• Energy Research

Abstract The amount of installed solar power in Finland is increasing as a result of decreasing photovoltaic (PV) system component prices. The growth is especially noticeable in residential systems, and ways to make PV electricity a more competitive choice for Finnish residents are studied. One of these ways is to decrease the solar PV electricity production costs by decreasing the investment costs by undersizing the inverter of the PV system. The objective of undersizing is to find the optimal array-to-inverter sizing ratio (AISR) where the ratio of the economic loss from the clipped energy to the economic gain from the decreased system investment achieved by an undersized inverter is lowest. In this paper, the economically most optimal AISRs are determined for different residential array sizes, orientations, and inclinations when operating in Finnish locations and conditions. Calculations for each inverter size are carried out by using recorded Finnish meteorological data and the current Finnish PV system cost distribution, and by analyzing existing 1-s resolution production measurement data of a Finnish PV system. It is concluded that it is necessary to use 1-s resolution data as the use of 1-h resolution production data would lead to more significant undersizing caused by the power clipping occurring within an hour. The optimal AISRs presented in this study are higher than the optimal ratios reported in previous studies for locations further south than Finland. This can be explained by the northern location of Finland, where the irradiance above Standard Test Conditions (STC) is lower than in central Europe, for example. This allows more significant undersizing as less energy is clipped even at higher ratios. In the case of south-oriented arrays in a 30° installation angle, the optimal AISRs for the 10 kW, 6 kW, and 3 kW inverters were 1.6, 1.8, and 2.08, respectively. Again, the AISRs for the southwest-southeast facade installations were 1.8, 1.9, and 2.17 for the inverters under study. They do not clip the produced energy as much as rooftop systems because their production is more evenly distributed throughout the day, yet they do not achieve as low production costs either. It is pointed out that if the PV self-consumption is optimized by using PV to heat water or batteries as a storage, limitation of the PV generation might not be the correct solution.

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.

Background: Climate change has been shown to be directly linked to multiple physiological sequelae and to impact health consequences. However, the impact of climate change on mental health globally, particularly among vulnerable populations, is less well understood. Objective: To explore the mental health impacts of climate change in vulnerable populations globally. Methods: We performed an integrative literature review to identify published articles that addressed the research question: What are the mental health impacts of climate change among vulnerable populations globally? The Vulnerable Populations Conceptual Model served as a theoretical model during the review process and data synthesis. Findings/Results: One hundred and four articles were selected for inclusion in this review after a comprehensive review of 1828 manuscripts. Articles were diverse in scope and populations addressed. Land-vulnerable persons (either due to occupation or geographic location), Indigenous persons, children, older adults, and climate migrants were among the vulnerable populations whose mental health was most impacted by climate change. The most prevalent mental health responses to climate change included solastalgia, suicidality, depression, anxiety/eco-anxiety, PTSD, substance use, insomnia, and behavioral disturbance. Conclusions: Mental health professionals including physicians, nurses, physician assistants and other healthcare providers have the opportunity to mitigate the mental health impacts of climate change among vulnerable populations through assessment, preventative education and care. An inclusive and trauma-informed response to climate-related disasters, use of validated measures of mental health, and a long-term therapeutic relationship that extends beyond the immediate consequences of climate change-related events are approaches to successful mental health care in a climate-changing world.

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