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Good governance is paramount to the sustainability of fisheries, and inclusiveness of stakeholder groups has become the centerpiece in the ethos of managing small-scale fisheries. Understanding the effect of governance network structures on fishery sustainability can help guide governance to achieve desired outcomes. Data on resource users, fishing methods, governance networks and classifications of stock health were compiled for 17 sea cucumber fisheries in the Indian Ocean. The subjective influence of the actors and the complexity of governance networks on the health of wild stocks were analyzed. The fisheries differed widely in their resource users, fishing methods and governance networks. Little correspondence was found between the number of nodes in the governance networks and the health (exploitation status) of wild stocks. Government entities dominated the networks but neither their relative influence in the networks nor their proportionate contribution to the number of entities in the networks greatly affected stock health. These findings do not refute the benefits of inclusive governance, but rather suggest that multiple other factors (e.g. inadequate regulations, weak enforcement, high number of fishers) are also likely to play a role in influencing sea cucumber fishery sustainability. These factors must be tackled in tandem with good governance.

Abstract This study developed a geographical information system-based methodology to maximize the environmental and economic efficiency of large-scale energy plants using different types of biomass. As a precursor to the optimization modelling, multi-criteria spatial analyses and then transport cost optimization are applied taking into account spatial biomass availability and existing road networks, in order to identify optimal biomass energy plant sites for different combinations of biomass types. A geographical information system integrated with a capacitated maximal covering location problem model was employed to assign biomass supply points to biomass energy plants. Capacity limits of the plants and a defined maximum transportation distance were taken into consideration. The resulting information was used to examine major challenges in establishing a large-scale biomass energy plant, biomass supply logistics, environment cost and anticipated traffic congestion. A sensitivity analysis was also carried out to review the influence of plant capacity and maximum transportation distance on economic and environmental sustainability. The proposed methodology was employed utilizing agricultural and forest residues for bioelectricity generation in Queensland, Australia. It was found that 100% bagasse had the highest eco-efficiency for bioelectricity generation from multiple biomass combinations. In contrast, the combination of 70% sawmill residues and 30% forest harvest residues showed the lowest level of traffic congestion (2 trucks per hour). In addition, the impact of traffic congestion of bulky sugarcane residues can be reduced by adding forest residues to the biomass supply. The advantages of the resulting solution are further enhanced when social impacts such as energy security, new income opportunities and job creation are taken into account in addition to the sustainability aspects.

Urban transportation issues continue to emerge and evolve as a result of rapid urbanization, and the systematic and scientific assessment of urban mobility is becoming increasingly essential. In this work, a Pressure-State-Response (PSR) model with 25 indicators was established to reflect the status of urban mobility. Then, the importance of indicators was determined with the interval-valued intuitionistic fuzzy analytic hierarchy process (IVIF-AHP) method, and the fuzzy comprehensive evaluation (FCE) method was applied to assess the overall status of urban mobility. The validity of the proposed model was demonstrated using the mobility system of Beijing as a case study, and the pressure, state, and response scores were calculated. The proposed assessment model can help to improve urban transportation monitoring and can also provide a scientific foundation for future urban transportation policymaking, planning, and traffic management, thereby further ensuring the sustainable development of urban transportation systems.

AbstractThe most recent Intergovernmental Panel on Climate Change assessment report concludes that the Atlantic Meridional Overturning Circulation (AMOC) could weaken substantially but is very unlikely to collapse in the 21st century. However, the assessment largely neglected Greenland Ice Sheet (GrIS) mass loss, lacked a comprehensive uncertainty analysis, and was limited to the 21st century. Here in a community effort, improved estimates of GrIS mass loss are included in multicentennial projections using eight state‐of‐the‐science climate models, and an AMOC emulator is used to provide a probabilistic uncertainty assessment. We find that GrIS melting affects AMOC projections, even though it is of secondary importance. By years 2090–2100, the AMOC weakens by 18% [−3%, −34%; 90% probability] in an intermediate greenhouse‐gas mitigation scenario and by 37% [−15%, −65%] under continued high emissions. Afterward, it stabilizes in the former but continues to decline in the latter to −74% [+4%, −100%] by 2290–2300, with a 44% likelihood of an AMOC collapse. This result suggests that an AMOC collapse can be avoided by CO2 mitigation.

Uncertainties associated with the loads and the output power of distributed generations create challenges in quantifying the integration limits of distributed generations in distribution networks, i.e., hosting capacity. To address this, we propose a distributionally robust optimization-based method to determine the hosting capacity considering the voltage rise, thermal capacity of the feeders and short circuit level constraints. In the proposed method, the uncertain variables are modeled as stochastic variables following ambiguous distributions defined based on the historical data. The distributionally robust optimization model guarantees that the probability of the constraint violation does not exceed a given risk level, which can control robustness of the solution. To solve the distributionally robust optimization model of the hosting capacity, we reformulated it as a joint chance constrained problem, which is solved using the sample average approximation technique. To demonstrate the efficacy of the proposed method, a modified IEEE 33-bus distribution system is used as the test-bed. Simulation results demonstrate how the sample size of historical data affects the hosting capacity. Furthermore, using the proposed method, the impact of electric vehicles aggregated demand and charging stations are investigated on the hosting capacity of different distributed generation technologies.

Bus services are a fundamental component of transportation networks in Latin America, but buses often account for a disproportionately large number of environmental externalities. Electric buses (e-buses) are emerging as an effective and pragmatic option for reducing greenhouse gas emissions and local pollutants. However, e-buses are difficult to procure in Latin America because of existing procurement challenges in the region, especially as those challenges relate to forming contracts to deal with high upfront costs and unknown risks. To overcome these procurement issues, this paper presents a new contractual model, based on literature and case study research. This new model suggests the separation of bus service responsibilities into three separate actors: multiple bus procurement companies, one or multiple bus depots and charging infrastructure companies, and multiple bus operating companies. By separating bus service responsibilities, the proposed model would bring about three concrete improvements: lower costs to the transit system, better quality of service, and lower-emission fleet deployment.

Transit bus passenger loading changes significantly over the course of a workday. Therefore, time-varying vehicle mass as a result of passenger load becomes an important factor in instantaneous energy consumption. Battery-powered electric transit buses have restricted range and longer “fueling” time compared with conventional diesel-powered buses; thus, it is critical to know how much energy they require. Our previous work has shown that instantaneous transit bus mass can be obtained by measuring the pressure in the vehicle’s airbag suspension system. This paper leverages this novel technique to determine the impact of time-varying mass on energy consumption. Sixty-five days of velocity and mass data were collected from in-use transit buses operating on routes in the Twin Cities, MN metropolitan area. The simulation tool Future Automotive Systems Technology Simulator was modified to allow both velocity and mass as time-dependent inputs. This tool was then used to model an electrified and conventional bus on the same routes and determine the energy use of each bus. Results showed that the kinetic intensity varied from 0.27 to 4.69 mi−1 and passenger loading ranged from 2 to 21 passengers. Simulation results showed that energy consumption for both buses increased with increasing vehicle mass. The simulation also indicated that passenger loading has a greater impact on energy consumption for conventional buses than for electric buses owing to the electric bus’s ability to recapture energy. This work shows that measuring and analyzing real-time passenger loading is advantageous for determining the energy used by electric and conventional diesel buses.

AbstractEnvironmental histories that span the last full glacial cycle and are representative of regional change in Australia are scarce, hampering assessment of environmental change preceding and concurrent with human dispersal on the continent ca. 47,000 years ago. Here we present a continuous 150,000-year record offshore south-western Australia and identify the timing of two critical late Pleistocene events: wide-scale ecosystem change and regional megafaunal population collapse. We establish that substantial changes in vegetation and fire regime occurred ∼70,000 years ago under a climate much drier than today. We record high levels of the dung fungus Sporormiella, a proxy for herbivore biomass, from 150,000 to 45,000 years ago, then a marked decline indicating megafaunal population collapse, from 45,000 to 43,100 years ago, placing the extinctions within 4,000 years of human dispersal across Australia. These findings rule out climate change, and implicate humans, as the primary extinction cause.