19 research outputs found
The influence of different allowance allocation methods on China's economic and sectoral development
<p>China launched its national carbon emissions trading scheme (ETS) in 2017. The choice of allowance allocation methods can strongly influence the political acceptance of an ETS by enterprises/sectors that are covered by it. This article builds a computable general equilibrium model to conduct a quantitative analysis of the effects of nine common allowance allocation methods on both the macro-economy and the industries covered by the ETS. The results of the model show that national gross domestic product (GDP) decreases by 0.37–0.44% during the 13th Five-Year Plan period against a backdrop of a 2% annual reduction in carbon emissions from the sectors covered by the ETS compared with the business-as-usual scenario. China's total emissions drop by 1.71–1.76%. When auctioning and allocation approaches without ex-post adjustment are used, the allowance price is 40–45 yuan/tCO<sub>2</sub>. When the dynamic allocation methods are used, the allowance price increases to 70–75 yuan/tCO<sub>2</sub>. Auctioning and allocation approaches without ex-post adjustment exert the same influence on macroscopic indicators (such as GDP and total emissions) and industry indicators (such as output and price). The dynamic allocation methods have a subsidy effect, which can significantly reduce the effect of the ETS on GDP and industry output while significantly increasing the allowance price and decreasing the economic efficiency of the ETS. The cement and steel industries are the most sensitive to the output subsidy effect of the dynamic allocation methods. This article suggests a limit on the use of dynamic allocation approaches to avoid excessively high allowance prices and excessive subsidies for overcapacity industries.</p> <p><b>Key policy insights</b></p><p>Auctioning and one-off allocation purely based on historical data are most economically efficient; dynamic allocation based on updated or actual output data could reduce the impact of the ETS on enterprises’ output, but will increase the allowance price and thus reduce the economic efficiency of the ETS.</p><p>Implementing a national ETS will have limited impact on China's GDP, but could promote emissions abatement of the whole economy in an efficient way.</p><p>Different allocation methods have almost the same impact on GDP, but the impacts on different sectors are significantly different.</p><p></p> <p>Auctioning and one-off allocation purely based on historical data are most economically efficient; dynamic allocation based on updated or actual output data could reduce the impact of the ETS on enterprises’ output, but will increase the allowance price and thus reduce the economic efficiency of the ETS.</p> <p>Implementing a national ETS will have limited impact on China's GDP, but could promote emissions abatement of the whole economy in an efficient way.</p> <p>Different allocation methods have almost the same impact on GDP, but the impacts on different sectors are significantly different.</p
PhID: An Open-Access Integrated Pharmacology Interactions Database for Drugs, Targets, Diseases, Genes, Side-Effects, and Pathways
The current network pharmacology
study encountered a bottleneck
with a lot of public data scattered in different databases. There
is a lack of an open-access and consolidated platform that integrates
this information for systemic research. To address this issue, we
have developed PhID, an integrated pharmacology database which integrates
>400 000 pharmacology elements (drug, target, disease, gene,
side-effect, and pathway) and >200 000 element interactions
in branches of public databases. PhID has three major applications:
(1) assisting scientists searching through the overwhelming amount
of pharmacology element interaction data by names, public IDs, molecule
structures, or molecular substructures; (2) helping visualizing pharmacology
elements and their interactions with a web-based network graph; and
(3) providing prediction of drug–target interactions through
two modules: PreDPI-ki and FIM, by which users can predict drug–target
interactions of PhID entities or some drug–target pairs of
their own interest. To get a systems-level understanding of drug action
and disease complexity, PhID as a network pharmacology tool was established
from the perspective of data layer, visualization layer, and prediction
model layer to present information untapped by current databases
Effectiveness of pilot carbon emissions trading systems in China
<p>China is in the process of establishing a national emissions trading system (ETS). Evaluating the implementation effectiveness of the seven pilot ETSs in China is critical for designing this national system. This study administered a questionnaire survey to assess the behaviour of enterprises covered by the seven ETS pilots from the perspective of: the strictness of compliance measures; rules for monitoring, reporting and verification (MRV); the mitigation pressure felt by enterprises; and actual mitigation and trading activities. The results show that the pilot MRV and compliance rules have not yet been fully implemented. The main factors involved are the lack of compulsory force of the regulations and the lack of policy awareness within the affected enterprises. Most enterprises have a shortage of free allowances and thus believe that the ETSs have increased their production costs. Most enterprises have already established mitigation targets. Some of the covered enterprises are aware of their own internal emission reduction costs and most of these have used this as an important reference in trading. Many enterprises have accounted for carbon prices in their long-term investment. The proportion of enterprises that have participated in trading is fairly high; however, reluctance to sell is quite pervasive in the market, and enterprises are mostly motivated to trade simply in order to achieve compliance. Few enterprises are willing to manage their allowances in a market-oriented manner. Different free allowance allocation methods directly affect the pathways enterprises take to control emissions.</p> <p><b>Key policy insights</b></p><p>In the national ETS, the compulsory force of ETS provisions should be strengthened.</p><p>A reasonable level of free allowance shortage should be ensured to promote emission reduction by enterprises.</p><p>Sufficient information should be provided to guide enterprises in their allowance management to activate the market.</p><p>To promote the implementation of mitigation technologies by enterprises, actual output-based allocation methods should be used.</p><p>The government should use market adjustment mechanisms, such as a price floor and ceiling, to ensure that carbon prices are reasonable and stable, so as to guide long-term low carbon investment.</p><p></p> <p>In the national ETS, the compulsory force of ETS provisions should be strengthened.</p> <p>A reasonable level of free allowance shortage should be ensured to promote emission reduction by enterprises.</p> <p>Sufficient information should be provided to guide enterprises in their allowance management to activate the market.</p> <p>To promote the implementation of mitigation technologies by enterprises, actual output-based allocation methods should be used.</p> <p>The government should use market adjustment mechanisms, such as a price floor and ceiling, to ensure that carbon prices are reasonable and stable, so as to guide long-term low carbon investment.</p
Data_Sheet_1_Nonlinear relationship between platelet count and 30-day in-hospital mortality in intensive care unit stroke patients: a multicenter retrospective cohort study.pdf
BackgroundEvidence of the relationship between platelet count and 30-day in-hospital mortality in ICU stroke patients is still scarce. Therefore, the purpose of this study was to explore the relationship between platelet count and 30-day in-hospital mortality among ICU stroke patients.MethodsWe conducted a multicenter retrospective cohort study using data from 8,029 ICU stroke patients in the US eICU-CRD v2.0 database from 2014 to 2015. Utilizing binary logistic regression, smooth curve fitting, and subgroup analyses, we examined the link between platelet count and 30-day in-hospital mortality.ResultsThe 30-day in-hospital mortality prevalence was 14.02%, and the mean platelet count of 223 × 109/L. Adjusting for covariates, our findings revealed an inverse association between platelet count and 30-day in-hospital mortality (OR = 0.975, 95% CI: 0.966, 0.984). Subgroup analyses supported the robustness of these results. Moreover, a nonlinear relationship was observed between platelet count and 30-day in-hospital mortality, with the inflection point at 163 × 109/L. On the left side of the inflection point, the effect size (OR) was 0.92 (0.89, 0.95), while on the right side, the relationship was not statistically significant.ConclusionThis study establishes an independent negative association between platelet count and 30-day in-hospital mortality in ICU stroke patients. Furthermore, a nonlinear relationship with a saturation effect was identified, suggesting that maintaining the platelet count around 163 × 109/L can reduce 30-day in-hospital mortality in these patients.</p
Correct and incorrect results made over different reaction distances.
<p>Correct and incorrect results made over different reaction distances.</p
Incorrect prediction KEGG reaction examples on different substrate specificities.
<p>Incorrect prediction KEGG reaction examples on different substrate specificities.</p
One KEGG reaction example, S-Adenosyl-L-methionine+L-Tryptophan< = >S-Adenosyl-L-homocysteine+Abrine (R00683, ‘enzyme not yet characterized’), and its closest training KEGG reaction, S-Adenosyl-L-methionine+Serotonin< = >S-Adenosyl-L-homocysteine+N-Methylserotonin (R02910), for EC assignment prediction.
<p>One KEGG reaction example, S-Adenosyl-L-methionine+L-Tryptophan< = >S-Adenosyl-L-homocysteine+Abrine (R00683, ‘enzyme not yet characterized’), and its closest training KEGG reaction, S-Adenosyl-L-methionine+Serotonin< = >S-Adenosyl-L-homocysteine+N-Methylserotonin (R02910), for EC assignment prediction.</p
Comparisons of several EC assignment methods by considering their method basis, if they are automatic for a whole reaction, and if there is a web server available.
<p>Comparisons of several EC assignment methods by considering their method basis, if they are automatic for a whole reaction, and if there is a web server available.</p
Incorrect prediction KEGG reaction examples on inter-molecular and intra-molecular transformations.
<p>Incorrect prediction KEGG reaction examples on inter-molecular and intra-molecular transformations.</p
Crassula barbata
<p>Cross-validation accuracies of reaction difference fingerprints with different lengths.</p