The dynamic relationship between nuclear energy, CO2 emissions, and economic growth: evidence from the richest countries in Europe and Asia.

This study examines the dynamic relationship between the share of nuclear energy, growth in CO2 emissions, and GDP growth for the wealthiest countries of the two continents, Europe and Asia, from 1965 to 2021. The results from the SVAR model show a significant positive relationship between GDP growth and the growth of CO2 emissions in all countries. However, the values of the coefficients vary in the case of different countries of both continents. Further, it has been observed that the relationship between the growth of nuclear energy and the growth in CO2 emissions is more complex and varies from country to country. The results indicate that there is a strong negative relationship between nuclear energy and CO2 emissions in France, Russia, the United Kingdom, and India. At the same time, there is no significant relationship between Germany, Italy, and China. Finally, the result shows that there is an insignificant relationship between the growth in the share of nuclear energy and the GDP growth rate. From the policy perspective, this study suggests that alternative energy sources like nuclear energy can be leveraged to obtain a clean environment.

From the Individual to the Group: Using Idiographic Analyses and Two-Stage Random Effects Meta-Analysis to Obtain Population Level Inferences for within-Person Processes.

In psychology, the use of portable technology and wearable devices to ease participant burden in data collection is on the rise. This creates increased interest in collecting real-time or near real-time data from individuals within their natural environments. As a result, vast amounts of observational time series data are generated. Often, motivation for collecting this data hinges on understanding within-person processes that underlie psychological phenomena. Motivated by the body of Dr. Peter Molenaar's life work calling for analytical approaches that consider potential heterogeneity and non-ergodicity, the focus of this paper is on using idiographic analyses to generate population inferences for within-person processes. Meta-analysis techniques using one-stage and two-stage random effects meta-analysis as implemented in single-case experimental designs are presented. The case for preferring a two-stage approach for meta-analysis of single-subject observational time series data is made and demonstrated using an empirical example. This provides a novel implementation of the methodology as prior implementations focus on applications to short time series with experimental designs. Inspired by Dr. Molenaar's work, we describe how an approach, two-stage random effects meta-analysis (2SRE-MA), aligns with recent calls to consider idiographic approaches when making population-level inferences regarding within-person processes.

How does tourism affect the urban heat island effect? A case study of the tourism heat footprint in Macao.

This paper uses the tourism heat footprint (THF) and a structural vector autoregressive model to investigate how tourism has affected the urban heat island effect in Macao, a typical urban tourism destination. The dynamic relationships between the THF, heat island intensity (HII), and quarterly average temperature (QAT) are investigated. The impulse response function and variance decomposition analysis are used to assess if a long-term causal relationship exists between the three indicators. The results show the following. (1) The hotel industry in Macao is the source of energy consumption and heat release. (2) A Granger causality relationship exists between the THF and QAT but not between the THF and HII. (3) The interaction effect between the growth rate of the THF and QAT is manifested as shocks with the same frequency and regular periodic fluctuations. (4) The heat island effect of this tourism destination is reflected in an increase in local temperature due to the energy consumption and heat release by tourists. Based on the results, policy implications for a sustainable tourism city are provided.

Study on the Dynamic Relationship between Chinese Residents' Individual Characteristics and Commercial Health Insurance Demand.

Based on the time series data of age characteristics, household registration characteristics, gender characteristics, education characteristics, marriage characteristics and commercial health insurance density of Chinese residents from 1997 to 2020, this paper aims to explore the dynamic relationship between the individual characteristics of Chinese residents and the demand for commercial health insurance by means of impulse response and variance decomposition analysis using an SVAR model. The results show that the age characteristics, household registration characteristics, gender characteristics, education characteristics and marriage characteristics of Chinese residents have a significant impact on the demand for commercial health insurance, but there is a time lag. There is a long-term equilibrium relationship between them: In terms of age characteristics and gender characteristics, the former has a positive effect in the short term and a significant inhibition on commercial health insurance demand in the long term, while the latter has the opposite. In terms of household registration characteristics, education characteristics and marriage characteristics, there are positive effects on the whole and negative effects in a particular period.

Autonomous demand and technical change: exploring the Kaldor-Verdoorn law on a global level.

This paper aims to explain labour productivity through the lens of a Kaldorian perspective. To assess the relationship between output, demand, capital accumulation, and labour productivity, we apply Panel Structural Vector Autoregressive (P-SVAR) modelling to a dataset of 52 countries observed over a long-time span as provided by the Penn World Table. Findings validate the Kaldorian perspective and show that demand shocks-measured by government expenditures and exports-produce positive and persistent effects on labour productivity. Findings are confirmed even when the full sample is broken down to consider developed and developing countries separately.

A study on the response of carbon emission rights price to energy price macroeconomy and weather conditions.

China's carbon emission trading market has gradually attracted worldwide attention. In this paper, a structural VAR model and Shenzhen, a typical city in China, are selected to study the dynamic relationships between China's carbon emission rights price, energy prices, macroeconomic level, and weather conditions. Shanghai crude oil futures, the first crude oil futures contract in China, is used to describe changes in oil market as a substitute for Daqing crude oil price. The results show that the price of carbon emission rights is mainly affected by its own historical price; and the price of carbon emission rights is positively correlated with crude oil price and natural gas price, but negatively correlated with coal price; the change of macroeconomic level will still have a relatively large impact on carbon emission rights price in the current stage of economic development in China, but this impact is not significant; The impact of weather conditions on the price of carbon emission rights is not obvious. It is found that the launch of the national unified carbon market has indeed achieved certain results, but the situation that China's carbon market is still in its infancy has not been changed; further efforts are needed.

Assessing economic growth-energy consumption-CO2 nexus by climate zone: international evidence.

The energy economics literature lacks a consensus on the short- and long-term linkages along with the Granger causality direction between economic growth and energy consumption. This paper examines the relationships between economic growth, fossil fuel and renewable energy consumption, CO2 emissions, temperature, and population in 56 countries from 1990 to 2019. We contribute to the literature by uniquely grouping countries by climate zone (i.e., tropical, arid, temperate, and continental) following the Köppen-Geiger climate classification approach and employing a panel structural vector autoregressive model (P-SVAR) to study these relationships. In addition, rather than mistakenly using a single energy consumption variable, we divide it into two categories: fossil fuel and renewable energy consumption. Our findings indicate that temperature change has the most negligible impact on economic growth, while CO2 and renewable energy have the two most significant effects. Our Granger causality tests support all four hypotheses (growth, feedback, conservative, and neutral) that describe the relationship between economic performance and energy consumption, indicating the level of difficulty in recommending an overarching energy policy worldwide. We find that conservative energy consumption and clean energy policies that avoid CO2 emissions may benefit the selected countries' economic prosperities.

Noise shocks and business cycle fluctuations in three major European Economies.

This paper investigates how supply noise and demand noise contribute to business cycle fluctuations in three major European economies. A structural vector autoregressive model is used to identify supply, demand, supply noise and demand shocks. The identification scheme is built on nowcast errors of output growth and the inflation rate that are derived from the Consensus Economics Survey. The results indicate that positive supply noise and positive demand noise shocks have an expansionary effect on output, but their magnitude differs across countries. The two shocks contribute equally to business fluctuations, and jointly, they account for around one quarter of the total variation in GDP in each of the three countries.

Exploring time-varying impact of world pandemic uncertainty on China's commodity prices using TVP-SVAR-SV model.

Since the outbreak of the COVID-19 pandemic, a growing body of literature has focused on the impact of the uncertainty of the world pandemic (WPU) on commodity prices. Using the quarterly data from the first quarter of 2008 to the second quarter of 2020, we run the TVP-SVAR-SV model to study the time-varying impact of WPU on China's commodity prices. Specifically, we select minerals, non-ferrous metals, energy and steel commodities for a categorical comparison and measure the impact of WPU accordingly. The findings are as follows. First, WPU has a significant time-varying impact on China's commodity prices, and the short-term effect is greater than the long-term effect. Second, compared with the global financial crisis in the fourth quarter of 2008 and China's stock market crash in the second quarter of 2015, WPU had a greatest impact on Chinese commodity prices during the COVID-19 pandemic event in the fourth quarter of 2019. Third, significant differences exist in the impact of WPU on the four major commodity prices. Among them, WPU has the largest time-varying impact on the price of minerals but the smallest time-varying impact on that of steel.

Policy Intervention and Financial Sustainability in an Emerging Economy: A Structural Vector Auto Regression Analysis.

The purpose of the study is to observe the impact of policy intervention on financial sustainability using the structural vector autoregression (SVAR) analysis. The population of the study is the manufacturing sector of Pakistan, which is an emerging economy. Data for 249 firms operating in the manufacturing sector are taken, collected from Datastream from 2005 to 2019, with total observations of 2,400. To conduct the analysis, R software is used for its better visualization. Results show that firm performance, corporate governance, and sectoral policies have a positive and long-term impact on financial sustainability, whereas earning management and financialization not only have a negative impact, but this impact affects the operations of the corporate for a longer period. This study would be helpful for policymakers as it gives a framework for financial sustainability based on the policies and strategies developed by the sector.

Time trends in the epidemiology of nonrheumatic aortic valve disease in Spain, 2003-2018.

INTRODUCTION AND OBJECTIVES: The impact of therapeutic improvements in nonrheumatic aortic valve disease (NRAVD) has been assessed at the patient level but not in the whole population with the disease. Our objective was to assess temporal trends in hospitalization rates, treatment and fatality rates in patients with a main or secondary NRAVD diagnosis. METHODS: Retrospective analysis of administrative claims from patients hospitalized with a main or secondary NRAVD diagnosis between 2003 and 2018 in Spain. Time trends in age- and sex-standardized hospitalization and procedure rates, baseline characteristics and case fatality rates by diagnosis type were assessed by Poisson regression and joinpoint analysis. RESULTS: Hospital admissions in patients with NRAVD increased from 69 213 in 2003 to 136 185 in 2018. The crude in-hospital fatality rate increased from 6.7% to 8.7% (IRR, 1.015; 95%CI, 1.012-1.018; P <.001) without changes after adjustment. Adjusted fatality rates decreased in patients with a main NRAVD diagnosis (5.5% to 3.5%; IRR, 0.953; 95%CI, 0.942-0.964) but increased in those with a secondary diagnosis (8.0% to 8.8%; IRR, 1.005; 95%CI, 1.002-1.009). Aortic valve replacements increased from 10.5 to 17.1 procedures per 100 000 population (IRR, 1.033; 95%CI, 1.030-1.037), mainly driven by transcatheter procedures (IRR, 1.345; 95%CI, 1.302-1.389). CONCLUSIONS: Hospitalizations in patients with NRAVD are increasing, with most being secondary diagnoses. The use of aortic valve replacement is increasing with a reduction in fatality rates but only in patients with a main diagnosis.

Financial uncertainty and interest rate movements: is Asian bond market volatility different?

The COVID-19 pandemic has given rise to a spike in financial market volatility. In this paper, we attempt to assess the effects of financial & news-driven uncertainty shocks in growing Asian economies, using country-specific bond volatility shocks as a measure of local interest rate uncertainty. Also, we contrast the effects of local uncertainty with global stock market uncertainty. Using bond market data from nine Asian markets, we uncover a transmission mechanism of uncertainty shocks via the bond market. The mechanism works as a crowding-out effect due to government-led excessive market borrowing with supply-side consequences for the private sector, as opposed to economic policy or global stock market uncertainty which works more like a demand shock as in the literature. We conclude that countries with growing fiscal deficits that entail a larger government bond market or higher current account deficits, tend to experience an increase in the cost of borrowing due to this bond market volatility or interest rate uncertainty shocks.

COVID-19-induced shocks and uncertainty.

Using statistical identification, we extract a COVID-19-induced shock by exploiting large daily jumps in financial markets caused by news about the pandemic. This shock depresses economic and financial indicators, increases risk and uncertainty measures, has sizeable distributional effects, and hits most harshly those industries relying on face-to-face interactions. Impulse response function analysis across various identification strategies leads us to interpret the statistical COVID-19-induced shock as a structural uncertainty shock.

Time-varying impact of pandemics on global output growth.

This paper analyses the dynamic impact of uncertainty due to global pandemics (SARS, H5N1, H1N1, MERS, Ebola, and COVID-19) on global output growth, using a TVP-SVAR model. We find that the negative effect of the coronavirus on the growth rate of output is unprecedented, with the emerging markets being the worst hit. We also find that since 2016, the comovement among the growth rates has increased significantly. Our results imply that policymakers would need to undertake massive expansionary policies, but it is also important to pursue well-coordinated policy decisions across the economic blocs.

Information Search and Financial Markets under COVID-19.

The discovery and sudden spread of the novel coronavirus (COVID-19) exposed individuals to a great uncertainty about the potential health and economic ramifications of the virus, which triggered a surge in demand for information about COVID-19. To understand financial market implications of individuals' behavior upon such uncertainty, we explore the relationship between Google search queries related to COVID-19-information search that reflects one's level of concern or risk perception-and the performance of major financial indices. The empirical analysis based on the Bayesian inference of a structural vector autoregressive model shows that one unit increase in the popularity of COVID-19-related global search queries, after controlling for COVID-19 cases, results in 0.038 - 0.069 % of a cumulative decline in global financial indices after one day and 0.054 - 0.150 % of a cumulative decline after one week.

The impact of natural disasters on China's macroeconomy.

This study attempts to construct an econometric model using China's natural disaster losses and macro-industry development data from 1980 to 2017 to explore the macroeconomic fluctuations caused by natural disasters. The structural vector autoregressive (SVAR) and the seemingly unrelated regression (SUR) models are employed in estimating the impact of natural disasters on China's macroeconomy and how the disasters specifically affect the three sectors of the economy: primary, secondary, and tertiary. This study concludes that even though natural disasters in China do not significantly affect the overall real GDP, they have adverse impacts on the production in the primary industry, causing a sudden reduction in the means of production in the market and directly affecting various industries, but the impact on the secondary and tertiary industries is weak. This study also shows that the effect of natural disasters on the primary sector reduced significantly following industry restructuring after China's accession to the World Trade Organization (WTO). The impact of natural disasters on the primary industry could be reduced by adjusting the industrial structure to deal with macroeconomic shocks caused by natural disasters in order to promote macroeconomic stability of both regional and national economies. Finally, national aid policy should focus on the primary industry since that sector is significantly affected by natural disasters shocks.

Functional and resting-state characterizations of a periventricular heterotopic nodule associated with epileptogenic activity.

The object of this study was to extensively characterize a region of periventricular nodular heterotopia (PVNH) in an epilepsy patient to reveal its possible neurocognitive functional role(s). The authors used 3-T MRI approaches to exhaustively characterize a single, right hemisphere heterotopion in a high-functioning adult male with medically responsive epilepsy, which had manifested during late adolescence. The heterotopion proved to be spectroscopically consistent with a cortical-like composition and was interconnected with nearby ipsilateral cortical fundi, as revealed by fiber tractography (diffusion-weighted imaging) and resting-state functional connectivity MRI (rsfMRI). Moreover, the region of PVNH demonstrated two novel characterizations for a heterotopion. First, functional MRI (fMRI), as distinct from rsfMRI, showed that the heterotopion was significantly modulated while the patient watched animated video scenes of biological motion (i.e., cartoons). Second, rsfMRI, which demonstrated correlated brain activity during a task-negative state, uniquely showed directionality within an interconnected network, receiving positive path effects from patent cortical and cerebellar foci while outputting only negative path effects to specific brain foci.These findings are addressed in the context of the impact on noninvasive presurgical brain mapping strategies for adult and pediatric patient workups, as well as the impact of this study on an understanding of the functional cortical architecture underlying cognition from a neurodiversity and evolutionary perspective.

How does the water-energy-food nexus work in developing countries? An empirical study of China.

As three resources that are necessary for human survival and production, water, energy and food are increasingly closely linked. In recent years, the water-energy-food nexus has attracted special attention from international organizations and academic circles. However, due to the lack of research on its internal mechanisms, there is still controversy on whether the water-energy-food nexus can be used as a new policy basis. The internal mechanisms of the water-energy-food nexus were analysed from the perspective of industrial linkages in this paper and empirically verified by constructing an SVAR (structuralvectorautoregression) model using China's data. The results showed that there were two forms of conduction in China's water-energy-food nexus: the water-energy-food nexus with nuclear power participation and that with natural gas participation. The characteristics of China's water-energy-food nexus were derived. For the interactions of the water-energy segment in China's water-energy-food nexus, the conduction from energy to water was consistent for different types of energy, while that from water to energy varied depending on the type of energy. Food production always had a negative impact on energy production, while the conduction from energy to food varied for different types of energy. The conduction between food and the water supply was not as significant as was generally considered. Especially, the impact of the water supply on food production was weak. The order of strength intensity and the duration were also available for reference. Accordingly, a new policy basis was presented under the framework of China's water-energy-food nexus. Both our research design and research findings are significant in contributing to understanding the internal mechanisms of the water-energy-food nexus, and the policy implications are also helpful for achieving better policy effects.

Understanding and using time series analyses in addiction research.

Time series analyses are statistical methods used to assess trends in repeated measurements taken at regular intervals and their associations with other trends or events, taking account of the temporal structure of such data. Addiction research often involves assessing associations between trends in target variables (e.g. population cigarette smoking prevalence) and predictor variables (e.g. average price of a cigarette), known as a multiple time series design, or interventions or events (e.g. introduction of an indoor smoking ban), known as an interrupted time series design. There are many analytical tools available, each with its own strengths and limitations. This paper provides addiction researchers with an overview of many of the methods available (GLM, GLMM, GLS, GAMM, ARIMA, ARIMAX, VAR, SVAR, VECM) and guidance on when and how they should be used, sample size det ermination, reporting and interpretation. The aim is to provide increased clarity for researchers proposing to undertake these analyses concerning what is likely to be acceptable for publication in journals such as Addiction. Given the large number of choices that need to be made when setting up time series models, the guidance emphasizes the importance of pre-registering hypotheses and analysis plans before the analyses are undertaken.