ABSTRACT Decarbonising its extensive, fossil-fuel-reliant maritime sector is crucial for Indonesia, which seeks to balance economic growth with its role in global climate change mitigation. This study uses a detailed System Dynamics (SD) model to evaluate the long-term effectiveness of diverse decarbonisation strategies by assessing their impact on economic and environmental dimensions within the Indonesian marine sector. We compared three policy pathways: continuing current practices (Business as Usual), a gross tonnage (GT) ship policy, and a carbon emission regulation framework utilising tokens. Findings identify the Carbon Token approach as the most promising for long-term sustainability, projecting an over 92% emission cut by 2045 alongside significant financial gains. GT-based policies offer short-term economic boosts but risk environmental compromise. We recommend integrated, sustainable policies to guide Indonesia towards an economically sound, ecologically responsible, low-carbon maritime future. A green shipping transition is possible and beneficial.

Driven by the “dual-carbon” strategy, the development of zero- and low-carbon parks has become a crucial approach to resolving the conflict between urban expansion and ecological limits. Using urban functional zoning and land use data, this study estimates carbon emissions in Xiamen and examines their spatial distribution at the functional zone level, along with an assessment of carbon balance zoning. The results indicate that (1) Carbon sources far exceed sinks, with spatial concentrations in southern and northern areas, respectively. Commercial, transportation, and industrial zones are major emission sources. (2) A significant negative spatial correlation in carbon emissions exists among functional zones, manifesting as an alternating pattern of high- and low-carbon zones. (3) 72% of the zones have an ecological support coefficient below one, indicating severe carbon imbalance. (4) Xiamen can be categorized into four carbon balance functional zones, with carbon-source regulation zones accounting for 70%, core carbon-source zones accounting for 5%, and carbon-sink stressed zones accounting for 25%. No core carbon sink zones are identified. Based on these findings, targeted strategies are proposed: ecological restoration in northern Xiamen, carbon emission regulation in central areas, and source reduction in the south. These measures provide a scientific foundation for supporting Xiamen’s low-carbon transition and sustainable development.

ABSTRACT Amid growing emphasis on sustainability and customer-driven market, supply chain must balance environmental responsibility with efficiency. Present study develops a joint economic lot-sizing model integrating outsourcing, green technology investment, carbon emission regulations, and customization strategies in a two-level supply chain comprising a manufacturer and a retailer. The manufacturer produces fresh products and outsources defective repairs, while the retailer customizes part of the output to meet individual preferences. Both chain-partners invest in emission-reduction technologies to comply with carbon tax policies. Considering price-sensitive demand, the model optimizes customization cost, emission-reduction investment, selling price, and production cycle to maximize centralized profit. The novelty lies in the integrated treatment of outsourcing, carbon control, and customization under trade credit. Results suggest improving quality to reduce defectives, moderating customization, and setting optimal credit periods to enhance profitability.

Regulation of black carbon emissions from arctic Shipping: Overcoming challenges and shaping future governance

Forest fires significantly impact the global climate through carbon emissions, yet the multi-scale coupling mechanisms among meteorological factors, fire behavior, and emissions remain uncertain. Focusing on tropical Asia, this study integrated satellite-based fire behavior products, meteorological datasets, and emission factors, and employed machine learning together with structural equation modeling (SEM) to explore the mediating role of fire behavior in the meteorological regulation of carbon emissions. The results revealed significant differences among vegetation types in both carbon emission intensity and sensitivity to meteorological drivers. For example, average gas emissions (GEs) and particle emissions (PEs) in mixed forests (MF, 323.68 g/m2/year for GE and 0.73 g/m2/year for PE) were approximately 172% and 151% higher, respectively, than those in evergreen broadleaf forests (EBF, 118.92 g/m2/year for GE and 0.29 g/m2/year for PE), which exhibited the lowest emission intensity. Mixed forests and deciduous broadleaf forests exhibited stronger meteorological regulation effects, whereas evergreen broadleaf forests were comparatively stable. Temperature and vapor pressure deficit emerged as the core drivers of fire behavior and carbon emissions, exerting indirect control through fire behavior. Overall, the findings highlight fire behavior as a critical link between meteorological conditions and carbon emissions, with ecosystem-specific differences determining the responsiveness of carbon emissions to meteorological drivers. These insights provide theoretical support for improving the accuracy of wildfire emission simulations in climate models and for developing vegetation-specific fire management and climate adaptation strategies.

Amid tightening global carbon emission regulations and rising consumer acceptance of eco-friendly mobility, luxury automakers are accelerating their transition toward electrification. Porsche, renowned for high-performance engineering, is phasing out gasoline-powered 718 models and introducing the 718 EV as a central pillar of its long-term sustainability strategy. Using 718 as a case study, this paper investigates the strategic, technical, and branding dimensions of legacy automakers electric shift. Strategically, the analysis situates Porsches move within the broader context of global policy frameworks, such as the European Unions climate goals and Chinas EV incentives, which shape product roadmaps and investment decisions. Technically, it examines the adoption of Porsches Premium Platform Electric (PPE), designed to deliver efficiency, range, and dynamic performance while preserving the driving emotion central to the brands identity. At the branding level, the study explores how Porsche seeks to maintain its DNA while competing with rivals like Tesla Roadster and Alpine A110 EV. Findings indicate that the transition, though complex, offers Porsche the opportunity to redefine performance and sustainability in the electric era, providing broader insights into how luxury automakers pursue decarbonization without eroding brand essence.

ABSTRACT Climate change is projected to intensify freeze–thaw cycles (FTCs) in the peatlands of Changbai Mountain, influencing soil biogeochemistry and carbon cycling. In order to elucidate microbial regulation of carbon emissions during FTCs, we performed controlled laboratory simulations using soils from a peatland in the Changbai Mountains, Northeast China. Our findings indicate that after 15 FTCs with small (−5°C to 5°C) and large amplitudes (−10°C to 10°C), the carbon dioxide (CO 2 ) emission rates from surface soils declined by 63.8% and 64.2%, respectively, compared to the constant‐temperature control; in deeper soils, the respective declines were 27.5% and 50.9%. We found that oxidase activities were negatively correlated with CO 2 emissions during FTCs and served as the primary driver of these emissions. Methane (CH 4 ) was oxidized during FTCs, with oxidation rates inversely related to FTC amplitude and greater under small amplitude than large amplitude conditions. Soil hydrolase activities were negatively correlated with CH 4 oxidation rates, functioning as the primary regulators of methane oxidation. The carbon emissions were subsequently influenced by microbial phospholipid fatty acids, which modulated enzyme activities. This investigation comprehensively explores the interactive effects of soil enzymes, organic carbon fractions, and microbial community composition on carbon emissions. The results underscore the central role of soil enzymes in mediating these processes. Collectively, these findings provide novel insights into the microbial mechanisms governing greenhouse gas emissions from peatlands during FTCs.

This study pioneers a groundbreaking approach to sustainable inventory management by developing a novel two-warehouse inventory model that simultaneously optimizes adjustments to the selling price and investments in preservation technology can help mitigate losses and optimize profitability. under the complex interplay of partial trade credit, inflation, and stringent carbon emission regulations. Through a meticulous analysis of the intricate dynamics between these factors, our model provides actionable insights that empower businesses to strike a harmonious balance between economic profitability and Eco-conscious initiatives sustainability.The study's key findings have far-reaching implications for industries navigating the dual challenges of inventory management and environmental stewardshipby providing insight into on the critical role of preservation technology also in sustainable inventory practices, this study offers a paradigm shift in strategic decision-making, enabling businesses to make informed choices that drive both economic growth and environmental responsibility. This pioneering research provides insights into significantly towards the existing framework on sustainable stock management, Offering a comprehensive framework for businesses to optimize their inventory management practices while minimizing their environmental footprint. The insights gleaned from this study can inform the development of sustainable supply chain strategies, enabling companies to enhance their competitiveness while reducing their environmental impact. Ultimately, this study's innovative approach to sustainable inventory management has the potential to transform the way businesses approach inventory management, encouraging a shift towards more environmentally responsible and economically viable practices that drive long-term sustainability and growth.

As the IMO and the EU strengthen carbon emission regulations, eco-friendly voyage planning is increasingly recognized by ship owners as one of the most important performance factors of the vessel fleet. The eco-friendly voyage planning aims to reduce carbon emissions and fuel consumption while satisfying voyage constraints. In this study, a novel route waypoint optimization method is proposed, which combines a fuel consumption forecasting model based on the Transformer and a Proximal Policy Optimization (PPO) algorithm for adaptive waypoint planning. The developed framework suggests a multi-objective methodology unlike the traditional approaches where a single objective is sought after, which characterizes fuel efficiency against navigational safety and operational simplicity. The methodology consists of three sequential phases. First, the transformer model is employed to predict ship fuel consumption using navigational and environmental data. Next, the predicted consumption values are utilized as a reward function in a PPO-based reinforcement learning framework to generate fuel-efficient routes. Finally, the number and placement of waypoints are further optimized with respect to terrain and bathymetric constraints, improving the practicality and safety of the navigational plan. The results show that the proposed method could decrease average fuel consumption by up to 11.33% across three real-world case studies: Busan–Rotterdam, Busan–Los Angeles, and Mokpo–Houston, compared to AIS-based routes. The transformer model outperformed Long Short-Term Memory (LSTM) and Random Forest baselines with the highest prediction accuracy, achieving an R2 score of 86.75%. This study is the first to incorporate transformer-based forecasting into reinforcement learning for maritime route planning and demonstrates how the method adaptively controls waypoint density in response to environmental and geographical conditions. These results support the practical application of the approach in smart ship navigation systems aligned with IMO’s decarbonization goals.

In the context of the dual-carbon goals, this study proposes a cooperative game-theoretic optimization strategy to enhance the energy utilization efficiency, operational efficiency, and cost-effectiveness of integrated energy systems (IESs) while simultaneously reducing carbon emissions, improving operational flexibility, and mitigating renewable energy variability. To achieve these goals, an IES framework integrating power-to-gas (P2G) technology and carbon capture and storage (CCS) facilities is established to regulate carbon emissions. The system incorporates P2G conversion units and thermal components—specifically, hydrogen fuel cells, electrolyzers, reactors, and electric boilers—aiming to maximize energy conversion efficiency and asset utilization. A cooperative game-theoretic optimization model is developed to facilitate collaboration among multiple stakeholders within the coalition, which employs the Shapley value method to ensure equitable distribution of the cooperative surplus, thereby maximizing collective benefits. The model is solved using an improved gray wolf optimizer (IGWO). The simulation results demonstrate that the proposed strategy effectively coordinates multi-IES scheduling, significantly reduces carbon emissions, facilitates the efficient allocation of cooperation gains, and maximizes overall system utility.

Abstract As Arctic sea ice recedes due to global warming, ship traffic is increasing, posing global climate risks, particularly from black carbon emissions. Emitted by ships burning heavy fuel oil, black carbon accelerates ice melt and contributes to climate change. Despite this urgency regulatory progress on the topic has been slow. The International Maritime Organization has debated Arctic black carbon emissions for over a decade with little advancement. Notably, regulatory efforts on the topic so far have been driven mainly by non-state actors rather than states. However, their regulatory influence is hindered by a critical barrier: a lack of transparency. This article analyses the crucial role of transparency in international law-making, specifically for non-state actors, using Arctic black carbon regulation as a case study. Drawing on semi-structured interviews, the article identifies transparency challenges and suggests recommendations to overcome them, thereby strengthening the role of non-state actors within the regulation.

This article develops a systematic literature review with a focus on the optimization of water harvesting through the use of artificial intelligence (AI) applications. These are framed in the search for sustainable solutions to the growing problem of water scarcity in urban environments. The analysis is oriented towards urban resilience and smart water management, incorporating interdisciplinary approaches such as systems thinking to understand the complex dynamics involved in water governance. The results indicate a growing trend in the utilisation of AI in various domains, including demand forecasting, leak detection, and catchment infrastructure optimization. Additionally, the findings suggest its application in water resilience modelling and adaptive urban planning. The text goes on to examine the challenges associated with the integration of technology in urban contexts, including the critical aspects of governance and regulation of AI, water consumption, energy and carbon emissions from the use of this technology, as well as the regulation of water management in digital transformation scenarios. The study identifies the most representative patents that combat the problem, and in parallel proposes lines of research aimed at strengthening the water resilience and sustainability of cities. The strategic role of AI as a catalyst for innovation in the transition towards smarter, more integrated and adaptive water management systems is also highlighted.

With the increasingly stringent regulation of ship carbon emissions by the International Maritime Organization (IMO), improving ship energy efficiency has become a key research direction in the current shipping industry. This paper proposes and evaluates a comprehensive energy-saving solution that integrates a wind-assisted propulsion system (WAPS) and an organic Rankine cycle (ORC) waste heat power generation system. By establishing an energy efficiency simulation model of a typical ocean-going cargo ship, the appropriate optimal system configuration parameters and working fluids are determined based on minimizing the total fuel consumption, and the impact of these two energy-saving technologies on fuel consumption is systematically analyzed. The simulation results show that the simultaneous use of these two energy-saving technologies can achieve the highest energy efficiency, with the maximum fuel savings of approximately 21%. This study provides a theoretical basis and engineering reference for the design of ship energy-saving systems.

Recently, as carbon emission regulations enforced by the International Maritime Organization (IMO) have become stricter and pressure from the World Trade Organization (WTO) to abolish tax-free fuel subsidies has increased, the demand for electric propulsion systems in the marine sector has grown. Most small domestic fishing vessels rely on tax-free fuel and have limited cruising ranges and constant-speed operation, which makes them well-suited for electric propulsion. This paper proposes replacing the internal combustion engine system of such vessels with an electric propulsion system. Based on real operating conditions, an Interior Permanent Magnet Synchronous Motor (IPMSM) was designed and optimized. The Savitsky method was used to calculate total resistance at a typical cruising speed, from which the required torque and output were determined. To reduce torque ripple, an asymmetric dummy slot structure was proposed, with two dummy slots of different widths and depths placed in each stator slot. These dimensions, along with the magnet angle, were set as optimization parameters, and a metamodel-based optimal design was carried out. As a result, while meeting the design constraints, torque ripple decreased by 2.91% and the total harmonic distortion (THD) of the back-EMF was lowered by 1.32%.

Effective inventory management is crucial for small and medium-sized enterprises (SMEs) to enhance efficiency, reduce costs, and adopt sustainable practices. This study introduces an inventory model that integrates demand dynamics, preservation technology, and sustainability measures, addressing price, time, and advertisement frequency to optimize strategies. Carbon emission regulations are incorporated to promote eco-friendly operations. AI-driven demand forecasting enables precise predictions by analyzing historical sales data, seasonal trends, and external market factors, while ML-based optimization dynamically adjusts inventory policies to minimize costs and improve supply chain performance. Automated inventory tracking using barcode and RFID technologies ensures real-time visibility and accuracy, reducing errors and streamlining warehouse operations. RFID, in particular, provides non-line-of-sight tracking, enhancing inventory monitoring capabilities. These technologies not only improve operational efficiency but also generate actionable insights for strategic decision-making. The theoretical properties of the model are validated through numerical examples and sensitivity analyses. The findings underscore the importance of integrating modern technologies and sustainability measures in inventory management, demonstrating that SMEs can reduce operational costs, enhance efficiency, and build resilient, eco-friendly supply chains. This research showcases how leveraging advanced inventory models enables SMEs to remain competitive, financially viable, and environmentally responsible.

The modelling and optimization of a manufacturing systems in the context of sustainable production under uncertainty remain a pivotal focus in control theory. The goal of this research is to develop a robust decision-making framework for a production-inventory system characterised by imperfect production with reworking processes, and interval valued non-linear demand rate which is dependent on green level, selling price, warranty period, and time. This study also considers the impact of carbon emission regulation taxes to demonstrate how CO2 emission control influences the best-found policy of the proposed system. To fulfil the goal, an interval-valued optimal control problem (IVOCP) is constructed using generalised variational principle and corresponding highly nonlinear interval maximization problem is obtained. To tackle this interval optimisation problem, an improved c-r optimisation technique and the meta-heuristic algorithm Sparrow Search algorithm (SSA) are employed. The best-found solution for the corresponding problem is numerically illustrated through four distinct scenarios based on the presence of green investment levels and warranty periods in the demand rate. The obtained best found results are compared by some other metaheuristic algorithms. Additionally, statistical tests and non-parametric tests are conducted to assess the effectiveness, consistency, and stability of the algorithms. Furthermore, sensitivity analyses have been made to observe how inventory system parameters impact the optimal policy. Based on these analyses, managerial insights are derived to aid in decision-making processes.

Driven by increasingly stringent carbon emission regulations from the International Maritime Organization (IMO), the maritime industry increasingly requires eco-friendly power systems and enhanced energy efficiency. Lithium-ion batteries, a core component of these systems, necessitate precise temperature management to ensure safety, performance, and longevity, especially under high-temperature conditions owing to the inherent risk of thermal runaway. This study proposes a sensorless temperature estimation method using a long short-term memory network. Using key parameters, including state of charge, voltage, current, C-rate, and depth of discharge, a MATLAB-based analysis program was developed to model battery dynamics. The proposed method enables real-time internal temperature estimation without physical sensors, demonstrating improved accuracy via data-driven learning. Operational data from the training vessel Hannara were used to develop an integrated organic Rankine cycle–energy storage system model, analyze factors influencing battery temperature, and inform optimized battery operation strategies. The results highlight the potential of the proposed method to enhance the safety and efficiency of shipboard battery systems, thereby contributing to the achievement of the IMO’s carbon reduction goals.

Optimizing the Sustainability in the Supply Chain for Imperfect Deteriorating Items under Remanufacturing and Carbon Emission Regulation

Carbon emission regulation and corporate financing constraints: A quasi-natural experiment based on China’s carbon emissions trading mechanism

This study aims to analyze the influence of economic growth, investment, industrial added value, trade value, and oil consumption on CO2 emissions in Indonesia in the period 1987-2019, before the COVID-19 pandemic. In addition, this study also considers the role of government policies in regulating carbon emissions and mitigating negative impacts on the environment. The method used in this study is the ECM (Error Correction Model) regression approach. The results show that in the long term, economic growth, industrial value-added, and oil consumption have a positive effect on increasing CO2 emissions in Indonesia, while investment (GFCF) and trade value have a negative effect on CO2 emissions. In the short term, industrial value added, and trade value have no significant effect on CO2 emissions, while economic growth and oil consumption encourage an increase in CO2 emissions. Investment (GFCF) contribute to the reduction of CO2 emissions. Indonesian government policies that focus on reducing carbon emissions through regulations that support renewable energy and energy efficiency, as well as controlling oil consumption, are expected to accelerate the transition to a low-carbon economy. The implication of these findings is that more assertive and integrated policies between the economic and environmental sectors are needed to effectively reduce CO2 emissions, while supporting economic growth and industrialization.