Search Results (48)

In respiratory patients, limited adherence to and misuse of devices hinder the effectiveness of inhalation therapy. Switching inhalers for non-clinical reasons poses a risk of deterioration of respiratory disease and/or promotes poor adherence to therapy. The objective of this work was to explore the impact of device changes for non-clinical reasons on clinical outcomes (primary) and costs (secondary), including carbon emissions in Spain. After a comprehensive literature search, the increased use of resources following worsening outcomes was apportioned using Spanish cost data and following the recommended pathways for care. We calculated the cost of re-training these patients and attributed carbon emissions in metric tons of CO₂ equivalent (tCO₂eq) to the excess resource use. In Spain, the impact of uncontrolled switching for non-clinical reasons in COPD has an annual estimated cost of EUR 923/patient, leading to an excess annual expenditure of more than EUR 216 million. For asthma patients, the annual impact is almost EUR 263/patient, representing an additional EUR 118 million excess annual expenditure. The environmental consequence of both conditions can be equated to almost 45 thousand tCO₂eq. Training all these patients on the new device would cost around EUR 35 million and would generate an extra impact reduction of about 2.6 thousand tCO₂eq in carbon emissions levy. Full article

This study examines the effects of the International Maritime Organization’s (IMO) market-based measures, with a particular focus on the greenhouse gas (GHG) levy and on the financial and operational performance of Korean shipping companies. The analysis estimates that these companies, which play a vital role in global trade, consume approximately 9211 kilotons of fuel annually and emit 28.5 million tons of carbon dioxide. Under the lowest proposed carbon tax scenario, the financial burden on these companies is estimated at approximately KRW 1.07 trillion, resulting in an 8.8% reduction in net profit, a 2.4% decrease in return on equity (ROE), and a 1.1% decline in return on assets (ROA). Conversely, under the highest carbon tax scenario, costs rise to KRW 4.89 trillion, leading to a significant 40.2% decrease in net profit, thereby posing a serious threat to the financial stability and competitiveness of these firms. These findings underscore the urgent need for strategic policy interventions to mitigate the financial impact of carbon taxation while promoting both environmental sustainability and economic resilience in the maritime sector. Full article

Tropical peatlands store approximately 105 gigatons of carbon (GtC), serving as vital long-term carbon sinks, yet remain critically underrepresented in climate policy. Indonesia peatlands contain 57GtC—the largest tropical peatland carbon stock in the Asia–Pacific. However, decades of drainage, fires, and lax enforcement practices have degraded vast peatland areas, turning them from carbon sinks into emission sources—as evidenced by the 1997 and 2015 peatland fires which emitted 2.57 Gt CO₂eq and 1.75 Gt CO₂eq, respectively. Using system theory validated against historical data (1997–2023), we develop a causal loop model revealing three interconnected feedback loops driving irreversible collapse: (1) drainage–desiccation–oxidation, where water table below −40 cm triggers peat oxidation (2–5 cm subsistence) and fires; (2) fire–climate–permafrost, wherein emissions intensify radiative forcing, destabilizing monsoons and accelerating Arctic permafrost thaw (+15% since 2000); and (2) economy–governance failure, perpetuated by palm oil’s economic dominance and slack regulatory oversight. To break these vicious cycles, we propose a precautionary framework featuring IoT-enforced water table (≤40 cm), reducing emissions by 34%, legally protected “Global Climate Stabilization Zones” for peat domes (>3 m depth), safeguarding 57 GtC, and ASEAN transboundary enforcement funded by a 1–3% palm oil levy. Without intervention, annual emissions may reach 2.869 GtCO₂e by 2030 (Nationally Determined Contribution’s business-as-usual scenario). Conversely, rewetting 590 km²/year aligns with Indonesia’s FOLU Net Sink 2030 target (−140 Mt CO₂e) and mitigates 1.4–1.6 MtCO₂ annually. We conclude that integrating peatlands as irreplaceable climate infrastructure into global policy is essential for achieving Paris Agreement goals and SDGs 13–15. Full article

The 83rd session of the IMO Maritime Environment Protection Committee (MEPC 83) approved a global pricing mechanism for the shipping industry, with formal adoption scheduled for October 2025. Proposed mechanisms include the International Maritime Sustainable Fuels and Fund (IMSF&F) and a combined approach integrating GHG Fuel Standards with Universal GHG Contributions (GFS&UGC). This study developed a model based on the marginal abatement cost curve (MACC) methodology to assess the cost-effectiveness of alternative fuels under both mechanisms. Sensitivity analyses evaluated the impacts of fuel prices, carbon prices, and the GHG Fuel Intensity (GFI) indicator on MAC. Results indicate that implementing the GFS&UGC mechanism yields higher net present values (NPVs) and lower MACs compared to IMSF&F. Introducing universal GHG contributions promotes a comparatively fairer transition to sustainable shipping fuels. Investments in zero- or near-zero-fueled (ZNZ) ships are unlikely to be recouped by 2050 unless carbon prices rise sufficiently to boost revenues. Bio-Methanol and bio-diesel emerged as the most cost-competitive ZNZ options in the long term, while e-Methanol’s poor competitiveness stems from its extremely high price. Both pooling costs and universal GHG levies significantly reduce LNG’s economic viability over the study period. MACs demonstrated greater sensitivity to fuel prices (P_fuel) than to carbon prices (P_carbon) or GFI within this study’s parameterization scope, particularly under GFS&UGC. Ratios of P_carbon%/P_fuel% in equivalent sensitivity scenarios were quantified to determine relative price importance. This work provides insights into fuel selection for shipping companies and supports policymakers in designing effective GHG pricing mechanisms. Full article

Rapid urbanization in China is intensifying travel demand while making transport the nation’s third-largest source of carbon emissions. Anticipating continued growth in private-car fleets, this study integrates vehicle-stock forecasting with multi-scenario emission modeling to identify effective decarbonization pathways for Chinese cities. First, Kendall rank and grey relational analyses are combined to screen the key drivers of car ownership, creating a concise input set for prediction. A Lévy-flight-enhanced Sparrow Search Algorithm (LSSA) is then used to optimize the smoothing factor of the Generalized Regression Neural Network (GRNN), producing the Levy flight-improved Sparrow Search Algorithm optimized Generalized Regression Neural Network (LSSA-GRNN) model for annual fleet projections. Second, a three-tier scenario framework—Baseline, Moderate Low-Carbon, and Enhanced Low-Carbon—is constructed in the Long-range Energy Alternatives Planning System (LEAP) platform. Using Ningbo as a case study, the LSSA-GRNN outperforms both the benchmark Sparrow Search Algorithm optimized Generalized Regression Neural Network (SSA-GRNN) and the conventional GRNN across all accuracy metrics. Results indicate that Ningbo’s car fleet will keep expanding to 2030, albeit at a slowing rate. Relative to 2022 levels, the Enhanced Low-Carbon scenario delivers the largest emission reduction, driven primarily by accelerated electrification, whereas public transport optimization exhibits a slower cumulative effect. The methodological framework offers a transferable tool for cities seeking to link fleet dynamics with emission scenarios and to design robust low-carbon transport policies. Full article

Maritime transport carries over 80% of global trade by volume and remains the most energy-efficient mode for long-distance goods movement. However, the sector contributes approximately 3% of global Greenhouse Gas (GHG) emissions, a share that could rise to 17% by 2050 without effective regulation. In response, the International Maritime Organization (IMO) has introduced initial and short-term measures to enhance energy efficiency and reduce emissions. In 2023, IMO Strategy expanded on these efforts with medium-term measures, including Market-Based Mechanisms (MBMs) such as a GHG levy, a feebate system, and fuel intensity regulations combined with carbon pricing. This study evaluates the economic and environmental impacts of these measures using an integrated computational simulation model that combines Ocean Engineering and Economics. Our results indicate that all proposed measures support the IMO’s intermediate emission reduction targets through 2035, cutting absolute emissions by more than 50%. However, economic impacts vary significantly across regions, with most of Africa, Asia, and South America experiencing the greatest adverse effects on GDP and trade. Among the measures, the GHG levy exerts the strongest pressure on economic activity and food prices, while a revised fuel intensity mechanism imposes lower costs, particularly in the short term. Revenue redistribution mitigates GDP losses but does so unevenly across regions. By leveraging a general equilibrium model (GTAP) to capture indirect effects often overlooked in prior studies, this analysis provides a comprehensive comparison of policy impacts. The findings underscore the need for equitable and pragmatic decarbonization strategies in the maritime sector, contributing to ongoing IMO policy discussions. Full article

Maritime transport accounts for approximately 3% of global greenhouse gas (GHG) emissions, a figure projected to rise by 17% by 2050 without effective mitigation measures. Achieving zero-emission shipping requires a comprehensive strategy that integrates regulatory frameworks, alternative fuels, and energy-saving technologies. However, existing studies often fail to provide an integrated analysis of regulatory constraints, economic incentives, and technological feasibility. This study bridges this gap by developing an integrated model tailored for international maritime transport, incorporating regulatory constraints, economic incentives, and technological feasibility into a unified framework. The model is developed using a predictive approach to assess decarbonization pathways for global shipping from 2018 to 2035. A multi-criterion decision analysis (MCDA) framework, coupled with techno-economic modeling, evaluates the cost-effectiveness, technology readiness, and adoption potential of alternative fuels, operational strategies, and market-based measures. The results indicate that technical and operational measures alone can reduce emissions by up to 44%, while market-based measures improve the diversity of sustainable fuel adoption. Biofuels, particularly BISVO and BIFAME, emerge as preferred alternatives due to cost-effectiveness, while green hydrogen, ammonia, and biomethanol remain unviable without additional policy support. A strict carbon levy increases transport costs by 46%, whereas flexible compliance mechanisms limit cost increases to 14–25%. The proposed approach provides a robust decision-support framework for policymakers and industry stakeholders, ensuring transparency in evaluating the trade-offs between emissions reductions and economic feasibility, thereby guiding future regulatory strategies. Full article

Taiwan will levy a carbon fee starting in 2025, according to the three-tier carbon accounting model and carbon emissions inventory measures. On 21 October 2024, Taiwan’s Ministry of Environment announced the carbon fee and that Taiwan had officially entered an era in which carbon emissions would be priced. The carbon fee officially took effect on 1 January 2025. Therefore, all manufacturing and power industries with annual carbon emissions exceeding 25,000 tons of carbon dioxide equivalents (tCO₂e) would be billed. The carbon fee system provides various preferential rate options and encourages all companies to propose voluntary reduction plans. Rate differences can help generate substantial carbon reduction action. However, the carbon fee system is flexible and can be adjusted in the future based on implementation status and industry change to help cope with changes in the net-zero transformation process. Full article

The implementation of the Carbon Border Adjustment Mechanism (CBAM) carries profound implications for China’s export trade with the EU. However, a comprehensive analysis of CBAM’s impact on provincial export trade, particularly one grounded in industrial linkages and incorporating diverse policy scenarios, remains limited. To address this gap, this study develops a mechanistic framework based on industrial linkage theory and dynamically integrates key factors such as the scope of industries covered by CBAM, carbon emission accounting boundaries, and carbon pricing into a multi-scenario quantitative model. Leveraging a refined multi-region input–output (MRIO) model, we quantitatively assess the effects of CBAM on China’s provincial exports to the EU under various scenarios. The findings show that CBAM significantly raises export costs, leading to a pronounced decline in the competitiveness of five highly vulnerable industries. As CBAM expands to include sectors covered by the EU Emissions Trading System (EU ETS), the total levies on affected industries increase considerably, ranging from USD 0.07 billion to USD 2.25 billion depending on the scenario. Conversely, seven provincial industries, such as the chemical industry in Shanxi, experience only limited impacts due to their low direct carbon intensity and minimal overall increases in carbon tariffs. Then, the study underscores the pivotal role of China’s domestic carbon pricing mechanism in mitigating the effects of CBAM. Higher domestic carbon prices enhance China’s capacity to respond effectively, thereby reducing the overall impact of the mechanism. By adopting an inter-industry linkage perspective, this study provides new insights into assessing the multidimensional impacts of CBAM on China’s exports to the EU across provinces under different policy design scenarios, providing lessons for different categories of provinces on how to cope with CBAM. Full article

This paper discusses the role of afforestation projects and other climate technologies in the green agenda for Russia and aims to justify the anchoring of jurisdictional carbon pricing in the cost approach to valuation, specifically, with reference to the cost economics for afforestation projects given their centrality to the agenda. Through that, and due to the inchoate state of carbon pricing in the study jurisdiction, this paper aims to advance price discovery for national carbon credits in both compliance and voluntary schemes. The cost approach framework, moderated by international market comparisons, indicates the fair price of carbon in Russian jurisdiction at the level of USD 20–25 per tonne of CO₂-eq, which is close to the global median but is more than double the amount of carbon levies set under the Sakhalin GHG quota experiment. It is argued that unless such a fair price for carbon is set in the country, the national carbon credits market will not achieve viable growth, nor will sustainable development be advanced, and funds for it be adequately collected. This represents a relevant contribution to the literature on the development of the national carbon credit markets. Full article

To achieve the optimal solution between construction costs and carbon emissions in the multi-target optimization scheduling, this paper proposes a multi-objective optimization scheduling design for wind–solar energy storage microgrids based on an improved oppositional gradient grey wolf optimization (OGGWO) algorithm. First, two new features were added to the traditional grey wolf optimization (GWO) algorithm to solve the multi-target optimization scheduling of grid-connected microgrids, aiming to improve solution quality and convergence speed. Furthermore, Gaussian walk and Lévy flight are introduced to enhance the search capability of the proposed OGGWO algorithm. This method expands the search range while sacrificing only a small amount of search speed, contributing to obtaining the global optimal solution. Finally, the gradient direction is considered in the feature search process, allowing for a comprehensive understanding of the search space, which facilitates achieving the global optimum. Experimental results indicate that, compared to traditional methods, the proposed improved OGGWO algorithm can achieve standard deviations of 4.88 and 4.46 in two different scenarios, demonstrating significant effectiveness in reducing costs and pollution. Full article

The dual-carbon objective aspires to enhance China’s medium- and long-term green power trading and facilitate the low-carbon economic operation of park microgrids from both medium- and long-term and spot market perspectives. First, the integration of medium- and long-term green power trading with spot trading was meticulously analyzed, leading to the formulation of a power purchase strategy for park microgrid operators. Subsequently, a sophisticated Bayesian fuzzy learning method was employed to simulate the interaction between supply and demand, enabling the prediction of the price for bilaterally negotiated green power trading. Finally, a comprehensive multi-objective optimization model was established for the synergistic operation of park microgrid in the medium- and long-term green power and spot markets. This model astutely considers factors such as green power trading, distributed photovoltaic generation, medium- and long-term thermal power decomposition, energy storage systems, and power market dynamics while evaluating both economic and environmental benefits. The Levy-based improved bird-flocking algorithm was utilized to address the multi-faceted problem. Through rigorous computational analysis and simulation of the park’s operational processes, the results demonstrate the potential to optimize user power consumption structures, reduce power purchase costs, and promote the green and low-carbon transformation of the park. Full article

This study aims to establish an accurate hybrid model for predicting residential daily carbon dioxide (CO₂) emissions, offering essential theoretical insights and data support for decision-makers in the construction industry. A hybrid model named CRLPSO-LSTM was proposed, which integrates an enhanced particle swarm optimization (CRLPSO) algorithm with a long short-term memory (LSTM) network. The CRLPSO algorithm enhances population quality, diversity, and global search efficiency by introducing improved circle chaotic mapping, optimizing worst mutations, and incorporating the Lévy flight strategy. The performance of the CRLPSO algorithm was rigorously evaluated using 23 internationally recognized standard test functions. Subsequently, the CRLPSO algorithm was employed to optimize the parameters of the LSTM model. Experimental validation was performed on three datasets from China, the United States, and Russia, each exhibiting distinct emissions characteristics: China with high emissions and high volatility, the United States with medium emissions and medium volatility, and Russia with low emissions and low volatility. The results indicate that the CRLPSO-LSTM hybrid model outperformed other hybrid models in predicting residential daily CO₂ emissions, as demonstrated by superior R², MAE, and MSE metrics. This study underscores the effectiveness and broad applicability of the CRLPSO-LSTM hybrid model, offering a robust theoretical foundation and data support for advancing the sustainable development goals. Full article

LEVIS is an innovation project funded by the EU Horizon 2020 program. Its main objective is to develop lightweight multi-material solutions based on bio-based materials and carbon fiber thermoplastic composites for electric vehicle components and demonstrating the technical, operational, and economic feasibility of applying eco-design and circular-economy principles into the design process. The project demonstrates the application of these materials in four case studies: a suspension control arm, a battery box, a battery module housing, and a cross-car beam. All demonstrators achieved a 20%-to-40% reduction in component weight, but environmental assessment results varied significantly, with emissions changes ranging from an increase for suspension control arms to a 65.5% decrease for battery modules. Efficient use of materials, particularly in the battery box using hybrid solutions and bonding technologies, showed notable emissions reduction. In contrast, replacing steel with CFRPs in suspension control arms led to increased emissions, suggesting that CFRPs are more effective for replacing high-polluting materials like aluminum. Recycled carbon fibers proved more beneficial for low-polluting materials like steel. The environmental performance of technologies depends on the expected use of EVs and the electricity grid mix, with better outcomes in coal-reliant grids. Finally, no single recycling method is universally superior; the optimal method depends on the specific technologies and the energy required for recycled materials. Full article

In line with global carbon-neutral policies, wind power generation has received widespread public attention, which can enhance the security of supply and social sustainability. Since wind with non-stationarity and randomness makes power systems unstable, precise wind speed forecasting is an integral part of wind farm scheduling and management. Therefore, a compound short-term wind speed forecasting framework based on numerical weather prediction (NWP) is proposed coupling a maximum information coefficient (MIC), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), shared weight gated memory network (SWGMN) with improved northern goshawk optimization (INGO). Firstly, numerical weather prediction is adopted to acquire the predicted variables with different domains, including the predicted wind speed and other predicted meteorological variables, after which the error is calculated using the predicted and actual wind speeds. Then, the correlation between the predicted variables and the error is obtained using the MIC to select the correlation factors. Subsequently, CEEMDAN is employed to decompose the correlation factors, corresponding the actual factors and the error into a series of subsequences, which are regarded as the input series. After that, the input series is fed into the proposed SWGMN to forecast each subsequent error, respectively, in which the shared gate is proposed to replace the input gate, the forgetting gate and the output gate. Meanwhile, the proposed INGO based on northern goshawk optimization (NGO), the levy flight disturbance strategy and the nonlinear contraction strategy is applied to calibrate the parameters of the SWGMN. Finally, the forecasting values are acquired by summing the forecasted error and the predicted wind speed from the NWP. The experimental results depict that the errors are small among all the models. Compared with the traditional method, the proposed framework achieves higher prediction accuracy and efficiency. The application of this framework not only assists in optimizing the operation and management of wind farms, but also reduces the dependence on fossil fuels, thereby promoting environmental protection and the sustainable use of resources. Full article