Volatility Modeling and Dependence Structure of ESG and Conventional Investments

The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of selected ESG indices and conventional ones and investigate dependence between them. Analysis of tail dependence is important to evaluate the diversification benefits between conventional investments and ESG investments, which is necessary in constructing optimal portfolios. It allows investors to diversify the risk of the portfolio and positively impact the environment by investing in environmentally friendly companies. Examples of institutions that are paying attention to ESG issues are banks, which are increasingly including products that support sustainability goals in their offers. This analysis could be also important for policymakers. The European Banking Authority (EBA) has admitted that ESG factors can contribute to risk. Therefore, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail-dependence coefficients from the copula-based approach are applied. The analysis period covered 2007 until 2019. The period of the COVID-19 pandemic has not been analyzed due to the relatively short time series regarding data requirements from models’ perspective. Results of the research confirm the higher dependence of extreme values in the crisis period (e.g., tail-dependence values in 2009–2014 range from 0.4820/0.4933 to 0.7039/0.6083, and from 0.5002/0.5369 to 0.7296/0.6623), and low dependence of extreme values in stabilization periods (e.g., tail-dependence values in 2017–2019 range from 0.1650 until 0.6283/0.4832, and from 0.1357 until 0.6586/0.5002). Diversification benefits vary in time, and there is a need to separately analyze crisis and stabilization periods.

A Survey of AIOps Methods for Failure Management

Modern society is increasingly moving toward complex and distributed computing systems. The increase in scale and complexity of these systems challenges O&M teams that perform daily monitoring and repair operations, in contrast with the increasing demand for reliability and scalability of modern applications. For this reason, the study of automated and intelligent monitoring systems has recently sparked much interest across applied IT industry and academia. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to Machine Learning, AI, and Big Data. However, AIOps as a research topic is still largely unstructured and unexplored, due to missing conventions in categorizing contributions for their data requirements, target goals, and components. In this work, we focus on AIOps for Failure Management (FM), characterizing and describing 5 different categories and 14 subcategories of contributions, based on their time intervention window and the target problem being solved. We review 100 FM solutions, focusing on applicability requirements and the quantitative results achieved, to facilitate an effective application of AIOps solutions. Finally, we discuss current development problems in the areas covered by AIOps and delineate possible future trends for AI-based failure management.

New Generation of Smart Highway: Framework and Insights

The new generation of smart highway (NGSH) has become an irresistible global trend to improve transport efficiency and safety. The exploration of the features and framework for NGSH can guide us to upgrade the current highway system. This paper summarizes the fundamental features of the NGSH from the perspective of the interactive evolution of automobile industry and road transport. In line with the popularity of automated and connected vehicles, the primary technical features of the NGSH are proposed as (I) complete elements sensing, (II) cyber-physical systems, (III) cooperative vehicle-infrastructure applications, and (IV) 5th generation mobile communication technology. The corresponding physical framework and data flow are introduced, in which three data attributes (data accuracy, dimensionality, and freshness) are highlighted to describe the data requirements for various scenarios. The development path of the NGSH is further discussed in terms of the different vehicle automation levels. The characteristics of five levels of NGSH are identified from R1 to R5. Different combinations of NGSH level and vehicle automation level lead to distinct system functions. Several urgent problems in the current stage are pointed out in terms of system compatibility, standard specification, and information security. This paper provides new insights for sustainable and reproducible highway reformation, drawing some implications for NGSH design.

Conceptual Design of a Comprehensive Farm Nitrogen Management System

Data that are required for nutrient management are becoming increasingly available in digital format, leading to a high innovation potential for digital nitrogen (N) management applications. However, it is currently difficult for farmers to analyze, assess, and optimize N flows in their farms using the existing software. To improve digital N management, this study identified, evaluated, and systematized the requirements of stakeholders. Furthermore, digital farm N management tools with varying objectives in terms of system boundaries, data requirements, used methods and algorithms, performance, and practicality were appraised and categorized. According to the identified needs, the concept of a farm N management system (FNMS) software is presented which includes the following modules: (1) management of site and farm data, (2) determination of fertilizer requirements, (3) N balancing and cycles, (4) N turnover and losses, and (5) decision support. The aim of FNMS is to support farmers in their farming practices for increasing N efficiency and reducing environmentally harmful N surpluses. In this study, the conceptual requirements from the agricultural and computer science perspectives were determined as a basis for developing a consistent, scientifically sound, and user-friendly FNMS, especially applicable in European countries. This FNMS enables farmers and their advisors to make knowledge-based decisions based on comprehensive and integrated data.

Key issues in the regulation of microbial bioprotectants in the European Union: challenges and solutions to achieve more sustainable crop protection

Microbial bioprotectants, like chemical pesticides, are required to pass a risk assessment and risk management procedure prior to use in plant protection, which in many countries is an obstacle for market access, in particular, the European Union. Administrative issues and data requirements, adapted from those used for chemicals, cause issues for both applicants and evaluators. These issues are reviewed and improvements are proposed. Biology should be the basis of the evaluation and data requirements for microorganisms, with an emphasis in this chapter on microbial compounds and testing methods. Political actions involving the use of pesticides are reviewed and recommendations are made on how to improve the system for microbial bioprotectants, including new uses. New legislation is suggested for all microorganisms used in agriculture and related uses based on the assumption that well-known microorganisms are of low risk to human health and the environment.

Missing interactions: the current state of multispecies connectivity analysis

Designing effective habitat and protected area networks, which sustain species-rich communities is a critical conservation challenge. Recent decades have witnessed the emergence of new computational methods for analyzing and prioritizing the connectivity needs of multiple species. We argue that the goal of multispecies connectivity prioritizations be the long-term persistence of a set of species in a landscape and suggest the index of metapopulation capacity as one metric by which to assess and compare the effectiveness of proposed network designs. Here we present a review of the literature based on 77 papers published between 2010 and 2020, in which we assess the current state and recent advances in multispecies connectivity analysis in terrestrial ecosystems. We summarize the four most employed analytical methods, compare their data requirements, and provide an overview of studies comparing results from multiple methods. We explicitly look at approaches for integrating multiple species considerations into reserve design and identify novel approaches being developed to overcome computational and theoretical challenges posed by multispecies connectivity analyses. We conclude that, while advances have been made over the past decade, the field remains nascent in its ability to integrate multiple species interactions into analytical approaches to connectivity. Furthermore, the field is hampered in its ability to provide robust connectivity assessments for lack of a clear definition and goal for multispecies connectivity, as well as a lack of common metrics for their comparison.

Integration of biowaste into chemical reaction networks

As the chemical sector looks to decarbonize, one promising solution is the utilization of bio-feedstocks and biowaste to produce functional molecules. There is, therefore, great interest in understanding how and where to integrate these resources within chemical supply chains. To assist such efforts, screening methodologies relying on large reaction networks have recently been proposed.1,2 However, they are currently hindered by a lack of data for region-specific heterogenous raw materials compositions, as well as upstream pretreatments to isolate the important feedstocks. This study illustrates the workflow and data requirements of early stage biowaste stream evaluation through a case study on the waste landscape in and around the Singapore region. We first investigate biowaste sources that are available, stable in quantities, underutilized, pure, and yielding the feedstocks of interest. Oil palm empty fruit bunch (EFB), a lignocellulosic biowaste stream widely available in Malaysia and Indonesia, meets these criteria. We then simulate an ethanol organosolv pretreatment process for the fractionation of cellulose, lignin and xylose from EFB, and characterise the economic and environmental performances of the process through its exergy profile; this enables a link to chemical pathway identification in reaction networks. This study outlines the initial steps towards generating open datasets on biowaste for development of sustainable supply chains.