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Forecasting carbon dioxide (CO2) emissions is necessary to estimate their climate impact and the required mitigation efforts. However, existing scenario frameworks are not consistent and limit the comparability of projection studies. In this study, the "Stochastic Impacts by Regression on Population, Affluence and Technology" model was combined with the shared socioeconomic pathways (SSPs) defined by the Intergovernmental Panel on Climate Change. Model forecasts were calculated for each SSP from 2020 to 2060 in Qinghai Province, an underdeveloped, carbon-sink-rich region in China. Total CO2 emissions in Qinghai Province consistently exhibited an upward then downward trend, with large differences between SSPs in the peak date and maximum value of total CO2 emissions. Considering the current socioeconomic development status and total carbon emission trends in Qinghai Province, SSP1 and SSP2 were the most suitable pathways, providing balance between carbon emissions reduction and population well-being. [ABSTRACT FROM AUTHOR]

The achievement of dual carbon goals varies significantly across Chinese provinces due to the differences in resource endowments and socioeconomic development levels. These variations impose distinct pressures for transitioning along different socioeconomic development paths. Exploration of orderly carbon reduction paths for each province is, thus, crucial, but current predictions have not adequately integrated provincial development plans and carbon emission convergence trends. This study combines the Intergovernmental Panel on Climate Change (IPCC's) shared socioeconomic pathways (SSPs) with a dual-layer target-setting approach based on trends in carbon emissions and each province's five-year plans. The scalable stochastic impacts by regression on population, affluence, and technology (STIRPAT) model and ridge regression method were employed to identify the factors influencing carbon emissions. Carbon peak values and timing were then predicted for provinces under different scenarios. The results indicate that total carbon emissions are primarily influenced by provincial economic level and population, with spatial and temporal variations in the driving factors. Carbon emissions are ranked from low to high in the following order: SSP1, SSP2, and SSP5. Provinces were categorized into four tiers based on their peak times: early, on-time, delayed, and significantly delayed peaking. Under SSP1, 24 provinces would achieve carbon peaking as scheduled. Under SSP2, only 17 provinces would achieve carbon peaking on time. Among the first-tier provinces, Beijing and Shanghai would achieve peak carbon five to eight years ahead of schedule; the second-tier provinces of Henan, Fujian, Guangdong, and Inner Mongolia would achieve peak carbon by 2030; the third tier would achieve carbon peak before 2035; and the fourth tier would gradually reach peak carbon before 2050. Policy implications for differentiated carbon peak paths are proposed for different regions based on these findings. [ABSTRACT FROM AUTHOR]

This study examines the interplay between ambient temperature, brown adipose tissue (BAT) function, and bone metabolism, emphasizing the effects of cold exposure and BAT mitochondrial activity on bone health. Utilizing ovariectomized (OVX) mice to model primary osteoporosis and BAT-specific mitochondrial dysfunction (BKO) mice, we evaluated the impact of housing temperature on bone density, immune modulation in bone marrow, and the protective role of BAT against bone loss. Cold exposure was found to universally reduce bone mass, enhance osteoclastogenesis, and alter bone marrow T-cell populations, implicating the immune system in bone remodeling under cold stress. The thermogenic function of BAT, driven by mitochondrial oxidative phosphorylation, was crucial in protecting against bone loss. Impaired BAT function, through surgical removal or mitochondrial dysfunction, exacerbated bone loss in cold environments, highlighting BAT's metabolic role in maintaining bone health. Furthermore, cold-induced changes in BAT function led to systemic metabolic shifts, including elevated long-chain fatty acids, which influenced osteoclast differentiation and activity. These findings suggest a systemic mechanism connecting environmental temperature and BAT metabolism with bone physiology, providing new insights into the metabolic and environmental determinants of bone health. Future research could lead to novel bone disease therapies targeting these pathways. [ABSTRACT FROM AUTHOR]

A promising research area in nanomaterials is the use of superatomic clusters as building blocks for creating novel molecules or materials with tailored properties. However, assembling these superatoms into functional materials is challenging, and a thorough understanding of this process is still lacking. In this study, we introduce a new concept called the "superatomic family," which refers to superatoms that share similar valence electron structures but differ in size. We demonstrate this concept with the synthesized [Au6{Ni3(CO)6}4]2− cluster and the designed [Au16{Ni6(CO)10}4]2− and [Au31{Ni10(CO)15}4]5− clusters. These serve as analogs to simple hydrocarbons, such as methane (CH4), silicon hydride (SiH4), and germanium hydride (GeH4). In these supermolecular structures, the central cores of Au6, Au16, and Au31 exhibit the formation of superatomic SP3 hybridized orbitals, which influence the molecular shape and bonding. Moreover, we explored superatomic bonding involving SP3–SP3 hybridized cores, representing a single superatomic bond between members of the superatomic family, analogous to CH3–SiH3 bonds. By integrating the concept of group superatoms into the Lewis structure framework, we present a powerful approach for predicting and engineering cluster properties, thus opening a vast landscape of nanomaterial design possibilities. [ABSTRACT FROM AUTHOR]

An energy transition programme based on the principles of energy justice is an important way to mitigate climate change; however, empirical studies on energy justice remain scarce. This study explores key aspects of energy transition policy implementation in China, using three dimensions of energy justice – distributional, recognition and procedural justice – to establish an analytical framework and help develop quantification methodologies. We focused on Luquan District, Shijiazhuang City (Hebei Province, China), to assess energy justice levels following the implementation of China's 'coal-to-gas' energy transition policy in rural regions. The findings indicate that China's rural energy policy has not delivered energy justice. Analysis revealed a markedly low procedural justice index, registering a mere 0.37 on a scale of 0–1, attributable to factors such as the lack of involvement of rural households and a lack of transparency in the design and formulation of policy, as well as a lack of access of rural households to policy-related information. Furthermore, insufficient acknowledgment of the needs of specific groups during the energy transition has negatively impacted recognition justice, resulting in a mid-level index value of 0.69. By comparison, this study shows reveals a reasonably high energy distributional justice index value of 0.89. These findings suggest the need for the government to enhance energy policy communication and responsiveness to its stakeholders, to acknowledge and fairly address the energy transition needs of rural consumers, and to implement targeted energy subsidies to augment distributional justice and preclude the waste of limited financial resources. The analytical framework and calculation methods presented here could contribute to quantifying energy justice levels and to informing energy transition policy both in China and more broadly elsewhere in the world. Since 2013, implementation of China's 'coal-to-gas' energy transition policy has engendered impacts on different dimensions of social justice that have influenced the realization of a just transition. A composite index system is established, encompassing distributional justice, recognition justice, and procedural justice, and quantitative exploration of energy justice is undertaken using a reverse deduction method. The greatest losses in justice, following implementation of China's 'coal-to-gas' energy transition policy, are related to procedural justice, whereas recognition justice incurs moderate losses, and distributional justice experiences the lowest level of losses. Acknowledging the disparities among different groups, the ideal allocation of energy subsidies should be actualized through preliminary surveys, consultation and coordination, thereby preventing resource wastage. Recognizing individual rights and guaranteeing that the policy implementation process is open, transparent and participatory, could enhance energy justice and movement toward a just transition in the context of climate change policy. [ABSTRACT FROM AUTHOR]