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This paper aims to investigate whether the establishment of commodity futures can effectively hedge systemic risk in the spot network, given the context of financialization in the commodity futures market.

Utilizing industry association data from the Chinese commodity market, the authors identify systemically important commodities based on their importance in the production process using multiple graph analysis methods. Then the authors analyze the effect of listing futures on the systemic risk in the spot market with the staggered difference-in-differences (DID) method.

The findings suggest that futures contracts help reduce systemic risks in the underlying spot network. Systemic risk for a commodity will decrease by approximately 5.7% with the introduction of each corresponding futures contract, since the hedging function of futures reduces the timing behavior of firms in the spot market. Establishing futures contracts for upstream commodities lowers systemic risks for downstream commodities. Energy commodities, such as crude oil and coal, have higher systemic importance, with the energy sector dominating systemic importance, while some chemical commodities also have considerable systemic importance. Meanwhile, the shortest transmission path for risk propagation is composed of the energy industry, chemical industry, agriculture/metal industry and final products.

The paper provides the following policy insights: (1) The role of futures contracts is still positive, and future contracts should be established upstream and at more systemically important nodes in the spot production chain. (2) More attention should be paid to the chemical industry chain, as some chemical commodities are systemically important but do not have corresponding futures contracts. (3) The risk source of the commodity spot market network is the energy industry, and therefore, energy-related commodities should continue to be closely monitored.

This study examines the risk spillovers between Indonesian sectorial stocks (Energy, Basic Materials, Industrials, Consumer Cyclicals, Consumer Non-cyclical and Financials), the aggregate index (IDX) and two commodities (gold and West Texas Intermediate Crude Oil [WTI] futures).

The study uses two methodologies: the TVP-VAR model of Antonakakis and Gabauer (2017) and the quantile connectedness approach of Ando et al. (2022). The data cover the period from October 04, 2010, to April 5, 2022.

The results show that the IDX, industrials and materials are net transmitters, while the financials, consumer noncyclical and energy sectors are the dominant shock receivers. Using the quantile connectedness approach, the role of each sector is heterogeneous and asymmetric, and the return spillover is stronger at lower and higher quantiles. Furthermore, the portfolio hedging results show that oil offers more diversification gains than gold, and hedging oil is more effective during the pandemic.

This study provides valuable insights for investors to diversify their portfolios and for policymakers to develop policies, regulations and risk management tools to promote stability in the Indonesian stock market. The results can inform the design of market regulations and the development of risk management tools to ensure the stability and resilience of the market.

This study is the first to examine the spillovers between commodities and Indonesian sectors, recognizing the presence of heterogeneity in the relationship under different market conditions. It provides important portfolio diversification insights for equity investors interested in the Indonesian stock market and policymakers.

This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

The presence of heavy metals in milk causes many acute and chronic physiological dysfunctions in human organs. The present study aims to investigate the heavy metals in cow's and buffalo's milk of two major cities, Karachi and Gujranwala, Pakistan to estimate metal intake by humans from this source.

In total, 48 milk samples from 2 cities were drawn from animals' udder to avoid contamination. Each sample was digested with nitric acid at 105 ^oC (degree Celsius) on a pre-heated electric hot plate to investigate the metals by atomic absorption spectroscopy (flame type). Air-acetylene technique analyzed chromium, cadmium and lead, and the hydride method analyzed arsenic in the milk samples.

The results revealed the highest mean lead concentration (19.65 ± 43.86 ppb) in the milk samples, followed by chromium (2.10 ± 2.33 ppb) and arsenic (0.48 ± 0.73 ppb). Cadmium was not detected in any sample, assuming cadmium's occurrence was below the detection level. The concentrations of all the metals in the samples of the two cities do not differ statistically. Lead concentrations in the buffalo's milk were higher than in cow's milk (p < 0.05). However, the concentrations of arsenic and chromium between buffalo's and cow's milk do not differ statistically. The present study reveals a lower level of metals in the milk than those conducted elsewhere. The mean concentrations of all the metals met the World Health Organization's (WHO) safety guidelines (1993).

Although cadmium causes toxicity in the human body, cadmium could not be measured because cadmium's concentration was below the detection level, which is 1 ppb.

This study will help reduce the toxic metals in our environment, and the sources of heavy metals, particularly from the industrial sector could be identified. The feed and water consumed by the milking animals could be carefully used for feeding them.

This study will help reduce the diseases and malfunction of human organs and organ systems since these heavy metals cause toxicity and carcinogenicity in humans. Arsenic and chromium cause cancer while lead causes encephalopathy (a brain disease).

The study reports heavy metal concentrations in the two attributes of four independent variables of raw milk samples that were scarcely reported from Pakistan.

This paper aims to examine the return and volatility transmission among economic policy uncertainty (EPU), geopolitical risk (GPR), their interaction (EPGR) and five tradable precious metals: gold, silver, platinum, palladium and rhodium.

Applying time-varying parameter vector autoregression (TVP-VAR) frequency-based connectedness approach to a data set spanning from January 1997 to February 2023, the study analyzes return and volatility connectedness separately, providing insights into how the data, in return and volatility forms, differ across time and frequency.

The results of the return connectedness show that gold, palladium and silver are affected more by EPU in the short term, while all precious metals are influenced by GPR in the short term. EPGR exhibits strong contributions to the system due to its elevated levels of policy uncertainty and extreme global risks. Palladium shows the highest reaction to EPGR, while silver shows the lowest. Return spillovers are generally time-varying and spike during critical global events. The volatility connectedness is long-term driven, suggesting that uncertainty and risk factors influence market participants’ long-term expectations. Notable peaks in total connectedness occurred during the Global Financial Crisis and the COVID-19 pandemic, with the latter being the highest.

Using the recently updated news-based uncertainty indicators, the study examines the time and frequency connectedness between key uncertainty measures and precious metals in their returns and volatility forms using the TVP-VAR frequency-based connectedness approach.

This study aims to address three research questions pertaining to climate neutrality within the supply chain of metal and mining industry: (1) How can an organization implement practices related to climate neutrality in the supply chain? (2) How do members of the supply chain adopt different measures and essential processes to assist an organization in responding to climate change-related concerns? (3) How can the SAP-LAP framework assist in analyzing and proposing solutions to attain climate neutrality?

To address the proposed research questions concerning climate neutrality, this study employs a case study approach utilizing the SAP-LAP (situation, actor, process–learning, action, performance) framework. Within the SAP-LAP framework, adopting a natural resource-based perspective, the study thoroughly examines the intricacies and interactions among existing situations, pertinent actors and processes that impact climate initiatives within a metal and mining company.

The study's findings suggest that organizations can achieve the objective of climate neutrality by prioritizing resources and capabilities that lead to reduced GHG emissions, lower energy consumption and optimal resource utilization. The study further proposes key elements that significantly influence the pursuit of climate neutrality within enterprises.

This study is one of the earliest contributions to the development of a holistic understanding of climate neutrality in the supply chain of the metal and mining industry.

The study will assist practitioners and policymakers in comprehending the present circumstances, actors and processes involved in enterprises' supply networks in order to attain climate neutrality in supply chains, as well as in taking the right steps to enhance performance.

This study presents a climate neutrality model and provides valuable insights into emission management, contributing to the achievement of the climate neutrality objective.

The purpose of this study is the synthesis and characterization of a CMYK palette (cyan of Cr-BiVO₄, magenta of Pr-CeO₂, yellow of Bi-(Ce,Zr)O₂ composite and black of YMnO₃) as an eco-friendly polyfunctional palette that combines (a) high near-infrared reflectance (cool pigments) that allows moderate temperatures in indoor environments and the urban heat island effect; (b) photocatalytic activity for the degradation of organic contaminants of emerging concern of substrates in solution (such as Orange II or methylene blue) and gaseous (NO_x and volatile organic compounds such as acetaldehyde or toluene); (c) X-ray radiation attenuators associated with bismuth ions; and (d) biocidal effect combined with co-doping with bactericidal agents.

Pigments were prepared by a solid-state reaction and characterized by X-ray diffraction, diffuse reflectance spectroscopy, photocatalytic activity over Orange II and scanning electron microscopy.

The behaviour of the proposed palette was compared to that of a commercial inkjet palette, and an improvement in all functionalities was observed.

The functionalities of pigments allow the building envelope and indoor walls to exhibit temperature-moderating effects (with the additional effects of moderating global warming and increasing air conditioning efficiency), purification and disinfection of both indoor and outdoor air, and radiation attenuation.

The proposed palette and its polyfunctional characterization are novel.

The purpose of this paper is to investigate the efficacy of bacterial exopolysaccharides (Eps) in reactive black 5 (RB5) textile dye wastewater bioremediation.

The Eps were produced by bacteria isolated from cotton gin trash soils collected from different cotton-growing regions in Kenya for comparison purposes. A broth medium reconstituted using molasses was assessed for its capacity to produce the Eps. RB5 textile dye wastewater was optimized for dye removal under different temperatures, times and molasses concentrations. Dye removal was studied by Lovibond-Day Light Comparator, UV–Vis spectrophotometer and FTIR.

It was found that cotton gin trash soils contained Eps-producing bacteria. Three of the Eps studied were found to have the capacity to remove at least 80% of the dye from the wastewater.

This research did not assess the efficacy of the RB5 dye removal from the wastewater by mixtures of the Eps.

Bioremediation of textile dye wastewater with Eps produced by bacteria cultured from cotton gin trash soil is significant because it will offer an effective and cleaner alternative to the chemical coagulants.

Alternative treatment of textile wastewater with the Eps would result in safer water being released into the water bodies as opposed to the chemically treated wastewater that contains remnant chemicals.

Research on the use of Eps produced by bacteria isolated from cotton gin trash soils for removal of RB5 dye from textile wastewater has not been done before.

India is not geographically close to either Russia or Ukraine. However, India's trade relations with them make it vulnerable to the consequences of the war between these countries. Thus, the present study aims to examine the impact of the Russia–Ukraine war on various sectoral indices of the Indian economy.

Event study methodology has been used in this study for analysis. The date of the war announcement is the event day. The sample studied includes ten sectors of the Indian economy listed on the National Stock Exchange (NSE). Results correspond to the period of −167 days to +20 days of the announcement of the war, i.e. from June 25, 2021, to March 28, 2022.

Almost all the sample sectors earned significantly positive abnormal returns in the post-event period. The metal industry has led this group by showcasing the highest abnormal returns. Though Indian sectors made overall positive returns, the market soon corrected itself and abnormal returns were wiped out.

These results can benefit portfolio managers, analysts, investors and policymakers in hedging risks and selecting suitable investments during increased global uncertainty. The study's conclusions help policymakers establish an institutional and supervisory framework that will make it easier to spot systematic risks and reduce them by putting countercyclical measures in place.

India has no geographical proximity or trade relations with Russia or Ukraine, as strong as any other European country. However, Russia has remained a strong ally to India in the trade of defense equipment. Similar is the case with Ukraine, a significant global partner for India. Thus, the impact of conflict between these two countries has not been limited to Europe only but has also engulfed related economies. Hence, the present study is one of the first attempts to examine the burns sustained by the Indian economy due to this war.

The criteria importance through intercriteria correlation (CRITIC) and range of value (ROV) combined methods were used to determine a single index for all multiple responses.

This paper used cold metal transfer (CMT) and pulse metal-inert gas (MIG) welding processes to study the weld-on-bead geometry of AA2099-T86 alloy. This study used Taguchi's approach to find the optimal setting of the input welding parameters. The welding current, welding speed and contact-tip-to workpiece distance were the input welding parameters for finding the output responses, i.e. weld penetration, dilution and heat input. The L9 orthogonal array of Taguchi's approach was used to find out the optimal setting of the input parameters.

The optimal input welding parameters were determined with combined output responses. The predicted optimum welding input parameters were validated through confirmation tests. Analysis of variance showed that welding speed is the most influential factor in determining the weld bead geometry of the CMT and pulse MIG welding techniques.

The heat input and weld bead geometry are compared in both welding processes. The CMT welding samples show superior defect-free weld beads than pulse MIG welding due to lesser heat input and lesser dilution.