Recognizing the complex inter-correlation between FDI, emission and the three economic determinants of emission, we constructed a simultaneous model to study the FDI-emission nexus in China by exploring both the dynamic recursive FDI entry decision and the linkage from FDI entry to final emission results under the intermediation of the scale, composition and technique effects. The model is then estimated on the panel data of China's 29 provinces' industrial SO2 emission. Result shows that, exerting through different channels; the total impact of FDI on industrial SO2 emission is very small. With 1% increase in FDI capital stock, industrial SO2 emission will increase by 0.099%, in which the emission increase caused by impact of FDI on economic growth and composition transformation cancels out the emission reduction result due to FDI's role in reinforcement of environmental regulation. By introducing to the simultaneous system the recursive dynamism that supposes FDI entry decision to depend on last period's economic growth and environmental regulation stringency, our model also provides convincing supportive evidences for ‘Pollution haven' hypothesis. Although FDI enterprises in China generally produce with higher pollution efficiency, the rise in environmental regulation stringency still has modest deterrent effect on FDI capital inflow. Furthermore, the composition transformation impact of FDI in China seems to be dominated by the inflow of foreign capital pursuing a ‘production platform' that provides lower pollution regulation compliance cost.
Abstract Background As the global population ages and life expectancies rise, improving the health and equity of middle-aged and older individuals has become a universal goal, especially with the economic benefits of the demographic dividend decreasing. Health investments (HI), which are crucial for improving health outcomes (HO) and protecting human capital, play a key role in achieving these objectives. This study aims to examine the impact of HI on the health status of middle-aged and elderly individuals, analyze issues of health equity among this population, and enhance their overall health level while fostering economic growth. Methods This study, based on Grossman’s health demand theory and China Health and Retirement Longitudinal Study (CHARLS) data from 2011 to 2020 (n = 11,138), examines middle-aged and elderly individuals (aged 45 years and above) across 28 Chinese provinces. A panel data model is used to assess HI and HO, with composite indices created using the entropy method. HI includes leisure, healthcare, and living environments, whereas HO covers self-reported short- and long-term health. A high-dimensional fixed-effects model is used to analyze the impact of HI on HO. Health equity is explored using the income Gini coefficient, health investment concentration index (I-CI), and health outcome concentration index (H-CI), with decomposition performed using the Shapley method. Results HI positively affects HO in middle-aged and elderly individuals in China. The key factors that influence HO are gender, age, household registration (HR), and income. Income inequality is significant, with an average Gini coefficient of 0.492. The I-CI averages 0.081, indicating higher investment concentration among wealthier groups. The major factors that influence the I-CI are household registration (34.9%), income (33.1%), employment (18.8%), and education (11.7%). The H-CI averages 0.033, with better outcomes associated with higher education. The key factors influencing H-CI are age (46.7%), gender (16.7%), income (15.2%), and education (10.7%). Conclusion HI significantly improves the HO and enhances the health human capital of middle-aged and elderly individuals. However, these investments tend to favor wealthier groups, whereas HO are more favorable among those with higher education. Income and education levels are the key drivers of inequity in both HI and HO.
Computer applications to medicine. Medical informatics, Public aspects of medicine
Investments constitute a fundamental component of most global economies. A primary strategic objective for Russia involves mobilizing idle financial resources and allocating them across all economic sectors. Given that attracting foreign capital is currently constrained, the savings of the Russian population (specifically retail savings) represent a significant financial potential. The financial sector increasingly relies on digital technologies and advanced devices, which fundamentally alter user strategies for managing personal savings. This digital transformation shifts the supply and demand dynamics of financial resources within the domestic market. Through economic and mathematical modeling, this study describes the impact of digitalization on retail savers’ behavior and assesses the current state of retail savings within the Russian economy. By identifying the economic nature of the retail savings market, the authors determine the effect of digital factors on savings patterns. Ultimately, digital technologies are a critical factor in projecting national-level savings behavior.
We propose a market-wide liquidity measure by exploiting the connection between the amount of arbitrage capital in the market and observed “noise” in U.S. Treasury bonds—the shortage of arbitrage capital allows yields to deviate more freely from the curve, resulting in more noise in prices. Our noise measure captures episodes of liquidity crises of different origins across the financial market, providing information beyond existing liquidity proxies. Moreover, as a priced risk factor, it helps to explain cross-sectional returns on hedge funds and currency carry trades, both known to be sensitive to the general liquidity conditions of the market. ∗Hu is from Faculty of Business and Economics, University of Hong Kong, and Pan (corresponding author) and Wang are from MIT Sloan School of Management, CAFR, and NBER. We are grateful to Cam Harvey (the Editor), the associate editor, two anonymous reviewers, Darrell Duffie, Mark Kritzman, Krishna Ramaswamy, Dimitri Vayanos, Adrien Verdelhan, and Haoxiang Zhu for valuable discussions. We also thank comments from seminar participants at Boston University, Shanghai University of Finance and Economics, University of Maryland, University of Michigan, University of Pennsylvania, University of Western Australia, Capula Investment Management LLC, 2011 NBER Asset Pricing Program Spring Meeting, Moody’s 2011 Credit Risk Conference, Morgan Stanley, and Q Group 2012 Spring Conference. The level of liquidity in the aggregate financial market is closely connected to the amount of arbitrage capital available. During normal times, institutional investors such as investment banks and hedge funds have abundant capital, which they can deploy to supply liquidity. Consequently, big price deviations from fundamental values are largely eliminated by arbitrage forces, and assets are traded at prices closer to their fundamental values. During market crises, however, capital becomes scarce and/or willingness to deploy it diminishes, and liquidity in the overall market dries up. The lack of sufficient arbitrage capital limits arbitrage forces and assets can be traded at prices significantly away from their fundamental values. Thus, temporary price deviations, or noise in prices, being a key symptom of shortage in arbitrage capital, contains important information about the amount of liquidity in the aggregate market. In this paper, we analyze the noise in the price of U.S. Treasuries and examine its informativeness as a measure of overall market illiquidity. Our basic premise is that the abundance of arbitrage capital during normal times helps smooth out the Treasury yield curve and keep the average dispersion low. This is particularly true given the presence of many proprietary trading desks at investment banks and fixedincome hedge funds that are dedicated to relative value trading with the intention to arbitrage across various habitats on the yield curve. During liquidity crises, however, the lack of arbitrage capital forces proprietary trading desks and hedge funds to limit or even abandon their relative value trades, leaving the yields to move more freely in their own habitats and resulting in more noise in the yield curve. We argue that abnormal noise in Treasury prices is a symptom of a market in severe shortage of arbitrage capital. More importantly, to the extent that capital is allocated across markets for major marginal players in the market, this symptom applies not only to the Treasury market, but also more broadly to the overall financial market.
Abstract The article introduces an innovative esg-metric quantitative methodology, developed as additive scoring model based on the outcomes of esg-questionnaires. A focused esg-assessment based on Environmental, Social & Governance attributes had been developed & thereafter coupled to a robust quantitative framework (topic of the present work). This lead to gain a synthetic scoring measure m, rating R, esg-sustainability probability p and esg-default like risk probability $$\psi =1-p$$ , (therefore a full metric) from the realized answers provided by the company ( answering the questionnaires). Starting from general quantitative assumptions, with an high degree of independence of the questions/business typology investigated, the realized score value is mapped into a normalized probability function p, with $$p\in (0,1)$$ , in order to make different questionnaires scales and score methodologies comparable. Main mathematical assumptions underlying the model had been summarized, justified & mathematical details are provided in the final Appendix. The instrument developed had been applied to few questionnaires (4) submitted to potential startups/targets of a Venture Capital, in order to outline first model application with real values. In future an higher number of esg-questionnaires will be studied with the proposed methodology. Introducing an abstract structured framework enables, other the main results here presented, demonstrating theorems like theorem 5, opening operational research opportunities in the field of applied quantitative methods for esg-assessment and even more in general in the theory of quantitative scoring.
Trap cropping is a pest management strategy where a grower plants an attractive "trap crop" alongside the primary crop to divert pests away from it. We propose a simple framework for optimizing the proportion of a grower's field or greenhouse allocated to a main crop and a trap crop to maximize agricultural yield. We implement this framework using a model of pest movement governed by trap crop attractiveness, the potential yield threatened by pests, and functional relationships between yield loss and pest density drawn from the literature. Focusing on a simple case in which pests move freely across the field and are attracted to traps solely by their relative attractiveness, we find that allocating 5-20 percent of the landscape to trap plants is typically required to maximize yield and achieve effective pest control in the absence of pesticides. For highly attractive trap plants, growers can devote less space because they are more effective; less attractive plants are ineffective even in large numbers. Intermediate attractiveness warrants the greatest investment in trap cropping. Our framework offers a transparent and tractable approach for exploring trade-offs in pest management and can be extended to incorporate more complex pest behaviors, crop spatial configurations, and economic considerations.
The capacity market provides economic guidance for generation investment and ensures the adequacy of generation capability for power systems. With the rapidly increasing proportion of renewable energy, the adequacy of flexibility and resilience becomes more crucial for the secure operation of power systems. In this context, this paper incorporates the flexibility and resilience demand into the capacity market by formulating the capacity demand curves for ramping capability, inertia and recovery capabilities besides the generation capability. The guidance on generation investment of the capacity market is also taken into account by solving the generation investment equilibrium among generation companies with a Nash Cournot model employing an equivalent quadratic programming formulation. The overall problem is established as a trilevel game and an iterative algorithm is devised to formulate the capacity demand curves in the upper level based on Genco's investment acquired from the middle and lower levels. The case study further demonstrates that to incorporate flexibility and resilience demand into the capacity market could stimulate proper generation investment and ensure the adequacy of flexibility and resilience in power systems.
Davoud Hosseinnezhad, Mel T. Devine, Seán McGarraghy
This paper employs a game-theoretic approach to analyze investment decisions in Ireland's electricity market. It compares optimal electricity investment strategies among energy generators under a perfect competition framework with an imperfect Nash-Cournot competition. The model incorporates market price based on competition among generators while accounting for the supply capacity of each firm and each technology, along with the System Non-Synchronous Penetration (SNSP) constraint to reflect operational limitations in renewable energy contribution to the power system. Both models are formulated as single-objective function optimization problems. Furthermore, unit commitment constraints are introduced to the perfect competition model, allowing the model to incorporate binary decision variables to capture energy unit scheduling decisions of online status, startup, and shutdown costs. The proposed models are evaluated under three different demand test cases, using Ireland's electricity generation projections for 2023 to 2033. The results highlight key differences in investment decisions, carbon emissions, and the contribution of renewable technologies in perfect and imperfect competition structures. The findings provide managerial insights for policymakers and stakeholders, supporting optimal investment decisions and generation capacity planning to achieve Ireland's long-term energy objectives.