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Microalgae are recognized as a third generation feedstock for biofuel production due to their rapid growth rates and lignin-free characteristics. In this study, a lipid extracted microalgal biomass residues was used as the raw material to produce isoprene, α-pinene and β-pinene with an engineered E. coli strain. We adopted an optimal sulfuric acid hydrolysis method (1:7 ratio of solid to acid solution, 32% (w/v) concentration of sulfuric acid solution at 90 °C for 90 min) to efficiently convert holocellulose into glucose efficiently (6.37 g/L). Futhermore, we explored a novel detoxification strategy (phosphoric acid/calcium hydroxide) to remove inhibitors and notably acetic acid, furfural and 5-hydroxymethylfurfural (5-HMF) were reduced by 5.32%, different number given later 99.19% and 98.22%, respectively. Finally, the fermentation concentrations of isoprene (223.23 mg/L), α-pinene (382.21 μg/L) and β-pinene (17.4 mg/L) were achieved using the detoxified hydrolysate as the carbon source, equivalent to approximately 86.02%, 90.16% and 88.32% of those produced by the engineered E. coli strain fermented on pure glucose, respectively.

The shipping industry faces a large challenge as it needs to significantly lower the amounts of Green House Gas emissions. Traditionally, reducing the fuel consumption for ships has been achieved during the design stage and, after building a ship, through optimisation of ship operations. In recent years, ship efficiency improvements using Machine Learning (ML) methods are quickly progressing, facilitated by available data from remote sensing, experiments and high-fidelity simulations. The data have been successfully applied to extract intricate empirical rules that can reduce emissions thereby helping achieve green shipping. This article presents an overview of applying ML techniques to enhance ships’ sustainability. The work covers the ML fundamentals and applications in relevant areas: ship design, operational performance, and voyage planning. Suitable ML approaches are analysed and compared on a scenario basis, with their space for improvements also discussed. Meanwhile, a reminder is given that ML has many inherent uncertainties and hence should be used with caution.

AbstractElevated carbon-dioxide concentration [eCO2] is a key climate change factor affecting plant growth and yield. Conventionally, crop modeling work has evaluated the effect of climatic parameters on crop growth, without considering CO2. It is conjectured that a novel multimodal ensemble approach may improve the accuracy of modelled responses to eCO2. To demonstrate the applicability of a multimodel ensemble of crop models to simulation of eCO2, APSIM, CropSyst, DSSAT, EPIC and STICS were calibrated to observed data for crop phenology, biomass and yield. Significant variability in simulated biomass production was shown among the models particularly at dryland sites (44%) compared to the irrigated site (22%). Increased yield was observed for all models with the highest average yield at dryland site by EPIC (49%) and lowest under irrigated conditions (17%) by APSIM and CropSyst. For the ensemble, maximum yield was 45% for the dryland site and a minimum 22% at the irrigated site. We concluded from our study that process-based crop models have variability in the simulation of crop response to [eCO2] with greater difference under water-stressed conditions. We recommend the use of ensembles to improve accuracy in modeled responses to [eCO2].

This is a postprint version of the Book Chapter. Information regarding the official publication is available from the link below - Copyright @ 2011 Springer Implementation of a Life Cycle Sustainability Management (LCSM) strategy can involve significant challenges because of competing or conflicting objectives between stakeholders. These differences may, if not identified and managed, hinder successful adoption of sustainability initiatives. This article proposes a conceptual framework for stakeholder management in a LCSM context. The framework identifies the key sustainability stakeholder groups and suggests strategic ambiguity as a management tool to harness dysfunctional conflict into constructive collaboration. The framework is of practical value as it can be used as a guideline by managers who wish to improve collaboration with stakeholders along the supply chain. The article also fills a gap in the academic literature where there is only limited research on sustainability stakeholder management through strategic ambiguity.

Plug-in hybrid electric vehicles (PHEVs) have a larger battery and can replace a certain amount of conventional fossil fuels with grid electricity, which differs from the traditional hybrid electric vehicles (HEVs). The application of the onboard electrical energy significantly influences the energy utilization efficiency and thus impacts the fuel economy. In this paper, the basic PHEV operation modes are defined as pure electric driving (PED), hybrid driving charge depleting (HDCD) and hybrid driving charge sustaining (HDCS) based on the battery state of charge (SoC) profile. For a plug-in hybrid electric bus (PHEB), three different energy management strategies, which are combined with two or three of the basic operation modes, are put forward and comparatively examined based on simulation models. If some trip information can be approximately known in advance such as the trip distance and the mean power demand, the PED + HDCD + HDCS strategy comprised optimally of the PED mode, the HDCD mode and the HDCS mode would be the best energy management strategy.

AbstractThe planetary boundary framework constitutes an opportunity for decision makers to define climate policy through the lens of adaptive governance. Here, we use the DICE model to analyze the set of adaptive climate policies that comply with the two planetary boundaries related to climate change: (1) staying below a CO2 concentration of 550 ppm until 2100 and (2) returning to 350 ppm in 2100. Our results enable decision makers to assess the following milestones: (1) a minimum of 33% reduction of CO2 emissions by 2055 in order to stay below 550 ppm by 2100 (this milestone goes up to 46% in the case of delayed policies); and (2) carbon neutrality and the effective implementation of innovative geoengineering technologies (10% negative emissions) before 2060 in order to return to 350 ppm in 2100, under the assumption of getting out of the baseline scenario without delay. Finally, we emphasize the need to use adaptive path-based approach instead of single point target for climate policy design.