A Statistical Framework for Evaluating the Effectiveness of Vegetation Management in Reducing Power Outages Caused during Storms in Distribution Networks

This paper develops a statistical framework to analyze the effectiveness of vegetation management at reducing power outages during storms of varying severity levels. The framework was applied on the Eversource Energy distribution grid in Connecticut, USA based on 173 rain and wind events from 2005–2020, including Hurricane Irene, Hurricane Sandy, and Tropical Storm Isaias. The data were binned by storm severity (high/low) and vegetation management levels, where a maximum applicable length of vegetation management for each circuit was determined, and the data were divided into four bins based on the actual length of vegetation management performed divided by the maximum applicable value (0–25%, 25–50%, 50–75%, and 75–100%). Then, weather and overhead line length normalized outage statistics were taken for each group. The statistics were used to determine the effectiveness of vegetation management and its dependence on storm severity. The results demonstrate a higher reduction in damages for lower-severity storms, with a reduction in normalized outages between 45.8% and 63.8%. For high-severity events, there is a large increase in effectiveness between the highest level of vegetation management and the two lower levels, with 75–100% vegetation management leading to a 37.3% reduction in trouble spots. Yet, when evaluating system reliability, it is important to look at all storms combined, and the results of this study provide useful information on total annual trouble spots and allow for analysis of how various vegetation management scenarios would impact trouble spots in the electric grid. This framework can also be used to better understand how more rigorous vegetation management standards (applying ETT) help reduce outages at an individual event level. In future work, a similar framework may be used to evaluate other resilience improvements.

Two Birds with One Stone: Bioplastics and Food Waste Anaerobic Co-Digestion

Following the BBC’s Blue Planet II nature documentary series on marine ecosystems, plastic packaging has come under public fire, with consumers demanding greener alternatives. The biodegradable properties of some bioplastics have offered a potential solution to the global challenge of plastic pollution, while enabling the capture of food waste through anaerobic digestion as a circular and energy-positive waste treatment strategy. However, despite their increasing popularity, currently bioplastics are being tested in environments that do not reflect real-life waste management scenarios. Bioplastics find their most useful, meaningful and environmentally-sound application in food packaging—why is there so little interest in addressing their anaerobic co-digestion with food waste? Here, we provide a set of recommendations to ensure future studies on bioplastic end-of-life are fit for purpose. This perspective makes the link between the environmental sustainability of bioplastics and the role of food waste anaerobic digestion as we move towards an integrated food–energy–water–waste nexus. It shines light on a novel outlook in the field of bioplastic waste management while uncovering the complexity of a successful path forward. Ultimately, this research strives to ensure that the promotion of bioplastics within a circular economy framework is supported across waste collection and treatment stages.

Analysis of Construction Implementation Time and Cost Scheduling by Adjusting Budget Changes Due to the Covid-19 Pandemic (Case Study on the Randugunting Dam Construction Project)

The Randugunting Dam, located in Blora Regency, is planned to have a capacity of 10.40 million m and is expected to irrigate an area of 630 Ha, providing a raw water supply of 0.15 m/second. The construction activity of the Randugunting Dam is one of the construction works that has been affected by the Covid-19 pandemic, namely cash flow is hampered. Therefore we need an analysis of cash flow management scenarios and determining strategies for handling the impact of the pandemic on implementation. The data used in this study is the result of interviews with the project implementation team for the Randugunting Dam construction, and data on the project cost budget. Cash flow management analysis is carried out by applying 3 scenarios, namely: scenario 1 (fixed time), scenario 2 (slowed down time), and scenario 3 (accelerated time). The analysis was carried out with the help of the Microsoft Project 2010. The determination of the handling strategy in this study used the DSS (Decision Support System) method, and the SWOT (Strength, Weakness, Opportunity, and Threat). The results of the study chose scenario 3, which is to speed up the time of 6 months. Acceleration is done by increasing the working time. The annual cost required is higher than scenario 1 (fixed time) and 2 (delayed time by 7 months), but the building is completed faster and can be used immediately. The strategy generated by SWOT analysis is Diversity Strategy and produces 8 (eight) strategies. Diversity Strategy is a strategy by maximizing internal strength factors (S), namely increasing the internal capabilities of project implementers and avoiding external threat factors (T), especially related to the Covid-19 pandemic.

Prescriptions For The Control Of A Clonal Invasive Species Using Demographic Models

Until recently, little focus has been given to determine the population dynamics of invasive species and evaluate their genetic variation. Consequently, not much is known of what drives clonal invasive species and their demography. Here we describe the population dynamics of Kalanchoe delagoensis (Crassulaceae), considered invasive to several countries. We quantified the demography of a population in central Mexico using integral projection models (IPM) in a population that reproduced asexually exclusively through plantlets. The effect of clonal recruitment on λ was evaluated by changing plantlet survival and simulating management scenarios that used previous data of watering and seven experimental herbicide treatments. The finite rate of population increase indicated that this Kalanchoe delagoensis population is growing (above one) with significant potential increases that correlated with water availability. The IPM showed that plantlet survival and recruitment were the most critical steps in the cycle for the population and simulations of different management scenarios showed that reducing plantlet survival significantly decreased λ only in two out of the seven herbicides used.

Image of the destination in knowledge management for the reactivation of the tourism economy in the face of Covid-19

The image of the destination is a central process in the tourism agenda. The establishment of a model or explanatory includes predictive variables the satisfactory experience in terms of transfer, stay and return. In this way, the objective of this work is to demonstrate the axes, trajectories and relationships between the determining variables of the destination image in order to anticipate knowledge management scenarios aimed at reactivating the tourist economy. A correlation work was carried out to with a selection of sources indexed to international repositories, considering the search for keywords in the period of the pandemic. The results show homogeneous random effects that suggest risk thresholds for the decision - making of the tourist experience based on its predictors, although these findings correspond to a data tracking and processing system that can be developed according to the prevalence of the literature.

Effect of Climate-Smart Agriculture Practices on Climate Change Adaptation, Greenhouse Gas Mitigation and Economic Efficiency of Rice-Wheat System in India

Conventional rice–wheat (RW) rotation in the Indo-Gangetic Plains (IGP) of South Asia is tillage, water, energy, and capital intensive. Coupled with these, crop residue burning contributes significantly to greenhouse gas (GHG) emission and environmental pollution. So, to evaluate the GHG mitigation potential of various climate-smart agricultural practices (CSAPs), an on-farm research trial was conducted during 2014–2017 in Karnal, India. Six management scenarios (portfolios of practices), namely, Sc1—business as usual (BAU)/conventional tillage (CT) without residue, Sc2—CT with residue, Sc3—reduced tillage (RT) with residue + recommended dose of fertilizer (RDF), Sc4—RT/zero tillage (ZT) with residue + RDF, Sc5—ZT with residue + RDF + GreenSeeker + Tensiometer, and Sc6—Sc5 + nutrient-expert tool, were included. The global warming potential (GWP) of the RW system under CSAPs (Sc4, Sc5, and Sc6) and the improved BAU (Sc2 and Sc3) were 33–40% and 4–26% lower than BAU (7653 kg CO2 eq./ha/year), respectively. This reflects that CSAPs have the potential to mitigate GWP by ~387 metric tons (Mt) CO2 eq./year from the 13.5 Mha RW system of South Asia. Lower GWP under CSAPs resulted in 36–44% lower emission intensity (383 kg CO2 eq./Mg/year) compared to BAU (642 kg CO2 eq./Mg/year). Meanwhile, the N-factor productivity and eco-efficiency of the RW system under CSAPs were 32–57% and 70–105% higher than BAU, respectively, which reflects that CSAPs are more economically and environmentally sustainable than BAU. The wheat yield obtained under various CSAPs was 0.62 Mg/ha and 0.84 Mg/ha higher than BAU during normal and bad years (extreme weather events), respectively. Thus, it is evident that CSAPs can cope better with climatic extremes than BAU. Therefore, a portfolio of CSAPs should be promoted in RW belts for more adaptation and climate change mitigation.

Comparison of Flexibility Factors and Introduction of A Flexibility Classification Using Advanced Heat Pump Control

With the increasing use of renewable energy, the energy flexibility of buildings becomes increasingly important regarding grid support. Therefore, there is a need to describe this flexibility in a concise manner. For the characterization of building energy flexibility, flexibility factors can be used. The comparison of a selection of existing flexibility factors shows that they are not easy to use or understand for designers and users. A simplification is necessary. The aim of this study is to introduce a flexibility classification that is easy to understand and shows in an easy way if a building already uses the lowest energy cost level or if further improvement is possible. The classification expresses the annual energy costs in colored classes: green (class A) for lowest up to red (class D) for highest level. Basically, the flexibility classes can be derived for any metric of interest, in this paper examples are shown for energy costs and CO2eq emissions. The results given are based on the simulation of load management scenarios with different penalty signals applied for the heat pump operation of a residential building.

Guidance for management of free-roaming community cats: a bioeconomic analysis

Objectives This study used computer simulation modeling to estimate and compare costs of different free-roaming cat (FRC) management options (lethal and non-lethal removal, trap–neuter–return, combinations of these options and no action) and their ability to reduce FRC population abundance in open demographic settings. The findings provide a resource for selecting management approaches that are well matched for specific communities, goals and timelines, and they represent use of best available science to address FRC issues. Methods Multiple FRC management approaches were simulated at varying intensities using a stochastic individual-based model in the software package Vortex. Itemized costs were obtained from published literature and expert feedback. Metrics generated to evaluate and compare management scenarios included final population size, total cost and a cost efficiency index, which was the ratio between total cost and population size reduction. Results Simulations suggested that cost-effective reduction of FRC numbers required sufficient management intensity, regardless of management approach, and greatly improved when cat abandonment was minimized. Removal yielded the fastest initial reduction in cat abundance, but trap–neuter–return was a viable and potentially more cost-effective approach if performed at higher intensities over a sufficient duration. Of five management scenarios that reduced the final population size by approximately 45%, the three scenarios that relied exclusively on removal were considerably more expensive than the two scenarios that relied exclusively or primarily on sterilization. Conclusions and relevance FRCs present a challenge in many municipalities, and stakeholders representing different perspectives may promote varying and sometimes incompatible population management policies and strategies. Although scientific research is often used to identify FRC impacts, its use to identify viable, cost-effective management solutions has been inadequate. The data provided by simulating different interventions, combined with community-specific goals, priorities and ethics, provide a framework for better-informed FRC policy and management outcomes.

Predicting Climate Change Impact on Water Productivity of Irrigated Rice in Malaysia Using FAO-AquaCrop Model

Water productivity (WP) is a key indicator of agricultural water management, since it affects the quantity of water used for crop yield in various management scenarios. This study evaluated the WP of irrigated rice due to a changing climate in the Northwest Selangor Rice Irrigation Scheme (NSRIS) by using field experimental data and the FAO-AquaCrop Model. Pertinent soil, water, climate, and crop data were acquired by executing a field investigation during the off-season (dry season, January–April) and main season (wet season, July–October) in 2017. The AquaCrop 6.0 model was calibrated and validated using the measured data. A Climate-smart Decision Support System (CSDSS) with an ensemble of 10 Global Climate Models (GCMs) was used to downscale climate variables under RCP4.5, RCP6.0, and RCP8.5 emission scenarios during baseline (1976 to 2005) and future (2020 to 2099) periods. The AquaCrop model fairly predicted rice yields under field conditions with root-mean-square error (RMSE), mean absolute error (MAE), prediction error (PE) and index of agreement (d) between the observed and estimated yields of 0.173, 0.157, −0.31 to 5.4 and 0.78, respectively for the off-season; and 0.167, 0.127, −5.6 to 2.3 and 0.73, respectively for the main season. It predicted a 10% decrease in actual crop evapotranspiration (ETc) in both crop seasons in the future. The WP of rice based on total water input (WPIrr+RF), applied irrigation (WPIrr), and actual crop evapotranspiration (WPETc) will likely increase by 14–24%, 14–19%, and 17–29%, respectively under the three RCP emission scenarios in the off-season. The likely increase in WP for the corresponding base is 13–22%, 15–24%, and 14–25% in the main season. Various agronomic management options linked to WP will most likely become important in making crucial decisions to cope with the risk of impacts on climate change.