Incorporating carbon footprints into seafood sustainability certification and eco-labels

The seafood industry has become increasingly interconnected at a global scale, with fish the most traded commodity worldwide. Travel to the farthest reaches of the oceans for capture is now common practice, and subsequent transport to market can require hundreds to thousands of miles of travel by sea and air. Refrigeration of seafood products is generally required at all stages of the journey from ocean to dinner plate, resulting in substantial energy expenditure. Energy input for aquaculture (including mariculture) products can also be high, namely due to the large amounts of feed required to support fish growth. As a result of these factors, the seafood industry has a substantial carbon footprint. Surprisingly, however, carbon footprints of seafood products are rarely integrated into assessments of their sustainability by eco-labels, sustainability certification, or consumer seafood sustainability guides. Suggestions are provided here for how carbon footprints could be incorporated within seafood sustainability schemes.

A new reinforcement learning-based memetic particle swarm optimizer

Developing an effective memetic algorithm that integrates the Particle Swarm Optimization (PSO) algorithm and a local search method is a difficult task. The challenging issues include when the local search method should be called, the frequency of calling the local search method, as well as which particle should undergo the local search operations. Motivated by this challenge, we introduce a new Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO) model. Each particle is subject to five operations under the control of the Reinforcement Learning (RL) algorithm, i.e. exploration, convergence, high-jump, low-jump, and fine-tuning. These operations are executed by the particle according to the action generated by the RL algorithm. The proposed RLMPSO model is evaluated using four uni-modal and multi-modal benchmark problems, six composite benchmark problems, five shifted and rotated benchmark problems, as well as two benchmark application problems. The experimental results show that RLMPSO is useful, and it outperforms a number of state-of-the-art PSO-based algorithms.

Measuring eco-roof mitigation on flash floods via GIS simulation

Purpose: The rapid and ongoing expansion of urbanised impervious areas could lead to more frequent flood inundation in urban flood-prone regions. Nowadays, urban flood inundation induced by rainstorm is an expensive natural disaster in many countries. In order to reduce the flooding risk, eco-roof systems (or green roof systems) could be considered as an effective mechanism of mitigating flooding disasters through their rainwater retention capability. However, there is still a lack of examining the stormwater management tool. The purpose of this paper is to evaluate the effects on flooding disaster from extensive green roofs. Design/methodology/approach: Based on geographical information system (GIS) simulation, this research presents a frame of assessing eco-roof impacts on urban flash floods. The approach addresses both urban rainfall-runoff and underground hydrologic models for traditional impervious and green roofs. Deakin University’s Geelong Waurn Ponds campus is chosen as a study case. GIS technologies are then utilised to visualise and analyse the effects on flood inundation from surface properties of building roofs. Findings: The results reveal that the eco-roof systems generate varying degrees of mitigation of urban flood inundation with different return period storms. Originality/value: Although the eco-roof technology is considered as an effective stormwater management tool, it is not commonly adopted and examined in urban floods. This study will bring benefits to urban planners for raising awareness of hazard impacts and to construction technicians for considering disaster mitigation via roof technologies. The approach proposed here could be used for the disaster mitigation in future urban planning.

The influence of eco-label knowledge and trust on pro-environmental consumer behaviour in an emerging market

Consumers are increasingly concerned about the negative environmental implications of purchasing goods, which in turn shape their behaviour. Yet, there are indications that consumers do not always act on these concerns, causing an attitude–behaviour gap. For consumers to make ecologically responsible purchases, they need relevant product environmental information. Therefore, marketers and firms are increasingly integrating more detailed environmental information in their offerings, including eco-labels with externally validated information. This study integrates consumers’ knowledge and trust in eco-labels with their environmental knowledge to determine how these affect pro-environmental consumer behaviour (PECB). The findings suggest environmental and eco-label knowledge is positively associated with attitudes towards the environment, and that positive environmental attitudes and trust in eco-labels affect PECB. This implies that firms, policy-makers and accreditation organisations (i.e. labelling) can educate consumers about eco-labels and the environment to increase PECB. Such strategies will also build consumer knowledge and trust in eco-labels, necessary for facilitating PECB.