This study examines the economic impact of soaring international energy prices during the Russia-Ukraine conflict from February 23, 2022, to May 31, 2022. Notably, by applying a CGE model, this study offers insights into energy policies at both macroeconomic and industrial levels, emphasizing the model\'s utility in analyzing complex economic interactions under geopolitical stress. Findings indicate that: (1) Russia, a critical energy-producing country, faced severe economic setbacks due to sanctions, with its GDP contracting by 5.5 %, household income decreasing by 4 %, and consumer spending dropping by 3.5 %. This was accompanied by a significant reduction in domestic investment by 6 %, a decline in output by 5 %, and a decrease in societal welfare indicators. (2) Other energy-producing countries or regions, such as the Middle Eastern oil-producing countries, Australia, Canada, Mexico, and Southeast Asia, experienced economic benefits from the global energy market\'s \"crowding-out effect.\" These regions saw an increase in GDP ranging from 2 % to 4.5 %, output growth by 3 %-6 %, and household income and consumption improvements by approximately 3 %-5 %. However, these benefits were tempered by a 1 %-2.5 % decline in domestic investment due to rising local energy costs. (3) Developed and developing regions, suffered adverse impacts, including the US, UK, EU, Japan, China, South Asia, Middle Eastern non-oil-producing countries, and Africa. These regions reported a decrease in GDP by 0.5 %-3 %, a decline in household income by 2 %-4 %, and lower consumption rates by 1.5 %-3.5 %. The economic strain was further exacerbated by an inflation increase of up to 2 % across these economies. This research offers valuable insights for governments and policymakers globally to address the challenges posed by the Ukraine crisis-induced energy crisis, underscoring the need for strategic energy policy adjustments and economic resilience planning.

The Russia-Ukraine conflict is a growing concern worldwide and poses serious threats to regional and global food security. Using monthly trade data for maize, rice, and wheat from 2016/1 to 2023/12, this paper constructs three international crop trade networks and an aggregate international food trade network. We aim to examine the structural changes following the occurrence of the Russia-Ukraine conflict. We find significant shifts in the number of edges, average in-degree, density, and efficiency in the third quarter of 2022, particularly in the international wheat trade network. Additionally, we have shown that political reasons have caused more pronounced changes in the trade connections between the economies of the North Atlantic Treaty Organization and Russia than with Ukraine. This paper could provide insights into the negative impact of geopolitical conflicts on the global food system and encourage a series of effective strategies to mitigate the negative impact of the conflict on global food trade.

As climate change and geopolitical conflicts intensify, understanding how geopolitical risks affect companies prioritizing Environmental, Social, and Governance (ESG) practices is crucial. This study investigates the dynamic relationship between global geopolitical risks and the performance of Environmental, Social, and Governance (ESG) and non-ESG companies, particularly their influence on green markets. Utilizing a robust methodological framework, including the dynamic time-varying parameters vector autoregression (TVP-VAR) model, and causal impact modeling, we analyze daily financial data from 2021 to 2024. The results reveal a substantial negative impact of geopolitical risks on non-ESG companies, contrasting with the resilience of ESG-committed counterparts. This suggests that ESG-committed companies demonstrate better resilience against geopolitical risks, emphasizing the protective role of ESG practices amid uncertainties. Additionally, the inclusion of ESG companies in green markets diminishes the severity of the negative impact of geopolitical risks, underlining the transformative role of ESG commitment in shaping investor behavior towards sustainable investments. Our findings offer insights for policymakers and investors navigating geopolitical risks and ESG performance, with a focus on environmental management, and provide guidance for effective risk mitigation and investment policies to enhance environmental sustainability.

This study analyzes the performance of the Shanghai Composite Index, S&P 500 index, WTI oil price, and LBMA gold price when wars took place, especially the Russia-Ukraine conflict. We employ empirical methods to explore the stability, instantaneous shock, and short-term shock regarding the abovementioned financial assets. We first adopt the event study method to ascertain whether the cumulative abnormal returns of the selected assets are significant when wars break out. Then, we use the permutation test to examine the significance of price level changes. Results show that only the Shanghai Composite Index is relatively stable. Second, the difference-in-differences model indicates that the 3 unstable assets all suffered positive shocks in their price levels within several days after the Russia-Ukraine conflict broke out. The parallel trend test confirms the validity of establishing the difference-in-differences model. Third, regression discontinuity is designed to measure the impact in a longer event window, suggesting the robustness of conclusions of the difference-in-differences model and revealing an upward trend before the conflict and a downward trend after the conflict of the financial assets. The study suggests that investors consider adjustments to investment strategies and governments take precautions to diminish the risk of the outbreak of wars.

The ongoing Russia-Ukraine conflict has led to significant upheaval in the worldwide natural gas sector. Accurate natural gas price forecasting, as an essential tool for mitigating market uncertainty, plays a crucial role in commodity trading and regulatory decision-making. This study aims to develop a hybrid forecasting model, the FS-GA-SVR model, which integrates feature selection (FS), genetic algorithm (GA), and support vector regression (SVR) to investigate Henry Hub natural gas price prediction amidst the Russia-Ukraine conflict. The results show that: (1) The feature selection automates model input variable selection, decreasing the time required while improving the model\'s accuracy. (2) The use of genetic algorithm for selecting support vector regression hyperparameters significantly improves the accuracy of natural gas price predictions. The algorithm leads to a decrease of approximately 70% in measurement indicators. (3) During the Russia-Ukraine conflict, the FS-GA-SVR hybrid model demonstrates more consistent and accurate predictions for natural gas spot prices than the base SVR model. This study serves as a valuable theoretical reference for energy policymakers and natural gas market investors worldwide, supporting their ability to anticipate fluctuations in natural gas prices.

We use an extended joint connectedness technique and the time-varying parameter vector autoregression (ETVP-VAR) method to examine connections between the ARK FinTech Innovation ETF (ARKF), Global X FinTech ETF (FINX), and energy volatility by connectedness as a quality of eight indicators from April 1, 2019, to September 26, 2022. Our results demonstrate that the pattern of ARKF and FINX is picked up as a crucial net shock transmitter that nearly permeates our analyzed sample. Since the COVID-19 epidemic, more people are adopting FinTech partly because of their concern about the disease spreading through social contact and cash handling. Moreover, green bonds are net shock recipients over the long term. Furthermore, during the COVID-19 duration and the Russo-Ukrainian War, shocks transmitted to green bonds soared sharply. By contrast, keeping with the clean energy and crude oil trend, these indicators transmit a network of shocks during the period under study. When considering wind power, it becomes clear that this signal first acts as a net shock transmitter before changing into a net receiver of shocks from mid-2021 onwards. We recognize that the system is a net shock receiver regarding clean power. The dynamics invariably lead the series to change to a net shock transmitter in mid-2021. By mid-2021, the developments always cause the series to transform into a net shock transmitter.

Considered contaminants of emerging concern, per- and polyfluoroalkyl substances (PFAS) are a class of toxic, manufactured chemicals found in commercial and consumer products such as nonstick cookware, food packaging, and firefighting foams. Human exposure to PFAS through inhalation and ingestion can cause a variety of harmful effects and negative health outcomes. Per- and polyfluoroalkyl substances possess high polarity and chemical stability, enabling them to resist degradation in most environmental conditions. These characteristics allow PFAS to be mobile in soil, air, and water, and bioaccumulate in living organisms. Due to their thermally resistant chemical properties, PFAS are used as binders in polymer-bonded explosives (PBX) and in various components of munitions. Thus, when munitions are detonated, PFAS are released into the environment as aerosols and can deposit in the soil, surface water, or biota. Air emission modeling suggests that ground-level and airborne detonation of munitions can increase PFAS deposition both locally and long range. Further, if industrial facilities with PFAS are damaged or destroyed, there is greater potential for environmental degradation from increased release of PFAS into the environment. As a consequence of their persistent nature, PFAS can remain in an environment long after armed conflict, indirectly affecting ecosystems, food sources, and human health. The toxic contamination from munitions could present a greater hazard to a larger population over time than acute detonation events. This article discusses methods for estimating war-related damage from PFAS by exploring predictive modeling approaches and postwar ground validation techniques. Integr Environ Assess Manag 2023;19:376-381. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).