3,099 research outputs found
The impact of climate change on credit cycles: Evidence from China's bond market
This study examines the long-term impacts of climate change on credit spreads using a sample of A-share listed companies that issued corporate bonds between 2014 and 2020. The results indicate that the greater the climate change risk a company faces, the greater its bonds' credit spread; the secondary bond market perceives the potential harm caused by climate change. This conclusion remains valid after endogeneity treatment and robustness tests. Furthermore, mechanism testing reveals that climate change affects bond credit spreads by increasing the probability of corporate bankruptcy, profit volatility, and negative investor sentiment. The heterogeneity test considers three aspects, namely, individual enterprises' internal characteristics, creditor protection measures, and regional green finance levels. Finally, this study introduces the impact of economic cycles to test the conclusions at the macro level and closely follow the topic. Thus, we provide new evidence and perspectives for exploring the impact of climate risk on the bond market and important policy implications for green finance and the healthy development of China's bond market
Corporate sustainability policies and corporate investment efficiency: Evidence from the quasi-natural experiment in China
This paper studies the impact of green disclosure on firm investment efficiency, leveraging a policy experiment in China. Since 2012, the Chinese government has begun to implement the Ambient Air Quality Standards (AQS), which have strengthened the requirements for green disclosure throughout the country. We exploit the rollout of the AQS and find that tightening the green disclosure requirements significantly increases corporate investment efficiency. This increase is primarily driven by a reduction in underinvestment among non-state-owned firms and firms with low institutional ownership. Further analysis suggests that the alleviation of agency problems and the reduction of financial constraints are the two main mechanisms through which green disclosure influences firm investment efficiency. Our findings provide valuable policy implications, indicating that strengthening green disclosure standards can have a substantial positive impact on firm investment outcomes
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors
Existing tensor factorization methods assume that the input tensor follows
some specific distribution (i.e. Poisson, Bernoulli, and Gaussian), and solve
the factorization by minimizing some empirical loss functions defined based on
the corresponding distribution. However, it suffers from several drawbacks: 1)
In reality, the underlying distributions are complicated and unknown, making it
infeasible to be approximated by a simple distribution. 2) The correlation
across dimensions of the input tensor is not well utilized, leading to
sub-optimal performance. Although heuristics were proposed to incorporate such
correlation as side information under Gaussian distribution, they can not
easily be generalized to other distributions. Thus, a more principled way of
utilizing the correlation in tensor factorization models is still an open
challenge. Without assuming any explicit distribution, we formulate the tensor
factorization as an optimal transport problem with Wasserstein distance, which
can handle non-negative inputs.
We introduce SWIFT, which minimizes the Wasserstein distance that measures
the distance between the input tensor and that of the reconstruction. In
particular, we define the N-th order tensor Wasserstein loss for the widely
used tensor CP factorization and derive the optimization algorithm that
minimizes it. By leveraging sparsity structure and different equivalent
formulations for optimizing computational efficiency, SWIFT is as scalable as
other well-known CP algorithms. Using the factor matrices as features, SWIFT
achieves up to 9.65% and 11.31% relative improvement over baselines for
downstream prediction tasks. Under the noisy conditions, SWIFT achieves up to
15% and 17% relative improvements over the best competitors for the prediction
tasks.Comment: Accepted by AAAI-2
Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. IV. H Time Lags and Implications for Super-Eddington Accretion
We have completed two years of photometric and spectroscopic monitoring of a
large number of active galactic nuclei (AGNs) with very high accretion rates.
In this paper, we report on the result of the second phase of the campaign,
during 2013--2014, and the measurements of five new H time lags out of
eight monitored AGNs. All five objects were identified as super-Eddington
accreting massive black holes (SEAMBHs). The highest measured accretion rates
for the objects in this campaign are , where
,
is the mass accretion rates, is the Eddington luminosity and
is the speed of light. We find that the H time lags in SEAMBHs are
significantly shorter than those measured in sub-Eddington AGNs, and the
deviations increase with increasing accretion rates. Thus, the relationship
between broad-line region size () and optical luminosity at
5100\AA, , requires accretion rate as an additional
parameter. We propose that much of the effect may be due to the strong
anisotropy of the emitted slim-disk radiation. Scaling by
the gravitational radius of the black hole, we define a new radius-mass
parameter () and show that it saturates at a critical accretion rate of
, indicating a transition from thin to slim
accretion disk and a saturated luminosity of the slim disks. The parameter
is a very useful probe for understanding the various types of accretion onto
massive black holes. We briefly comment on implications to the general
population of super-Eddington AGNs in the universe and applications to
cosmology.Comment: 53 pages, 12 figures, 7 tables, accepted for publication in The
Astrophysical Journa
2016 Consensus statement on prevention of atherosclerotic cardiovascular disease in the Hong Kong population
published_or_final_versio
Minimal intervention for controlling nosocomial transmission of Methicillin-Resistant Staphylococcus aureus in resource limited setting with high endemicity
Objective: To control nosocomial transmission of methicillin-resistant Staphylococcus aureus (MRSA) in resource-limited healthcare setting with high endemicity. Methods: Three phases of infection control interventions were implemented in a University-affiliated hospital between 1- January-2004 and 31-December-2012. The first phase of baseline period, defined as the first 48-months of the study period, when all MRSA patients were managed with standard precautions, followed by a second phase of 24-months, when a hospital-wide hand hygiene campaign was launched. In the third phase of 36-months, contact precautions in open cubicle, use of dedicated medical items, and 2% chlorhexidine gluconate daily bathing for MRSA-positive patients were implemented while hand hygiene campaign was continued. The changes in the incidence rates of hospital-acquired MRSA-per- 1000-patient admissions, per-1000-patient-days, and per-1000-MRSA-positive-days were analyzed using segmented Poisson regression (an interrupted time series model). Usage density of broad-spectrum antibiotics was monitored. Results: During the study period, 4256 MRSA-positive patients were newly diagnosed, of which 1589 (37.3%) were hospitalacquired. The reduction of hospital-acquired MRSA per 1000-patient admissions, per 1000-patient-days, and per 1000- MRSA-positive-days from phase 1 to 2 was 36.3% (p<0.001), 30.4% (p<0.001), and 19.6% (p = 0.040), while the reduction of hospital-acquired MRSA per 1000-patient admissions, per 1000-patient-days, and per 1000-MRSA-positive-days from phase 2 to 3 was 27.4% (p<0.001), 24.1% (p<0.001), and 21.9% (p = 0.041) respectively. This reduction is sustained despite that the usage density of broad-spectrum antibiotics has increased from 132.02 (phase 1) to 168.99 per 1000 patient-days (phase 3). Conclusions: Nosocomial transmission of MRSA can be reduced with hand hygiene campaign, contact precautions in open cubicle, and 2% chlorhexidine gluconate daily bathing for MRSA-positive despite an increasing consumption of broadspectrum antibiotics. © 2014 Cheng et al.published_or_final_versio
A targeted gene panel that covers coding, non-coding and short tandem repeat regions improves the diagnosis of patients with neurodegenerative diseases
Genetic testing for neurodegenerative diseases (NDs) is highly challenging because of genetic heterogeneity and overlapping manifestations. Targeted-gene panels (TGPs), coupled with next-generation sequencing (NGS), can facilitate the profiling of a large repertoire of ND-related genes. Due to the technical limitations inherent in NGS and TGPs, short tandem repeat (STR) variations are often ignored. However, STR expansions are known to cause such NDs as Huntington\u27s disease and spinocerebellar ataxias type 3 (SCA3). Here, we studied the clinical utility of a custom-made TGP that targets 199 NDs and 311 ND-associated genes on 118 undiagnosed patients. At least one known or likely pathogenic variation was found in 54 patients; 27 patients demonstrated clinical profiles that matched the variants; and 16 patients whose original diagnosis were refined. A high concordance of variant calling were observed when comparing the results from TGP and whole-exome sequencing of four patients. Our in-house STR detection algorithm has reached a specificity of 0.88 and a sensitivity of 0.82 in our SCA3 cohort. This study also uncovered a trove of novel and recurrent variants that may enrich the repertoire of ND-related genetic markers. We propose that a combined comprehensive TGPs-bioinformatics pipeline can improve the clinical diagnosis of NDs
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