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The concept of resilience is a crucial part in crafting visions of desirable futures designed to withstand the widest variety of external shocks to the system. Backcasting scenarios are widely used to envision desirable futures with a discontinuous change from the present in mind. However, less effort has been devoted to developing theoretical frameworks and methods for building backcasting scenarios with a particular focus on resilience, although resilience has been explored in related sustainability fields. This paper proposes a method that helps design backcasting scenarios for resilient futures. A characteristic of the method is to delineate “collapse” futures, based upon which resilient futures are described to avoid the various collapsed states. In the process of designing backcasting scenarios, fault tree analysis (FTA) is used to support the generation of various risk factors and countermeasures to improve resilience. In order to test the effectiveness of the proposed method, we provide a case study to describe resilient energy systems for a Japanese community to 2030. Four expert workshops involving researchers from different disciplines were organized to generate diversified ideas on resilient energy systems. The results show that three scenarios of collapsed energy systems were described, in which policy options to be taken toward achieving resilient energy systems were derived.

Abstract Many countries committed themselves in the Kyoto protocol to reduce greenhouse gas (GHG) emissions. Some of these targeted emission reductions could result from a switch from coal-fired to gas-fired electricity generation. The focus in this work lies on Western Europe, with the presence of the European Union Emission Trading Scheme (EU ETS). For the switching to occur, several conditions have to be fulfilled. First, an economical incentive must be present, i.e. a sufficiently high European Union Allowance (EUA) price together with a sufficiently low natural gas price. Second, the physical potential for switching must exist, i.e. at a given load, there must remain enough power plants not running to make switching possible. This paper investigates what possibilities exist for switching coal-fired plants for gas-fired plants, dependent on the load level (the latter condition above). A fixed allowance cost and a variable natural gas price are assumed. The method to address GHG emission reduction potentials is first illustrated in a methodological case. Next, the GHG emission reduction potentials are addressed for several Western European countries together with a relative positioning of their electricity generation. GHG emission reduction potentials are also compared with simulation results. GHG emission reduction potentials tend to be significant. The Netherlands have a very widespread switching zone, so GHG emission reduction is practically independent of electricity generation. Other counties, like Germany, Spain and Italy could reduce GHG emissions significantly by switching. With an allowance cost following the switch level of a 50% efficient gas-fired plant and a 40% efficient coal-fired plant in the summer season (like in 2005), the global GHG emission reduction (in the electricity generating sector) for the eight modeled zones could amount to 19%.

Electric vehicles (EVs) offer an attractive long-term solution to reduce the dependence on fossil fuel and greenhouse gas emission. At the same time, charging a large fleet of EVs distributed across the residential area poses a challenge for the distribution network. In this paper, we formulate this problem by building on the optimal power flow (OPF) framework to model the network constraints that arises from charging EVs at different locations. To overcome the computational challenge when the control horizon is long, we study a nested optimization approach to decompose the joint OPF and EV charging problem. We characterize the optimal EV charging schedule to be a valley-filling profile, which allows us to develop an efficient offline algorithm with significantly lower computational complexity compared to centralized interior point solvers. Furthermore, we propose a decentralized online algorithm that dynamically tracks the valley-filling profile. Our algorithms are evaluated on the IEEE 14 bus system with real residential load profiles, and the simulations show that our online algorithm performs almost optimally under different settings.

A reasonable plan for charging stations is critical to the widespread use of electric vehicles. In this paper, we propose an optimal planning method for electric vehicle charging stations. First of all, we put forward a forecasting method for the distribution of electric vehicle fast charging demand in urban areas. Next, a new mathematical model that considers the mutual benefit of electric vehicle users and the power grid is set up, aiming to minimize the social cost of charging stations. Then, the model is solved by the Voronoi diagram combined with improved particle swarm optimization. In the end, the proposed method is applied to an urban area, simulation results demonstrate that the proposed method can yield optimal location and capacity of each charging station. A contrasting case is carried out to verify that improved particle swarm optimization is more effective in finding the global optimal solution than particle swarm optimization.

Carbon emission reduction is a long-term strategy for China to promote its economic and social development. However, emission reduction often involves a huge amount of technological investment, which could vary substantially across different provinces due to their discrepancy in economic and technological development levels. Emission trading as a useful policy instrument may help different provinces achieve their emission reduction targets cost-effectively. This paper models the economic performance of an interprovincial emission reduction quota trading scheme in China. The marginal abatement cost curve of each province in China is first estimated. A nonlinear programming model is further developed to evaluate the economic performance of interprovincial emission reduction quota trading. Five equity criteria are used to conduct the initial allocation of emission reduction targets between different provinces. Our modeling results show that China’s total emission abatement cost could decrease by over 40% through implementing such an interprovincial emission reduction quota trading scheme. Of the five alternative criteria, the CO2 emissions and population criteria look fairer and are recommended for use in the initial allocation of CO2 emission reduction targets.

The rapidly evolving global warming is triggering all levels of actions to reduce industrial carbon emissions, while capturing carbon dioxide of industrial origin via microalgae has attracted increasing attention. This article attempted to offer preliminary analysis on the carbon capture potential of microalgal cultivation. It was shown that the energy consumption-associated with operation and nutrient input could significantly contribute to indirect carbon emissions, making the microalgal capture of carbon dioxide much less effective. In fact, the current microalgae processes may not be environmentally sustainable and economically viable in the scenario where the carbon footprints of both upstream and downstream processing are considered. To address these challenging issues, renewable energy (e.g., solar energy) and cheap nutrient source (e.g., municipal wastewater) should be explored to cut off the indirect carbon emissions of microalgae cultivation, meanwhile produced microalgae, without further processing, should be ideally used as biofertilizer or aquafeeds for realizing complete nutrients recycling.

In the view of the construction of green university throughout the world, this paper summarizes the principles, connotation and methods of the evaluation green university index system. An index system is established for assessment of green university, which contains the green concept, the green culture, the green research, the green education and the green campus. By applying the multi-level grey approach, the article tries to innovate the evaluation approach of green university and develop Green Star rating system. Taken Jiangnan university as a case study, a comprehensive evaluation model is employed to puts forward a feasible and useful way for green university evaluation.

Globally, acceptance among the general public of waste-to-energy (WtE) incinerators is a crucial factor in implementing national waste-to-energy policies. This study aims to shed light on the acceptance of WtE incinerators, with a focus on anti-incinerator sentiment and the influence and interactions of place-, trust-, and fairness-based factors, with a case study in China. A total of 338 residents in the Asuwei area in North Beijing completed a survey on a proposed WtE incinerator in the vicinity. Hierarchical regression analyses indicate that place attachment positively enhances anti-incinerator sentiment through direct effects, as well as moderation and mediation effects between risk perception and opposing willingness. Furthermore, institutional trust negatively moderates the impact of perceived risk on anti-incinerator sentiment, in addition to directly reducing perceived risk. Trust also influences anti-incinerator sentiment via risk perception, attesting to the effectiveness of a casual model of trust. Likewise, fairness perception acts as another determinant of opposing sentiment, similarly to trust. These findings demonstrate the importance of using a range of instrumental and more effective strategies to promote the acceptance of renewable energy infrastructure.