Search Results (179)

The contagious COVID-19 disease not only brought about a global health crisis but also a disruption in the global economy. The uncertainty levels regarding the impact of the disease increased volatility. This study analyses stock market volatility and Economic Policy Uncertainty (EPU) of South Africa (SA) with that of the United States of America (USA) and Brazil, Russia, India, and China (BRIC) during the COVID-19 pandemic. The study aims to analyse volatility spillovers from a developed market (USA) to emerging markets (BRIC countries) and also to examine the causality between EPU and stock returns during the COVID-19 pandemic. By employing the GARCH-in-Mean model from a sample of daily returns of national equity market indices from 1 January 2020 to 31 March 2022, SA and China are shown to be the most volatile during the pandemic. By using the diagonal Baba, Engle, Kraft, and Kroner (BEKK) model to analyse spillover effects, evidence of spillover effects from the US to the emerging countries is small but statistically significant, with SA showing the strongest impact from US market shocks. From the Granger causality test, Brazil’s and India’s equity markets are shown to be highly sensitive to changes in EPU relative to the other countries. Full article

Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

CCS/CCUS is considered vital for global climate mitigation, especially in decarbonizing hard-to-abate sectors like upstream oil and gas. In Indonesia, however, its deployment remains limited due to fragmented stakeholder views and lack of integrated policy support. This study explores multi-stakeholder perspectives, including government, academia, business, finance, media, and civil society, on the role and feasibility of CCS/CCUS in achieving the country’s net zero emissions (NZE) target. Using a mixed-method approach, we conducted structured surveys (n = 39) and in-depth interviews (n = 34). Findings reveal broad support for CCS/CCUS but highlight ongoing concerns about economic viability, regulatory uncertainty, and environmental risks. Stakeholders emphasize the need for stronger government incentives and cross-border financing mechanisms. The study underscores the importance of inclusive policymaking, enhanced fiscal support, and integration of CCS/CCUS into Indonesia’s carbon economic value framework to ensure a more participatory and sustainable climate policy pathway. Full article

Using time-varying Granger causality, Neural Networks Nonlinear VAR, and Wavelet Coherence analysis, we evidence the unstable effect of the money market rate on industrial production and consumer price index in Tunisia. The effect is asymmetric and depends on geopolitical risk (low versus high). We show that global geopolitical risk has both detriments and benefits sides—it is a threat and an opportunity for monetary policy transmission mechanisms. Interacted local projections (LPs) reveal short–medium-term volatility or dampening effects, suggesting that geopolitical uncertainty might weaken the immediate impact of monetary policy on output and prices. In uncertain environments (e.g., high geopolitical risk), economic agents—households and businesses—may adopt a wait-and-see approach. They delay consumption and investment decisions, which could initially mute the impact of monetary policy. Agents may delay their responses until they gain more information about geopolitical developments. Once clarity emerges, they may adjust their behavior, aligning with the long-run effects observed in the Vector Error Correction Model (VECM). Furthermore, we identify an exacerbating investor sentiment following tightening monetary policy, during global and local geopolitical episodes. The impact is even more pronounced under conditions of high domestic weakness. Evidence is extracted through a novel composite index that we construct using Principal Component Analysis (PCA). Our results have implications for the Central Bank’s monetary policy conduct and communication practices. Full article

This study examines the impact of economic policy uncertainty (EPU) on agricultural land conversion. Using a newspaper-based index of EPU and a comprehensive panel dataset covering 270 prefecture-level cities in China, we estimate a city fixed effects model to explore this relationship. Our results indicate that a one-standard-deviation increase in EPU leads to a 22.2% increase in the conversion of agricultural land to urban residential, commercial, and industrial uses. This finding suggests that the surge in EPU triggered by the global financial crisis accounts for approximately 45% of the increase in agricultural land conversion. The adverse effect on agricultural land preservation mainly stems from intensified fiscal pressures and heightened demands on local governments to meet economic growth targets. To address potential endogeneity concerns, we employ the one-period lagged U.S. EPU index and its temporal variations as an instrument for China’s EPU, leveraging cross-country spillover effects. Our instrumental variable estimates confirm the validity of the land conversion effect and its underlying mechanisms. Furthermore, we find that the effects of EPU are particularly pronounced in cities located in non-eastern China and those that depend heavily on fixed asset investment for local economic development. Finally, our analysis of potential policy interventions to mitigate EPU-induced agricultural land loss suggests that strengthening market-oriented reforms and reducing province-level quotas on agricultural land conversion can effectively offset the impact of rising EPU. Full article

This study explores the use of Life Cycle Assessments (LCAs), Total Sustainability Assessment, and Life Cycle Sustainability Assessment (LCSA) as tools to evaluate the environmental, social, and economic impacts in Agri-industry. It highlights the unique trajectory of LCA and LCSA implementation in Latin America, shaped by the region’s distinct environmental, social, and economic contexts, contrasted with global research trends. Evidence shows the importance of biodiversity, conservation, and deforestation mitigation in Latin American LCA applications, which differ from the urban-focused impacts seen in regions like Europe or North America. Furthermore, it emphasizes the significant role of LCSA in addressing socio-economic challenges unique to Latin America, such as inequality and labor conditions. The research reveals the benefits of LCA and LCSA methodologies in the agro-industrial sector, particularly in addressing social issues like land use rights and rural community welfare. Despite challenges such as limited access to high-quality data and the need for capacity building, the innovative application of these methodologies in Latin America offers valuable insights for the global community. Our work relies on Latent Dirichlet Allocation (LDA) to analyze the LCSA literature from 1990 to 2024, identifying evolving trends and research focal areas in sustainability. The analysis herein presented highlights the need for a multi-dimensional and holistic approach to sustainability research and practice. Our findings also emphasize the importance of developing comprehensive models and integrated methodologies to effectively address complex sustainability challenges. Environmental information remains crucial for policy processes, acknowledging uncertainties in estimations and the connection between land use change, agriculture, and emissions from the global food economy and bioenergy sectors. The research underscores the dynamic nature of LCSA and the importance of continually reassessing sustainability efforts to address pressing challenges. Full article

The global transition toward low-carbon energy systems necessitates fundamental innovations in demand-side flexibility, particularly in industrial load regulation. This study presents a systematic review and critical analysis of 90 key research works (2015–2025) to establish a comprehensive framework for industrial load flexibility enhancement. We rigorously examined the tripartite interdependencies among the following: (1) Multi-energy flow physical coupling, addressing temporal-scale disparities in electricity-thermal-gas coordination under renewable penetration; (2) Uncertainty quantification, integrating data-driven and physics-informed modeling for robust decision-making; (3) Market mechanism synergy, analyzing demand response, carbon-P2P hybrid markets, and regulatory policy impacts. Our analysis reveals three fundamental challenges: the accuracy-stability trade-off in cross-timescale optimization, the policy-model disconnect in carbon-aware scheduling, and the computational complexity barrier for real-time industrial applications. The paper further proposes a roadmap for next-generation industrial load regulation systems, emphasizing co-optimization of technical feasibility, economic viability, and policy compliance. These findings advance both academic research and practical implementations for carbon-neutral power systems. Full article

Oil-dependent countries face persistent challenges, such as energy supply–demand imbalances, overreliance on fossil fuels, declining economic diversification, and environmental degradation. In response, policymakers are increasingly advocating for comprehensive energy transitions to enhance energy and environmental security while promoting sustainable development. This study evaluates Iran’s energy transition through the modeling of five scenarios using the EnergyPLAN software V16.3. These scenarios, ranging from increased fossil fuel production to renewable energy deployment, subsidy reform, and energy efficiency, were developed based on a systematic literature review and expert interviews. Key indicators such as carbon emissions, primary energy demand, and supply–demand balance were used to assess the long-term impacts of each scenario through 2040. The Transition Scenario Policy (TSP), which integrates elements of all other scenarios, emerged as the most effective pathway for reducing emissions, correcting supply–demand imbalances, and aligning with sustainable development goals. The novelty of this study lies in its mixed-method approach, combining qualitative stakeholder insights with quantitative modeling, offering a replicable framework for energy transition planning in similar oil-dependent contexts. The practical implications support evidence-based policy making, while the results open avenues for future research on adaptive energy governance, policy trade-offs, and resilience under global uncertainty. Full article

Family businesses play a vital role in the global economy as an organizational form that has evolved over time. However, Chinese family firms generally suffer from insufficient investment in research and development. Based on panel data of Chinese listed family firms from 2008 to 2022, this study investigates the impact of government green subsidies on family firms’ green innovation, along with the heterogeneity of such effects under different scenarios. The results show that government green subsidies significantly promote both strategic and substantive green innovation. The moderating effect analysis reveals that economic policy uncertainty weakens the baseline effect. Further analysis confirms that the positive impact of green subsidies is achieved by alleviating firms’ R&D funding constraints. Heterogeneity analysis indicates that green subsidies have a stronger effect on non-heavily polluting firms; they promote substantive green innovation more effectively in firms with low managerial green cognition, and strategic green innovation in those with high cognition. Additionally, the effects vary across the firm life cycle: green subsidies enhance strategic green innovation during the growth and maturity stages, and substantive green innovation during the growth and decline stages. This study reveals the mechanisms through which government green subsidies affect green innovation in family firms and offers policy implications for promoting sustainable development in the family business sector. Full article

This paper examines the impact of various uncertainty channels on stock market returns in Saudi Arabia, with a focus on the Tadawul All Share Index (TASI). It examines factors such as Saudi-specific Geopolitical Risk, Global Oil Price Uncertainty, Climate Policy Uncertainty, and U.S. Monetary Policy Uncertainty. Using monthly data from November 1998 to June 2024 and the Local Projections (LP) methodology, the study examines how these uncertainties impact market returns across various time horizons, taking into account potential structural breaks and nonlinear dynamics. Our findings indicate significant variations in the market’s response to the uncertainty measures across two distinct periods. During the first period, geopolitical risks have a strong positive impact on market returns. Conversely, the second period reveals a reversal, with negative cumulative effects, suggesting a shift in risk–return dynamics. Oil Price Uncertainty consistently exhibits a negative impact in both periods, highlighting the changing nature of oil dependency in the Saudi market. Additionally, Climate Policy Uncertainty is becoming more significant, reflecting increased market sensitivity to global environmental policy changes. Our analysis reveals significant asymmetries in the effects of various uncertainty shocks, with Monetary Policy Uncertainty exhibiting nonlinear effects that peak at intermediate horizons, while commodity-related uncertainties exhibit more persistent impacts. These findings, which remain robust across various tests, offer critical insights for portfolio management, policy formulation, and risk assessment in emerging markets undergoing substantial economic changes. Full article

Amid growing global concerns regarding sustainable governance, understanding the drivers of ESG disclosure is vital for promoting transparency and responsible corporate behavior. This study examines the peer effects of ESG disclosure among 32,187 observations from Chinese A-share listed firms between 2010 and 2021. This research employs an instrumental variable approach based on stock-specific idiosyncratic returns estimated via the Carhart four-factor model to address endogeneity concerns. The results confirm significant peer effects, suggesting that firms adjust ESG practices in response to their industry counterparts. These effects are significantly moderated by firm-level characteristics, including information asymmetry, corporate reputation, and market competition, as well as by external conditions such as economic policy uncertainty, business environment volatility, and institutional quality. This research defines peer groups by industry affiliation and conducts robustness tests using ESG risk clustering to address classification bias. This study contributes to the literature by strengthening causal inference and refining the understanding of peer-driven ESG behavior by integrating institutional theory, signaling theory, and information economics. The findings offer practical implications for policymakers, investors, and corporate managers seeking to promote ESG convergence through peer-driven incentives in diverse regulatory contexts. Full article

The rapid evolution of autonomous vehicles (AVs) has ignited widespread interest in their potential to transform mobility and transportation ecosystems. However, despite significant technological advances, the acceptance of AVs by the public remains a complex and multifaceted challenge. This state-of-the-art review explores the key factors influencing AV acceptance, focusing on the intersection of artificial intelligence (AI) services, user experience, social dynamics, and regulatory landscapes across diverse global regions. By analyzing trust, perceived safety (PS), cybersecurity, and user interface design, this paper delves into the psychological and behavioral drivers that shape public perception of AVs. It also highlights the role of demographic segmentation and media influence in accelerating or hindering adoption. A comparative analysis of AV acceptance across North America, Europe, Asia, and emerging markets reveals significant regional variations, influenced by regulatory frameworks, economic conditions, and social trends. Also, this review reveals critical insights into the perceived safety associated with AV technology, including legal uncertainties and cybersecurity concerns, while emphasizing the future potential of AVs in urban environments, public transit, and autonomous logistics fleets. This review concludes by proposing strategic roadmaps and policy implications to accelerate AV adoption, offering a forward-looking perspective on how advances in technology, coupled with targeted industry and government initiatives, can shape the future of autonomous mobility. Through a comprehensive examination of current trends and challenges, this paper provides a foundation for future research and innovation aimed at enhancing public acceptance and trust in AVs. Full article

The international seabed area (“the Area”) harbors abundant metal mineral resources that are critical to address global metal supply–demand and sustainable development. However, exploitation of mineral resources in the Area faces complex risks spanning politics, economy, technology, science, environment, society, industry, and law. No commercial-scale deep-sea mining operations have been conducted to date. Systematic risk identification and prioritization can inform strategic planning for stakeholders. This study employs literature analysis and an 80-expert questionnaire to identify key risk factors affecting mineral exploitation in the Area. Using the Analytic Hierarchy Process (AHP), we quantitatively assess the relative importance and weightings of these risks. Our results indicate that Level 1 risk groups prioritize (1) policy and public opinion risk, (2) extended continental shelf (ECS) delineation risk, (3) high sea marine protected areas (HSMPAs) establishment risk, and (4) mining area economic value risk. The five most critical Level 2 risk factors are (i) policy changes in contractor states, (ii) ECS-mining area boundary conflicts, (iii) environmental provisions in exploitation regulations at the international seabed (ER), (iv) ER implementation delays, and (v) mineral resource uncertainty. These findings provide actionable insights for contractors, policymakers, and stakeholders to optimize decision making in deep-sea mining projects. Full article

This study evaluates the economic viability of the South Pasopati Coal Project in Indonesia, addressing market volatility, carbon pricing policies, and the country’s energy transition towards Net Zero Emissions (NZE). Given Indonesia’s reliance on coal and the increasing global shift toward renewable energy, traditional valuation methods such as Discounted Cash Flow (DCF) may not adequately capture uncertainty and strategic flexibility. The study applies Real Options Valuation (ROV), integrating Monte Carlo Simulation (MCS) and Binomial Lattice Modeling, to assess project feasibility under various scenarios. The research compares three valuation scenarios: the base scenario (eastern route), an alternative scenario (western route), and a carbon pricing scenario. Results indicate that while the DCF method estimates a positive Net Present Value (NPV) for the base scenario, it fails to incorporate price volatility risks. The ROV method, however, captures managerial flexibility and provides a more robust valuation, showing an Expanded NPV (ENPV) that better reflects market uncertainties. Findings suggest that implementing ROV improves decision-making, particularly in volatile markets. The study underscores the necessity for Indonesia to adopt more flexible valuation frameworks to enhance investment decisions in the coal sector while aligning with international environmental standards. Full article