Examining industrial air pollution embodied in trade: implications of a hypothetical China-UK FTA.

Very few developed economies have a full free trade agreement (FTA) with China. This study employs one GTAP model and builds an extended environmental multi-region input-output model to investigate a hypothetical China-UK FTA, concerning embodied industrial emissions of SO2, PM2.5, NOX, and NH3. The economic sectors are also classified based on their embodied pollution intensity and trade advantage index under various FTA scenarios. Results show that the UK's GDP and welfare and China's welfare will increase, along with changes in their trade structures. Overall, this FTA brings about larger net impacts on embodied emissions of SO2 than on PM2.5, NOX and NH3, and both countries are net importers of the latter three pollutants. Key sectors such as non-metallic mineral products, chemical products, and agriculture are inclined to become less competitive and less polluting under the FTA. The inclusion of agri-food sectors exhibits slight counteracting effects in general. The findings are of policy importance as they provide insights into how best to target key sectors, seeking a balance between trade development and environmental protection. Supplementary Information: The online version contains supplementary material available at 10.1007/s10668-022-02612-z.

A bibliometric review on carbon accounting in social science during 1997-2020.

A social science perspective to carbon accounting is essential for determining the appropriate allocation of reduction responsibility, and thus contributing to addressing the climate crisis. It is crucial to have a comprehensive review of the literature in this field to better understand how relevant research has evolved, and to identify gaps that future studies need to work on. Based on the bibliographic database from the Web of Science (WOS), we identified 897 publications relevant to carbon accounting in the social sciences published between 1997 and 2020. Bibliometric analysis is applied to analyze the trends and features of carbon accounting research in the social sciences. The results show that international trade has spurred considerable scholarly interest in responsibility allocation from a consumption perspective. IO (input-output) analysis that can be used to derive embodied emissions in trade has therefore become the most popular method in this domain. It is also revealed that few publications have addressed quantification of emissions at organizational level. In consideration of the importance of organizations especially corporations in emission reduction, a shift of priority to this particular area is needed for further research. Carbon label and supply chain have emerged as a subject in keywords analysis, but have not been addressed enough either. To achieve carbon neutrality, solely relying on actions at country and organizational level may not be sufficient. Greener consumption behaviors of the public and individuals could play a remarkable role. Thus, it is important to formulate a consistent framework for labeling carbon embodied in products and investigate the drivers of consumers' low-carbon choices.

Theoretical Calculation of Hydrogen Generation and Delivery via Photocatalytic Water Splitting in Boron-Carbon-Nitride Nanotube/Metal Cluster Hybrid.

Solar-driven water splitting is an appealing strategy to produce hydrogen energy. However, the non-negligible chance of reverse reactions due to a mixture of hydrogen molecules (H2) with oxygen species poses challenges for safe H2 collection and delivery, which hinders its applications. Using first-principles simulations, we propose a hybrid structure design where metal clusters of TM4 (TM = Au/Pt) are encapsulated in boron-carbon-nitride nanotube (BCNNT) decorated with CuN3 group. It can readily absorb ultraviolet-visible solar light to generate charge carriers. The energetic electrons and holes would be separately delivered to the reduction site of TM4 and the oxidation site of the BCNNT layer. Then, protons generated by water dissociation at the BCNNT layer will penetrate through BCNNT and consequently meet electrons at the TM4 site to be reduced into H2. As a selective sieve, BCNNT prevents oxygen species from going inside and H2 from crossing out so that H2 can be completely isolated. Further, the sufficient space of the tubular cavity endows the transportation feasibility of the produced H2 along the nanotube for collection. This proposed design combines photocatalytic hydrogen production and safe delivery, which may help in developing a practical solution for a photodriven hydrogen production.

Using Machine Learning to Predict the Dissociation Energy of Organic Carbonyls.

Bond dissociation energy (BDE), an indicator of the strength of chemical bonds, exhibits great potential for evaluating and screening high-performance materials and catalysts, which are of critical importance in industrial applications. However, the measurement or computation of BDE via conventional experimental or theoretical methods is usually costly and involved, substantially preventing the BDE from being applied to large-scale and high-throughput studies. Therefore, a potentially more efficient approach for estimating BDE is highly desirable. To this end, we combined first-principles calculations and machine learning techniques, including neural networks and random forest, to explore the inner relationships between carbonyl structure and its BDE. Results show that machine learning can not only effectively reproduce the computed BDEs of carbonyls but also in turn serve as guidance for the rational design of carbonyl structure aimed at optimizing performance.

Probing Cellular and Molecular Mechanisms of Cigarette Smoke-Induced Immune Response in the Progression of Chronic Obstructive Pulmonary Disease Using Multiscale Network Modeling.

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disorder characterized by progressive destruction of lung tissues and airway obstruction. COPD is currently the third leading cause of death worldwide and there is no curative treatment available so far. Cigarette smoke (CS) is the major risk factor for COPD. Yet, only a relatively small percentage of smokers develop the disease, showing that disease susceptibility varies significantly among smokers. As smoking cessation can prevent the disease in some smokers, quitting smoking cannot halt the progression of COPD in others. Despite extensive research efforts, cellular and molecular mechanisms of COPD remain elusive. In particular, the disease susceptibility and smoking cessation effects are poorly understood. To address these issues in this work, we develop a multiscale network model that consists of nodes, which represent molecular mediators, immune cells and lung tissues, and edges describing the interactions between the nodes. Our model study identifies several positive feedback loops and network elements playing a determinant role in the CS-induced immune response and COPD progression. The results are in agreement with clinic and laboratory measurements, offering novel insight into the cellular and molecular mechanisms of COPD. The study in this work also provides a rationale for targeted therapy and personalized medicine for the disease in future.