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As presented in the first companion paper, distributed mixed-integer fuzzy hierarchical programming (DMIFHP) was developed for municipal solid waste management (MSWM) under complexities of heterogeneities, hierarchy, discreteness, and interactions. Beijing was selected as a representative case. This paper focuses on presenting the obtained schemes and the revealed mechanisms of the Beijing MSWM system. The optimal MSWM schemes for Beijing under various solid waste treatment policies and their differences are deliberated. The impacts of facility expansion, hierarchy, and spatial heterogeneities and potential extensions of DMIFHP are also discussed. A few of findings are revealed from the results and a series of comparisons and analyses. For instance, DMIFHP is capable of robustly reflecting these complexities in MSWM systems, especially for Beijing. The optimal MSWM schemes are of fragmented patterns due to the dominant role of the proximity principle in allocating solid waste treatment resources, and they are closely related to regulated ratios of landfilling, incineration, and composting. Communities without significant differences among distances to different types of treatment facilities are more sensitive to these ratios than others. The complexities of hierarchy and heterogeneities pose significant impacts on MSWM practices. Spatial dislocation of MSW generation rates and facility capacities caused by unreasonable planning in the past may result in insufficient utilization of treatment capacities under substantial influences of transportation costs. The problems of unreasonable MSWM planning, e.g., severe imbalance among different technologies and complete vacancy of ten facilities, should be gained deliberation of the public and the municipal or local governments in Beijing. These findings are helpful for gaining insights into MSWM systems under these complexities, mitigating key challenges in the planning of these systems, improving the related management practices, and eliminating potential socio-economic and eco-environmental issues resulting from unreasonable management.

Abstract Food waste (FW), primary sludge (PS) and waste activated sludge (WAS) were characterized and found to be complementary in the concentrations of carbohydrates, total Kjeldahl nitrogen (TKN), PO4–P and some metal for biological hydrogen production. Moreover, FW was found to have low pH buffering capacity while the values for PS and WAS were relatively higher. An anaerobic toxicity analysis (ATA) derived from a methanogenic ATA protocol showed that these waste materials had no toxicity to hydrogen production. Adding phosphate buffer to the FW significantly improved hydrogen production while initial pH was 7.0. Co-digestion of FW and sewage sludge was studied using a batch respirometric cultivation system. All combinations of the feedstocks (FW+PS, FW+WAS and FW+PS+WAS) showed enhanced hydrogen production potential as compared with the individual wastes. A mixing ratio of 1:1 was found to be the best among the ratios tested for all three co-digestion groups. A hydrogen yield of 112 mL/g volatile solid (VS) added was obtained from a combination of FW, PS and WAS. This yield was equivalent to 250 mL/g VS added if only FW contributed to hydrogen production. The reason for the enhancement of hydrogen production was postulated to be multifold in which the increase in buffer capacity in the co-digestion mixture was verified.

Abstract. The assessment of wind conditions in complex terrain requires the use of Computational Fluid Dynamics (CFD) simulations incorporating an accurate parameterization of forest canopy effects and variable thermal stability effects. This study aims to investigate how incorporating the presence of trees can improve flow predictions. A three-dimensional steady Reynolds-averaged Navier-Stokes (RANS) equations model is set up using OpenFOAM to simulate the flow over a complex terrain site comprising two parallel ridges located near Perdigão, Portugal. A 7.5 km × 7.5 km terrain of the Perdigao site is constructed from elevation data centered around a 100 m met-mast located on the northeast ridge. A 30-min averaged stationary period corresponding to near-neutral conditions on a single met-mast tower is simulated. The impact of incorporating different source terms is studied such as forest canopy, Coriolis forces as well as also buoyancy forces. The prediction capability of the models is analyzed for different groups of towers on the South-West ridge, inside the valley, and on the North-East ridge based on the flow topology. The inclusion of a canopy model is shown to improve predictions close to the ground for most of the towers, while reducing prediction accuracy on top of the ridges, illustrating the need to represent terrain heterogeneity.

Abstract Greenhouse gas emissions from the burning of fossil fuels are one of the main causes of anthropogenic climate change. Large-scale deployment of renewable energy can play an immense role in transforming the global energy system and mitigating the emissions. This paper describes the development of a novel framework called MArket Penetration ModeLing of Renewable Energy Technologies in Electric Power Sector (MAPLET-PS). MAPLET-PS assesses the impacts of policy measures such as carbon price and financial incentives on the adoption of renewable energy technologies. The framework was used to develop a case study for the electric power sector of Alberta, a fossil-dominated province in Western Canada. The results show that implementing a carbon price on fossil fuel electric power sources and incentives for renewable energy, along with the phase-out of coal-fired electricity generation, can mitigate 29% of Alberta's electricity sector 2020 GHG emissions by 2050 and reduce GHG emissions from 46.5 Mt of CO2 eq. in 2020 to 23.6 and 29.1 Mt of CO2 eq. per year in 2030 and 2050, respectively, in Alberta. Moreover, these changes can increase the share of renewable energies from 12.5% in 2018 to 30% in 2050. These rates can be achieved by implementing a carbon price along with a 1000 $ incentive per kW new capacity development and 70 $ incentive per MWh electric power generation from renewable sources, from 2021 to 2025, primarily from wind turbines.

Meteorological forcing at the air-water interface is the main determinant of the heat balance of most lakes (Edinger et al., 1968; Sweers, 1976). Year-to-year changes in the weather therefore have a major effect on the thermal characteristics of lakes. However, lakes that differ with respect to their morphometry respond differently to these changes (Gorham, 1964), with deeper lakes integrating the effects of meteorological forcing over longer periods of time. Other important factors that can influence the thermal characteristics of lakes include hydraulic residence time, optical properties and landscape setting (e.g. Salonen et al., 1984; Fee et al., 1996; Livingstone et al., 1999). These factors modify the thermal responses of the lake to meteorological forcing (cf. Magnuson et al., 2004; Blenckner, 2005) and regulate the patterns of spatial coherence (Chapter 17) observed in the different regions (Livingstone, 1993; George et al., 2000; Livingstone and Dokulil, 2001; Jarvinen et al., 2002; Blenckner et al., 2004)

In this study, an agroforestry ecosystem project (AEP) is developed for confronting the conflict between agricultural development and forest protection. A fuzzy stochastic programming with Laplace scenario analysis (FSL) is proposed for planning water resources in an AEP issue under uncertainties. FSL can not only deal with spatial and temporal variations of hydrologic elements and meteorological conditions; but also handle uncertainties that are expressed in terms of probability, possibility distributions and fuzzy sets; meanwhile, policy scenario analysis with Laplace’s criterion (PSL) is introduced to handle probability of each scenario occurrence under the supposition of no data available. The developed FSL can be applied to an AEP issue in Xixian county, located in north of China. The result of ecological effects, water allocation patterns, pollution mitigation schemes and system benefits under various scenarios are obtained, which can support policymakers adjusting current strategy to improve regional ecological function with cost-effective and sustainable manners. Meanwhile, it can support generating a robust water plan for regional sustainability in an AEP issue under uncertainties.

Abstract A validated computational fluid dynamics simulation tool is used to study the long-term performance of a centralized latent heat thermal energy storage system (LHTES). The LHTES system is integrated with a building mechanical ventilation system. Paraffin RT20 was used as a phase change material (PCM) and fins are used to enhance its performance. To reduce the computational time, artificial neural networks (ANN) was used to relate the relationships between the LHTES inputs and output parameters. Extensive CFD simulations were carried out to identify all the influential parameters for the development of ANN. They include phase change temperature range, air flow rate, the geometrical configuration of a LHTES system, fin size, and the unit's length. Further CFD simulations were carried out to provide sufficient data for proper training and testing of the ANN. The ANN model was used to predict the LHTES's outlet air-temperature. There was a good agreement between the ANN prediction and CFD model's prediction. The ANN model then was used to study the annual performance of a LHTES for application in Montreal. We found that the potential of use the centralized LHTES system to reduce the cooling load is high with a wider phase change temperature range. The centralized LHTES system contributes to reducing the cooling load from 21% to 36% when the length of the centralized LHTES system is increased from 500 to 650 mm at a flow rate of 1.5 m/s.

This research investigates the relationships among renewable energy consumption, economic growth, and financial development in five sub-Saharan African nations utilizing panel data from 2000 to 2020. Econometric methods are used to ascertain the existence or absence of cross-sectional dependence and the short-run and long-run connections between the following factors: Pesaran cross-sectional dependence (CD) and cross-sectionally augmented IPS (CIPS) unit root tests, pooled mean group (PMG), and dynamic ordinary least squares (DOLS) estimations. The presence of cross-sectional dependence is found and represented with the CIPS unit root test. No significant short-run relationship is found between the variables of the study, yet a significant long-run relationship is present among them. A positive relationship exists between CO2 emissions and financial development, while financial development and renewable energy consumption are found to have negative relationships with CO2 emissions. The study also supports the scale effect of the environmental Kuznets curve hypothesis. Additionally, no causality is found among the variables, and impulse response and variance decomposition estimation are carried out to recommend future effects. Policy implications of findings are discussed, with accompanying suggestions.