The study proposes applying an efficient but straightforward multi-objective constrained optimization model for optimal water allocation among irrigation and environmental sectors. The model has been ...implemented in the Muhuri Irrigation Project (MIP), Bangladesh, where the irrigation systems lead to unjustifiable use of groundwater. This study explores how water can be optimised to increase agricultural production and sustain the local environment in the MIP. Hence, the paper has two objectives—to maximise the net return and minimise the deficit in environmental flow. The study uses a Non-Dominating Sorting Genetic Algorithm, NSGA-II, to solve the research problem. Results indicate that crops more profitable to trade should be cultivated. Furthermore, the rainfall has more impact on the net return and environmental flow deficit than water inflow. The findings of this study can help plan irrigation water and cropland resources and be a reference for further studies.
At the zero lower bound, the dynamic Nelson–Siegel (DNS) model and even the Svensson generalization of the model have trouble in fitting the short maturity yields and fail to grasp the ...characteristics of the Japanese government bonds (JGBs) yield curve. During the zero interest rate policy regime, the short end of the yield curve is flat and yields corresponding to various maturities have asymmetric movements. Therefore, closely related generalized versions of Nelson–Siegel model—with and without no-arbitrage restriction (GAFNS and GDNS)—that have two slopes and curvatures factors are considered and compared empirically in terms of in-sample fit as well as out-of-sample forecasts with the standard Nelson–Siegel model—with and without no-arbitrage restriction (AFNS and DNS). The affine-based models provide a more attractive fit of the yield curve than their counterpart DNS-based models. Both extended models are capable to restrict the estimated rates from becoming negative at the short end of the curve and distill the JGBs term structure of interest rate quite well. The affine-based extended model leads to a better in-sample fit than the simple GDNS model. In terms of out-of-sample accuracy, both non-affine models outperform the affine models at least for 1- and 6-month horizons. The out-of-sample predictability of the GDNS for the 1- and 6-month-ahead forecasts is superior to the GAFNS for all maturities, and for longer horizons, i.e., 12-month-ahead, the former is still compatible to the latter, particularly for short- and medium-term maturities.
PurposeThis study aims to investigate how a firm's management team's capacity to efficiently use its resources affects the firm's exposure to climate change. Specifically, the authors investigate the ...intriguing question – does managerial ability affect a firm's climate change exposure?Design/methodology/approachThe authors use an unbalanced panel dataset of 4,230 US based firms listed on Compustat from 2002–2019 and test the hypothesis by panel regression analysis. To mitigate endogeneity concerns, difference-in-differences and instrumental variable approaches are used.FindingsThe baseline analysis shows a negative, statistically significant impact of managerial ability on climate change exposure. The findings hold after controlling for endogeneity using two-stage least squares regression and difference-in-differences tests. The authors find the negative effect is stronger for managers engaged in socially responsible activities, and after climate change issues receiving greater public awareness following the 2006 release of the Stern Review and the 2016 signing of the Paris Accord.Research limitations/implicationsMotivated by the resource-based theory and the natural resource-based view of the firm model, the empirical results support the view that greater managerial ability protects the firm against environmental challenges through efficient use of firm resources. Compared with traditional climate change measures that are plagued by disclosure issues, the use of the Sautner, Van Lent, Vilkov and Zhang's machine learning based dataset utilizing earning conference calls provides stronger, robust findings that will be useful to management and investors in environmental performance assessments.Originality/valueMotivated by the resource-based theory and the natural resource-based view of the firm model, the empirical results support the view that greater managerial ability protects the firm against environmental challenges through efficient use of firm resources. Compared with traditional climate change measures that are plagued by disclosure issues, the use of the machine learning based dataset utilizing earning conference calls provides stronger, robust findings that will be useful to management and investors in environmental performance assessments.
This study examines tail risk contagion across returns series of (i) ten major electricity markets and (ii) five raw materials used for electricity production during crises, using data from 2006M07 ...to 2023M03. The crises covered, in the study to examine tail risk contagion, are the global financial crisis, the European debt crisis, the COVID-19 pandemic and the Russia-Ukraine war. We estimate tail risk using the Conditional Autoregressive Value at Risk (CAViaR) method and employ the quantile vector autoregression (QVAR) connectedness approach to examine the tail risk spillover. In addition, we examine the effect of uncertainty factors on tail risk spillover. The QVAR result shows significant contagion across the electricity markets during crises, particularly pronounced in extreme quantiles. We identify geopolitical risk as the substantial uncertainty factor driving the contagion across these electricity markets. The findings have significant implications for regulators in formulating policies to reduce the effect of crises and uncertainty factors.
•We examine the tail risk contagion across electricity markets during economic crises.•Tail risk is estimated using Conditional Autoregressive Value at Risk.•QVAR approach shows an upsurge in tail risk connectedness during crises.•Geopolitical risk is the major driver of contagion.
The improvement of the catalytic activity of a heterogeneous chiral catalyst is one of the most critical issues, as are its recovery and reuse. The design of a heterogeneous chiral catalyst, ...including the immobilization method and the support polymer, is of significance for the catalytic activity in asymmetric reactions. An ionic, core-corona polymer microsphere-immobilized MacMillan catalyst (ICCC) was successfully synthesized by the neutralization reaction of sulfonic acid functionalized core-corona polymer microsphere (CCM–SO3H) with a chiral imidazolidinone precursor. We selected the core-corona polymer microsphere as the polymer support for the improvement of catalytic activity and recovery. The MacMillan catalyst was immobilized onto the pendant position of the corona with ionic bonding. ICCC exhibited excellent enantioselectivity up to 92% enantiomeric excess (ee) (exo) and >99% ee (endo) in the asymmetric Diels-Alder (DA) reaction of (E)-cinnamaldehyde and 1,3-cyclopentadiene. ICCC was quantitatively recovered by centrifugation because of the microsphere structure. The recovered ICCC was reused without significant loss of the enantioselectivity.
Since the emergence of blockchain technology, several digital assets such as cryptocurrencies, DeFi, and NFTs have gained considerable attention from investors and policymakers. However, the ...blockchain market has significant negative ramifications for the environment that may transmit shocks towards eco-friendly financial assets. We use the rolling window wavelet correlation (RWWC) model and the quantile-based time-varying (QVAR) connectedness framework to analyze the dynamic price correlation and connectedness between the blockchain market and green (eco-friendly) financial assets. As a representative of the blockchain market, we use the price returns of four cryptocurrencies, DeFi, and NFTs. For green equities, we use the MSCI Global Environment Price Index and the S&P Green Bond Price Index. We find a low correlation between the blockchain market and green financial assets before the outbreak of COVID-19 and a strong correlation during the COVID-19 and the Russia-Ukraine war. The quantile VAR results show symmetric connectedness of the examined and identical spillovers between extremely positive and strongly negative returns. Green bonds and stocks are the system's major shock receivers. The transmission network results imply major shock transmissions are driven by short-term frequency, whereas there is a lower transmission in the long-term.
Managerial ability and supply chain power Wali Ullah, G M; Luo, Juan; Yawson, Alfred
Journal of contemporary accounting & economics,
August 2024, 2024-08-00, Volume:
20, Issue:
2
Journal Article
Peer reviewed
Open access
This paper investigates how major customer firms, managed by highly capable managers, can gain bargaining power over their suppliers. Our results document a positive association between managerial ...ability and the supply chain power a major customer firm holds over its suppliers. The results are robust to endogeneity concerns, tested through two-stage least squares (2SLS) regressions and difference-in-differences estimates surrounding forced CEO turnovers. We find the positive association to be stronger for durable goods manufacturers and higher ability managers engaged in socially responsible activities and corporate innovation. We provide evidence that higher-ability managers use their enhanced bargaining power to secure greater supplier trade credit.
Well-defined functional poly(p-phenyl styrenesulfonate) and poly(p-phenyl styrene-sulfonate-co-styrene) were successfully synthesized by the atom transfer radical polymerization (ATRP) using ...CuBr/bpy(PMDETA) catalyst and 1-phenylethyl bromide (1-PEBr) as an ATRP initiator in diphenyl ether (DPE) or dimethyl formamide (DMF). In both homo- and copolymers, the CuBr/PMDETA catalytic system in DPE or DME showed higher yield than CuBr/bpy and the polydispersity index (PDI) of polymer was low. Using PMDETA or bpy as a ligand in DMF, the high yield with high PDI was obtained than in DPE. We found that the CuBr/PMDETA catalyzed ATRP of p-phenyl styrenesulfonate and copolymerization with styrene comonomer in DPE proceeded in a controlled manner. The polymers containing sulfonic acid were obtained by the chemical deprotection of protecting group, followed by acidification. The molecular structure, molecular weights and thermal properties of the copolymers were determined by nuclear magnetic resonance (
1
H NMR) spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, size exclusion chromatography (SEC), differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), respectively.
Fog computing is one of the major components of future 6G networks. It can provide fast computing of different application-related tasks and improve system reliability due to better decision-making. ...Parallel offloading, in which a task is split into several sub-tasks and transmitted to different fog nodes for parallel computation, is a promising concept in task offloading. Parallel offloading suffers from challenges such as sub-task splitting and mapping of sub-tasks to the fog nodes. In this paper, we propose a novel many-to-one matching-based algorithm for the allocation of sub-tasks to fog nodes. We develop preference profiles for IoT nodes and fog nodes to reduce the task computation delay. We also propose a technique to address the externalities problem in the matching algorithm that is caused by the dynamic preference profiles. Furthermore, a detailed evaluation of the proposed technique is presented to show the benefits of each feature of the algorithm. Simulation results show that the proposed matching-based offloading technique outperforms other available techniques from the literature and improves task latency by 52% at high task loads.
This study investigates the influence of Russia-Ukraine war and associated economic sanctions sentiments on the returns of cryptocurrencies, NFTs, and DeFi assets. We analyse daily returns of twelve ...blockchain-based assets by employing quantile-on-quantile regression (QQR) and an asymmetric time-varying parameter vector autoregression (TVP-VAR) connectedness approach. The QQR reveals that the war sentiment has varying effects on the returns of digital assets, with negative (positive) impacts in bullish (bearish) markets. Notably, there is a heterogeneous effect observed in normal market conditions. Results from the TVP-VAR-based asymmetric connectedness approach demonstrate a time-varying influence of war sentiment, particularly heightened post-invasion. The war sentiment emerges as a significant transmitter (receiver) of price shocks in bullish (bearish) market conditions. These findings offer extensive implications for investors and policymakers when modelling market behavior during geopolitical events.
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•Asymmetric effect of war sentiment on cryptoassets during the Russia-Ukraine war.•Quantiles of RUWESsent have a negative (positive) effect on cryptoassets in bullish (bearish) markets.•Time-varying impact of RUWESsent increased after the invasion.•RUWESsent is a major transmitter (receiver) of price shock.
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