In this paper, we proposed a novel concept of using thermal diode bridge (TDB) and phase change materials (PCMs) to control the temperature in the built environment continuously without any external energy consumption. In the daytime, the solar energy was efficiently harvested and stored in the PCMs so the indoor temperature was significantly reduced. At night, the thermal energy stored in the PCMs was used to heat the room. A thermodynamic model was established to evaluate the performance of the system. It was found that the temperature as well as the temperature variation in the built environment can be significantly reduced with zero energy consumption and green gas emission.
With China’s urbanization process, a number of medium-sized cities in the south eastern coastal areas have also entered a period of rapid development. Fast-growing cities are facing many challenges, such as housing demand and resource depletion in connection with the rapid population growth. Building industry, which contributes to a large amount of resource use, plays an important role in climate change. Along with the updating of energy saving standards, the thermal condition of building envelopes has also been dramatically improved. Therefore, energy consumption for space heating and cooling has been significantly reduced in recent years. However, there is still a lack of research concerning the embodied energy mainly caused by material use. A benchmark of life-cycle resource use in the building sector is urgently required in practice in order to achieve a more sustainable development in China. This research took the city Qingdao as a case study and examined lifecycle resource use of typical office and residential buildings with different structures and thermal conditions. The method and result could be considered as a reference for a more complete research on a building lifecycle resource use in the future. In the end of this paper, a neighborhood in Qingdao was taken as a case study to show the application of resource use benchmark in the future.
Accurate prediction of air conditionerâ€™s dynamic operation is very important for advanced control and fault diagnosis method. The prediction of vehicle air conditionersâ€™ performance faces many challenges like unstable working conditions and frequent â€œon-offâ€ operation. This research proposes a long short-term memory (LSTM) recurrent neural network-based method to tackle this tricky issue. The proposed model is trained and tested with field operation data to prove its capability.
This paper presents a game theory-based modeling framework for government subsidy optimization and renewable multi-energy system (MES) design. The government offers subsidy for renewable technologies, while consumers return rational response to government subsidy on deploying renewable technologies in their respective MES. The game theory-based subsidy optimization and MES design is first formulated as a mixed-integer bilevel nonlinear programming problem and then transformed into a single level mixed-integer linear programming problem using Karush-Kuhn-Tucker conditions and linearization strategies. The results show that the government needs to provide a total subsidy of 3.86 million USD in order to achieve a renewable penetration of 60% in a small urban city composed of four towns. With government subsidy, the total net present costs for the four towns are 8.24, 6.7, 8.32, and 8.93 million USD, respectively.
Waste, especially that of plastics, is a global problem that carries serious economic, social and, particularly, environmental impacts. Many studies and statistics have demonstrated the continuously accelerating trend in plastic production, and that plasticassociated problems are becoming increasingly worse due to its ubiquitous nature in human life. Also, issues associated with technological and economical limitations in waste sorting and recycling processes cannot effectively tackle the problem with recyclable plastics. In a reality, a huge amount of recyclable plastic ends up in landfills. One of the ways in which to reduce landfilled plastics, which is considered to be losing scarce resources, is to use plastic-to-energy technologies. Catalytic pyrolysis is a promising technology to treat plastics and produce valuable energy fuels. Despite a lack of studies in this area, catalytic pyrolysis has showed more advantages than many other plastic treatment technologies in terms of environmental and economic impacts. More research studies are needed to determine all the benefits, and indeed pitfalls, of using this technology. In our study, we compare centralized and decentralized catalytic pyrolysis systems for comingled post-consumer waste plastic mixtures via lifecycle assessment (LCA) and economic analysis. The novelty of this topic is that the comparison of centralized and decentralized catalytic pyrolysis systems has not been previously been considered, and the advantages and disadvantages of catalytic pyrolysis using comingled post-consumer waste plastic mixtures is not particularly well understood by other researchers. LCA can determine the global warming and eutrophication potential of centralized and decentralized catalytic
pyrolysis systems that use plastics. Economic analysis can identify which of the two is more economically efficient by comparison of facility cost, maintenance and operating costs, and absolute revenues. Ultimately, social, economic, and environmental impacts can be interpreted based on LCA and economic analyses.
High penetration of renewable energy and the random real-time charging of large-scale electric vehicles are challenging to traditional urban power system technically and economically. This urgently calls for accelerating the development of smart energy solutions to improve the resilience of urban power system, and mobilized and distributed battery is believed to have the potential to provide the solution due to the advantages of high energy density, fast response, and convenient installation. Aiming at effectively satisfying the enormous urban power demand and realizing the cost-effectiveness and environmental-sustainability of power supply, this paper develops a two-stage method to achieve logistics and scheduling optimization of batteries at various temporal and spatial scales between renewable energy power plants and cities. The stage-one model is a battery transportation and logistics optimization problem, in which the objective function is to minimize the total cost of battery purchase and transportation considering the railway transport capacity, battery balance and other technical constraints. Based on the forecast results of available supply and demand of fully charged batteries in each renewable energy power plant and city, the detail battery transportation route and volume can be obtained. The stage-two model is a railway scheduling problem to determine the departure time of railway loaded with fully-charged batteries to achieve the purpose of maximizing the peak load regulation of urban power system. Finally, the proposed approach is applied to six cities in China, and the results demonstrate that the battery logistics and scheduling model can effectively improve the penetration of renewable energy and alleviate the peak power consumption of urban power system, thereby realizing the low-carbon development of urban energy system.
SO2 is a common air pollutant, which is harmful to human physical and mental health. This study construct, an individual-family-province three-level linear regression model to reveal the heterogeneity effect of SO2 pollution and household health care expense on depression. The findings are as follows: (1) 28.73% and 1.91% depression differences are caused by family and province differences. (2) At the family level, the higher the family health care expenditure, the greater the inequality of depression caused by the differences of social status and physical health. (3) At the province level, the depression difference between men and women was smaller under high SO2 concentration. The inequality in depression caused by education level disparity increased with the increase in SO2 concentration. The depression difference between individuals with and without chronic disease was larger under high SO2 concentration. The inequality in depression caused by relative income disparity increased with the increase in SO2 concentration. This study is helpful for the government to formulate relevant health service policies for depression and promote the equal development of public mental health.
China is a country which has different regions development and has distinct seasonal characteristic. The air pollution spilloverâ€™s characteristic in different regions and seasons have different feature. To analyze how to distinguish the regions of air pollution, we used complex network algorithm to discover the topological features of Chinese cities air pollution spillover system. We divided the cities which have synchronization to same region. Then we analyzed the air pollutionâ€™s spillover between regions in different seasons by motif algorithm. In this research, we divided the cities to different regions easy to control air pollution spillover between cities. After that we analyzed the air pollutionâ€™s spillover between regions and found that there is a core region in every season and different have different core regions. This region could influence most other regions in this season. Therefore, we could control air pollutionâ€™s spillover between regions by control the air pollution in core region.
The rapid development of the economy and urbanization leads to sharp increases in the generation of municipal solid waste (MSW). MSW can threaten urban ecosystems if not properly managed. The understanding of the influential factors of waste gene generation is essential to developing sustainable waste management plans. It can also provide useful information for the formulation of pollution control policies.
In this work, the autoregressive distributed lag (ARDL) model is used to analyze the impact of economic development (per capita GDP), consumer price index (CPI), and population growth on MSW output (e.g. waste arising per capita) during the period 2004-2020 monthly from Scottish governmentâ€™s statistics data. Furthermore, the study predicts the next decadeâ€™s potential output based on the 2004-2020 MSWâ€™s data in Glasgow, Scotland. Based on the prediction of MSW generation in the coming decades and three types of advanced technologies, i.e. pyrolysis, gasification, and concentrated solar thermal gasification, the waste-to-energy potential is evaluated for Glasgow. The results will help governmental agencies to design measures to address the increasing waste generation effectively.
China’s urbanization has entered a new phase, and more and more migrant workers have become urban residents, and rapid urbanization is expected to further increase global energy consumption. Meanwhile, climate change will increase the refrigeration demand of residents, which is generally met with electric air conditioners and fans. It is necessary to be concerned about the energy consumption of migrant workers to ensure that society develops sustainably and stably. This paper compares the influence of temperature on the electricity consumption by rural residents, migrant workers and urban residents in Guangzhou in 2017.The results show that the 1Â°C rise in temperature leads to a 3% increase in electricity consumption by rural households, a 7% increase by rural migrant worker, a 9% increase by urban household. More seriously, the actual price of the migrant workers cost burden of electricity consumer spending is the biggest of the three types of families, make life in the city of migrant workers families facing serious electricity consumption inequality in hot weather.