Search Results (247)

The cement industry is a significant contributor to global environmental impacts, and Life Cycle Assessment (LCA) has emerged as a critical tool for evaluating and managing these burdens. This review uniquely synthesizes recent advancements in the LCA methodology and provides a detailed comparison of cement production impacts across major producing regions, notably highlighting China’s role as the largest global emitter. It covers the core LCA phases, including goal and scope definition, inventory analysis, impact assessment, and interpretation, and emphasizes the role of LCA in quantifying cradle-to-gate impacts (typically around 0.9–1.0 t CO₂ per ton of cement), evaluating the emissions reductions provided by alternative cement types (such as ~30–45% lower emissions using limestone calcined clay cements), informing policy frameworks like emissions trading schemes, and guiding sustainability certifications. Strategies for environmental load reduction in cement manufacturing are quantitatively examined, including technological innovations (e.g., carbon capture technologies potentially cutting plant emissions by up to ~90%) and material substitutions. Persistent methodological challenges—such as data quality issues, scope limitations, and the limited real-world integration of LCA findings—are critically discussed. Finally, specific future research priorities are identified, including developing country-specific LCI databases, integrating techno-economic assessment into LCA frameworks, and creating user-friendly digital tools to enhance the practical implementation of LCA-driven strategies in the cement industry. Full article

In alignment with the United Nations’ Sustainable Development Goals (SDGs) 12 (Responsible Consumption and Production) and 13 (Climate Action), this research explores the sustainable valorization of mangosteen peels into mangosteen peel powder (MPP), a value-added product with pharmaceutical properties. Mangosteen peels are an abundant agricultural waste in Thailand. This study evaluates six MPP production schemes, each employing different drying methods. Life Cycle Assessment (LCA) is utilized to assess the global warming potential (GWP) of these schemes, and the quality of the MPP produced is also compared. The results show that a combination of frozen storage and freeze-drying (scheme 4) has the highest GWP (1091.897 kgCO₂eq) due to substantial electricity usage, whereas a combination of frozen storage and sun-drying (scheme 5) has the lowest GWP (0.031 kgCO₂eq) but is prone to microbial contamination. Frozen storage without coarse grinding, combined with hot-air drying (scheme 6), is identified as the optimal scheme in terms of GWP (11.236 kgCO₂eq) and product quality. Due to the lack of an onsite hot-air-drying facility, two transportation strategies are integrated into scheme 6 for scenarios A and B. These transportation strategies include transporting mangosteen peels from orchards to a facility in another province or transporting a mobile hot-air-drying unit to the orchards. The analysis indicates that scenario B is more favorable both operationally and environmentally, due to its lower emissions. This research is the first to comparatively assess the GWP of different MPP production schemes using LCA. Furthermore, it aligns with the growing trend in international trade which places greater emphasis on environmentally friendly production processes. Full article

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

This study explores how carbon pricing and electricity market liberalization jointly contribute to China’s sustainable energy transition. Using a dynamic computable general equilibrium (CGE) model (CEEEA2.0), we simulate three policy scenarios—business as usual, emissions trading scheme (ETS) with regulated electricity prices, and ETS with market-based pricing—under a unified emissions cap. The results demonstrate that electricity market liberalization enhances carbon pricing efficiency by eliminating price distortions, leading to a 0.06% increase in GDP and a 12% reduction in emission abatement costs. However, liberalization also raises electricity and consumer prices, disproportionately affecting rural and low-income households. These findings underscore the need to balance economic efficiency and social equity in sustainability-oriented energy reforms. Our analysis emphasizes the importance of designing inclusive and just transition policies to ensure that carbon mitigation efforts support long-term environmental, economic, and social sustainability goals. Full article

This paper investigates the impact of carbon emissions trading schemes (CETSs) on agricultural green total factor productivity (AGTFP) using a multi-temporal DID model. Using Chinese prefecture-level city data collected from 2004 to 2022, we find that CETSs enhance AGTFP through technological innovation, with stronger effects in eastern and western regions and positive spillover to neighboring cities. These findings underscore the significant role of CETSs in influencing agricultural productivity and highlight the various factors that contribute to improving AGTFP. Full article

This study proposes a novel image reconstruction algorithm for nuclear medicine imaging based on the maximum likelihood expectation maximization (MLEM) framework with dynamic ElasticNet regularization. Whereas conventional the L1 and L2 regularization methods involve trade-offs between noise suppression and structural preservation, ElasticNet combines their strengths. Our method further introduces a dynamic weighting scheme that adaptively adjusts the balance between the L1 and L2 terms over iterations while ensuring nonnegativity when using a sufficiently small regularization parameter. We evaluated the proposed algorithm using numerical phantoms (Shepp–Logan and digitized Hoffman) under various noise conditions. Quantitative results based on the peak signal-to-noise ratio and multi-scale structural similarity index measure demonstrated that the proposed dynamic ElasticNet regularized MLEM consistently outperformed not only standard MLEM and L1/L2 regularized MLEM but also the fixed-weight ElasticNet variant. Clinical single-photon emission computed tomography brain image experiments further confirmed improved noise suppression and clearer depiction of fine structures. These findings suggest that our proposed method offers a robust and accurate solution for tomographic image reconstruction in nuclear medicine imaging. Full article

New Zealand’s Emissions Trading Scheme (ETS) enables growers to earn payments by accumulating carbon units as their forests increase in carbon stock. For forests of less than 100 hectares, growers use predefined lookup tables (LUTs) to estimate carbon stock changes based on forest age. Using a combination of growth models and productivity surfaces, underpinned by data from 1360 growth plots, the objective of this study was to provide draft updates for the Exotic Hardwoods LUTs. The updated LUTs were based on growth rates of three Eucalyptus species, E. fastigata, E. regnans, and E. nitens, which comprise a major proportion of the Exotic Hardwoods forest type in New Zealand. Carbon tables were first derived for each species. Then, a draft LUT was generated for New Zealand’s North Island, using a weighted average of the species-specific tables based on the relative importance of the species, while the E. nitens table was used for the South Island where this is the predominant Eucalyptus species. Carbon stock predictions at ages 30 and 50 years were 820 and 1340 tonnes CO₂ ha⁻¹ for the North Island, and slightly higher at 958 and 1609 tonnes CO₂ ha⁻¹ for the South Island. Regional variation was significant, with the highest predicted carbon in Southland (1691 tonnes CO₂ ha⁻¹ at age 50) and lowest in Hawke’s Bay/Southern North Island (1292 tonnes CO₂ ha⁻¹). Predictions closely matched the current Exotic Hardwood LUT to age 20 years but exceeded it by up to 45% at age 35. Growth and carbon sequestration rates were similar to other established Eucalyptus species and slightly higher than Acacia species, though further research is recommended. These findings suggest that the three Eucalyptus species studied here could serve as the default species for a revised Exotic Hardwoods LUT and that the current national tables could be regionalised. However, the government may consider factors other than the technical considerations outlined here when updating the LUTs. Full article

As industries come under growing pressure to minimize carbon emissions without compromising the efficiency of operations, the integration of energy-aware production scheduling with emerging energy markets, renewable energy, and policy mechanisms is critical. This paper identifies critical shortcomings in current academic and industrial approaches—namely, an excessive reliance on deterministic assumptions, a limited focus on dynamic operational realities, and the underutilization of regulatory mechanisms such as carbon trading. We advocate for a paradigm shift to more robust, adaptable, and policy-compliant scheduling systems that provide space for on-site renewable generation, battery energy storage systems (BESSs), demand-response measures, and real-time electricity pricing schemes like time-of-use (TOU) and real-time pricing (RTP). By integrating recent advances and their critical analysis of limitations, we map out a future research agenda for the integration of uncertainty modeling, machine learning, and multi-level optimization with policy compliance. In this paper, we propose the need for joint efforts from researchers, industries, and policymakers to collectively develop industrial scheduling systems that are both technically efficient and adherent to sustainability and regulatory requirements. Full article

Assessing the environmental and economic impacts of the carbon emissions trading scheme (ETS) is both timely and essential. This study investigates the effects of the ETS on energy transition by analyzing panel data from 30 provinces and municipalities across mainland China. The findings highlight three key points. First, the ETS significantly promotes energy transition. Robustness tests confirm the validity of this conclusion. Compared with non-pilot provinces, pilot provinces achieve a 4.83% increase in energy transition levels. Second, the energy transition effect of the ETS is mainly achieved by changing the incentive and constraint behavior of local governments. Third, the ETS exerts a more pronounced impact on energy transition in regions with higher levels of marketization and stronger innovation capabilities. Furthermore, the effects of the ETS vary across different quantiles of energy transition levels. This study provides a novel perspective on achieving the synergistic development of economic growth and environmental sustainability. Full article

Amid intensified global climate mitigation efforts, integrating rail freight into carbon emissions trading schemes became critical under China’s “Dual-Carbon” strategy. Despite rail’s significantly lower emissions intensity compared to road transport, existing pricing frameworks inadequately internalized its environmental externalities, which limited its competitive advantage. To address this gap, this study systematically reviewed international and domestic practices of integrating transport into carbon trading systems and developed a novel “four-layer, three-dimensional” emissions trading scheme (ETS) framework tailored specifically for China’s rail freight sector. Employing a Stackelberg bilevel optimization model, this study analyzed how carbon quotas and pricing influenced rail operators’ pricing and investment decisions. The results showed that under optimized quotas and carbon prices, railway enterprises were able to generate surplus carbon credits, creating new revenue streams and enabling freight rate reductions. This “carbon revenue–freight rate feedback loop” not only delivered environmental benefits but also enhanced rail’s economic competitiveness. Overall, this study significantly advances the understanding of carbon-based pricing mechanisms in railway freight, providing robust theoretical insights and actionable policy guidance for achieving sustainable decarbonization in China’s transport sector. Full article

This study examines the price dynamics of the UK Emission Trading Scheme (UK ETS) by integrating advanced computational methods, including deep learning and statistical modelling, to analyze and simulate carbon market behaviour. By analyzing long-memory effects and price volatility, it assesses whether UK carbon prices align with theoretical expectations from carbon pricing mechanisms and market efficiency theories. Findings indicate that UK carbon prices exhibit persistent long-memory effects, contradicting the Efficient Market Hypothesis, which assumes price movements are random and fully reflect available information. Furthermore, regulatory interventions exert significant downward pressure on prices, suggesting that policy uncertainty disrupts price equilibrium in cap-and-trade markets. Deep learning models, such as Time-series Generative Adversarial Networks (TGANs) and adjusted fractional Brownian motion, outperform traditional approaches in capturing price dependencies but are prone to overfitting, highlighting trade-offs in AI-based forecasting for carbon markets. These results underscore the need for predictable regulatory frameworks, hybrid pricing mechanisms, and data-driven approaches to enhance market efficiency. By integrating empirical findings with economic theory, this study contributes to the carbon finance literature and provides insights for policymakers on improving the stability and effectiveness of emissions trading systems. Full article

Renewable power generation has experienced significant global deployment, leading to the replacement of synchronous generators, which traditionally defined the slow dynamics of power systems. As a result, stability issues related to converter dynamics are becoming increasingly prominent. It is crucial for the grid system to be sure that the renewable generation is robust with regard to the converter dynamics to avoid instability issues. This paper focuses on enhancing wind farm robustness to minimize the risk of converter-driven stability phenomena, considering both grid-feeding and grid-forming control schemes. Three software solutions to improve the stability criteria at the wind turbine level are evaluated, assessing their impact on system performance across various frequency ranges. Additionally, a second solution at the plant level, separate from the software solutions, is also included in the scope of the paper. Moreover, a trade-off analysis was carried out to evaluate these different solutions. Finally, the results showed that the stability criteria can be improved by adopting software solutions without additional costs, but the filter as a plant solution could mitigate the harmonic emission and provide extra reactive power capabilities. Full article

The deepening operation of the carbon emission trading market has reshaped the cost–benefit structure of the power generation side. In the process of participating in the market quotation, power suppliers not only need to calculate the conventional power generation cost but also need to coordinate the superimposed impact of carbon quota accounting on operating income, which causes the power suppliers a multi-time-scale decision-making collaborative optimization problem under the interaction of the carbon market, power market, and coal market. This paper focuses on the multi-market-coupling decision optimization problem of thermal power suppliers. It proposes a collaborative bidding decision framework based on a multi-agent deep deterministic policy gradient (MADDPG). Firstly, aiming at the time-scale difference of multi-sided market decision making, a decision-making cycle coordination scheme for the carbon–electricity–coal coupling market is proposed. Secondly, upper and lower optimization models for the bidding decision making of power suppliers are constructed. Then, based on the MADDPG algorithm, the multi-generator bidding scenario is simulated to solve the optimal multi-generator bidding strategy in the carbon–electricity–coal coupling market. Finally, the multi-scenario simulation based on the IEEE-5 node system shows that the model can effectively analyze the differential influence of a multi-market structure on the bidding strategy of power suppliers, verifying the superiority of the algorithm in convergence speed and revenue optimization. Full article

In response to the International Maritime Organization’s (IMO) 2020 sulfur cap and China’s stricter emission control policies, this study investigates the strategic interaction between port authorities and shipowners concerning air pollution emissions from ships in port areas. Using supervisory game theory, we construct a model that captures the cost–benefit trade-offs between inspection efforts by regulators and compliance behavior by ship operators. Empirical data from Guangzhou Port in 2020—including government inspection costs, fuel substitution costs, subsidy schemes, and fine levels—are incorporated into the model to simulate equilibrium outcomes. Results indicate that while the current level of inspection has a significant deterrent effect, the probability of full compliance remains low at 34.36%, highlighting the importance of a balanced regulatory approach combining inspection, fines, and subsidies. Policy implications suggest that increased financial incentives and stronger penalties can reduce both regulatory costs and non-compliance risks. This study contributes to the literature on maritime environmental governance by providing a quantitative supervisory framework grounded in real-world port data. Full article

In this paper, a novel technical route, namely combining the low-pressure exhaust gas recirculation (LP-EGR) and variable compression ratio (VCR), is proposed to address the inferior fuel economy for marine dual-fuel engines of low-pressure gas injection in diesel mode. To validate the applicability of the proposed technical route, firstly, a zero-dimensional/one-dimensional (0-D/1-D) engine simulation model with a predictive combustion model DI-Pulse is established using GT-Power. Then, parametric investigations on two LP-EGR schemes, which is implemented with either a back-pressure valve (LP-EGR-BV) or a blower (LP-EGR-BL), are performed to qualitatively identify the combined impacts of exhaust gas recirculation (EGR) and compression ratio (CR) on the combustion process, turbocharging system, and nitrogen oxides (NOx)-brake specific fuel consumption (BSFC) trade-offs. Finally, an optimization strategy is formulated, and an optimization program based on response surface methodology (RSM)–particle swarm optimization (PSO) is designed with the aim of improving fuel economy while meeting Tier III and various constraint conditions. The results of the parametric investigations reveal that the two LP-EGR schemes exhibit opposite impacts on the turbocharging system. Compared with the LP-EGR-BV, the LP-EGR-BL can achieve a higher in-cylinder pressure level. NOx-BSFC trade-offs are observed for both LP-EGR schemes, and the VCR is confirmed to be a viable approach for mitigating the penalty on BSFC caused by EGR. The optimization results reveal that for LP-EGR-BV, compared with the baseline engine, the optimized BSFC decreases by 10.16%, 11.95%, 10.32%, and 9.68% at 25%, 50%, 75%, and 100% maximum continuous rating (MCR), respectively, whereas, for the LP-EGR-BL scheme, the optimized BSFC decreases by 10.11%, 11.93%, 9.93%, and 9.58%, respectively. Furthermore, the corresponding NOx emissions level improves from meeting Tier II regulations (14.4 g/kW·h) to meeting Tier III regulations (3.4 g/kW·h). It is roughly estimated that compared to the original engine, both LP-EGR schemes achieve an approximate reduction of 240 tons in annual fuel consumption and save annual fuel costs by over USD 100,000. Although similar fuel economy is obtained for both LP-EGR schemes, LP-EGR-BV is superior to LP-EGR-BL in terms of structure complexity, initial cost, maintenance cost, installation space requirement, and power consumption. The findings of this study provide meaningful theoretical supports for the implementation of the proposed technical route in real-world engines. Full article