• Energy Research
• Closed Access close
• Open Source close
• 11. Sustainability close
• CA close
• IR close

Abstract For 118 million residential housing units in the U.S., there is currently a gap between the potential energy savings that can be achieved through the use of existing energy efficiency technologies, and the actual level of energy savings realized, particularly for the 37% of housing units that are considered residential rental properties. Additional quantifiable benefits are needed beyond energy savings to help further motivate residential property owners to invest in energy efficiency upgrades. This research focuses on assessing the adoption of energy efficient upgrades in U.S. residential housing and the impact on rental prices. Ten U.S. cities are chosen for analysis; these cities vary in size across multiple climate zones, and represent a diverse set of housing market conditions. Data was collected for over 159,000 rental property listings, their characteristics, and their energy efficiency measures listed in rental housing postings across each city. Following an extensive data quality control process, over thirty different types energy efficient features were identified. The level of adoption was determined for each city, ranging from 5.3% to 21.6%. Efficient lighting and appliances were among the most common, with many features doubling as energy efficient and other desirable aesthetic or comfort improvements. Then using propensity score matching and conditional mean comparison methods, the relative impact on rent charged in each city was calculated, which ranged from a 6% to 14.1% increase in rent for properties with energy efficient features, demonstrating a positive economic impact of these features, particularly for property owners. This was further subdivided into five types of energy efficiency upgrade and three housing types. Single family homes generally demanded higher premiums with energy efficient features, however there was not a consistent pattern across the types of efficient upgrades. The results of this work demonstrate that investment in energy efficient technologies has quantifiable benefits for rental property owners in the U.S. beyond just energy savings. This methodology and results can also be used in other cities and by property owners, utility companies, or others, ultimately encouraging further investment and positive economic impact in residential energy efficiency and in turn improving energy and resource conservation in the building sector.

Abstract This study develops a novel sustainability assessment methodology for energy systems using life-cycle emission factors and sustainability indicators. Here, the global warming and environmental impact dimensions of the sustainability assessment methodology are examined in detail. A comparative analysis shows that a wind-battery system produces fewer potential global warming, stratospheric ozone depletion, air pollution, and water pollution impacts compared to a gas-fired system. The wind-battery system generates 13% of the life-cycle GHG emissions and 22% of the life-cycle ozone-depleting substance emissions of a gas-fired power plant. Nitrous oxide contributes more than 90% of the stratospheric ozone depletion potential of gas-fired and wind-battery systems.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

Abstract Geological sequestration of CO 2 is an immediately available and technologically feasible means of reducing CO 2 emissions into the atmosphere, which is particularly suited to landlocked sedimentary basins. Geoscience, engineering, economic and public issues need addressing by governments and industry before proceeding with full scale implementation. Specific site selection should be based on a suitability analysis, a proper inventory of potential sites, an assessment of the fate of the injected CO 2 and a capacity determination, together with surface criteria such as CO 2 capture and transport. The suitability analysis, both at the basin and regional scales, is based on geological, geothermal, hydrodynamic, basin maturity, economic and societal criteria. The inventory of sequestration sites needs also identification of major CO 2 point sources and a cost benefit analysis. The potential for CO 2 escape and migration is a deciding factor in screening out unsafe sites. Site capacity should be determined based on in situ conditions and CO 2 properties and behavior. Transforming the geological space into the CO 2 space is an important step along the road map for selection of suitable CO 2 injection sites that allows the identification of safe large capacity sites. An example of application from the Alberta basin is presented.

Abstract Eradicating poverty and mitigating greenhouse gas (GHG) emissions are core issues of global sustainable development goals (SDGs), and China is struggling in realizing these targets. The poverty reduction that leads to popualtion structure and lifestyle changes would have an impact on GHG emission changes. However, few studies have assessed the historical and future impacts of the poverty allevation on China's emissions. Here by linking Chinese Multi-Regional Input Output (MRIO) database to the global MRIO database EXIOBASE, and using provincial household consumption data, we identified the distribution of Chinese household greenhouse gas footprints (HGFs) by income groups in 2015 at the national and provinical levels. Moreover, we focused on the historical impact of poverty alleviation on HGFs during 2010–2015, and developed four scenarios to project future HGFs changes due to poverty alleviation by 2030. We find that eradicating extreme poverty in the secanrio S2, i.e., bringing people to an income above $1.9 daily, does not cause a large emission impact with current technological level. However, lifting people from a higher poverty line of $5.5 per day in the sceanrio S4 results in a 1.6% increase in emissions compared with the scenario S1 without any poverty reduction goals. Furthermore, realizing a higher poverty reduction target will result in an increase of emissions contribution from internatioanl supply chains due to the differences in consumption patterns among different income groups. Our study highlights the conflict between the high poverty alleviaition goal and emission reduciton in China, and reminds us of the need to make more technological efforts for avoiding the large emissions embodied in international supply chains.

Abstract In order to further reduce the impact of climate fluctuations on the typical meteorological year (TMY) database, this paper introduces an ensemble empirical model decomposition method to extract the periodic fluctuation and random fluctuation data from outdoor climate data separately, and to construct a comprehensive description parameter that eliminates the influence of random fluctuation data. An innovative TMY based on the comprehensive description parameter was developed in six selected cities of different climate zones in China. Compared with the existing Chinese TMY development method and outdoor design parameters, it is found that the typical meteorological months (TMMs) of each city and the outdoor design parameters from the improved TMY database have changed to a certain extent. Through the correlation analysis between improved TMY database and the cumulative long-average meteorological data, it reveals that the improved TMY can better describe the local average climatic characteristics. Finally, this paper discusses the impact of the improved TMY on the building heat loss index and outdoor thermal comfort in different building shapes. The results demonstrate that the energy demand and outdoor thermal comfort analysis based on the improved TMY are closer to long-term averaged outdoor climate, and the calculation deviations compared with conventional method are reduced by 1.18%–21.08% and 53.42%–76.82% respectively. This research will refine outdoor climate data for building design and analysis.

Abstract The global steel production has been growing for the last 50 years, from 200 Mt in 1950s to 1 240 Mt in 2006. Iron and steel making industry is one of the most energy-intensive industries, with an annual energy consumption of about 24 EJ, 5% of the world's total energy consumption. The steel industry accounts for 3%–4% of total world greenhouse gas emissions. Enhancing energy efficiency and employing energy saving/recovering technologies such as coke dry quechning (CDQ) and top pressure recovery turbine (TRT) can be short-term approaches to the steel industry to reduce greenhouse gas emission. The long-term approaches to achieving a significant reduction in CO2 emissions from the steel industry would be through developing and applying CO2 breakthrough technologies for iron and steel making, and through increasing use of renewable energy for iron and steel making. Thus, an overview of new CO2 breakthrough technologies for iron and steel making was made.

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.