Search Results (1,969)

This paper presents a comprehensive power system transition-planning model positioned between conventional generation and transmission expansion planning (GTEP) formulations and broader macro-energy system (MES) tools. Existing planning models are typically unable to simultaneously represent detailed network constraints, adaptive long-term uncertainty, and a broad set of grid-enhancing transition technologies within a single tractable optimization framework; this work enables such integrated, scenario-based planning. The framework remains rooted in detailed electrical system modeling while expanding the decision space to include transition-relevant technologies: conventional and renewable generation, transmission, advanced flow-control devices, dynamic line rating, energy storage, and retrofit options, all within a long-term-planning model under uncertainty. The contribution is the integrated representation of these options and the modeling constructs required to capture their interactions, including expressions enabling concurrent investment decisions across FACTS, dynamic line rating, and transmission expansion; network-embedded modeling of series compensation devices; a battery degradation model that avoids exogenous degradation cost proxies; and a GIS-based zoning resolution methodology balancing spatial fidelity and computational tractability. The resulting formulation is a mixed-integer multi-stage stochastic program. Analytical value is demonstrated through a detailed small-scale example based on Alberta’s power system. To overcome the computationally prohibitive results encountered when scaling the formulation to a practical test case consistent with Alberta’s long-term power-system-planning practices, Stochastic Dual Dynamic Programming is employed in parallel. The resulting solution demonstrates the feasibility of a subclass of highly detailed, transition-oriented electrical system planning models that are otherwise intractable under monolithic workstation-based approaches. Full article

Out-of-school-time (OST) programs in the United States offer significant opportunities for youth to engage with and develop their identities in science, technology, engineering, and mathematics (STEM). However, professional learning that supports OST educators in developing identity-affirming STEM facilitation remains chronically underfunded and undervalued. Dominant approaches to measuring program “costs”—often centered on per-participant expenditures or short-term cost-effectiveness—obscure the systemic, relational, and capacity-building investments required to sustain high-quality OST STEM practices. This commentary examines how available cost frameworks shape what is rendered visible as “value” in OST STEM professional learning and where they fall short. To ground this analysis, I draw on the Afterschool Coaching for Reflective Educators in STEM (ACRES) program, a long-running national coaching initiative, as an illustrative case through which to examine how investments unfold over time and across contexts. Using ACRES, I demonstrate how costs are more productively understood as multidimensional investments in infrastructure, human capacity, relationships, and knowledge—forms of value that resist per-participant or short-horizon accounting. I offer an alternative tool, the Capacity-Based Cost Assessment (CBCA), to facilitate reflection on the outcomes of these investments. I include recommendations for how to define, document, and evaluate investments in OST STEM professional learning. Full article

Background: Natural disasters can trigger hazardous material (Hazmat) releases and damage valuable assets, increasing human, environmental, and economic losses. Effective pre-disaster relocation planning is therefore critical but operationally challenging. Methods: This study develops a mixed-integer programming model for the pre-disaster relocation of Hazmat and valuable assets (HVAs). The model jointly optimizes safe-location activation, fleet allocation, and trip scheduling within a limited warning period, subject to vehicle availability, storage and capacity limits. The objective minimizes total cost, including facility activation, transportation, and penalties for unrelocated inventories. The model is solved using the Gurobi Optimizer. A base scenario and sensitivity analyses on fleet size and safe-location capacity are conducted using data from a cyclone-prone region in Oman. Results: In the base scenario, 73.4% of HVAs are relocated by activating 10 safe locations. Sensitivity analysis shows rapid gains at small fleet sizes, followed by diminishing returns beyond a threshold. Over 95% of HVAs are relocated by doubling safe-location capacities with 80 vehicles or tripling capacities with 65 vehicles. Conclusions: Total vehicle capacity, time-window, and safe-location capacity constraints become binding at different thresholds, highlighting the need for balanced investments. The proposed model provides an analytics-driven decision-support tool for risk-aware, time-bounded disaster relocation planning. Full article

Drought is a slow-onset hazard whose economic impacts can propagate across sectors with multi-year delays. This study develops a non-linear autoregressive model with exogenous drought inputs (ARX) to assess whether U.S. drought severity, measured by the Drought Severity and Coverage Index (DSCI), contains incremental predictive information for monthly stock returns. Using weekly DSCI and stock price data from 2013 to 2023, we constructed monthly compound returns and multi-year drought lags spanning 1–5 years for four sector-representative firms: a water utility (American Water Works, AWK), two food service firms (Chipotle Mexican Grill, CMG; Starbucks, SBUX), and an industrial manufacturer (Tesla, TSLA). We compared regularized linear ARX baselines (Elastic Net, Ridge) with a non-linear Histogram Gradient Boosting Regressor (HGB) ARX model and used permutation importance to diagnose drought-relevant lag horizons. Results showed a clear, delayed drought signal for the water utility, with a dominant ~48-month drought lag, consistent with multi-year transmission through operations, regulation, and investment cycles. In contrast, drought lags added limited or unstable information for the food service firms and negligible information for TSLA, whose dynamics were dominated by non-drought drivers. Overall, the findings indicate that drought–return relationships are sector-specific and can emerge at multi-year lags, and that non-linear ARX models provide a flexible tool for detecting these delayed climate-risk signals. Full article

Background: Artificial Intelligence (AI) is increasingly adopted across logistics and service operations, yet limited research explains how it supports back-end event logistics or what factors enable or hinder its implementation. This study investigates how AI can be applied across event logistics processes and identifies the key barriers and critical success factors shaping its adoption. Methods: A sequential exploratory qualitative design was employed. First, semi-structured interviews with experienced event professionals generated context-specific insights. These findings informed a two-round Delphi study with 10 experts, where items were prioritised and consensus assessed using Kendall’s coefficient of concordance (W) and chi-square tests. Results: The results indicate that AI delivers the greatest value in pre-event planning activities, particularly scheduling and supplier coordination. Resistance to change and the lack of industry-specific AI tools emerged as the main adoption barriers, while technological infrastructure, system integration, and change management were identified as critical success factors. Conclusions: The study provides practical guidance for event organisers and technology providers by highlighting where AI investments are most likely to generate operational benefits and how organisational readiness can be strengthened. It also underscores the need for improved sustainability-focused tools and better data practices. Full article

Integrated landscape (bioregional, territorial) management (ILM) is a model for place-based planning and development that integrates values of healthy nature, regenerative economies, human well-being, and social solidarity. This review paper analyzes the support system for ILM to achieve transformative change, highlighting 20 dimensions in five support sub-systems. Though landscape partnerships (LPs) are now widespread, they have little coordinated support to form and have weak capacities, inadequate long-term operational funding, and limited cultural resonance. Landscape programs have proliferated and gained notable system-level support, but LP coalitions and alliances are just emerging, and there is little coordinated provision of LP support services. Despite widespread developments in the knowledge base, methods, and tools for local ILM design, there is little coordinated system support and limited dedicated work on data and IT, impact assessment, or strategic research. Landscape finance tools and business engagement with LPs are being explored, but economic valuation is inadequate, and little financing has shifted to coordinated landscape investments. In public policy, professional planners and international policy frameworks are adopting ILM, but government policies and tenure systems provide sparse support. High-leverage actions can accelerate progress in each dimension. But to fully realize the transformative potential of ILM will require more coherent support strategies. Full article

In the context of global climate change and promoting green development, the industrial sector’s significant contribution to carbon emissions necessitates effective policies for low-carbon energy transition. Understanding the role of carbon emission trading policy (CETP) in facilitating the low-carbon transformation of industrial energy (LTIE) is important. However, the effectiveness of CETP in driving LTIE and the mechanisms through which it operates remain underexplored. This study systematically examines the effects of CETP on LTIE and its underlying mechanisms across 30 Chinese provinces from 2005 to 2021. The findings show that first, CETP substantially facilitates LTIE, and the results remain consistent after undergoing parallel-trend, placebo, and other robustness tests. Second, CETP primarily facilitates this transformation through multiple channels, namely, energy-saving and carbon-reduction innovations, industrial structure transformation, and government support. Third, heterogeneous results indicate that the policy effect is highly pronounced in inland regions and areas with low levels of industrial governance investment. This study demonstrates that CETP is an effective tool for advancing China’s industrial energy transformation toward low-carbon models, whose effectiveness is influenced by regional characteristics and governance levels. These findings provide a scientific basis for formulating differentiated environmental strategies. Full article

Management of food organics and garden organics (FOGO) has emerged as a critical policy priority due to methane emissions from landfilled organics in Australia. Here, the responsibility for organics recovery rests with state and local governments, resulting in fragmented implementation, differing regulatory settings, and variable landfill levy designs. This study examines the viability of FOGO systems by drawing on three Queensland regional case studies: Lockyer Valley, Rockhampton, and Bundaberg. The study uses qualitative document analysis and comparative case study methods, supported by systems mapping, to examine interactions between policy, governance, infrastructure, and community factors. Seven key domains were identified as being central to system performance: (1) government waste strategy, (2) waste regulation, (3) political acceptance, (4) collection systems, (5) cost and funding, (6) community acceptance, and (7) compost processing. Examining these components collectively demonstrated that effective FOGO delivery relies on their alignment, with each layer reinforcing or constraining the others. To highlight waste regulation tools, the study compared landfill levies as a central economic and governance instrument in two contrasting Australian jurisdictions. In Queensland, the levy operates primarily as a fiscal tool rather than as a behavioural driver, limiting councils’ ability to invest in new services. By contrast, New South Wales’s mandatory FOGO implementation and a more mature regulatory framework have driven widespread service rollout but have also revealed the complexities of enforcing a universal policy in diverse regional contexts. The paper offers new insights into the financial and governance dynamics shaping regional waste policy, demonstrating how whole-of-system coherence is essential for advancing circular economy transitions in dispersed local contexts. Full article

Background: Supply Chain Flexibility (SCF) has transitioned from being viewed merely as a competitive edge to now being essential for survival during the current “high-impact, low-frequency” disruption era. Most research treats Supply Chain Flexibility enablement as a list of unrelated elements. Methods: To address this gap, this study develops an Interpretive Structural Modelling (ISM) to illustrate how Supply Chain Flexibility elements impact “Data-Driven Organisational Culture” so they can provide core technology and operational capabilities to Supply Chain Flexibility. Results: The hierarchical structure developed indicates that the “Nature of Customers” is the main building block of Supply Chain Flexibility, whereas “Strategic Redundancies” are viewed as the result of an advanced Supply Chain Flexibility system rather than as an initial factor in Supply Chain Flexibility. This study develops an integrated framework to align the macro-level Supply Chain Flexibility elements with the five operational areas such as market, delivery, logistics, organisational and volume flexibility. Conclusions: The finding provides practical guidance for practitioners to prioritise foundational cultural and strategic investments before adopting software tools or other surface-level solutions, thereby supporting the systematic development of robust and sustainable Supply Chain Flexibility. Full article

Hybrid renewable energy systems are increasingly important for enabling sustainable and resilient energy supply in rural smart communities, yet existing tools often lack the ability to integrate environmental variability, multi-technology interactions, and economic–environmental assessment in a unified framework. This study presents Hybrid Smart Micro Energy Community (HySMEC), a novel modeling approach that combines high-resolution meteorological data, technology-specific generation models, detailed demand characterization, and financial analysis to evaluate hybrid configurations of hydropower, solar PV, wind, battery storage, and grid interaction. Hourly simulations capture seasonal dynamics and system behavior under realistic technical efficiencies, investment costs, and emission factors, enabling a transparent assessment of energy flows, self-consumption, and grid dependence. The results show that hybrid systems can achieve competitive economic performance, low Levelized Costs of Energy, and significant CO₂ emission reductions across diverse rural community profiles, even when space or demand constraints are present. The analysis confirms the technical feasibility and environmental benefits of integrating multiple renewable sources with storage, highlighting the importance of self-consumption ratios in improving system profitability. Overall, HySMEC provides a robust and scalable tool to support data-driven design and optimization of distributed energy systems, offering valuable insights for researchers, planners, and decision-makers involved in sustainable rural energy development. Full article

This study examines sector-specific patterns of remarketing implementation in manufacturing and service industries and evaluates their effectiveness during digital transformation from a sustainability perspective. Using a mixed-method approach, the research combines descriptive analysis of enterprises’ digital maturity with Monte Carlo simulation modeling of remarketing campaign performance based on key parameters such as budget allocation, conversion efficiency, customer lifetime value, personalization intensity, and investment in AI-driven analytics. The results demonstrate that remarketing enhances traffic, user engagement, and return on investment; however, its sustainability depends on sectoral characteristics and behavioral responsiveness. AI-powered personalization is identified as a critical factor in reducing ad fatigue and improving conversion stability. While manufacturing firms tend to achieve higher but more volatile returns, service-sector companies demonstrate more stable outcomes due to greater digital adaptability and more intensive use of dynamic advertising tools. The findings highlight that sustainable remarketing strategies require sector-specific adaptation to balance economic efficiency, technological investment, and long-term consumer engagement, thereby supporting resilient and sustainable business development in the digital economy. Full article

Cross-border e-commerce, as a vital form of digital trade, is emerging as a new engine for corporate internationalization. This study employs China’s cross-border e-commerce pilot zones (established since 2015) as a quasi-natural experiment to investigate their causal effects on Chinese cities’ outward foreign direct investment (OFDI) and the underlying mechanisms. Distinct from previous trade-focused studies, this paper innovatively adopts a greenfield investment perspective. By integrating the Global Greenfield Investment Database (2010–2022) with the China City Statistical Yearbook, we constructed a greenfield OFDI dataset spanning the city–destination–target industry dimensions. Based on this dataset, this study employs a time-varying DID approach combined with PSM-DID, parallel trend tests, and placebo tests to empirically analyze how cross-border e-commerce development influences OFDI and its underlying mechanisms. The findings reveal that establishing cross-border e-commerce pilot zones boosts local outward investment by approximately 18.8%. A binary marginal decomposition analysis indicates that this effect primarily manifests through the extensive margin—significantly driving investment into new destination markets. Additionally, the mechanism operates by reducing information search costs and enhancing factor allocation efficiency. Furthermore, the outward investment promotion effect of cross-border e-commerce pilot zones is more pronounced in samples where the destination is a developed country, the target industry is high-tech, and the origin is eastern China. This study not only expands the dimensions for assessing the economic effects of cross-border e-commerce but also provides concrete empirical evidence for governments to optimize digital trade policy arrangements and for enterprises to leverage digital tools to overcome the “Liability of Foreignness” and achieve internationalization. Full article

This paper examines whether oil price volatility accelerates or decelerates the global sustainable energy transition by analyzing the differential responses of oil exporters and oil importers. Using a Panel Autoregressive Distributed Lag (P-ARDL) estimation on a balanced panel of 30 countries (2002–2023), this study investigates the long-term sustainability of energy shifts under market uncertainty. We find significant asymmetric impacts: oil-exporting countries demonstrate a strong, positive long-run response to volatility, suggesting that price uncertainty acts as a catalyst for economic diversification and sustainable investments in renewables as a strategic risk management tool. Conversely, oil-importing countries show no significant volatility response; their transition toward sustainability is primarily driven by economic growth rather than oil market forces. The error correction mechanisms reveal annual adjustment rates of 35.9% for importers and 21.5% for exporters, confirming stable long-run sustainable development relationships. These findings challenge the hypothesis of a uniform global transition, highlighting that achieving sustainability goals is highly dependent on a nation’s position in international oil markets, necessitating tailored policy frameworks for a resilient energy future. Full article

Green hydrogen production is a fundamental component of the sustainable energy transition; however, the success of such projects largely depends on the strategic selection of reliable and sustainable equipment suppliers. Supplier selection plays a critical role in aligning operational performance with long-term objectives, including technological competitiveness, environmental sustainability, and societal acceptance. Nevertheless, conventional multi-criteria decision-making (MCDM) approaches remain insufficient in adequately capturing the uncertainty, subjectivity, and group decision-making dynamics inherent in real-world supplier evaluation processes. To address this gap, this study proposes a group decision-making supported Spherical Fuzzy TOPSIS (SF-TOPSIS) framework for selecting sustainable green hydrogen production equipment suppliers. Within the model, ten evaluation criteria covering technical, economic, environmental, and social dimensions are defined to ensure alignment between supplier selection decisions and the strategic orientation of the business unit. The empirical findings, based on aggregated global fuzzy weights and relative closeness values, indicate that technical criteria such as electrolyzer efficiency and technical competence (C1), hydrogen safety (C2), and system robustness (C3) are decisive in the evaluation process. Moreover, the social criterion representing local supplier contribution and societal acceptance (C9) has been identified as playing a critical role, highlighting the increasing importance of social legitimacy and regional integration in sustainable hydrogen investments. These findings are derived directly from the model’s quantitative outputs, without relying on prior assumptions, reflecting the strategic significance of the criteria for operational reliability and long-term sustainability. The primary methodological contribution of this study lies in the development of a spherical fuzzy group decision-making framework capable of addressing multidimensional uncertainties across technical, economic, environmental, and social dimensions. This framework provides decision-makers with a reliable, systematic ranking tool for selecting sustainable hydrogen production equipment suppliers under complex uncertainty. From a practical perspective, the proposed model enables stakeholders to quantitatively assess trade-offs between technological performance and socio-economic impacts and serves as a guiding tool for strategic decision-making. Full article

This paper re-examines the role of trade and FDI inflows in accelerating the process of industrial growth involving countries belonging to the “Association of Southeast Asian Nations (ASEAN)” region. Trade openness and foreign direct investment (FDI) have improved the growth performance of numerous economies and regions over the years. However, the specific role of both trade openness and FDI inflows in advancing the industrial growth process of economies has yet to be investigated in the case of economies belonging to ASEAN. This study analyzes data from 2000 to 2023 and employs several relevant econometric tools, including the “Pooled Ordinary Least Squares (POLS)”, “Fixed Effects Filter (FEF)”, “Feasible Generalized Least Squares (FGLS)” and “Two Stages Least Squares (TSLS)”, to assess the specific impact of both trade openness and FDI inflows on industrial growth. Our findings show that both trade openness and FDI have advanced the industrial growth of ASEAN member economies. In terms of relative importance, the impact of trade openness is higher as compared to FDI inflows on the industrial sector. Similarly, the results demonstrate that the industrial growth of ASEAN economies could be explained positively by increased domestic investment and government expenditures. Moreover, our results indicate that the inflation rate and the natural resource sector have adversely impacted industrial growth. Finally, the labor force has not had the desirable positive impact on the industrial progress of ASEAN economies. The obtained results are robust across alternative specifications and estimation techniques. Therefore, our results have important policy implications for ASEAN economies. Full article