• Energy Research
• 11. Sustainability close
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Abstract Hydrothermal treatment (HT) is one of the efficient approaches for upgrading municipal solid waste (MSW). In the present study, emission characteristics of polycyclic aromatic hydrocarbons (PAHs) from hydrothermally treated municipal solid waste (H-MSW) combustion alone and H-MSW/coal co-combustion were investigated at different temperatures. The results showed that for all fuel combustion, the majority of PAHs were 3- or 4-ring PAHs. In addition, flue gas had the highest yields of PAHs followed by fly ash and bottom ash, while the ring number of dominated PAHs in fly ash was higher than those in flue gas and bottom ash. Compared to MSW, H-MSW combustion generated less PAHs at the value of 1131.95–7649.24 μg/g. The blending of H-MSW and coal reduced total PAH emissions and positive interactions were observed between H-MSW and coal during co-combustion. The toxicity equivalent quantity (TEQ) values of the PAHs from combustion were in the order MSW > H-MSW > H-MSW/coal, which was consistent with the total PAH emissions. The present study illustrated that significant reduction of PAH emissions and toxicity from combustion could be achieved by HT and the blending of H-MSW and coal.

Abstract Hydrothermal treatment (HT) is one of the efficient approaches for upgrading municipal solid waste (MSW). In the present study, emission characteristics of polycyclic aromatic hydrocarbons (PAHs) from hydrothermally treated municipal solid waste (H-MSW) combustion alone and H-MSW/coal co-combustion were investigated at different temperatures. The results showed that for all fuel combustion, the majority of PAHs were 3- or 4-ring PAHs. In addition, flue gas had the highest yields of PAHs followed by fly ash and bottom ash, while the ring number of dominated PAHs in fly ash was higher than those in flue gas and bottom ash. Compared to MSW, H-MSW combustion generated less PAHs at the value of 1131.95–7649.24 μg/g. The blending of H-MSW and coal reduced total PAH emissions and positive interactions were observed between H-MSW and coal during co-combustion. The toxicity equivalent quantity (TEQ) values of the PAHs from combustion were in the order MSW > H-MSW > H-MSW/coal, which was consistent with the total PAH emissions. The present study illustrated that significant reduction of PAH emissions and toxicity from combustion could be achieved by HT and the blending of H-MSW and coal.

Effectively analyzing and then treating energy-related air pollution requires examining every factor, from the pollution source to the end of treatment. This paper applies index decomposition analysis and a whole process treatment perspective to identify the factors facilitating air pollution reduction across three stages: source prevention, process control, and end-of-pipe treatment. Empirical research using data from China’s Jiangsu Province and its 13 cities reveals differences in local approaches to pollution prevention. At the provincial level, end-of-pipe treatment remains the primary approach to control air pollution emissions, indicating that the pattern of “pollute first, govern later” has not yet been fundamentally reversed. At the city level, 13 cities can be divided into four types, based on their approach to air pollution treatment: the leading type, process-dependent type, end-dependent type, and lagging type. Of these, 7 cities are using multiple control approaches, reflecting the comprehensive effect of whole process treatment. The Jiangsu Province should consider further strengthening effective whole process air pollution treatment models, by transitioning to pollution control, adjusting industrial structure, promoting technological progress, and consuming clean energy.

Effectively analyzing and then treating energy-related air pollution requires examining every factor, from the pollution source to the end of treatment. This paper applies index decomposition analysis and a whole process treatment perspective to identify the factors facilitating air pollution reduction across three stages: source prevention, process control, and end-of-pipe treatment. Empirical research using data from China’s Jiangsu Province and its 13 cities reveals differences in local approaches to pollution prevention. At the provincial level, end-of-pipe treatment remains the primary approach to control air pollution emissions, indicating that the pattern of “pollute first, govern later” has not yet been fundamentally reversed. At the city level, 13 cities can be divided into four types, based on their approach to air pollution treatment: the leading type, process-dependent type, end-dependent type, and lagging type. Of these, 7 cities are using multiple control approaches, reflecting the comprehensive effect of whole process treatment. The Jiangsu Province should consider further strengthening effective whole process air pollution treatment models, by transitioning to pollution control, adjusting industrial structure, promoting technological progress, and consuming clean energy.

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.

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.

Abstract Accurate rooftop solar energy potential characterization is critically important for promoting the wide penetration of renewable energy in high-density cities. However, it has been a long-standing challenge due to the complex building shading effects and diversified rooftop availabilities. To overcome the challenge, this study proposed a novel 3D-geographic information system (GIS) and deep learning integrated approach, in which a 3D-GIS-based solar irradiance analyzer was developed to predict dynamic rooftop solar irradiance by taking shading effects of surrounding buildings into account. A deep learning framework was developed to identify the rooftop availabilities. Experimental validations have shown their high accuracies. As a case study, a real urban region of Hong Kong was used. The results showed that the annual solar energy potential of the entire building group was reduced by 35.7% due to the shading effect and the reduced rooftop availability. The reductions of individual buildings varied from 13.4% to 74.5%. In spite of the substantial reductions of the annual solar energy, the shading effect could only slightly reduce the peak solar power. In fact, the annual solar energy reduction could be five times higher than the peak solar power reduction. Further analysis showed that simple addition of the respective solar energy potential reductions, caused by the shading effect and the rooftop availability, tends to highly overestimate the total reduction by up to 26%. For this reason, their impacts cannot be considered separately but as joint effects. The integrated approach provides a viable means to accurately characterize rooftop solar energy potential in urban regions, which can help facilitate solar energy applications in high-density cities.

Abstract Accurate rooftop solar energy potential characterization is critically important for promoting the wide penetration of renewable energy in high-density cities. However, it has been a long-standing challenge due to the complex building shading effects and diversified rooftop availabilities. To overcome the challenge, this study proposed a novel 3D-geographic information system (GIS) and deep learning integrated approach, in which a 3D-GIS-based solar irradiance analyzer was developed to predict dynamic rooftop solar irradiance by taking shading effects of surrounding buildings into account. A deep learning framework was developed to identify the rooftop availabilities. Experimental validations have shown their high accuracies. As a case study, a real urban region of Hong Kong was used. The results showed that the annual solar energy potential of the entire building group was reduced by 35.7% due to the shading effect and the reduced rooftop availability. The reductions of individual buildings varied from 13.4% to 74.5%. In spite of the substantial reductions of the annual solar energy, the shading effect could only slightly reduce the peak solar power. In fact, the annual solar energy reduction could be five times higher than the peak solar power reduction. Further analysis showed that simple addition of the respective solar energy potential reductions, caused by the shading effect and the rooftop availability, tends to highly overestimate the total reduction by up to 26%. For this reason, their impacts cannot be considered separately but as joint effects. The integrated approach provides a viable means to accurately characterize rooftop solar energy potential in urban regions, which can help facilitate solar energy applications in high-density cities.

This paper aims to develop a green building meta-model for a representative passively designed high-rise residential building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic building model and provide reliable building performance predictions. Input parameters are selected from different passive design strategies including the building layout, envelop thermophysics, building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust building performance predictions associated with the green building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.