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Unraveling the dynamics of the global carbon cycle and assessing the sustainability of terrestrial ecosystems are critically dependent on a comprehensive understanding of vegetation biomass. This exploration delves into the pivotal role of biomass within vegetation communities, emphasizing its impact on ecosystem health, productivity, and community structure development. These insights are invaluable for advancing ecological science and conservation efforts. The synthesis of aboveground (AGB) and belowground (BGB) biomass data from 4485 and 3442 locations across China, respectively, collates a wide range of published sources. Integrating this extensive dataset with environmental parameters and applying advanced machine learning techniques facilitated an in-depth analysis of AGB and BGB spatial patterns within China. Techniques such as variance decomposition analysis and piecewise structural equation modeling were employed to dissect the factors contributing to the spatial variability of vegetation biomass. Significant spatial heterogeneity in biomass distribution was uncovered, with vegetation biomass in the northwest markedly lower than in the southern and northeastern regions. It was observed that AGB consistently surpassed BGB. Climatic conditions, soil characteristics, and soil nutrients were found to significantly explain 53 % and 48 % of the total variance in AGB and BGB, respectively. Specifically, solar radiation and soil total nitrogen were identified as critical factors influencing variations in AGB and BGB. The findings offer profound contributions to the understanding of the global carbon balance and the evaluation of terrestrial ecosystems sustainability. Moreover, they provide essential insights into the ecosystems' response mechanisms to global changes, serving as a fundamental reference for future studies on terrestrial ecosystem carbon cycling and carbon sequestration potentials.

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Given Korea's status as a small, open economy, it exhibits a pronounced sensitivity to external shocks. Consequently, this article seeks to elucidate the impact of external financial and monetary policy shocks on the fluctuation of critical macroeconomic variables within Korea. Employing Bayesian estimation alongside the impulse response function for empirical analysis, the findings reveal that external financial and monetary policy shocks precipitate declines in exports, output, employment, real wages, consumption, investment, and imports. Conversely, these shocks are associated with increases in both the price level and inflation, highlighting the multifaceted effects of external pressures on the domestic economic landscape. Further, through forecast error variance decomposition, this study demonstrates that, relative to shocks stemming from productivity, terms of trade, and real exchange rate variations, external financial and monetary policy shocks exert a considerably milder impact on the fluctuations of Korea's key macroeconomic variables. This insight suggests a potential area for enhancement in the existing Korean literature on this topic, advocating for the integration of these findings to enrich understanding and analysis. In summary, by delving into the nuanced effects of external shocks on Korea's economy, this article contributes valuable perspectives to the discourse, suggesting avenues for further research and policy formulation. The integration of these results into the broader body of Korean economic literature could significantly augment current understandings and interpretations of Korea's economic dynamics in the face of global financial and monetary turbulence.

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Under the background of "double carbon", exploring the growth path of green logistics and enhancing the driving force of technological innovation is the urgent need of our country to comply with the green transformation of its economy and realize high-quality economic growth. Taking the panel data of 30 Chinese provinces from 2010 to 2020 as the sample, the green logistics index evaluation system is constructed based on the driver-press-state-impact-response (DPSIR) theoretical framework, and the green economic efficiency of each province in the sample period is measured by using the non-expected output Super- Slacks-based measure (SBM) model, and by constructing the panel vector autoregressive (PVAR) model including technological innovation is used to systematically elaborate the dynamic influence paths among the three. The study found that: China's green economy, technological innovation, and green logistics all have their own mechanisms for growth, which will gradually diminish over time. In the near and long term, green logistics will promote technological innovations and the evolution of a green economy, but there is a lag in the long-term benefits of green logistics on technological progress. In the short term, technological innovation does not lend support to the growth of a green economy, but over time, the impact of technological innovation on the growth of that economy will shift from negative to positive. This shows that improving technological innovation capability is an important path for green logistics to promote green economic efficiency. The findings of the study provide a basis for decision making to achieve the emission reduction target and improve the efficiency of the green economy.

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Objective:To explore the current situation of binary coping in patients with perimenopausal syndrome and analyze its influencing factors, in order to provide a basis for improving the level of binary coping.Methods:Using convenience sampling method, a total of 210 patients with perimenopausal syndrome and their spouses from the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine were cross-sectional surveyed by a general data questionnaire, the Binary Coping Scale, and the Modified Kupperman Score Scale. The influencing factors of binary coping level in patients with perimenopausal syndrome were explored by univariate analysis and variance decomposition model analysis.Results:A total of 200 valid questionnaires were retrieved.The patients aged (50.52 ± 2.89) years old. The binary coping score was (79.64 ± 22.74) points. The variance decomposition model analysis showed that marriage age, type of medical insurance, number of children, education level, family monthly income, spouse′s education level, presence of major comorbidities in spouse, modified Kupperman score, presence of generalized anxiety in spouse were the main influencing factors of binary coping in patients with perimenopausal syndrome (all P<0.05). Conclusions:The binary coping scores of patients with perimenopausal syndrome are lower than normal, and considering the influence and involvement of patients' spouses, nursing staff should pay special attention to patients who are married relatively early, have more children, have lower education levels, and have lower family monthly incomes. Additionally, attention should be given to spouses who experience widespread anxiety, have a lower level of education, and suffer from major chronic diseases. By developing and implementing comprehensive intervention measures aimed at improving the Kupperman score and the level of binary coping, both parties can be encouraged to support each other more effectively, thereby improving the marital relationships of patients during the perimenopausal period.

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Logistic regression is one of the most commonly used approaches to develop clinical risk prediction models. Developers of such models often rely on approaches that aim to minimize the risk of overfitting and improve predictive performance of the logistic model, such as through likelihood penalization and variance decomposition techniques. We present an extensive simulation study that compares the out-of-sample predictive performance of risk prediction models derived using the elastic net, with Lasso and ridge as special cases, and variance decomposition techniques, namely, incomplete principal component regression and incomplete partial least squares regression. We varied the expected events per variable, event fraction, number of candidate predictors, presence of noise predictors, and the presence of sparse predictors in a full-factorial design. Predictive performance was compared on measures of discrimination, calibration, and prediction error. Simulation metamodels were derived to explain the performance differences within model derivation approaches. Our results indicate that, on average, prediction models developed using penalization and variance decomposition approaches outperform models developed using ordinary maximum likelihood estimation, with penalization approaches being consistently superior over the variance decomposition approaches. Differences in performance were most pronounced on the calibration of the model. Performance differences regarding prediction error and concordance statistic outcomes were often small between approaches. The use of likelihood penalization and variance decomposition techniques methods was illustrated in the context of peripheral arterial disease.

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This study delved into the intricate relationships between green bonds (GB), Environmental National Cap (ENC), the European Commission's green growth metrics (EC), and innovative financial mechanisms (INN_FM). Employing the Pesaran CD test, the research underscored significant cross-sectional dependence among the examined countries. The subsequent unit root tests affirmed the first-order integration of variables, causing the panel vector autoregressive (PVAR) approach for deeper insights. The findings indicated that while GB notably influenced the EC's metrics, and its effect on ENC was relatively subdued. Notably, INN_FM appeared to insignificantly influence the issuance of GB. By leveraging variance decomposition, we discerned that the dynamics between these factors, especially in green economic growth, is complex and can vary across regulatory and national contexts. This research provides an essential foundation for policymakers, regulators, and investors to understand the multifaceted interplays in green finance mechanisms and craft strategies to optimize their impact on sustainability outcomes. Hence, the study provides multiple policy implications for the associated stakeholders.

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In Europe, there has been a significant shift in the movement of people and things. Nonetheless, despite the fact that transportation is an important component of the supply chain, its environmental consequences pose a severe threat to the ecosystem as a whole. As a result, we intend to explore the relationship between transportation, economy, and CO2 emissions. We used the Static method with Pooled OLS, then tested the Granger causality to validate the use of dynamic approach via the GMM system. The major findings revealed that GDP and trade openness had a considerable impact on CO2 emissions. Although the three modes of transportation have different effects on CO2 emissions, road density has a positive and considerable impact on CO2 emissions. The railway network is inversely connected to CO2 emissions. While the quantity of flight passengers has no substantial effect on emissions. In terms of the impulse response function, there is an initial shock in period 2 for the response of air passengers carried to CO2 emissions, followed by convergence back to zero in period 6, whereas road density has a slight decrease in period 2 with a post shock peak in period 4, followed by convergence back to zero in period 5. The variance decomposition results reveal a little increase until the fifth period for road density, air passengers, and trade openness with coefficients equal to 0.0893, 0.636, and 1.573, respectively, after which these three variables offer decreasing coefficients.

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We assess the relative and joint contributions of genetic and environmental factors on health during childhood and assume that parental resources are part of the environmental factors shaping children's health. We discuss theoretical background and empirical evidence concerning the effects of parental resources and heritability on children's health. Based on these findings we formulate six hypotheses guiding our empirical analysis, using data from TwinLife, a nationally representative sample of same sex twin pairs in Germany. We analyze self-rated health of 1,584 twin pairs aged 4-18. We did find strong support for the idea that parental resources influence children's health: household income and fathers' education consistently show positive effects. In contrast to our expectation, we did not find that genetic factors influence the health of well-off children less than the health of children living in families with lower SES. We also did not find that the genetic influence on health increases during childhood and adolescence. On the contrary our results indicate that the role played by genetic factors diminishes whereas environmental factors gain importance for health of children while growing up. This finding is good news for those interested in improving health chances of children from lower SES backgrounds because it demonstrates the malleability of children's health.

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OBJECTIVES: Recent work suggests that views of aging (VOA; a meta-construct reflective of individuals' aging-related thoughts, beliefs, feelings, and experiences) fluctuate within persons in day-to-day life. This study characterized the extent of daily variability in VOA and explored differences in variability patterns based on measure to enhance understanding of the dynamic nature of VOA. METHODS: An online sample of 122 adults aged 26-78 years completed multiple measures of VOA (subjective age, age group identity, aging attitudes, implicit theories of aging, awareness of age-related losses or gains) on each of 7 consecutive days. We partitioned variance in responses to each measure at the person level and day level to assess between-person and within-person variability, respectively. RESULTS: Between-person variability accounted for most of the total observed variation in VOA, whereas within-person variability accounted for a smaller amount. Different measures exhibited different ratios of between-person to within-person variation, with the lowest ratios observed for subjective age. Exploration of potential differences between age groups also suggests lower ratios in younger compared to older adults. DISCUSSION: Analyses suggest relative stability in daily measures of VOA over a 1-week period. Further study of measures (and age groups) showing greater within-person variability (evidenced by lower ratios of between-person to within-person variation) can increase understanding about constructs with greater sensitivity to fluctuating contexts. It can also inform future work linking VOA to other phenomenon in daily life.

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To deeply analyze and understand the macro-financial impact of climate change, this paper investigates the effect of climate risk on systemic financial risks by employing a network approach. The results demonstrate that climate risk not only affects a single financial market but also induces risk co-movement, which aggravates potential systemic financial risks. Specifically, the system-wide connectedness across the financial system respectively increased by 2.52% and 1.76% after the withdrawal of the US from the Kyoto Protocol and the Copenhagen UN Climate Change Conference. The bond and stock markets are the primary transmitters of climate shocks, while the forex and commodity markets appear to be more sensitive to climate-related information. In addition, the vulnerability of financial asset price fluctuations to climate risk changes substantially over time. Quantile regressions reveal the positive impact of climate risk on total connectedness across the financial system. This study provides novel insight into how the financial system responds to climate-related information and how systemic risk dynamics materialize.

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Understanding the gap between potential productivity and the actual productivity of vegetation (vegetation productivity gap, VPG) is the basis to explore the potential productivity improvement and identify its constraints. In this study, we used the classification and regression tree model to simulate the potential net primary productivity (PNPP) based on the flux-observational maximum net primary productivity (NPP) across different vegetation types, that is, potential productivity. The actual NPP (ANPP) is obtained from the grid NPP averaged over five terrestrial biosphere models, and the VPG is subsequently calculated. On this basis, we used the variance decomposition method to separate the effects of climate change, land-use change, CO2, and nitrogen deposition on the trend and the interannual variability (IAV) of VPG from 1981 to 2010. Meanwhile, the spatiotemporal variation characteristics and influencing factors of VPG under future climate scenarios are analyzed. The results showed an increasing trend in PNPP and ANPP, while there was a decreasing trend of VPG in most parts of the world and this trend is more significant under representative concentration pathways (RCPs). The turning points (TP) of VPG variation are found under RCPs and the reduction trend of VPG before TP is more than that after TP. The VPG reduction in most regions was caused by the combined effects of PNPP and ANPP (41.68 %) from 1981 to 2010. However, the dominant factors of global VPG reduction are changing under RCPs, and the increment of NPP (39.71 % - 49.3 %) has become the dominating factor of VPG variation. CO2 plays a decisive role in the multi-year trend of VPG, while climate change is the main factor determining the IAV of VPG. Under changing climate, temperature and precipitation are negatively correlated with VPG in most parts of the world, and the relationship between radiation and VPG from weak negative to positive correlation.

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The global navigation satellite system (GNSS) daily position time series are often described as the sum of stochastic processes and geophysical signals which allow to study global and local geodynamical effects such as plate tectonics, earthquakes, or ground water variations. In this work, we propose to extend the Generalized Method of Wavelet Moments (GMWM) to estimate the parameters of linear models with correlated residuals. This statistical inferential framework is applied to GNSS daily position time-series data to jointly estimate functional (geophysical) as well as stochastic noise models. Our method is called GMWMX, with X standing for eXogenous variables: it is semi-parametric, computationally efficient and scalable. Unlike standard methods such as the widely used maximum likelihood estimator (MLE), our methodology offers statistical guarantees, such as consistency and asymptotic normality, without relying on strong parametric assumptions. At the Gaussian model, our results (theoretical and obtained in simulations) show that the estimated parameters are similar to the ones obtained with the MLE. The computational performances of our approach have important practical implications. Indeed, the estimation of the parameters of large networks of thousands of GNSS stations (some of them being recorded over several decades) quickly becomes computationally prohibitive. Compared to standard likelihood-based methods, the GMWMX has a considerably reduced algorithmic complexity of order O { log ( n ) n } for a time series of length n. Thus, the GMWMX appears to provide a reduction in processing time of a factor of 10-1000 compared to likelihood-based methods depending on the considered stochastic model, the length of the time series and the amount of missing data. As a consequence, the proposed method allows the estimation of large-scale problems within minutes on a standard computer. We validate the performances of our method via Monte Carlo simulations by generating GNSS daily position time series with missing observations and we consider composite stochastic noise models including processes presenting long-range dependence such as power law or Matérn processes. The advantages of our method are also illustrated using real time series from GNSS stations located in the Eastern part of the USA.

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As a result of a greater worldwide aspiration for wealth and economic progress, increased use of natural resources for diverse industries resulted in increased pollution emissions, mainly carbon dioxide. Energy security, economic stability, job security, biodiversity loss, climate change, and global warming all require reconciliation and resolution now, more than ever before. This paper explores the causal relationship between CO2 emissions, economic growth, available energy, and employment for a panel of eight South-Eastern European countries from 1995 to 2019. We investigate the relationship using panel unit root tests, panel cointegration methods, and panel causality tests. The results show a short-run bidirectional panel causality between CO2 emissions and employment and between available energy and employment. The results further indicate a unidirectional causality from available energy and employment to GDP. The long-run causal relationship results show that the estimated coefficients of the lagged ECT in the CO2 emissions, GDP, and employment equations are statistically significant, implying that these variables could play a significant role in the system's adjustment process as it departs from long-run equilibrium. We also conducted a variance decomposition analysis, which allowed us to compare the extent of the individual factors' contributions to each other over the next 5 years.

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This study aims to examine the interrelationships and interdependencies between corporate governance (CG), capital structure (CS), and firm performance (FP) of companies listed on the Stock Exchange of Mauritius from 2009 to 2019 along with a comparison between financial and non-financial firms. A panel vector autoregression (PVAR) approach is used in this study to determine the relationship dynamics between CG, CS and FP. The findings reveal a positive and significant bidirectional association between CS and FP, supporting the trade-off theory. The results also show that CG and FP jointly help to increase CS while CG and CS jointly boost the profitability of firms. A strong bidirectional relationship with varied signs between CG and CS is found only for financial firms. The results of the forecast error variance decomposition analysis support the selection of FP as the most endogenous variable. Robustness tests also support the findings. This study is the first to examine the dynamic and interdependent relationships using a PVAR model between CG, CS and FP that presents new contributions to the existing CG and CS literature with insights from an emerging economy.

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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.

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It is unknown how co-rumination, or perseverating on problems or feelings with another person, unfolds in the daily lives of romantic couples. Using a variance decomposition procedure on data from a 14-day dyadic diary, we assessed how much variance in co-rumination was attributable to temporally stable and varying factors, as well as whether co-rumination is better measured as a couple-level or individual-level process. Within-person, within-couple fluctuations in co-rumination contributed most (~33%) to the total variance and summary scores based on these fluctuations were reliable. Stable between-couple differences accounted for ~14% of the total variance and could also be reliably assessed. However, within-couple agreement in co-rumination was low, such that the reliability at the level of within-couple change was inadequate. Research is needed to understand these divergent perceptions of co-rumination and potential downstream consequences. We conclude by considering how these results inform theory and can be applied to similar dyadic constructs.

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This paper examines the shock spillovers between US sectors and their dependence on the intersectoral business linkages. Our forecast error variance decompositions reveal significant shock transmissions among trading sectors, especially in turbulent periods such as the financial crisis and the COVID-19 pandemic. The dymamics of shock spillovers reflect the impacts of the pandemic on economic sectors. Shock spillovers are shown to be influenced by the strength of the intersectoral trading relationships. Shocks to a sector's important supplier have a strong impact on the forecast error variance of the sector's stock return. The total directional spillovers from/ to a sector are linked with the number of close commercial linkages between that sector and other sectors. Supplementary Information: The online version contains supplementary material available at 10.1007/s10479-022-04979-8.

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Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementarity of different feature spaces, a joint model is fit on multiple feature spaces simultaneously. To adapt regularization strength to each feature space, ridge regression is extended to banded ridge regression, which optimizes a different regularization hyperparameter per feature space. The present paper proposes a method to decompose over feature spaces the variance explained by a banded ridge regression model. It also describes how banded ridge regression performs a feature-space selection, effectively ignoring non-predictive and redundant feature spaces. This feature-space selection leads to better prediction accuracy and to better interpretability. Banded ridge regression is then mathematically linked to a number of other regression methods with similar feature-space selection mechanisms. Finally, several methods are proposed to address the computational challenge of fitting banded ridge regressions on large numbers of voxels and feature spaces. All implementations are released in an open-source Python package called Himalaya.

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Background and purpose: Diagnostic information about cell density variations and microscopic tissue anisotropy can be gained from tensor-valued diffusion magnetic resonance imaging (MRI). These properties of tissue microstructure have the potential to become novel imaging biomarkers for radiotherapy response. However, tensor-valued diffusion encoding is more demanding than conventional encoding, and its compatibility with MR scanners that are dedicated to radiotherapy has not been established. Thus, our aim was to investigate the feasibility of tensor-valued diffusion MRI with radiotherapy dedicated MR equipment. Material and methods: A tensor-valued diffusion protocol was implemented, and five healthy volunteers were scanned with different resolutions using conventional head coil and radiotherapy coil setup with fixation masks. Signal-to-noise-ratio (SNR) was evaluated to assess the risk of signal bias due to rectified noise floor. We also evaluated the repeatability and reproducibility of the microstructure parameters. One patient with brain metastasis was scanned to investigate the image quality and the transferability of the setup to diseased tissue. Results: A resolution of 3 × 3 × 3 mm3 provided images with SNR > 3 for 93 % of the voxels using radiotherapy coil setup. The parameter maps and repeatability characteristics were comparable to those observed with a conventional head coil. The patient evaluation demonstrated successful parameter analysis also in tumor tissue, with SNR > 3 for 93 % of the voxels. Conclusion: We demonstrate that tensor-valued diffusion MRI is compatible with radiotherapy fixation masks and coil setup for investigations of microstructure parameters. The reported reproducibility may be used to plan future investigations of imaging biomarkers in brain cancer radiotherapy.

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NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model's performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Impact of various data learning approaches on NIR spectral data.

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