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The purpose of this study is to assess the performance of fuel blends containing ethanol and gasoline in spark ignition engines. The aim is to explore alternative fuels that can enhance performance while minimizing or eliminating adverse environmental impacts, particularly in the context of limited fossil fuel availability and the need for sustainable alternatives.

The authors used the Ricardo Wave software to evaluate the performance of fuel blends with varying ethanol content (represented as E0, E10, E25, E40, E55, E70, E85 and E100) in comparison to gasoline. The assessment involved different composition percentages and was conducted at various engine speeds (1,500, 3,000, 4,500 and 6,000 rpm). This methodology aims to provide a comprehensive understanding of how different ethanol-gasoline blends perform under different conditions.

The study found that, across all fuel blends, the highest brake power (BP) and the highest brake-specific fuel consumption (BSFC) were observed at 6,000 rpm. Additionally, it was noted that the presence of ethanol in gasoline fuel blends has the potential to increase both the BP and BSFC. These findings suggest that ethanol can positively impact the performance of spark-ignition engines, highlighting its potential as an alternative fuel.

This research contributes to the ongoing efforts in the automotive industry to find sustainable alternative fuels. The use of Ricardo Wave software for performance assessment and the comprehensive exploration of various ethanol-gasoline blends at different engine speeds add to the originality of the study. The emphasis on the potential of ethanol to enhance engine performance provides valuable insights for motor vehicle manufacturers and researchers working on alternative fuel solutions.

The purpose of this paper is to review studies on mathematical optimization of the sustainable gasoline supply chain to help decision-makers understand the current situation, the exact dimensions of the problem and the models provided in the literature. So, a more realistic mathematical optimization model can be achieved by fully covering all dimensions of the supply chain of this product.

To evaluate and comprehend the mathematical optimization of the sustainable gasoline supply chain research area, a systematic literature review is undertaken that covers material collection, descriptive analysis, content analysis and material evaluation steps. Finally, based on this process, 69 related articles were carefully investigated.

The results of the systematic literature review show the main areas of the published papers on mathematical optimization of sustainable gasoline supply chain problems and the gaps for future research in this field presented based on them.

This approach is subject to limitations because the protocol of the systematic review of the research literature only included searching for the considered combination of keywords in the Scopus and ProQuest databases. Furthermore, the protocol used in this paper restricts documents to English.

The results have significant implications for both academicians and practitioners in this field. It can be useful for academics to comprehend the gaps and future trends in this field. Also, for practitioners, it can be useful to identify and understand the parts of the mathematical optimization model, which can help them model this problem effectively and efficiently.

No systematic literature review has been done in this field by considering gasoline to the best of the authors’ knowledge and delivers new facts for the future development of this field.

This study aims to explore the possibility of using magnetic biochar composite (MBCC) derived from Heglig tree bark (HTB) powder (agricultural solid waste) and cobalt ferrite (CoFe₂O₄, CFO) for oil spill removal from seawater surface.

One-pot co-precipitation route was used to synthesize MBCC. The prepared materials were characterized by X-ray diffraction, scanning electron microscopy-energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy. The densities of the prepared materials were also estimated. Crude, diesel engine and gasoline engine oils were used as seawater pollutant models. The gravimetric oil removal (GOR) method was used for removing oil spills from seawater using MBCC as a sorbent material.

The obtained results revealed that the prepared materials (CFO and MBCC) were able to remove the crude oil and its derivatives from the seawater surface. Besides, when the absorbent amount was 0.01 g, the highest GOR values for crude oil (31.96 ± 1.02 g/g) and diesel engine oil (14.83 ± 0.83 g/g) were obtained using MBCC as an absorbent. For gasoline engine oil, the highest GOR (27.84 ± 0.46 g/g) was attained when CFO was used as an absorbent.

Oil spill removal using MBCC derived from cobalt ferrite and HTB. Using tree bark as biomass (eco-friendly, readily available and low-cost) for magnetic biochar preparation also is a promising method for minimizing agricultural solid wastes (e.g. HTB) and obtaining value-added-products.

There is a lot of talk about the electric car today, but these vehicles are not new. Indeed, thebeginning of the 20th century saw electricity and the automobile take hold in North American society, so that by 1910, the electric car was everywhere. Until the turn of the 1920s, a new era dawned for transportation in the USA, but without the electric car. The purpose of this study is to question Why did it happen.

This paper develops such a comparison, not of the cars themselves, through a detailed engineering analysis, but rather of the marketing of electric vehicles in the USA in 1910 and 2010, as it appeared in the marketing strategies of the manufacturers.

There are many technical and economic reasons for this, but not only; there are also commercial strategy reasons. The position of manufacturers, especially through advertising and the press, can tell us about this golden age of the electric car, what precipitated its fall, and its reappearance a century later.

It is a comparison of images, of how electric vehicles had been and are proposed to the public, through the exploration of mainly promotional material and newspaper articles.

This paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis.

The authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot.

From the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets.

The quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions.

The outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds.

This paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.

This paper aims to investigate a condition under which identity salience effects are weakened. By examining how identity salience influences individuals’ product judgment in a domain of trade-offs, the current research demonstrates that the utilitarian value of a product is an important determinant of the effectiveness of identity salience on product judgment.

This research consists of two experiments. In Experiment 1, the authors examined whether identity salience effects were mitigated when the level of the perceived utilitarian value of an identity-incongruent product was greater than that of an identity-congruent product. In Experiment 2, the authors examined the effectiveness of internal attribution as a moderator that strengthens identity salience effects when the perceived utilitarian value of an identity-incongruent (vs. identity-congruent) product is higher.

In Experiment 1, the authors show that when the utilitarian value of a product with an attribute congruent (vs. incongruent) with one’s salient identity is lower, individuals do not show a greater preference for the identity-congruent (vs. identity-incongruent) product, mitigating the identity salience effects. Experiment 2 demonstrates that when individuals with a salient identity attribute a decision outcome to the self, they display a greater preference for the identity-congruent product even when its utilitarian value is lower compared to that of the identity-incongruent product.

The research contributes to previous research examining conditions under which identity salience effects are weakened [e.g. social influence by others (Bolton and Reed, 2004); self-affirmation (Cohen et al., 2007)] by exploring the role of the utilitarian value of a product, which has not been examined yet in prior research. Also, by doing so, the current research adds to the literature on identity salience in a domain of trade-offs (Benjamin et al., 2010; Shaddy et al., 2020, 2021). Finally, this research reveals that when a decision outcome is attributed to the self, identity salience effects become greater. By finding a novel determinant of identity salience effects (i.e. internal attribution), the present research contributes to the literature that has examined factors that amplify identity salience effects [e.g. cultural relevance (Chattaraman et al., 2009); social distinctiveness (Forehand et al., 2002); different types of groups (White and Dahl, 2007)].

The findings provide managerial insights on identity-based marketing by showing a condition under which identity-based marketing does not work [i.e. when the utilitarian value of an identity-congruent (vs. identity-incongruent) product is lower] and how to enhance the effectiveness of identity-based marketing by using internal attribution.

By exploring the role of utilitarian value, not yet examined in prior research, the present research adds to the knowledge of the conditions under which identity salience effects are weakened. Furthermore, by finding a novel determinant of identity salience effects (i.e. internal attribution), the research contributes to the literature on factors that amplify identity salience effects.

Waiting for a bus may represent a period of intense exposure to traffic particles in hot and noisy conditions in the street. To lessen the particle load and tackle heat in bus stops a shelter was equipped with an electrostatic precipitator and a three-step adiabatic cooling system capable of dynamically adjust its operation according to actual conditions. This study evaluates the effectiveness of the Airbitat Oasis Smart Bus Stop, as the shelter was called, to provide clean and cool air.

The particle exposure experienced in this innovative shelter was contrasted with that in a conventional shelter located right next to it. Mass concentrations of fine particles and black carbon, and particle number concentration (as a proxy of ultrafine particles) were simultaneously measured in both shelters. Air temperature, relative humidity and noise level were also measured.

The new shelter did not perform as expected. It only slightly reduced the abundance of fine particles (−6.5%), but not of ultrafine particles and black carbon. Similarly, it reduced air temperature (−1 °C), but increased relative humidity (3%). Its operation did not generate additional noise.

The shelter's poor performance was presumably due to design flaws induced by a lack of knowledge on traffic particles and fluid dynamics in urban environments. This is an example where harnessing technology without understanding the problem to solve does not work.

It is uncommon to come across case studies like this one in which the performance and effectiveness of urban infrastructure can be assessed under real-life service settings.

This study aims to examine the links between carbon emissions, electric vehicles, economic growth, energy use, and urbanisation in 15 countries from 2010 to 2020.

This study adopts seminal panel methods of moments quantile regression with fixed effects to trace the distributional aspect of the relationship. The reliability of methods is confirmed via fully modified ordinary least squares coefficients.

This study reveals that fossil fuel use, economic activity, and urbanisation negatively impact environmental quality, whereas renewable energy sources have a significant positive long-term effect on environmental quality in the selected panel of countries.

The main limitation of this study is the generalisability of the findings, as the study is confined to a limited number of countries, and focuses on non-renewable and renewable energy sources.

Finally, this study proposes several policy recommendations for decision-makers and policymakers in the 15 nations to address climate change, boost sales of electric vehicles, and increase the use of renewable energy sources.

This study calls for a comprehensive transition towards green energy in the transportation sector, enhancing economic growth, fostering employment opportunities, and improving environmental quality.

Given the importance of green finance in a discussion of energy efficiency and clean energy, it is critical to evaluate its implications for the growth of renewable energy. This study examines the impact of green finance on renewable energy development in Singapore.

The dynamic ordinary least squares (DOLS) regression was used in this work to test such a connection.

Using the DOLS for the period 2000–2020, it was discovered that green finance aids renewable energy development in Singapore. Additionally, the findings revealed that economic growth, oil prices, energy consumption, carbon dioxide emissions and institutional factors are all positively associated with renewable energy growth, resulting in a boost in renewable energy development.

Hence, as a result, the monetary authorities of Singapore, such as financial institutions, non-governmental organisations and corporations, should prioritise renewable energy projects under green finance initiatives to boost renewable energy growth. This may assist in raising investment flows to green projects; hence, accelerating the adoption of renewable energy.

Increased Singapore's initiatives to accelerate green finance have prompted this study to examine the research question of whether green finance has a significant impact on renewable energy growth. Thus, to the best of the authors’ knowledge, this will be the first empirical study to explore the impact of green finance on renewable energy growth in the case of Singapore.

The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues.

This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized.

This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious.

This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.