TRIPI: A global dataset and codebase of the total resources in physical infrastructure encompassing road, rail, and parking

Construction materials are associated with significant environmental and resource impacts. The circular use of materials already in use as stocks may provide an opportunity to reduce these impacts. We provide a dataset describing the potential global urban mine consisting of transportation infrastructure in an open database based on geospatial data from OpenStreetMaps. We reveal the significant opportunities of the embedded materials in this huge stock. With this Total Resources in Physical Infrastructure, or TRIPI, the database we provide easy access to a global dataset covering 175 countries and sub-regions, allowing researchers to select an area of study, and find the location as well as the material composition of the physical infrastructure. Material stocks are reported on a national level and commonly used regional aggregations. Material stocks are reported per kg, kg per capita, and kg per area; and for the physical type of infrastructure that is available in kilometres and area (km2). This dataset can be used in various research applications such as Material Flow Analysis, Material stock inventories, Country-level comparisons of infrastructure density, and others, and inform policy on harnessing the opportunities of the urban mine.

Gaussian Bayesian network model of healthcare, food and energy sectors in the pandemic: Türkiye case

Healthcare, food, and energy are the basic needs of life in the globalizing world. Humankind's quest to maintain a healthy life and find ways to meet its needs has continued since its existence. The causal relations between the healthcare, food, and energy sectors are explored using data from the pandemic when COVID-19 was a global risk, and human health sustainability underwent a complicated process. It aims to examine the interaction between the healthcare, food, and energy sectors and model the causal relationship within the framework of probabilistic dependencies. For this purpose, the relationships between these sectors during the pandemic are modeled via Bayesian Networks (BNs). This highly successful inference method makes the complex structure of causal relationships graphically understandable. The data consists of stock returns at the end of the business day between March 11, 2020, when the pandemic was declared, and December 26, 2022. Data on 13 stocks actively traded on the Istanbul Stock Exchange (BIST) during the 700 days were obtained from tr.investing.com. Causal modeling uses Gaussian Bayesian Networks (GBNs) for continuous variables. To make the inferences drawn from the data more successful and minimize the loss of information, the GBN model is built with continuous variables. The posterior Probability Density Functions (PDFs) of the stocks in the network are constructed over the structure of the Directed Acyclic Graphs (DAG) of the BNs, and inferences are made by querying possible cases (tips). Markov Chain Monte Carlo (MCMC) simulations are performed with the posterior PDFs, and measures of the central tendency of the stocks are calculated. GBNs are used to generate daily return estimates for ULKER with the lowest MSE (1.06e-03) and RMSE (3.22e-02) values and ULUUN with the highest MSE (3.43e-03) and RMSE (5.83e-02) values.

Bitcoin volatility in bull vs. bear market-insights from analyzing on-chain metrics and Twitter posts

Cryptocurrencies have emerged as a popular investment vehicle, prompting numerous efforts to predict market trends and identify metrics that signal periods of volatility. One promising approach involves leveraging on-chain data, which is unique to cryptocurrencies. On-chain data, extracted directly from the blockchain, provides valuable information, such as the hash rate, total transactions, or the total number of addresses that hold a specified amount of cryptocurrency. Some studies have also explored the relationship between social media sentiment and Bitcoin, using data from platforms such as Twitter and Google Trends. However, the quality of Twitter sentiment analysis has been lackluster due to suboptimal extraction techniques. This research proposes a novel approach that combines a superior sentiment analysis technique with various on-chain metrics to improve predictions using a deep learning architecture based on long-short term memory (LSTM). The proposed model predicts outcomes for multiple time horizons, ranging from one day to 14 days, and outperforms the Martingale (random walk) approach by over 9%, as measured by the mean absolute percentage error metric, as well as recent results reported in literature. To the best of our knowledge, this study may be among the first to employ this combination of techniques to improve cryptocurrency market prediction.

The impact of containment measures and monetary and fiscal responses on US financial markets during the COVID-19 pandemic

This paper analyses the effects of containment measures and monetary and fiscal responses on US financial markets during the Covid-19 pandemic. More specifically, it applies fractional integration methods to analyse their impact on the daily S&P500, the US Treasury Bond Index (USTB), the S&P Green Bond Index (GREEN) and the Dow Jones (DJ) Islamic World Market Index (ISLAM) over the period 1/01/2020-10/03/2021. The results suggest that all four indices are highly persistent and exhibit orders of integration close to 1. A small degree of mean reversion is observed only for the S&P500 under the assumption of white noise errors and USTB with autocorrelated errors; therefore, market efficiency appears to hold in most cases. The mortality rate, surprisingly, seems to have affected stock and bond prices positively with autocorrelated errors. As for the policy responses, both the containment and fiscal measures had a rather limited impact, whilst there were significant announcement effects which lifted markets, especially in the case of monetary announcements. There is also evidence of a significant, positive response to changes in the effective Federal funds rate, which suggests that the financial industry, mainly benefiting from interest rises, plays a dominant role.

What to Do about Plastics? Lessons from a Study of United Kingdom Plastics Flows

Plastics are one of the most widely used materials on the planet, owing to their usefulness, durability, and relatively low cost. Yet, making, using, and disposing of plastics create important environmental impacts, most notably greenhouse gas emissions and waste pollution. Reducing these impacts while still enjoying the benefits of plastic use requires an integrated assessment of all of the life cycles of plastics. This has rarely been attempted due to the wide variety of polymers and the lack of knowledge on the final uses and applications of plastics. Using trade statistics for 464 product codes, we have mapped the flows of the 11 most widely used polymers from production into six end-use applications for the United Kingdom (UK) in 2017. With a dynamic material flow analysis, we have anticipated demand and waste generation until 2050. We found that the demand for plastics seems to have saturated in the UK, with an annual demand of 6 Mt, responsible for approximately 26 Mt CO₂e/a. Owing to a limited recycling capacity in the UK, only 12% of UK plastic waste is recycled domestically, leading to 21% of the waste being exported, labeled as recycling, but mostly to countries with poor practices of waste management. Increasing recycling capacity in the UK could both reduce GHG emissions and prevent waste pollution. This intervention should be complemented with improved practices in the production of primary plastics, which currently accounts for 80% of UK plastic emissions.

Hedging strategies among financial markets: the case of green and brown assets

Recognizing the growing importance of the green energy market-renewable energy stocks and bonds-and its classification as a viable financial asset, this paper examines hedging strategies with brown market instruments-gold, oil, bond and the composite S&P500-on the green energy markets. That is, we examine whether, and to what extent brown assets can provide a hedge for green assets, using variants of the multivariate GARCH framework (DCC, ADCC and GO-GARCH). Our dataset spans the period 01/12/2008 to 30/09/2021. To account for the influence of the COVID-19 pandemic, we split the dataset into two-pre-covid (1/12/2008-10/03/2020) and covid-era (11/03/202-30/09/2021). Two key findings emanate from our results: first, conventional bonds and stocks provide the most consistent hedge for investment in the green markets. Second, the results are sensitive to the state of the market-hedging effectiveness declined during the covid period in the green stock market. Among other things, it is recommended that investors include instruments of the green market in portfolio allocation.

Spatial trends of trace elements bioaccumulation in the most endangered dolphin from the Southwestern Atlantic Ocean: The franciscana (Pontoporia blainvillei)

Trace elements bioaccumulation patterns can be an important tool to assess differences among cetaceans' populations. In this work, their use as potential chemical markers to differentiate franciscanas (Pontoporia blainvillei) populations was evaluated. Franciscanas were collected from three states in southeastern Brazil, which comprise three different Franciscana Management Areas (FMAs): Espírito Santo (FMA Ia), southern Rio de Janeiro (FMA IIa), and central São Paulo (FMA IIb). The concentrations of As, Cd, Cu, Fe, Hg, Mn and Zn were determined in the muscle, liver and kidney of the animals. Cadmium was the most valuable chemical marker to differentiate stocks, separating at least FMA IIa from the others. The higher Cd levels in FMA IIa, along with dietary information, indicate that the predominant consumption of cephalopods by this population is the main reason for the differences found. Additionally, environmental characteristics of the areas should also be considered as divergent sources of trace elements. Our findings suggest that non-essential trace elements, such as Cd, can be successful markers to differentiate populations. The Mn concentrations in FMA Ia raised concern and must be carefully monitored, as well as other elements that compose the iron ore tailings that have impacted the Espírito Santo coastal area. Additionally, this is the first study to report trace element concentration in the franciscanas from FMA IIa (southern Rio de Janeiro). Trace element concentrations found in franciscanas may represent different contamination levels in their preys and environments, which might pose specific threats to distinct populations. Therefore, our findings are important to characterize and differentiate franciscana populations and to guide precise management and conservation actions for the distinct stocks of this endangered species.

Connectedness of stock markets with gold and oil: New evidence from COVID-19 pandemic

This paper sets out to explore the impact of COVID-19 pandemic on the dynamic connectedness among gold, oil and five leading stock markets by applying a new DCC-GARCH connectedness approach. We find stronger connectedness between these markets during the COVID-19 pandemic than in the pre-pandemic period. We also find that during this pandemic, gold is a receiver of shocks from the five stock markets, whereas the oil is a net transmitter of shocks.

Influence of tree species on selenium and iodine partitioning in an experimental forest ecosystem

Storage of selenium and iodine can greatly vary between forest ecosystems, but the influence of tree species on partitioning and recycling of those elements remains elusive. In this study, contents of Se and I were measured in tree compartments, litterfall, humus, and soil horizons in monospecific stands of Douglas fir, pine, spruce, beech, and oak under identical climatic and edaphic conditions. The cycle of each element was characterized in terms of stocks and fluxes. Lowest concentrations were in wood (Se: 8-13 μg kg^-1; I: <16.5 μg kg^-1). Senescing organs had higher Se and I content, than the living parts of trees due to direct exposure to atmospheric deposition, with some variation between coniferous and deciduous trees. For all stands, low amounts of Se and I were involved in biological cycle as reflected by low root uptake. In humus, the enrichment of elements greatly increased with the stage of organic matter (OM) degradation with average factors of 10 and 20 for Se and I. OM degradation and element persistence in humus was influenced by tree species. Deciduous trees, with low biomass, and fast degradation of OM stored less Se and I in humus compared to fir and spruce with high humus biomass. Interestingly, tree species did not affect soil reserves of Se and I. Concentration ranges were 331-690 μg Se kg^-1 and 4.3-14.5 mg I kg^-1. However, the divergent vertical profiles of the elements in the soil column indicated greater mobility of I. Selenium concentrations regularly decreased with depth in correlation with OM and Fe oxides content. For iodine, the maximum iodine concentration at a soil depth of 15 to 35 cm was caused by a parallel precipitation/sorption behavior of aluminium and organic iodine dissolved in the topsoil.

COVID-19 impact on the shipping industry: An event study approach

The COVID-19 pandemic, apart from leading to human cases and deaths, is also distracting the shipping stock market and the Baltic Indices. While event studies, as well as macroeconomic research has been conducted in the literature, we have not witnessed any effort yet to investigate how external shocks - and in particular the COVID-19 outbreak - may impinge on the shipping markets. Therefore, our research tries to fill in this gap by studying how a sanitary incident might influence shipping freight rates and stock values. We have used a market-model event study approach to investigate how fast and comprehensively shipping markets react upon certain latest evidence. To quantify the pandemic's economic impact, we estimated the abnormal returns; in a phase before and after the event, they may work as a measure of the unexpected effect of the event on a shipping firm's performance. The data that we have used in stock analysis come from a major shipping index, while for our freight study, time-series come from all main Baltic indices. Our results show that according to the key date set as the event window, different results appear of how pandemic-proof the dry market, the tanker market, and the shipping stock market have proven to be.

Dynamic causality interplay from COVID-19 pandemic to oil price, stock market, and economic policy uncertainty: evidence from oil-importing and oil-exporting countries

In this study we examine the time-varying causal effect of the novel COVID-19 pandemic in the major oil-importing and oil-exporting countries on the oil price changes, stock market volatilities and the economic uncertainty using the wavelet coherence and network analysis. During the period of the pandemic, we explore such relationship by resorting to the wavelet coherence and gaussian graphical model (GGM) frameworks. Wavelet analysis enables us to measure the dynamics of the causal effect of the novel covid-19 pandemic in the time-frequency space. Regarding the findings displayed herein, we first found that the COVID-19 pandemic has a severe influence on oil prices, stock market indices, and the economic uncertainty. Second the intensity of the causality effect is stronger in the longer horizon than in the short ones, suggesting that the causality exercise continues. Our findings also provide evidence that the COVID-19 pandemic and oil price changes in oil-importing countries mirror those in oil-exporting countries and vice versa. Further, the COVID-19 pandemic has a profound immediate time-frequency effect on the US, Japanese, South Korean, Indian, and Canadian economic uncertainties. A better understanding of oil and stock market prices in the oil-importing and oil-exporting countries is vital for investors and policymakers, specially since the novel unprecedented COVID-19 crisis has been recognized among the most serious ever happened. Thus, the findings suggest that the authorities should strongly take efficient actions to minimize risk.

Gold and US sectoral stocks during COVID-19 pandemic

In this study, we examine the hedging relationship between gold and US sectoral stocks during the COVID-19 pandemic. We employ a multivariate volatility framework, which accounts for salient features of the series in the computation of optimal weights and optimal hedging ratios. We find evidence of hedging effectiveness between gold and sectoral stocks, albeit with lower performance, during the pandemic. Overall, including gold in a stock portfolio could provide a valuable asset class that can improve the risk-adjusted performance of stocks during the COVID-19 pandemic. In addition, we find that the estimated portfolio weights and hedge ratios are sensitive to structural breaks, and ignoring the breaks can lead to overestimation of the hedging effectiveness of gold for US sectoral stocks. Since the analysis involves sectoral stock data, we believe that any investor in the US stock market that seeks to maximize risk-adjusted returns is likely to find the results useful when making investment decisions during the pandemic.

Genetic population structure of the Blackspot seabream (Pagellus bogaraveo): contribution of mtDNA control region to fisheries management

Marine fisheries management models have traditionally considered biological parameters and geopolitical boundaries. The result is the existence of fisheries management units that do not match genetic populations. However, this panorama is changing with the contribution of genetic and genomic data. Pagellus bogaraveo is a commercially important sparid in the northeast Atlantic, with three stock components being considered by ICES: the Celtic Sea and Bay of Biscay, Atlantic Iberian waters and the Azores. The northern stock collapsed (1975-1985) and is essential to characterize the genetic makeup of the species, particularly in the Iberian Peninsula, where it is managed as a single stock. The mitochondrial control region was used to screen the intraspecific diversity and population structure of individuals from six locations across the species range. The genetic diversity found is similar among sites, and there is differentiation between the Azores and the remaining locations.

Determinants of gold price movements: An empirical investigation in the presence of multiple structural breaks

This study investigates the short- and long-run determinants of gold price movements in financial markets by taking into account multiple structural breakpoints using an ARDL-based error correction approach. The study used daily time series data from December 19, 2018 to May 15, 2020. The key variables used include international stocks and bond funds that are frequently traded on stock exchanges around the world. The results, based on the fourth breakpoint regime, reveal a significant positive relationship between gold price movements and LSE, Nikkei stocks, T.Rowe global multi-sector bond funds, and CBOE volatility index; and a significant negative association with Gmo emerging country debt and Pimco emerging markets local currency bond funds both in the short- and long-run. Other stocks, like NASDAQ, DJI, S&P500, only revealed negative short-run relationships; except for NYSE that was found to have a positive short-run association with gold price movements. Conversely, Goldman Sachs bonds revealed a significant positive long-run relationship with gold price movements. These results have significant policy implications for gold producers and investors, as both stocks and bonds are an important source of information in the determination of gold price movements both in the short- and long-run.

Display clutter and its effects on visual attention distribution and financial risk judgment

Display clutter is a widely studied phenomenon in ergonomics, where information density and other properties of task-relevant visualizations are related to effective user performance and visual attention. This paper examines the impact of clutter in the context of financial stock visualizations. Depending on their expertise, traders can use a variety of different cues to judge the current and future value of a stock and to assess its riskiness. In our study, two groups of participants (novices and experts) judge the riskiness of 28 pairs of stocks under two clutter conditions (low and high). Consistency of judgments and group concordance serve as measures for judgment performance, while mean fixation duration, fixation frequency, and transition matrix density are employed to capture visual attention. Our results reveal significant effects of display clutter and expertise on both the performance measures as well as the visual attention measures.

Flows, stocks, and emissions of DEHP products in Japan

The usage of products containing Bis (2‑ethylhexyl) Phthalate (DEHP) is widespread, mainly through the great variety of PVC products. However, DEHP has become a worldwide concern, due to the potential health and environmental risks it presents. In this study, material flow analysis and emission estimations for DEHP products in Japan, from 1948 to 2030, were performed. Moreover, an evaluation of the potentially damaging impacts on human health and the environment was completed through a lifecycle impact assessment approach. The analysis focused on three representative lifecycle phases - Production, Use and Treatment and Disposal. The peak flows of DEHP from Production to the Use phase were in 1996 with 285,300 tons for shipment and the stocks peaked in 2001 with 1,981,908 tons. Accordingly, in 2006 the peak of DEHP waste to the Treatment and disposal phase was 190,792 tons. The primary emissions were observed in the Use phase, due to the large stocks, with DEHP mostly being released to the pedosphere. The total emissions from the Use phase reached the maximum of 48,960 tons in 2000, whereas in the Production and Treatment and disposal phase it was 248 tons and 15 tons, respectively. Subsequently, concerning the evaluation of impacts, the damage to the human health was the most widespread impact, totaling 13,782 disability-adjusted life years (DALYs), compared with the damage to the ecosystems, with 0.12 species·year. Furthermore, the risk-risk tradeoffs between the lifecycle phases were clarified throughout the years.

Emission of greenhouse gases and soil carbon sequestration in a riparian marsh wetland in central Ohio

Wetlands are a C sink, but they also account for a large natural source of greenhouse gases (GHG), particularly methane (CH₄). Soils of wetlands play an important role in alleviating the global climate change regardless of the emission of CH₄. However, there are uncertainties about the amount of C stored and emitted from wetlands because of the site specific factors. Therefore, the present study was conducted in a temperate riverine flow-through wetland, part of which was covered with emerging macrophyte Typhus latifolia in central Ohio, USA, with the objective to assess emissions of GHGs (CH_4, CO₂, N₂O) and measure C and nitrogen (N) stocks in wetland soil in comparison to a reference upland site. The data revealed that CH₄ emission from the open and vegetated wetland ranged from 1.03-0.51 Mg C/ha/y and that of CO₂ varied from 1.26-1.51 Mg C/ha/y. In comparison, CH₄ emission from reference upland site was negligible (0.01 Mg C/ha/y), but CO₂ emission was much higher (3.24 Mg C/ha/y). The stock of C in wetland soil was 85 to 125 Mg C/ha up to 0.3 m depth. The average rate of emission was 2.15 Mg C/ha/y, but the rate of sequestration was calculated as 5.55 Mg C/ha/y. Thus, the wetland was actually a C sink. Emission of N₂O was slightly higher in vegetated wetland (0.153 mg N₂O-N/m²/h) than the open wetland and the reference site (0.129 mg N₂O-N/m²/h). Effect of temperature on emission of GHGs from the systems was also studied.

The effect of herd formation among healthcare investors on health sector growth in China

Background

China has become the world‘s second largest healthcare market based on a recent report by the World Health Organization. Eventhough China achieved universal health insurance coverage in 2011, representing the largest expansion of insurance coverage in human history achieved; health inequality remains endemic in China. Lessons from the effect of market crisis on health equity in Europe and other places has reignited interest in exploring the potential healthcare market aberrations that can trigger distributive injustice in healthcare resource allocation among China’s provinces. Recently, many healthcare investors in China have become more concerned about capital preservation, and are responding by abandoning long term investments strategies in healthcare. This investment withdrawal en mass is perceived to be influenced by herding tendencies and can trigger or consolidate endemic health inequality.

Methods

Our study simultaneously employs four testing models (two state spaced models and two return dispersion models) to establish the existence of procyclical (herding) behavior among the stocks and its health equity implications. These are applied to a large set of data to compare and contrast results of herd formation among investors in fourteen healthcare sectors in China.

Results

The study reveals that apart from the cross sectional standard deviation (CSSD) model, the remaining two models and our augmented state space model yields significant evidence of herding in all subsectors of the healthcare market. We also find that the herding effect is more prominent during down movements of the market.

Conclusion

Herding behavior may lead to contemporaneous loss of investor confidence and capital withdrawal and thereby deprive the healthcare sector of the much needed capital for expansion. Thus there may be obvious delay in efforts to bridge the gap in access to healthcare facilities, medical support services, medical supplies, pharmaceuticals, biotechnology, diagnostic substances, medical laboratory and advanced medical equipment across China. Moreover, a potential crash in the healthcare market is possible in the healthcare sector as a result of persistent herding tendencies among investors and that may have more damaging consequences for health inequality in China.