promising candidate to sustain long-term stability and high performances concurrently to address this obstacle. The current research article presents the finding of simulation-based studies performed on novel device architecture consisting of ITO/Nb-Ti2O3/3D Perovskite/2D Perovskite/Spiro-OMeTAD/Au. Using optical simulation features of SCAPS, absorption of light is computed in the proposed device. The computational results show that the thickness of the 2D perovskite layer badly affects the solar cell parameters. A thin 2D perovskite behaves as a capped coating that avoids the deterioration of 3D perovskite in open-air environments. The effect of a multivalent defect in the 3D perovskite layer is mathematically modelled, and their impact on overall performance parameters are analyzed. The findings are compared to the same configuration results, except where the absorber layer’s multivalent defect has been substituted by a neutral defect of the same defect density of about (1011 cm−3). Results show that the multivalent defect leads to an underestimation of the efficiency by 4.2%.

Abstract This study investigates the possibility of using and developing hydrokinetic power to supply reliable, affordable and sustainable electricity to rural, remote and isolated loads in rural South Africa where reasonable water resource is available. Simulations are performed using the Hybrid Optimization Model for Electric Renewable (HOMER) and the results are compared to those from other supply options such as standalone Photovoltaic system (PV), wind, diesel generator (DG) and grid extension. Finally the paper points out some major challenges that are facing the development of this technology in South Africa.

The shipping industry faces a large challenge as it needs to significantly lower the amounts of Green House Gas emissions. Traditionally, reducing the fuel consumption for ships has been achieved during the design stage and, after building a ship, through optimisation of ship operations. In recent years, ship efficiency improvements using Machine Learning (ML) methods are quickly progressing, facilitated by available data from remote sensing, experiments and high-fidelity simulations. The data have been successfully applied to extract intricate empirical rules that can reduce emissions thereby helping achieve green shipping. This article presents an overview of applying ML techniques to enhance ships’ sustainability. The work covers the ML fundamentals and applications in relevant areas: ship design, operational performance, and voyage planning. Suitable ML approaches are analysed and compared on a scenario basis, with their space for improvements also discussed. Meanwhile, a reminder is given that ML has many inherent uncertainties and hence should be used with caution.

AbstractElevated carbon-dioxide concentration [eCO2] is a key climate change factor affecting plant growth and yield. Conventionally, crop modeling work has evaluated the effect of climatic parameters on crop growth, without considering CO2. It is conjectured that a novel multimodal ensemble approach may improve the accuracy of modelled responses to eCO2. To demonstrate the applicability of a multimodel ensemble of crop models to simulation of eCO2, APSIM, CropSyst, DSSAT, EPIC and STICS were calibrated to observed data for crop phenology, biomass and yield. Significant variability in simulated biomass production was shown among the models particularly at dryland sites (44%) compared to the irrigated site (22%). Increased yield was observed for all models with the highest average yield at dryland site by EPIC (49%) and lowest under irrigated conditions (17%) by APSIM and CropSyst. For the ensemble, maximum yield was 45% for the dryland site and a minimum 22% at the irrigated site. We concluded from our study that process-based crop models have variability in the simulation of crop response to [eCO2] with greater difference under water-stressed conditions. We recommend the use of ensembles to improve accuracy in modeled responses to [eCO2].

KOF Working Papers, 327