Search Results (396)

This paper proposes an incentive scheme based on a public–private partnership (PPP) to encourage low-carbon travel behavior by inducing the mode choice shift from private cars to public transit. The scheme involves three key entities: travelers, the government, and the private sector. Travelers can choose between private cars and public transit, producing different emissions. As the leader, the government aims to reduce total emission to a certain level with limited budgets. The private sector, as an intermediary, invests subsidies in low-carbon rewards to attract green travelers and benefits from a larger user pool. A two-layer multi-objective optimization model is proposed, which includes travel time, monetary cost, and emission. The objective of the upper level is to maximize the utilities of the private sector and minimize social costs to the government. The lower layer is the user equilibrium of the travelers. The numerical results obtained through heuristic algorithms demonstrate that the proposed scheme can achieve a triple-win situation, where all stakeholders benefit. Moreover, sensitivity analysis finds that prioritizing pollution control strategies will be beneficial to the government only if the unit pollution control cost coefficient is below a low threshold. Contrary to intuition, larger government subsidies do not necessarily lead to better promotion of low-carbon travel. Full article

The transformer-based deep learning approach represents the current state-of-the-art in machine translation (MT) research. Large-scale pretrained transformer models produce state-of-the-art performance across a wide range of MT tasks for many languages. However, such deep neural network (NN) models are often data-, compute-, space-, power-, and energy-hungry, typically requiring powerful GPUs or large-scale clusters to train and deploy. As a result, they are often regarded as “non-green” and “unsustainable” technologies. Distilling knowledge from large deep NN models (teachers) to smaller NN models (students) is a widely adopted sustainable development approach in MT as well as in broader areas of natural language processing (NLP), including speech, and image processing. However, distilling large pretrained models presents several challenges. First, increased training time and cost that scales with the volume of data used for training a student model. This could pose a challenge for translation service providers (TSPs), as they may have limited budgets for training. Moreover, CO₂ emissions generated during model training are typically proportional to the amount of data used, contributing to environmental harm. Second, when querying teacher models, including encoder–decoder models such as NLLB, the translations they produce for low-resource languages may be noisy or of low quality. This can undermine sequence-level knowledge distillation (SKD), as student models may inherit and reinforce errors from inaccurate labels. In this study, the teacher model’s confidence estimation is employed to filter those instances from the distilled training data for which the teacher exhibits low confidence. We tested our methods on a low-resource Urdu-to-English translation task operating within a constrained training budget in an industrial translation setting. Our findings show that confidence estimation-based filtering can significantly reduce the cost and CO₂ emissions associated with training a student model without drop in translation quality, making it a practical and environmentally sustainable solution for the TSPs. Full article

Air quality monitoring IoT is one of the approaches to achieving a sustainable future. However, the large area of IoT and the high number of monitoring microsites pose challenges for device maintenance to guarantee quality of service (QoS) in monitoring. This paper proposes a novel maintenance programming model for a large-area IoT containing 1500 monitoring microsites. In contrast to classic device maintenance, the addressed programming scenario considers the division of appropriate microsites into batches, the determination of the batch maintenance date, vehicle routing for the delivery of maintenance services, and a set of hard constraints such as QoS in air quality monitoring, the maximum number of labor working hours, and an upper limit on the total CO₂ emissions. Heuristics are proposed to generate the batches of microsites and the scheduled maintenance date for the batches. A genetic algorithm is designed to find the shortest routes by which to visit the batch microsites by a fleet of vehicles. Simulations are conducted based on government open data. The experimental results show that the maintenance and transportation costs yielded by the proposed model grow linearly with the number of microsites if the fleet size is also linearly related to the microsite number. The mean time between two consecutive cycles is around 17 days, which is generally sufficient for the preparation of the required maintenance materials and personnel. With the proposed method, the decision-maker can circumvent the difficulties in handling the hard constraints, and the allocation of maintenance resources, including budget, materials, and engineering personnel, is easier to manage. Full article

The maritime sector faces urgent decarbonisation pressures due to regulatory instruments, such as the International Maritime Organization’s (IMO) Carbon Intensity Indicator (CII), which mandates reductions in greenhouse gas emissions per transport work. This paper investigates the challenge of identifying CII-compliant strategies that are also financially viable for UK shipowners. To address this, operational and technical data from UK-flagged vessels over 5000 GT are analysed using a capital budgeting framework. This includes scenario-based evaluation of speed reduction, payload limitation, and retrofitting with dual-fuel LNG and methanol engines. The analysis integrates carbon taxation, and pilot fuel use to assess impacts on emissions and profitability. The findings reveal that while the short-term operational measures examined offer modest gains, long-term compliance and financial performance are best achieved through targeted retrofitting supported by carbon taxes and favourable market conditions. The study provides actionable insights for shipowners and policymakers seeking to align commercial viability with regulatory obligations under the evolving CII framework. Full article

Healthy peatlands provide valuable ecosystem services. Peat extraction damages peatlands, leading to carbon emissions. One of the main reasons for peat extraction is for use in horticulture. Replacing peat with recycled organic materials in horticulture is critical to preserve the valuable ecosystems provided by peatlands and to establish more circular supply chains that are reliant on recycling rather than extraction. Despite the strong policy commitment and budget allocation to restore peatlands, the demand for peat-based growing media remains high and drives most of the peat demand. In our research, we mapped the growing media supply chain, held semi-structured interviews with key stakeholders representing different interests, and surveyed amateur gardeners in the UK to understand the bottlenecks experienced by each profile in ending peat use and how to overcome them. We employed semi-structured key expert surveys to understand the supply chain dynamics and consumer demand, informed by these early interviews and the previous literature, we prepared and distributed an online consumer survey and interviewed supply-side stakeholders to understand their perspectives. The findings indicate that the barriers of availability, cost, and performance are shared between the supply-and-demand-side stakeholders. A portfolio of financial, educational and logistic interventions is required to simultaneously support the supply side to accelerate the transformation of production and supply patterns and to aid the demand side to adapt to growing with compost of recycled organic materials. The policies promoting recycled organic material use in horticulture must be coordinated within the UK and with other parts of Europe focusing on the elimination of the peat content in products rather than peat extraction to avoid extraction and the associated destruction of peat stocks elsewhere. Full article

Ensuring consistent progress toward cities’ net-zero emission goals requires understanding key dimensions of urban climate governance—particularly the motivations driving municipalities toward net zero and the critical barriers and enablers along this pathway. Current knowledge on these critical aspects is fragmented, lacking a holistic framework and empirical prioritization of key factors. We developed an integrated analytical framework and empirically distilled the most salient motivations, barriers, and enablers through a large-scale survey targeting 489 net-zero-committed municipalities—known as “Zero Carbon Cities”—across Japan. With responses from 309 municipalities, we deliver the first systematic mapping of factors perceived as most influential by Japanese local authorities. The results indicate that municipalities are primarily motivated by seizing local economic development opportunities (enhanced local energy conditions, financial gains and savings, and local industry revitalization), future-proofing communities against disasters, and enhancing the local quality of life. Key barriers and enablers were identified across four categories: municipal resources and authority (budgets, dedicated staff, and empowered climate agencies), knowledge and expertise (staff climate competence), institutional coherence (cross-departmental coordination and stakeholder involvement), and political will and leadership (the presence of climate champions and awareness within city halls and among residents). Accordingly, we discuss implications and derive recommendations toward strengthened local action in Japan and beyond. Full article

This study examines how the Russia–Ukraine conflict has affected the robustness of the global liquefied natural gas (LNG) trade network—an essential component of the global energy transition. As environmental concerns intensify worldwide, LNG is gaining strategic importance due to its cleaner emissions and greater flexibility compared to traditional fossil fuels. However, the global LNG trade network remains vulnerable to geopolitical shocks, particularly due to its concentrated structure. In this context, we construct the LNG trade network from 2020 to 2023 and employ complex network analysis to explore its structural characteristics. We assess network robustness under various attack strategies, budget constraints, and phases of the conflict. Furthermore, we utilize the difference-in-differences (DID) method to evaluate the conflict’s impact on network robustness. Our findings reveal that the global LNG trade network exhibits a distinct center–periphery structure and regional clustering. Although the network scale has continuously expanded, its connectivity still requires improvement. The Russia–Ukraine conflict has significantly weakened network robustness, with negative impacts intensifying across attack phases and under greater budget constraints. The optimal attack strategy causes the most severe degradation, followed by high-importance attacks, while random and low-importance attacks exert limited influence. Our DID-based analysis further confirms the conflict’s significant negative impact. To strengthen its resilience, the global LNG trade network should diversify its partnerships and invest in infrastructure enhancements. Full article

Prescribed fire has become an increasingly important strategy for removing biomass from forests and mitigating the risk of severe wildfire. When considering where and to what extent prescribed fire should be applied, land resource managers must consider a host of concerns including biomass density, moisture content, and meteorological conditions. These variables will not only affect how effective the burn will be, but also what sort of smoke is produced by the prescribed fire and how that smoke impacts individuals and local communities. After briefly summarizing how prescribed fire practices have evolved, this review describes how the properties of prescribed fire smoke depend on prescribed fire conditions and the methods used to measure molecular and particulate species in prescribed fire smoke. The closing section of this review identifies areas where advances in smoke monitoring and characterization can continue to improve our understanding of prescribed fire behavior. Full article

Phthalic acid esters (PAEs), a class of synthetic semi-volatile organic compounds, are extensively incorporated into decorative materials. However, their specific occurrence, migration behaviors, and environmental impact on these materials—which comprise the largest surface areas in residential settings—remain insufficiently understood. This study investigated the distribution, emission dynamics, and environmental burdens of PAEs in flooring commonly used in Chinese households. The results showed that PAEs are widespread in flooring, with total concentrations ranging from 1220 to 166,000 ng/g (14,100 ng/g, median value). Solid wood flooring (55,900 ng/g) exhibited significantly higher PAE levels compared to engineered flooring (22,600 ng/g) and laminate flooring (4000 ng/g) (p < 0.05). Migration experiments revealed that solid wood flooring tended to continuously release PAEs, laminate flooring showed a pronounced capacity for PAE absorption, and engineered flooring exhibited both release and absorption behaviors. The initial PAE concentration is the dominant factor influencing migration rates, while the flooring type and substrate density also contribute to varying degrees. The estimated environmental burdens of PAEs resulting from flooring in newly renovated Chinese households ranged from 3.63 × 10⁹ ng to 3.45 × 10¹¹ ng, with a median value of 1.23 × 10¹⁰ ng. Households in the eastern and southwestern regions exhibited the highest PAE burdens, while the southern region showed the lowest. Socioeconomic factors such as residential floor area, number of rooms, household income, and renovation budget significantly influenced the environmental burden of PAEs derived from flooring. Full article

Under the “Dual Carbon” strategy, collaborative governance of port atmospheric pollutants and carbon emissions is critical for low-carbon transformation. Focusing on Ningbo-Zhoushan Port (48% regional ship emissions), this study examines government, port enterprises, and public interactions. A tripartite evolutionary game model with numerical simulation reveals dynamic patterns and key factors. The results show the following: (1) A substitution effect exists between government incentive costs and penalty intensity—increased environmental governance budgets reduce the probability of government incentives, whereas higher public reporting rewards accelerate corporate emission reduction convergence. (2) Public supervision exhibits cyclical fluctuations due to conflicts between individual rationality and collective interests, with excessive reporting rewards potentially triggering free-rider behavior. (3) The system exhibits two stable equilibria: a low-efficiency equilibrium (0,0,0) and a high-efficiency equilibrium (1,1,1). The latter requires policy cost compensation, corporate emission reduction gains exceeding investments, and a supervision benefit–cost ratio greater than 1. Accordingly, the study proposes a three-dimensional “Incentive–Constraint–Collaboration” governance strategy, recommending floating penalty mechanisms, green financial instrument innovation, and community supervision network optimization to balance environmental benefits with fiscal sustainability. This research provides a dynamic decision-making framework for multi-agent collaborative emission reduction in ports, offering both methodological innovation and practical guidance value. Full article

Quantitative greenhouse gas (GHG) budgets for Mediterranean pepper cultivation are still missing, limiting evidence-based nitrogen management. Furthermore, mitigation value of fertigation respect to granular fertilization in vegetable systems remains uncertain. This study therefore compared the GHG footprint and productivity of ‘papaccella’ pepper under two nitrogen fertilization methods: granular fertilization versus low-frequency fertigation with urea, each supplying about 63 kg N ha⁻¹. Eight automated static chambers coupled to a cavity ring-down spectrometer monitored soil CO₂ and N₂O fluxes throughout the season. Cumulative emissions did not differ between treatments (CO₂: 811 ± 6 g m⁻² vs. 881 ± 4 g m⁻²; N₂O: 0.038 ± 0.008 g m⁻² vs. 0.041 ± 0.015 g m⁻², fertigation vs. granular), and marketable yield remained at ~11 t ha⁻¹, leaving product-scaled global warming potential (GWP) unchanged. Although representing less than 2% of measured fluxes, “hot moments,” burst emissions exceeding four standard deviations (SD) from the mean, accounted for up to 4% of seasonal CO₂ and 19% of N₂O. Fertigation doubled the frequency of these events but reduced their peak magnitude, whereas granular application produced fewer but more extreme bursts (>11 SD). Results showed that fertigation did not mitigate GHGs emission nor improve productivity for Mediterranean pepper, mainly due to the low application frequency and the use of a urea fertilizer. Moreover, we can highlight that in horticultural systems, omitting ‘hot moments’ leads to systematic underestimation of emissions. Full article

Multimodal transportation is of strategic significance in improving transportation efficiency, reducing costs and achieving low-carbon development, all of which contribute to sustainable transportation. However, in actual operation, it often encounters multiple uncertain challenges such as demand, transportation time and carbon trading price, making it difficult for traditional fixed-parameter route optimization to meet the requirements of complex situations. Based on robust optimization and Box uncertainty set, this paper constructs a hybrid robust stochastic optimization model of multimodal transportation routes with uncertain demand, transportation time and carbon trading price, designs a hybrid algorithm, and verifies the effectiveness and rationality of the model through a numerical example. The results indicate that different types of uncertainty influence the routing decisions through distinct mechanisms. That is, demand uncertainty mainly affects capacity allocation and cost structure, transportation time uncertainty increases time penalties, and carbon trading price uncertainty drives preference for low-emission modes. Compared with the single genetic algorithm and the simulated annealing algorithm, the hybrid algorithm has better performance in terms of cost and stability. The hybrid robust stochastic optimization model can handle the multimodal transportation route selection problems where the probability distribution of parameters is unknown well. It is beneficial for decision-makers to adjust the uncertain budget level according to their preferences to formulate scientific transportation plans, so as to achieve sustainable transportation development. Full article

The Earth’s degassing is an important factor in evaluating global carbon budget estimates and understanding the carbon cycle. As a result, numerous studies have focused on this topic. However, current estimates predominantly focus on subaerial CO₂ emissions and CO₂ deep submarine emissions, particularly along mid-ocean ridges (MORs), whereas very few and only spatially limited estimates of shallow submarine CO₂ emissions have been reported, despite being widespread features of the seafloor. This study reports the results of measuring the dissolved CO₂ concentrations in shallow submarine environments along the coast of Vulcano Island (Aeolian Islands, Italy). For the areas exhibiting the highest concentrations, we calculated the amount of diffuse degassing by computing the sea–air CO₂ flux. The results revealed extremely high dissolved CO₂ concentrations, reaching up to 24 vol.% in areas with visible hydrothermal activity, including one location far from the island’s main crater. Notably, elevated CO₂ levels were also detected in areas with minimal or no apparent hydrothermal discharge, indicating the occurrence of diffuse degassing processes in these areas. In addition, the calculated diffuse degassing flux was comparable in magnitude to the CO₂ flux directly emitted into the atmosphere from the island’s main bubbling pools. Full article

The role of public housing in improving wellbeing for tenants and society is an important public policy issue. Public housing tenants in Aotearoa New Zealand have constrained incomes and their mode of transport has implications for their budgets, their wellbeing, and carbon emissions. Tenants’ daily life choices and wellbeing are influenced by the set of transport options available to them and the constraints and opportunities these options entail. What is important for wellbeing is also dependent on culture. Little is known, however, about the specific influences of transport on the wellbeing of public housing tenants and how that is mediated by the culture of particular groups, particularly Māori and Pacific people, who make up the majority of people in public housing in Aotearoa. In this article we review the literature on public housing, transport, and wellbeing, to establish what is known about how transport, and the access it affords, influence the wellbeing of public housing tenants. We searched Scopus and Web of Science for academic journal articles, published in English and available online, about public housing tenant wellbeing with regard to the transportation and location characteristics of public housing. We found that creating highly accessible public housing developments with options of various modes of travel is important for the wellbeing of tenants. We also found that understanding the specific needs and preferences of tenants, ensuring tenants have agency over how they travel, and engaging with tenants during transport decision-making are particularly important and often under-recognised for people in public housing. Finally, we identified substantial gaps in the literature around understanding transport needs and experiences from Māori and Pacific perspectives, emphasising the importance of including indigenous and ethnic minority views in future research. Full article

The laser pulse detection probability of a scanning direct time-of-flight light detection and ranging (LiDAR) measurement is evaluated based on the optical signal distribution on a multipixel single photon avalanche diode (SPAD) array. These detectors intrinsically suffer from dead-times after the successful detection of a single photon and, thus, allow only for limited counting statistics when multiple returning laser photons are imaged on a single pixel. By blurring the imaged laser spot, the transition from single-pixel statistics with high signal intensity to multipixel statistics with less signal intensity is examined. Specifically, a comparison is made between the boundary cases in which (i) the returning LiDAR signal is focused through optics onto a single pixel and (ii) the detection is performed without lenses using all available pixels on the sensor matrix. The omission of imaging optics reduces the overall system size and minimizes optical transfer losses, which is crucial given the limited laser emission power due to safety standards. The investigation relies on a photon rate model for interfering (background) and signal light, applied to a simulated first-photon sensor architecture. For single-shot scenarios that reflect the optimal use of the time budget in scanning LiDAR systems, the lens-less and blurred approaches can achieve comparable or even superior results to the focusing system. This highlights the potential of fully solid-state scanning LiDAR systems utilizing optical phase arrays or multidirectional laser chips. Full article