Emission Reduction Research Articles

Synergistic mechanisms of ethanol and butanol in gasohol blends in 4-stroke SI engines for green sustainable energy solutions: revolutionizing engine efficiency, power output and emission reduction for net-zero transportation systems

Abstract This study systematically investigates ethanol, butanol, and gasohol blends in a 4-stroke spark-ignition (SI) engine to explore sustainable and efficient fuel alternatives. The research focuses on understanding the interactions between engine performance and emissions while highlighting the combined effects of ethanol and butanol in gasohol formulations. The research methodology involves a comprehensive investigation into the intricate relationship between power generation, emissions, and efficiency by testing fuel blends with varying ethanol and butanol proportions (5 %, 8 %, 10 %, 12 %, and 15 % volume fractions). A range of performance parameters – including Brake Power (BP), Brake Specific Fuel Consumption (BSFC), Mechanical Efficiency (ME), and Exhaust Gas Temperature (EGT) – are assessed using a state-of-the-art experimental setup. Additionally, the study evaluates the environmental impact of each fuel blend by analyzing emissions of CO, CO2, NOx, and HC. The comparative analysis identifies optimal ethanol-butanol ratios that enhance engine performance while reducing emissions. The results indicate that increasing the alcohol content improves power output and mechanical efficiency while lowering carbon emissions. Specifically, the ethanol-butanol blend with a 12 % volume fraction demonstrated a 6.8 % increase in Brake Power and a 9.5 % reduction in CO emissions compared to conventional gasohol. However, higher ethanol content, due to its greater latent heat of vaporization, led to a 4.2 % decrease in NOx emissions but also a slight increase in BSFC by 3.1 %, highlighting the trade-offs in combustion efficiency. This underscores the need for precise engine calibration, including adjustments to spark timing and fuel injection, to maintain optimal combustion characteristics. By examining six distinct fuel compositions, this research provides valuable insights into the trade-offs between performance and emissions in alternative fuel applications. The findings support the transition toward biofuels by demonstrating their potential to enhance efficiency and sustainability. As the automotive industry advances in alternative fuel technologies, this study offers guidance for optimizing ethanol-butanol blends to develop cleaner and more efficient combustion systems.

Overview of Sustainable Maritime Transport Optimization and Operations

With the continuous expansion of global trade, achieving sustainable maritime transport optimization and operations has become a key strategic direction for transforming maritime transport companies. To summarize the current state of research and identify emerging trends in sustainable maritime transport optimization and operations, this study systematically examines representative studies from the past decade, focusing on three dimensions, technology, management, and policy, using data sourced from the Web of Science (WOS) database. Building on this analysis, potential avenues for future research are suggested. Research indicates that the technological field centers on the integrated application of alternative fuels, improvements in energy efficiency, and low-carbon technologies in the shipping and port sectors. At the management level, green investment decisions, speed optimization, and berth scheduling are emphasized as core strategies for enhancing corporate sustainable performance. From a policy perspective, attention is placed on the synergistic effects between market-based measures (MBMs) and governmental incentive policies. Existing studies primarily rely on multi-objective optimization models to achieve a balance between emission reductions and economic benefits. Technological innovation is considered a key pathway to decarbonization, while support from governments and organizations is recognized as crucial for ensuring sustainable development. Future research trends involve leveraging blockchain, big data, and artificial intelligence to optimize and streamline sustainable maritime transport operations, as well as establishing a collaborative governance framework guided by environmental objectives. This study contributes to refining the existing theoretical framework and offers several promising research directions for both academia and industry practitioners.

Experimental assessment of performance characteristics and emission reduction using different blended biodiesel

The current research is to investigate the performance and exhaust emission of variable compression ratio (VCR) of diesel engine by using 50 ppm α-Al 2 O 3 catalyst with each blend of chicken fat CF5 (5% chicken fat and 95% diesel), waste cooking oil WC5 (5% waste cooking oil and 95% diesel) and combined chicken fat with waste cooking oil CF&WC5 (2.5% chicken fat + 2.5% waste cooking oil and 95% diesel). Biodiesel’s performance and emissions parameters are analyzed at CR = 17 & 18, considering the load varies from 25% to 100%, compared with pure diesel. The results show that the 75% and 100% loading CF&WC5 have maximum brake thermal efficiency of 25.49% and 24.72%, respectively, for CR = 17 with BSFC of 0.35 kg/kWh. However, as CR = 18 increases, CF&WC5 develops a BTE of 25.66% with BSFC of 0.33 kg/kWh at 100% loading conditions. This indicates that the CF&WC5 reduces BSFC by 5.71%, resulting in a higher CR of 18. The novelty of the experiment is to reduce the NO x emission of 498 ppm and the CO x emission of 0.12 volume at full load condition for CR = 18. Thus, a higher compression ratio reduces about 14% of volume compared to pure diesel CO emission. It is also observed that the HC emission of CF&WC5 reduces 39% of vol. with smoke opacity up to 3% of vol.

Comparative impact of blast temperature and coke oven gas pre-heating on CO 2 emissions in blast furnace

The global commitment to achieving carbon neutrality has placed immense pressure on the steel industry, especially since blast furnace processes are responsible for most of the CO 2 emissions in steel production. Hydrogen-rich gases have efficient reduction of iron ore and non-polluting emission characteristics, notably, coke oven gas has become a focal point due to its ease of acquisition. This paper establishes a mass and heat balance model for a hydrogen-enriched blast furnace, and improves the associated CO 2 emission evaluation method. This study investigates the effects of various external heat sources on operational parameters, process indices and CO 2 emission reduction under different conditions. Results indicate that increasing gas pre-heating temperature enhances coke replacement rates and expands the operational window; however, it adversely affects blast furnace carbon emission reduction efforts. Under the investigated operating conditions, CO 2 emissions can be reduced to 1030.23 kg CO 2 /tHM, with maximum reduction potentials reaching 153.81 kg CO 2 /tHM. The relationship between increasing blast temperature and hydrogen-enriched gas pre-heating with carbon emissions requires further validation. Some carbon reduction strategies are proposed to address these challenges.

Integrated optimization of energy storage and green hydrogen systems for resilient and sustainable future power grids

Abstract This study presents a novel multi-objective optimization framework supporting nations sustainability 2030–2040 visions by enhancing renewable energy integration, green hydrogen production, and emission reduction. The framework evaluates a range of energy storage technologies, including battery, pumped hydro, compressed air energy storage, and hybrid configurations, under realistic system constraints using the IEEE 9-bus test system. Results show that without storage, renewable penetration is limited to 28.65% with 1538 tCO2/day emissions, whereas integrating pumped hydro with battery (PHB) enables 40% penetration, cuts emissions by 40.5%, and reduces total system cost to 570 k$/day (84% of the baseline cost). The framework’s scalability is confirmed via simulations on IEEE 30-, 39-, 57-, and 118-bus systems, with execution times ranging from 118.8 to 561.5 s using the HiGHS solver on a constrained Google Colab environment. These findings highlight PHB as the most cost-effective and sustainable storage solution for large-scale renewable integration.

The Potential Role of Hydrogen in Decarbonization: Exploring Global Supply Chain Impacts and Hydrogen Use in the United Kingdom.

Decarbonization of all sectors is needed to mitigate the impacts of climate change. To accomplish this, hydrogen use has been suggested in many industries that currently rely on fossil fuels. Yet, the emissions intensity of hydrogen depends on how it is produced and distributed. Additionally, it is unclear whether hydrogen use leads to a reduction in GHG emissions compared to alternative decarbonization options such as electrification with renewables. Here, we systematically compare the decarbonisation potential of supplying hydrogen to the United Kingdom from a wide range of global supply chains. We do this by assessing 37,000 configurations of the hydrogen supply chain from primary energy production through to end-use. We find that imports of green hydrogen production are unlikely to be compatible with the UK Low Carbon Hydrogen Standard. The maximum mitigation potential is achieved when electrification is prioritized, and hydrogen used only for applications where electrification is not viable. This leads to a reduction of up to 280 Mt CO2e/a across all sectors considered in the UK. In the short term, use of domestic green hydrogen infrastructure should focus on displacing existing gray hydrogen use.

Research on the Application of Nano-Additives in Gel-like Lubricants

In the field of mechanical motion, friction loss and material wear are common problems. As one of the essential components for enhancing the lubricating performance of gel-like lubricants, nano-additives leverage their unique physical and chemical properties to form an efficient protective film on friction surfaces. This effectively reduces friction resistance and inhibits wear progression, thereby playing a significant role in promoting energy conservation, emissions reduction, and the implementation of green development principles. This study first introduces the physical and chemical preparation processes of gel-like lubricant nanoadditives. It then classifies them (mainly based on metal bases, metal oxides, nanocarbon materials, and other nanoadditives). Then, the performance of gel-like lubricant nano-additives is evaluated (mainly in terms of anti-wear, friction reduction, oxidation resistance, and load carrying capacity), and the surface analysis technology used is described. Finally, we summarize the application scenarios of gel-like lubricant nano-additives, identify the challenges faced, and discuss future prospects. This study provides new insights and directions for the design and synthesis of novel gel-like lubricants with significant lubricating and anti-wear properties in the future.

Coalitions Improve the Coordination and Provision of Public Goods: Theory and Experimental Evidence

ABSTRACTWe study a public goods game with heterogeneous agents who care about their own payoff as well as that of the player who receives the lowest payoff. The weight of own payoff varies across players and is private information. We first develop a theoretical model and then test the predictions of our model in a laboratory setting under different parameter conditions. In both our model and experiments, introducing a coalition formation stage before making a contribution decision enables sorting of players according to their preferences, resulting in higher contributions to the public good. Additionally, we find that participants in our experiment take previous period outcomes into account while making current period decisions. These results help explain successful coalitions, like International Environmental Agreements, that are effective in creating real‐world public goods like reductions in carbon emissions.

Climate Change Mitigation at the Individual Level: Harnessing the 5Rs to Curb Greenhouse Gas Emissions

In the face of escalating climate change, identifying effective individual-level actions to reduce greenhouse gas (GHG) emissions has become increasingly vital. This study investigates the impact of personal choices, particularly in consumption, energy use, transportation, and diet on GHG emissions, within the framework of the 5Rs: Refuse, Reduce, Reuse, Recycle, and Recreate. The research adopts a multi-method approach, including a comprehensive review of existing literature, analysis of secondary data on individual behavioural patterns, and evaluation of government recommendations and educational content in Indian school curricula. Emissions reduction estimates for various lifestyle changes are critically examined to identify the most impactful personal strategies. The study also explores behavioural antecedents influencing residential energy conservation and sustainable transport decisions, such as mode of commuting and grocery shopping practices. Findings highlight that informed consumer choices guided by environmental education and the 5Rs framework can substantially mitigate emissions. The paper advocates for integrating individual action into broader climate strategies, particularly in the Indian context, and underscores the transformative potential of grassroots engagement in achieving climate resilience.

How does climate risk affect bank performance and systemic risk—evidence from Chinese commercial banks

ABSTRACT The carbon trading market established to address climate challenges achieves significant emission reductions but brings about new challenges for banks. This article estimates the bank climate-driven losses and examines the impact of climate-driven losses on bank performance and systemic risk. The findings imply that large state-owned commercial banks suffer the highest climate risk and bear the greatest losses. City commercial banks are more flexible in adapting to market signals. They have lower climate-driven losses than Joint-stock banks. Rural commercial banks are mainly oriented to rural areas, which makes them have the lowest climate-driven losses. Climate-driven losses reduce banks’ performance and amplify the systemic risk of commercial banks. Investor sentiment and asset price volatility have significant mediation effects. Climate policy uncertainty, the 30–60 target and capital adequacy can mitigate the negative impacts of climate-driven losses on commercial banks. The findings provide policy recommendations for banks and regulators to address climate-driven losses.

TF-CEP: carbon emission prediction with data augmentation and temporal-frequency fusion contrasting

In the context of low-carbon power development, accurate prediction of the carbon emission intensity of the power system can provide data support for the optimization strategy of carbon emission reduction, thus helping to reduce the carbon emissions of the power system. At present, carbon emission prediction methods can be broadly categorized into traditional prediction methods and artificial intelligence-based methods. Traditional prediction methods are prone to prediction bias during application, which affects forecasting accuracy. In contrast, artificial intelligence-based methods have been widely adopted due to their superior learning capabilities. However, existing AI-based prediction methods still have limitations in carbon emission forecasting, as they struggle to fully capture the temporal and frequency domain features of power data and lack effective consideration of other real-world influencing factors, which restricts their predictive performance. Therefore, this paper proposes a carbon emission prediction method-Temporal-Frequency Contrastive Enhanced Prediction, called TF-CEP. In order to improve the generalization ability of the model, GAN is used for data augmentation of the power data, and 1-Dimensional Convolutional Neural Network and frequency enhanced channel attention Mechanism methods are used to learn the temporal domain feature information and the frequency domain feature information of the power data, respectively. The predictive performance of the model is further enhanced by the fusion contrast of temporal and frequency domain features. Finally, the prediction of carbon emissions is carried out by introducing an attention mechanism and combining it with other weather information, wire state information and energy storage element state information. In our experimental evaluation, compared to advanced baseline models, our method reduces the prediction metrics MSE, MAE, and MAPE to 0.008, 0.064, and 9.32%, respectively, outperforming other baseline models.

Priority analysis framework for equitable seismic and energy-efficiency renovation of residential buildings applied to mainland Portugal

The European construction sector faces significant challenges due to seismic vulnerabilities and widespread energy inefficiency of its residential building stock. It becomes thus crucial to prioritize regions with higher needs for building performance upgrading using frameworks that encompass single and multi-sectoral indicators. Such integrated frameworks should combine seismic risk mitigation, energy efficiency, and socioeconomic factors, to create safer and more sustainable urban environments. In line with the European strategy, Portugal has committed to carbon neutrality by 2050 and has set ambitious targets for emissions reductions by 2030. This study performs a prioritization analysis on the mainland Portuguese residential building stock combining multiple indicators related to three macro sectors, namely seismic, energy, and socioeconomic vulnerability. Using a multidisciplinary approach and the most recently available building fragility models, data on energy use, and regional socioeconomic factors, this study highlights critical regions for resource allocation and targeted renovations. The results provide valuable insights for policymakers, aiming to strengthen community seismic resilience, reduce carbon emissions, and support equitable risk mitigation measures across different regions.

A parametric analysis of cold start emissions reduction with secondary air injection based post-oxidation actuation in exhaust manifold of a turbocharged GDI engine

In this research, an experimental investigation of secondary air injection-based post-oxidation phenomena and its influence on emissions, particularly during cold start operations, was conducted. The main purpose of this research is to reduce the conversion load to save the light-off time of the catalyst with the help of post-oxidation actuation. A suitable chiller and secondary air injection (SAI) setup were integrated with a commercial 4-cylinder 1.6 L gasoline direct injection (GDI) engine which has a compression ratio of 10.5, to achieve the research objectives. The engine was coupled with a low inertial dynamometer to facilitate the experiments. The SAI was delivered at the exhaust port. The emissions were measured upstream (TC up) and downstream (TC down) of the turbocharger and downstream of the three-way catalyst (TWC down). It was observed that with early spark timing (SA) and a lower percentage of SAI, effective post-oxidation could not be triggered, preventing an increase in exhaust temperature. The SAI implementation led to a reduction in both in-cylinder and exhaust port temperature while also increasing emissions of carbon monoxide (CO) and total hydrocarbon (THC) emissions due to a shift in the in-cylinder combustion toward a rich mode. However, it was also noted that with a further retardation of SA (−15 deg. bTDCf), and increment in SAI, the post-oxidation possibility can be increased and hence can reduce the THC, CO, and hydrogen (H 2 ) emissions along with minimizing the catalyst light-off timing. It was noted that despite these benefits, further retarding SA causes to loss of brake power, and hence thermal efficiency decreased which needs to be optimized. With retarded SA, the H 2 concentration which is the key factor of post-oxidation actuation was also measured at TC down and TWC down and found lower than TC up. This was attributed due to H 2 consumed in the post-oxidation as adequate exhaust temperature attained in the exhaust manifold when SA retarded. The H 2 concentration was also increased at TC up and TC down as SAI was introduced due to in-cylinder combustion shifting to the rich mode. Moreover, the THC oxidation rate was determined to be higher in the cold state, as higher THC emission was emitted and favorable conditions such as sufficient temperature and oxygen (O 2 ) availability were achieved for the oxidation process. Due to the higher THC in a cold state, the time characteristic for the THC oxidation was noted higher compared to a warm state. However, characteristic time in chemical reaction decreased due to the post-oxidation improvement when spark timing was further retarded.

Analysis of the effects of carbon emission reduction policies in key emission sectors in China

Analysis of the effects of carbon emission reduction policies in key emission sectors in China

Blockchains for Environmental Monitoring: Theory and Empirical Evidence from China

Abstract We explore the effects of a blockchain-based environmental monitoring technology on emissions. Our model of firm competition in the presence of regional regulators reveals that blockchain adoption reduces industrial pollution but triggers business relocation, creating trade-offs between local emission reduction and economic contraction. When pollution-induced social losses are highly dispersed across cities, a partial-adoption equilibrium fails to mitigate aggregate emissions because of the pollution leakage. We further present the first piece of empirical evidence corroborating model predictions, by taking advantage of a recent regulation change in China. The concentrations of SO2, NO2, and CO in blockchain-adopting cities are on average 16.6%, 7.9% and 4.6%, respectively, lower than other cities. However, blockchain-based monitoring disproportionately hurts the industrial sector, and the average economic activities is reduced by 1.8%-2.6%. Firms in adopting cities open more non-local plants to avoid regulation.

The impact of ‘greening’ finance reform on carbon emissions in border regions: evidence from China

ABSTRACT Green finance reform, centred on market mechanisms and financial tools, stands as a pivotal approach to breaking through the high energy consumption and pollution prevalent in border regions. By manually matching the carbon emissions data of 285 cities administrative boundaries from 2009 to 2020 and utilizing the method of multi-period difference in difference, this paper delves into the impacts and mechanisms of the policy on carbon emissions in administrative border regions. The results indicate that the policy has effectively curbed carbon emissions in border regions. Mechanism analysis reveals that the green finance reform and innovation pilot zone policy promotes the transformation and upgrading of border regions by reshaping the financial investment and financing mechanism and strengthening the competition within industries, but fails to efficiently force the heavy border regions to achieve technological progress and optimize the energy structure. Heterogeneity analysis demonstrates that the emission reduction effect of policy is more pronounced in urban border regions, regions with higher levels of digital economy development, and regions with robust environmental protection enforcement.

Green technology and stringent environmental policy interactions in a spatial context: Assessing domestic and cross-border CO2 emission reductions in border sharing OECD economies.

Green technology and stringent environmental policy interactions in a spatial context: Assessing domestic and cross-border CO2 emission reductions in border sharing OECD economies.

Implementation of advanced control with artificial intelligence in anhydrous ethanol production

The growing demand for cleaner energy and the reduction of greenhouse gas emissions have highlighted anhydrous ethanol as a key renewable fuel due to its applications as a fuel, gasoline additive, and biodiesel reactant. Traditional dehydration via azeotropic distillation with cyclohexane is energy-intensive and environmentally harmful, prompting the search for safer alternatives. This study modeled and simulated the ethanol dehydration process using monoethylene glycol in Aspen Plus v12.1, implementing conventional control strategies optimized by Luyben’s method, which identified tray 31 as the system’s most sensitive point. Dynamic simulations demonstrated process robustness, with rapid recovery from disturbances in feed and heat duty, maintaining 99.999 wt% ethanol purity and solvent recovery. Additionally, artificial intelligence (AI) models—specifically decision tree, random forest, and LightGBM—were developed to control top product composition using easily measurable variables. These models significantly outperformed linear regression, with the decision tree achieving R² = 0.9970, MAE = 4.19 × 10⁻⁷, and RMSE = 2.20 × 10⁻⁶, maintaining ethanol molar fractions above 0.9996 even under dynamic disturbances. Despite their strong performance, the industrial adoption of AI-based controllers is still limited. However, with the implementation of real-time validation, significant advancements in computational processing capacity, and improvements in techniques to prevent overfitting, artificial intelligence models can become a promising tool for industrial process control.Graphical abstract

Navigating the Impact, Challenges and Future Prospects of Carbon Neutrality Under Green Finance Initiatives

ABSTRACTThis article presents a detailed and state‐of‐the‐art analysis of Green Finance (GF) and its impact on net‐zero energy transitions from the perspective of carbon neutrality. It systematically analyzes the interaction between financial mechanisms and the seven pillars of carbon neutrality: development of renewable energy resources, reduction of carbon emissions, reduction of energy consumption, innovation of low‐carbon technologies, electric transport, and green buildings. This review aims to bridge knowledge gaps and provide actionable insights for stakeholders by analyzing advancements, barriers, and policy frameworks. Specifically, the study is motivated by the need to develop a deeper understanding of how GF can contribute to sustainable economic growth while aligning with the ambitious target of global carbon neutrality. Under the role of various stakeholders, further, this article offers actionable recommendations that include (a) mechanisms to improve synergy and cooperation among stakeholders, (b) theoretical and analytical models that guide stakeholders toward practical implementation, and (c) various methodologies for long‐term impact assessment. Thus, this study serves as a critical resource for researchers, policymakers, and industry practitioners, guiding how to effectively leverage GF in order to achieve a sustainable, low‐carbon future. As a result of syncing current research and practice, the article demonstrates how GF can play a key role in enabling a global energy transition and the attainment of carbon neutrality.

Spatiotemporal pattern evolution and quantitative prediction of electrical carbon emissions from a demand-side perspective in urban areas

Amid global climate change, analyzing spatiotemporal patterns and predicting urban demand-side electrical carbon emissions is vital for regional low-carbon transitions. This study focuses on a developed coastal region in Guangdong, China. Utilizing high-frequency monitoring data from 3000 distribution network stations (May–Sept 2018), it creates an integrated ’spatiotemporal evolution-data driven prediction’ framework to reveal emission dynamics and enhance forecast accuracy. Breaking through the limitations of traditional single-scale analysis, the study innovatively integrates monthly, daily and hourly time series with standard deviation ellipses and Kriging spatial interpolation technology, achieving a combination of spatial and dynamic spatiotemporal evolution analysis. The study found that the center of gravity of carbon emissions showed a significant southwest-northeastward migration trajectory, and there was a spatial differentiation feature of central urban agglomeration and peripheral area dispersion. The logarithmic mean divisia index analysis shows that finance and taxation are the primary positive driving factors, while the impact of values of industrial output and commercial consumption shows significant spatiotemporal scale differences. On this basis, the study proposed a prediction method that integrates feature engineering and bidirectional gated recurrent unit (Bi-GRU) to effectively capture carbon emission fluctuations, with an accuracy of 82.83. The analysis framework and prediction model can provide methodological support for formulating emission reduction policies in the region and have significant application value.