Special Issue on Catalytic Activation and Selective Conversion of Energy-Related Molecules

The activation and selective conversion of energy-related molecules is an important research area of energy chemistry.The depletion of petroleum has stimulated research activities into the utilization of non-petroleum carbon resources such as natural gas(including conventional and

Uncovering the coupling effect with energy-related carbon emissions and human development variety in Chinese provinces

Lessening energy-related carbon emissions has become a crucial measure to achieve Chinese carbon neutrality.This study is the first to construct a Difference in Carbon pressures-adjusted Human Development Index(DCHDI)model for the purpose of exploring the coupling effect between carbon emissions and human development variety from 2000 to 2019 in Chinese provinces.We demonstrate the following.(1)The total energy-related carbon footprint of 30 provinces in China reached 10.2 billion tons in 2019,with an average annual growth rate of 6.93% over the past two decades;and the provinces with the highest carbon emissions per capita are InnerMongolia,Ningxia,and Shanxi.(2)At the provincial level,we observed that the Human Development Index(HDI),which includes life expectancy,education,and income,has been rising,while Beijing,Shanghai,and Tianjin entered the super-high HDI level before 2008.(3)The entire coupling effect of 30 Chinese provinces has been broadly fortified in the last 20 years,but the growth rate of DCHDI values in 2011-2019 has slowed down compared with that in 2000-2010;the clustering phenomenon demonstrated that this discovery is associated with historical peaks in total carbon emissions.(4)The co-ordination degree of carbon emissions per capita and HDI was verified,and 96% of the data points were found in the range of super high coupling coordination degree.Overall,this study provides the government with worthwhile guidance for decision-making and carbon reduction strategies for other countries struggling to advance human sustainable development.

Unsupervised learning of load signatures to estimate energy-related building features using surrogate modelling techniques

Characterization of an existing building’s energy-related features is critical to inform maintenance and retrofit decisions.However,existing field-scale characterization methods tend to be labour intensive,invasive,and require high fidelity longitudinal data gathered through tightly regulated experiments.This highlights the need for a low cost,scalable,and efficient screening method.This paper puts forward a surrogate model-based approach to rapidly estimate energy-related building features.To this end,EnergyPlus models for 12 midrise office archetypes,all with a rectangular footprint,are developed.Ten thousand variants of each archetype are generated by altering envelope,causal heat gain,and heating,ventilation,and air conditioning operation features.A unique load signature is derived for each variant’s heating and cooling energy use.The parameters of the load signatures are clustered,then each cluster is associated with a set of plausible energy-related features.The accuracy of the results was evaluated using five test buildings not seen by the algorithm.The method could effectively identify building features with reasonable accuracy and no significant degradation in performance across all 12 archetypes.

Recent progress in the synthesis of graphene/CNT composites and the energy-related applications

Graphene and carbon nanotube(CNT) are representative carbon nanomaterials which have aroused numerous research interest due to their extraordinary material properties and promising application potentials,especially in the energy storage and conversion areas.However,the agglomeration happening in these materials has largely blocked their applications.Hybridization of CNT with graphene can,on one hand,prevent the agglomeration behavior,on the other hand,generate a synergistic effect between them with enhanced physical and chemical properties.There have been many studies conducted to find out the suitable approaches to synthesize graphene/CNT composites,and realize the application potentials of these structures.Based on the recent advances,this paper reviews the current research progress that has been achieved in synthesizing graphene/CNT composites,and the energy-related applications.Through this review,we aim at stimulating more significant research on this subject.

Energy-related Severe Accident Database(ENSAD):cloud-based geospatial platform

The Energy-related Severe Accident Database(ENSAD)is the most authoritative resource for comparative risk analysis of accidents in the energy sector.Although ENSAD contains comprehensive,worldwide data,it is a non-spatial database in Microsoft Access format.Therefore,spatial characteristics of the data cannot be fully utilised as well as analysed directly.Based on these premises,a new web-based version of ENSAD with GIS-capabilities–named ENSAD v2.0–is designed and developed using state-of-the-art,open source technologies.The ENSAD v2.0 consists of two main components,i.e.a spatial database and a responsive web application.For the spatial database,the current accident data are georeferenced and migrated from Microsoft Access,using a tiered approach.The responsive web application can be accessed from desktops as well as mobile devices,and provides both a 2D and 3D mapping platform that is developed on cloud-based,serverless architecture.ENSAD v2.0 also allows assigning different user roles with specific access rights,and a public version with advanced visualisation capabilities has also been developed.Lastly,a case study was carried out using a spatial analysis to visualise the potential impact radius of a natural gas pipeline explosion and to assess its consequences in terms of economic damage and casualties.

Low-carbon economic development in Central Asia based on LMDI decomposition and comparative decoupling analyses

Low-carbon economic development is a strategy that is emerging in response to global climate change. Being the third-largest energy base in the world, Central Asia should adopt rational and efficient energy utilization to achieve the sustainable economic development. In this study, the logarithmic mean Divisia index(LMDI) decomposition method was used to explore the influence factors of CO2 emissions in Central Asia(including Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan and Turkmenistan) during the period 1992–2014. Moreover, decoupling elasticity and decoupling index based on the LMDI decomposition results were employed to explore the relationship between economic growth and CO2 emissions during the study period. Our results show that the total CO2 emissions decreased during the period 1992–1998, influenced by the collapse of the Soviet Union in 1991 and the subsequent financial crisis. After 1998, the total CO2 emissions started to increase slowly along with the economic growth after the market economic reform. Energy-related CO2 emissions increased in Central Asia, mainly driven by economic activity effect and population effect, while energy intensity effect and energy carbon structure effect were the primary factors inhibiting CO2 emissions. The contribution percentages of these four factors(economic activity effect, population effect, energy intensity effect and energy carbon structure effect) to the total CO2 emissions were 11.80%, 39.08%, –44.82% and –4.32%, respectively, during the study period. Kazakhstan, Uzbekistan and Turkmenistan released great quantities of CO2 with the annual average emissions of 189.69×106, 45.55×106 and 115.38×106 t, respectively. In fact, their economic developments depended on high-carbon energies. The decoupling indices clarified the relationship between CO2 emissions and economic growth, highlighting the occurrence of a ’’weak decoupling’’ between these two variables in Central Asia. In conclusion, our results indicate that CO2 emissions are still not completely decoupled from economic growth in Central Asia. Based on these results, we suggest four key policy suggestions in this paper to help Central Asia to reduce CO2 emissions and build a resource-conserving and environment-friendly society.

Analysis of Chinese Accounting Standards for the Oil and Gas Industry and Related Enterprises

Accounting standards are the tools for distribution of the revenues. Their development trend is influenced by their stakeholders. The evolution of American oil and gas accounting standards has been shaped by the profit-maximizing process of American oil and gas company shareholders, which for outside lobbying relied on their huge capital and organization. The development and perfection of Chinese new oil and gas accounting standards should consider not only the criterion of standards but also the real political fact in China oil and gas industry. The research on oil and gas accounting standards is an academic study as well as a political analysis.

基于LMDI分解和STIRPAT模型的哈萨克斯坦能源碳排放影响因素的定量分析（英文）

Quantitative analysis of the impact factors in energy-related CO2 emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO_2 emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO2 emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO2 emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO2 emissions, which produced 110.86 Mt CO2 emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO2 emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused –110.90 Mt CO2 emissions with a contribution rate of –43.94%, followed by the energy carbon structure effect resulting in –18.76 Mt CO2 emissions with a contribution rate of –7.43%. In order to provide an in-depth examination of the change response between energy-related CO2 emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO_2 emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.