• Energy Research
• other engineering and technologies close
• 7. Clean energy close
• Sustainability close

China’s research on and specific implementation of energy saving for buildings are mainly concentrated in urban areas, but according to 2016 statistics, the rural population accounts for 42.65% of the total population, so rural housing has considerable energy-saving potential. However, the degree of attention to the energy consumption of rural houses needs to be improved. Regarding the research on and implementation of passive energy-saving strategies for residences, compared with centralized urban high-rise residences, rural residences mainly have independent courtyards, with a flexible layout and easier transformation. In this study, a system that uses the common cold lanes in traditional villages and buildings’ exterior walls was constructed, and the indoor spaces of courtyard buildings in southern Shaanxi were completely passively cooled in summer. This system can be completely separated from the supply of artificial energy by relying on the accumulation and buoyancy effects of air in patios and cold lanes and the hot-pressure ventilation in buildings to cool the buildings and greatly improve indoor ventilation efficiency. As the building is ventilated and cooled, the air wall formed in the system can effectively prevent direct contact between the outdoor and indoor temperatures and reduce the impact of thermal wall radiation on the interior. In previous studies on the passive design of courtyard houses, scholars considered the effect of thermal wall radiation on indoor temperature in simulations. Therefore, in this study, we also separately calculated whether to consider the difference between the situation with and without wall heat radiation (WHR) when simulating thermal conversion. The final results show that when the cooling system was adopted, the annual cooling load of the whole building was 4786.494 kW·h without WHR. However, with WHR, the cooling load reduction was 2989.128 kW·h, a difference of 1797.336 kW·h.

To meet international efforts to prevent climate change, the South Korean government is seeking to transform its main power source from coal to natural gas (NG), which emits less carbon dioxide (CO2) than coal. The government needs information about public preferences for increasing NG-fired generation. Therefore, the purpose of this paper is to provide the government with this information by gathering the data on public preferences for increasing NG-fired generation for reducing CO2 emissions, analyzing these data, and obtaining the implications from the analysis. To this end, a survey of 1000 people, after explaining the merits and demerits of NG-fired power, were asked to determine the proper ratio of NG-fired generation by choosing one of six examples: lower than 10%, 10%–20%, 20%–30%, 30%–40%, 40%–50%, and higher than 50%. An interval data model was employed to deal with the data. The average value of the ratio was estimated to be 26.7% with statistical significance, which is greater than the actual value of 22.2% from 2017. Finally, whether and how much some variables such as education level, age, gender, and income level of the respondent affected their preferences for NG-fired generation were investigated.

3D city models are a useful tool to analyze the solar potential of neighborhoods and cities. These models are built from buildings footprints and elevation measurements. Footprints are widely available, but elevation datasets remain expensive and time-consuming to acquire. Our hypothesis is that the GIS cadastral data can be used to build a 3D model automatically, so that generating complete cities 3D models can be done in a short time with already available data. We propose a method for the automatic construction of 3D models of cities and neighborhoods from 2D cadastral data and study their usefulness for solar analysis by comparing the results with those from a hand-built model. The results show that the accuracy in evaluating solar access on pedestrian areas and solar potential on rooftops with the automatic method is close to that from the hand-built model with slight differences of 3.4% and 2.2%, respectively. On the other hand, time saving with the automatic models is significant. A neighborhood of 400,000 m2 can be built up in 30 min, 50 times faster than by hand, and an entire city of 967 km2 can be built in 8.5 h.

Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.

This paper discusses the effect of various climatic conditions that pertain to passive design measurements and their relationships with building configurations to improve indoor thermal comfort based on the different climate zones in Egypt to support Egypt’s sustainability agenda 2030. We find the most appropriate design settings that can increase the indoor thermal comfort, such as building orientation and shape. These settings can be modeled using DesignBuilder software combined with Egyptian meteorological data. This software is used accompanied by computational fluid dynamics to numerically assess the outcomes of different changes, by simulating indoor climate condition factors such as wind speed and temperature. Natural ventilation simulations were performed for four different shapes to create comprehensive dataset scenarios covering a general range of shapes and orientations. Seven scenarios were optimized to put forward a series of building bioclimatic design approaches for the different characteristic regions. The results indicated that the temperature decreased by about 3.2%, and the air velocity increased within the study domain by 200% in the best and the worst cases, respectively, of the four different shapes. The results of the study gave evidence that the configuration of buildings, direction, and wind speed are very important factors for defining the natural ventilation within these domains to support the green building concept and the sustainable design for a better lifestyle.

In recent years, the global economy has become more closely related among countries, and people’s pursuit of economic growth has caused the destruction of the environment. This paper selected panel data from 30 provinces in China from 1997 to 2020 to investigate the dynamic relationship between trade liberalization, financial development and carbon dioxide emissions by constructing a PVAR model. We also consider technology as an important variable for studying the effect on carbon dioxide emissions. We draw the following conclusions. First, financial development promotes carbon dioxide emissions, while trade liberalization has no significant impact on carbon dioxide emissions. Second, China’s trade liberalization promotes financial development, which has limited support for international trade. Third, there is a two-way causal relationship between financial development and carbon dioxide emissions, and there is also a two-way causal relationship between trade liberalization and financial development. Finally, there is a significant inverted “U” curve relationship between trade liberalization and innovation efficiency, environmental regulation and innovation. According to the results, we believe that openness to trade impacts emissions of carbon dioxide, opening a new function path: namely, trade openness and financial development result in high carbon dioxide emissions; consequently, China has relied on this process in the development of their financial system.

Closed-loop supply chain networks are gaining research popularity due to environmental, economic and social concerns. Such networks are primarily designed to overcome carbon footprints and to retrieve end of life products from customers. This study considers a multi echelon closed-loop supply chain in the presence of machine disruption. A multi-objective model is presented to optimize the total cost, the total time and emissions in a closed-loop supply chain network. The aim is to analyze the trade-off between the objectives of cost, time, and emissions and how these decisions are impacted by the selection of different available machines. A number of solution approaches are tested on a case study from the tire industry. The results suggest the improved performance of the hybrid heuristic and the importance of controlling disruption in a closed-loop supply chain network. Furthermore, there is a trade-off between the different objective functions which can help the decision maker to choose a particular solution according to the preference of an organization. Finally, conclusion and future research avenues are provided.

Significant harm from heat has become a key restriction for deep metal mining with increasing mining depth. This paper proposes a refrigeration ventilation system for deep metal mines combined with an existing air cycling system and builds an experimental platform with six stope simulation boxes. Using the heat current method and the driving-resistance balance relationship, the heat transfer and flow constraints of the system were constructed. An artificial neural network was used to establish models of heat exchangers and refrigerators with historical experimental data. Combining the models of the system and stope simulation box, an algorithm that iterates the water outlet temperature of the evaporator and condenser of the refrigerator was proposed to design the coupled simulation model. The heat balance analysis and comparison of the air outlet temperatures of the stope, as well as the heat transfer rates of the heat exchangers with the experimental data, validated the coupled simulation model. Additionally, the effects of cooling fans and the air inlet temperature of the cooling tower were discussed, which provided a powerful modelling method for the coupled model of a refrigeration ventilation system, helps to reduce energy consumption, and improves the sustainability of mining production.