Explore the vast range of titles available.

The transport sector is the fourth largest industrial CO 2 emitter in China, next to power sector, iron and steel industries, and nonmetallic mineral product industry, and plays an important role in reducing China’s CO 2 emissions. In this study, a temporal decomposition analysis model, i.e., Logistic Mean Division Index (LMDI), is developed to analyze the influencing factors of CO 2 emissions in China’s transport sector during 2000–2015. Then, a multi-regional spatial decomposition model is employed to identify the key factors to induce the differences in CO 2 emissions of China’s 30 regional transport sectors in 2000, 2005, 2010, and 2015. Based on the empirical results, we find that both in the temporal and spatial perspectives, the main factors that affect CO 2 emissions in the transport sector are the same ones. From the temporal perspective, the income effect is the dominant factor increasing CO 2 emissions of transport sector, while energy intensity effect and transportation structure effect are the key influencing factors that curb the CO 2 emissions of China’s transport sector, during the whole study period. From the spatial perspective, the income effect, energy intensity effect, and transportation structure effect are the key influencing factors that enlarge the gap of CO 2 emissions of various transport sectors in the key study years. More importantly, the less-developed regions and high energy intensity regions (i.e., the lower energy efficiency regions) are identified to have the great potential to reduce CO 2 emissions of transport sector. Therefore, differentiated mitigation measures and interregional collaborations are encouraged to reduce transport sector’s CO 2 emissions in China.

The energy transition from coal and oil to renewable energy, nuclear energy, and natural gas is a fundamental way for emission reduction of China’s power generation sector. Until now, research on the drivers of CO 2 emissions from China’s power generation sector has generally evaluated the energy mix as a whole, with a lack of exploration of the decomposition of different types of energy. This paper uses both index decomposition analysis (IDA) and structural decomposition analysis (SDA) to explore the impacts of energy transition on CO 2 emissions in the power generation sector during periods of 2002–2007, 2007–2012, and 2012–2017. We find that the results of IDA and SDA are almost consistent, indicating that our results are robust. During the whole study period, CO 2 emissions of power generation sector increased by 2447 Mt, of which the fossil fuel structure significantly contributed 642 Mt of incremental emissions (IDA). The thermal power generation efficiency was a dominator for reducing emissions, with a total reduction of 586 Mt (IDA). Simultaneously, the impacts of renewable energy and nuclear energy on emission reduction tend to be strengthening over time, with values changing from 38 Mt and −5 Mt in 2002-2007 to −219 Mt and −83 Mt (IDA) in 2012-2017, respectively. Based on the results, we put forward some suggestions such as promoting coal-to-gas, renewable energy, and nuclear energy in power generation to cut down CO 2 emissions of China’s power generation sector.

In order to meet climate change mitigation goals, nations such as Japan need to consider strategies to reduce the impact that lifestyles have on overall emission levels. This study analyzes carbon footprints from household consumption (i.e., lifestyles) using index and structural decomposition analysis for the time period from 1990 to 2005. The analysis identified that households in their 40s and 50s had the highest levels of both direct and indirect CO2 emissions, with decomposition identifying consumption patterns as the driving force behind these emissions and advances in CO2 reduction technology having a reducing effect on lifestyle emissions. An additional challenge addressed by this study is the aging, shrinking population phenomenon in Japan. The increase in the number of few-member and elderly households places upward pressure on emissions as the aging population and declining national birth rate continues. The analysis results offer two mitigatory policy suggestions: the focusing of carbon reduction policies on older and smaller households, and the education of consumers toward low-carbon consumption habits. As the aging, shrinking population phenomenon is not unique to Japan, the findings of this research have broad applications globally where these demographic shifts are being experienced.

China faces urgent road transportation carbon emission (RTC) reduction under its “dual carbon” goals. Achieving RTC reduction necessitates not only identifying key driving factors but also clarifying the sectoral linkages closely associated with RTC. Within an input–output framework, this study integrated structural decomposition analysis (SDA) and production‐theoretical decomposition analysis (PDA) to construct a two‐stage decomposition method. Applying this approach, we examined the drivers and sectoral connections of RTC from 2002 to 2020. The findings reveal that (1) due to its service‐oriented nature, 72.6% of RTC stemmed from transportation services fulfilling intermediate demand from other sectors, with the construction sector exhibiting the largest intermediate demand and thus contributing most significantly to RTC. (2) The final demand effect was the dominant driver behind RTC growth, accounting for a cumulative increase of 590.3 Mt, though its influence has weakened since 2017. (3) The steady decline in emission intensity served as the primary factor reducing RTC, resulting in a cumulative decrease of 190.8 Mt, attributable mainly to advances in energy technology, improved energy efficiency, and shifts in energy structure. We recommend that the transportation department should strive to improve energy technology and enhance efficiency, thereby effectively reducing carbon emission intensity. Meanwhile, government should actively adjust industrial structure and optimize transportation layout to further reduce RTC.

The linear relations between adsorption energies are one of the cornerstones of contemporary catalysis in view of the simplicity and predictive power of the computational models built upon them. Despite their extensive use, the exact nature of scaling relations is not yet fully understood, and a comprehensive theory of scaling relations is yet to be elaborated. So far, it is known that scalability is dictated by the degree of resemblance of the adsorbed species. In this work, density functional theory calculations show that CO and OH, two dissimilar species, scale or not depending on the surface facet where they adsorb at Pt alloys. This peculiar behavior arises from an interplay of ligand and geometric effects that can be used to modulate adsorption‐energy scalability. This study opens new possibilities in catalysis, as it shows that surface coordination is a versatile tool to either balance or unbalance the similarities among adsorbates at alloy surfaces. Currently, it is widely accepted that adsorption‐energy scaling relations exist or not depending on the similarity of the adsorbates. Here, we show that CO and OH scale linearly or not on Pt alloys depending on the coordination of the adsorption sites. Hence, ligand and geometric effects can be used to modulate scaling relations, thereby opening new possibilities in catalysis.

AbstractThe structure, electronic properties, and aromaticity of the (para-C5H4X)Ir(PH3)3 iridabenzene complexes (X = NH2, OH, Me, H, F, Cl, CF3, CN, NO2) were illustrated by applying the hybrid density functional MPW1PW91 theory. The electron donor groups (EDG) and electron withdraw groups (EWG) effects on structure, bonding properties, and aromaticity were investigated. The nature of the chemical bond between the [Ir(PH3)3]3+ and [para-C5H4X]3– fragments was investigated with energy decomposition analysis (EDA). Charge decomposition analysis (CDA) and extended charge decomposition analysis (ECDA) were used for study of electron transfer between [Ir(PH3)3]3+ and [para-C5H4X]3– fragments in the studied complexes. The Ir–C, Ir–P and CC bond characters were illustrated through the application of the quantum theory of atoms in molecules analysis (QTAIM). Para-delocalization index (PDI) was used for explanation of the aromaticity of six-membered rings.

The required decarbonization of the energy system is a complex task, with ambitious targets under the Paris Agreement, and related policy analysis should consider possible impacts on the economy and society. By coupling the energy system model TIMES PanEU with the impact assessment model EcoSense and the computable general equilibrium model NEWAGE, we present an integrated assessment toolbox for the European energy system capable of internalizing health damage costs of air pollution while simultaneously accounting for demand changes in energy services caused by economic feedback loops. The effects of each coupling step are investigated in a scenario analysis. Additionally, CO2 decomposition analysis is applied to identify the main drivers to decarbonize the energy system. Our results show that integrating externalities forces the system to take early action, which provides benefits on the societal level. Including macro-economic variables has a negative effect on energy service demands and generally reduces the need for structural change, which are still the main drivers of decarbonization. The tighter the models are coupled, the fewer the iterations needed and the lower the CO2 prices resulting from the carbon cap and trade system. In this aspect, an integrated view can provide valuable insights to determine efficient and effective decarbonization paths.

In this work, we optimized XH···C 20 (X = F, Cl, Br) systems at the LC-ωPBE /6-311G( d,p ) level of theory and illustrated structural, electronic and vibrational properties in these systems. The relative stabilities of the D-containing isotopomers (XD···C 20 ) and XH···C 20 systems were compared. Bond critical point of C···H interactions were clarified by quantum theory of atoms in molecules (QTAIM) calculations. Binding energy of hydrogen bonds were computed based on electron density at BCP of H-bond. Charge decomposition analysis (CDA) was considered to understanding of the electron transfer between XH and C 20 molecules. Reduced density gradient (RDG) was used to analyze the C···X interactions.

In this work, we reported a computational investigation on the interaction between a cycloplatinated thiosemicarbazone (CT) as antiparasitic and antitumor agents with C20 molecule. The solvent impacts were considered by the SCRF based on PCM. The relationships of solvation energies, interaction energy, dipole moment, and N-H stretching frequencies (v(NH)) values with modified-Buckingham function were illustrated. ETS-NOCV, CDA, and EDA results provided valuable understanding into the interaction between two fragments.

To become the fastest-growing large economy in the world, India has set a target growth rate of 9%, reaching an economy of $5 trillion by 2024-25. It is an immense challenge to meet the growth target and keep the CO2 emissions under control. The present paper aims to discover the determinants for explaining CO2 emissions in India by conducting a complete decomposition analysis, where the residuals are fully distributed to the determinants for the country from 1990-2018. The analysis reveals that the biggest contributor to the rise in CO2 emissions in India is the expansion of the economy (scale effect). The intensity of CO2 and the change in the composition of the economy, which nearly move in tandem, also contribute to the rise in CO2 emissions, although more slowly. A declining energy intensity of the Indian economy is responsible for a considerable reduction in CO2 emissions. As a typical result for an upcoming economy, this paper did not find evidence for an environmental Kuznets curve. This implies that continued economic growth will lead to increased CO2 emissions.