Soka University Repository / 創価大学機関リポジトリ
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Research on the Impact of ESG Rating on the Corporate Bonds: Analysis of Yield Spreads and Performance
Get PDFThe primary goal of most businesses is to generate profit, but there is increasing recognition that their responsibilities extend beyond profit-making to include environmental, social, and governance (ESG) factors. ESG investing has become a dominant trend, particularly in green bonds, and is expected to gain further prominence.Although there is substantial research on the relationship between ESG factors and corporate financial performance, the impact of ESG ratings on corporate bond spreads, especially in emerging markets, remains underexplored. This study investigates the connection between ESG ratings and corporate bond spreads, focusing on whether strong ESG performance can reduce financing costs in China's market.Analyzing data from firms listed on the China Stock Exchange from 2009 to 2020, the study examines the effects of ESG ratings on corporate bond spreads in both the current and lagged periods. The results indicate that higher ESG ratings significantly lower corporate bond spreads, with the effect intensifying over time, suggesting a lasting impact of ESG performance on financing costs.The study also observes that the influence of ESG ratings on bond spreads has grown in recent years, particularly following the 2018 inclusion of ESG factors in corporate governance codes by the China Securities Regulatory Commission. Moreover, the nature of the corporation—state-owned versus private—significantly affects bond spreads, with state-owned enterprises benefiting from lower spreads due to perceived creditworthiness.Lastly, while the money supply positively correlates with bond spreads, other macroeconomic factors like Shibor do not. Financial performance indicators, such as interest coverage ratio and return on assets (ROA), show no significant correlation with bond spreads.departmental bulletin pape
On the vicissitudes of a lack of belonging in adolescence
Get PDF本研究では、大学生を対象に、思春期に感じた居場所感のなさを量的・質的な面から探索的に研究することで、自己形成の過程で、居場所感のなさが生起しうることを知ることで、思春期の子どもが周囲の大人への援助要請力を高めることや思春期の子どもに対する適切な支援のための知見を得ることを目的とした。研究の結果、量的研究では、直接的な〈居場所感〉や人との〈つながり〉を感じているかどうかによって孤独感との相関関係に違いが見られた。また、質的研究からは居場所感がない感覚と、居場所感がある感覚との間に【かりそめの居場所感】が存在すること、思春期に居場所感のなさを感じている子どもは援助要請に困難があることが示唆された。今後は、学校以外の空間における居場所感の質的なプロセス変化について検討すること、質的研究の協力者に個別で支援が必要な臨床群が含まれているかどうかを考慮することを課題として挙げた。departmental bulletin pape
保育の質と「よい」についての理論的研究 ─幼児の表現に関する保育者の理解─
Get PDF保育所等の定員充足率は逓減傾向にあり、保育所等は今後淘汰されていくことが予想されている。保育者は単にサービスの提供者ではなく、専門的役割を担った教師である。そのため、保育者は自身の言葉で保育実践の教育的意義を語ることが求められる。幼児教育段階の保育施設は園によって特色があり、保育理念や活動内容に大きな差があるという特徴がある。園における取組について、保護者の理解を得る際には「なぜよいのか」を語るであろう。その語りの根拠となりうるものを探るため、幼稚園教育要領等の整理検討を行った。結果、保育者が教育的視座に基づいて幼児の表現を解釈し、そこに込められた意義を語る際、その主観さえも専門性に結び付けることができると示唆された。また、保育者同士が教育観を開示し、話し合いを行うなかに同僚性と園文化の醸成を伴う「よい保育」の検討が行われうることを示唆し、今後の研究の展望としている。departmental bulletin pape
教育実習生の自己の強みの自覚と、実習後の教職志望の意識との関連性
Get PDF近年、教員不足が加速しており、その背景には、教員志望者の減少が要因の一つにあるものと考えられる。そのため、教職の魅力向上が喫緊の課題であり、養成・採用・研修の一体化事業（文部科学省、2023）では、理論と実践を通じた教育実習等の在り方の研究が取り上げられており、養成段階から学校現場での質の高い学びが重要であることが明確である。また、教職を志す学生にとっても、教育実習（以下、「実習」とする）は、教職を志望するモチベーションを維持、さらには向上するための重要な機会となり得る。本研究の目的は、教育実習生の自己の強みの自覚と実習後の教職志望度との関連性を明らかにすることである。本研究では、東京都内中規模大学教職課程履修学生を対象に、実習生の自己の強みの自覚と実習前後のアンケート調査を実施した。得られたデータについて、因子分析を行い、各尺度を構成する概念と相互の関係性を明らかにした。自己の強みは、「自己効力感と成長志向」、「他者貢献と社会性」、「建設的・チーム志向」の3 つの因子として示すことができた。実習生が認識するこれらの自己の強みと実習における学びの内容には関係性があり、それにより教職志望度に影響を及ぼすという知見が得られ、教職課程のあり方についての課題を見いだした。departmental bulletin pape
リモートセンシング技術を使用したエチオピアのタナ湖におけるホテイアオイ (Eichhornia crassipes) の時空間的変動解析とその影響
Get PDF創価大学博士（工学）This study examined the spatiotemporal dynamics of water hyacinth (WH) and its impact on hydrology and water quality in Lake Tana, Ethiopia. Three non-parametric machine learning algorithms were evaluated for WH detection. All classifiers achieved >95% accuracy with Sentinel-2 and >90% with Landsat-8. Although the performance differences between the methods were small, Random Forest demonstrated the highest accuracy and was used to estimate the spatiotemporal variability of WH distribution. High WH populations were concentrated in Lake Tana’s northeastern sector, with spatial coverage increasing significantly from 2015 to 2023. Water loss due to WH evapotranspiration also increased significantly during this period. Lake surface water temperature (LSWT) decreased significantly across all seasons except the dry season. Turbidity declined significantly in all seasons except the pre-rainy season. Chlorophyll-a (Chl-a) decreased in pre-rainy and rainy seasons but showed a non-significant increasing trend during dry and post-rainy seasons. WH biomass had a non-significant positive correlation with LSWT (r = 0.18), while a significant negative correlation with turbidity (r = -0.33) and Chl-a (r = -0.35). This study identified RF as the most accurate method for WH detection and comprehensively quantified its spatiotemporal distribution and impacts on the ecosystem using remote sensing technology for the first time.doctoral thesi
Measuring the Phillips Curve by Machine Learning
Get PDFIn recent years, machine learning and Bayesian modeling have been applied in various fields. Machine learning includes supervised learning, unsupervised learning, and reinforcement learning, and regression analysis, which is often used in economics, is a typical example of supervised learning. Dynamic programming, a fundamental tool for dynamic macroeconomic models, is also an important topic in reinforcement learning. However, regression analysis in machine learning is fundamentally different from traditional regression models and is called Gaussian process regression. It is more computationally expensive than conventional methods, but it is more flexible and provides more accurate forecasts. This paper introduces Gaussian process regression and Bayesian modeling, which are unfamiliar to many economists, by estimating the Phillips curve for Japan. Gaussian process regression is a stochastic model of a functional relationship, in which the probability distribution of the function allows the uncertainty of the forecast to be made explicit. Traditionally, parametric models such as linear regression models have been used in empirical research in economics. However, various problems arise when applying conventional models to big data, which has recently come into use. A nonparametric approach should be applied to big data. In econometrics, approaches based on the frequentist approach of statistics have been predominant, with an emphasis on obtaining maximum likelihood estimators of parameters. Although the maximum likelihood estimator has consistency and invariance under general conditions, analytical solutions are often not available for nonlinear models. There is also the problem of multiple local maximum points depending on the shape of the likelihood function. Bayesian approaches based on alternatives to frequency-based approaches have been applied in many fields, but in economics, they are limited to a few, such as estimating the parameters of DSGE and VAR models. The main reason is that it requires a huge amount of computation, but powerful tools such as the MCMC method are now available. In Section 2, we estimate the Phillips curve for Japan using Bayesian methods.departmental bulletin pape
Educational Practice Report: Beethoven Book Fair Curated by Students for the 200th Anniversary of the Premiere of the Ninth Symphony
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The Development of Value Creating Education in the Republic of Kenya
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