• Energy Research
• 2016-2025close
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The Chinese electric power industry, including its coal industry and other energy industries that are not efficient, contributes to China’s serious energy shortages and environmental contamination. The governing authority considers energy conservation to be one of the most prominent national targets, and has formulated various plans for decarbonizing the power system. Applying the trans-log cost function, this paper examined the trans-log cost function to analyze the potential inter-factor substitution among energy, capital and labor. We also investigated what role human capital played in energy substitution for the electric power sector during the period from 1981 to 2017. Three key results were derived: (1) energy is price-insensitive, (2) there exists large substitution sustainability between both capital and labor with energy, and (3) human capital input not only enhances the extent of energy substitutability with capital and labor but also is a substitute to energy itself. These findings imply that the liberalization of the electric price mechanism is conducive to lessening energy use and augmenting non-energy intensiveness, and that energy conservation technology could become more sustainable by investing more capital in the electricity sector, thereby achieving a capital–energy substitution and a decrease of CO2 emissions. We further suggest that the priority for the Chinese electric power industry should be to attach more importance to increasing human capital input.

Simple indicators are often used to summarize the complexity of systems or products, commonly through color-coded labels paired with letters. These labels, like those indicating energy efficiency or nutritional ratings, help users quickly understand essential characteristics. Building on this approach, the Quality Urban Label (QUL) has been developed to assess public space adaptation to urban climate change. The QUL utilizes four key indicators, called quality components: air quality (pollutants), noise pollution, thermal comfort, and visual comfort. It ranges from 0 to 25 and is represented by a color and letter code (green, A, ranging from 0 to 2; blue, B, ranging from 6 to 11; orange, C, ranging from 15 to 19; and red, D, ranging from 22 to 25), with green representing better quality and red poorer quality. The QUL aims to evaluate public spaces based on energy consumption reduction, greenhouse gas emissions reduction, and progress toward carbon neutrality. This article explores some ecological and climate benefits of the QUL, especially in warm Mediterranean cities. An objective label that quantifies the alignment of urban public space with climate neutrality has numerous advantages, which are discussed in the article. In addition, it is a key tool for urban project planning, focused on reducing urban social inequalities and promoting a just energy transition of urban public space.

This paper addresses some questions that climate change raises for international law. It focuses in particular on the request for an advisory opinion submitted by the United Nations General Assembly to the International Court of Justice (ICJ) on 29 March 2023. This request is analysed as a further manifestation of the international community’s concern to clarify the international responsibility of states to prevent, mitigate and remedy the damage caused by climate change. The study argues that the ICJ could clarify obligations under existing treaties. There may also be a particular opportunity for the Court to expand its jurisprudence on other sources of international law. In particular, it asks whether the ICJ could confirm the emergence of a specific customary rule on the issue.

SignificanceEstimates of nutrient allocation in different plant tissues and the relationships between the nutrient contents and photosynthetic capacity are critical to predicting ecosystem carbon sequestration under global change. Here, we provide an assessment of large-scale patterns of community-level nitrogen and phosphorus concentrations in different plant tissues and then examine how nutrient allocations are coupled with plant productivity. The results show that nutrient concentrations in leaves are less responsive to abiotic environments than those in woody stems and roots (stable leaf nutrient concentration hypothesis); the relationships between vegetation primary productivity and leaf nutrient contents are stronger when less nutrients are allocated to the woody tissues (productivity–nutrient allocation hypothesis) and are stronger in deciduous than in evergreen vegetation (productivity–leaf lifespan hypothesis).

A promising technology for renewable energy is energy piles used to heat and cool buildings. In this research, the effects of bio-cementation via microbially induced calcite precipitation (MICP) using mixed calcium and magnesium sources and the addition of fibres on the thermal conductivity of soil were investigated. Firstly, silica sand specimens were treated with cementation solutions containing different ratios of calcium chloride and magnesium chloride to achieve maximum thermal conductivity improvement. Three treatment cycles were provided, and the corresponding thermal conductivity was measured after each cycle. It was found that using 100% calcium chloride resulted in the highest thermal conductivity. This cementation solution was then used to treat bio-cemented soil samples containing fibres, including polyethylene, steel and glass fibres. The fibre contents used included 0.5%, 1.0% and 1.5% of the dry sand mass. The results show that the glass fibre samples yielded the highest thermal conductivity after three treatment cycles, and SEM imaging was used to support the findings. This research suggests that using MICP as a soil improvement technique can also improve the thermal conductivity of soil surrounding energy piles, which has high potential to effectively improve the efficiency of energy piles.

Pedestrian inertial navigation technology plays an important role in indoor positioning technology. However, low-cost inertial sensors in smart devices are affected by bias and noise, resulting in rapidly increasing and accumulating errors when integrating double acceleration to obtain displacement. The data-driven class of pedestrian inertial navigation algorithms can reduce sensor bias and noise in IMU data by learning motion-related features through deep neural networks. Inspired by the RoNIN algorithm, this paper proposes a data-driven class algorithm, RBCN-Net. Firstly, the algorithm adds NAM and CBAM attention modules to the residual network ResNet18 to enhance the learning ability of the network for channel and spatial features. Adding the BiLSTM module can enhance the network’s ability to learn over long distances. Secondly, we construct a dataset VOIMU containing IMU data and ground truth trajectories based on visual inertial odometry (total distance of 18.53 km and total time of 5.65 h). Finally, the present algorithm is compared with CNN, LSTM, ResNet18 and ResNet50 networks in VOIMU dataset for experiments. The experimental results show that the RMSE values of RBCN-Net are reduced by 6.906, 2.726, 1.495 and 0.677, respectively, compared with the above networks, proving that the algorithm effectively improves the accuracy of pedestrian navigation.