Environmental Volatility Research Articles

Digital financial inclusion, environment volatility and investment efficiency: demand and supply side experiences of Chinese listed renewable energy firms

Digital financial inclusion, environment volatility and investment efficiency: demand and supply side experiences of Chinese listed renewable energy firms

The interplay of digital transformation, agility, environmental volatility, and innovation to spur enterprise performance: Evidence from Chinese electric vehicle firms

Given the rapid growth of China's electric vehicle (EV) industry, with sales expected to reach over 10 million units annually by 2025, there is an urgent need to understand how digital logistics, supply chain agility, and innovation can drive sustainable performance amidst increasing environmental volatility. This study utilises the dynamic capability theory and proposes a framework that examines the interplay between digital transformation, agility, dual innovation, environmental volatility, and enterprise performance. A survey was circulated among the managers working in the EV industry. We received 197 responses. The data was analysed using Smart PLS software using structural equation modelling (SEM). The findings reveal that digital transformation has significant positive relationships with enterprise performance, agile capability, and innovation capability. Additionally, agile and innovation capabilities have positive effects on enterprise performance. The study recommends investment in digital transformation programs and fostering an agile, innovative culture in the EV industry.

CSR in French companies between performance target and environmental volatility: an empirical study

CSR in French companies between performance target and environmental volatility: an empirical study

Strategic Adaptation to Environmental Volatility: Evaluating the Long-Term Outcomes of Business Model Innovation

In a period, when the risk of environmental distortions becomes higher and more unpredictable, business struggle to carry on or even stay in business is obvious, unless they are able to adjust their strategies properly. This paper takes business model innovation as the primary strategic response to environmental turbulence into account. Through an advanced empirical methodology, this study looks at the long-term results of diverging businesses from various industries moving toward a cutting edge adaptation plot instead of those that contended for the simple replication of those that already existed. The analysis, first and foremost, demonstrates significant disparities in prosperity rates, which may be attributed to interplay of evolving business strategies and market dynamics along with organizational resources and capabilities. In addition to this, the document presents strategic foresight and the imperative of agility as a means of creating a platform where innovation and sustainable growth are realizable. The results provide a detailed framework for CEOs, owners and politicians, underscoring the imperative of avant-garde techniques that will enable them to adapt effectively to the continuously evolving market environment.

Green Process Innovation on Msmes Performance Moderate Perceived Environmental Volatility: Study on Food and Beverage MSMEs in Solo City

Based on Stakeholder Theory and Social Cognitive Theory. This research aims to determine the influence of the relationship between green process innovation on the performance of MSMEs and moderating perceived environmental volatility. This research uses a survey. Research population of MSME owners in Solo City, Indonesia. Convenience sampling was used with a sample size of 120 respondents. Data analysis used PLS-SEM version 3.0. The research results show that green transformational leadership has a significant effect on the performance of MSMEs. Green process innovation has a significant effect on the performance of MSMEs. Moderation of perceived environmental volatility in the relationship between green process innovation has a significant effect on the performance of MSMEs and has a positive but not significant effect. The current research provides useful insights into green process innovation and perceived environmental volatility that can improve MSME performance

Balancing act: Trade‐offs and synergies within Sustainable Development Goals 1st, 10th, and 13th—Poverty, inequality, and climate actions

AbstractWhile the existing body of research has exhaustively examined several Sustainable Development Goals (SDGs), it fails to adequately reflect the significance of trade‐offs and synergies among the SDGs. This study aims to address the existing gap by doing a complete analysis of the SDGs in developing nations, specifically examining the trade‐off relationship between SDG‐1, pertaining to poverty reduction; SDG‐10, concerning reducing inequality; and SDG‐13, focusing on environmental activities. The analysis encompasses a sample of 40 developing countries. To accomplish this, a Generalized Method of Movement (GMM)‐style Panel‐Vector Autoregressive (PVAR) model is utilized, with carbon emissions (CO2Em) serving as a measure for environmental volatility. The findings confirm the existence of the Environmental Phillips Curve (EPC), which signifies a trade‐off between SDG‐1 (poverty), SDG‐10th (inequality), and SDG‐13th (CO2Em). Additionally, the processes of industrialization, trade, and population expansion have adverse effects on the attainment of SDG‐13. Conversely, industrialization and trade can contribute positively towards SDG‐1. A conventional sys‐GMM model is utilized for the robustness check, supporting the main findings acquired from the GMM‐style PVAR model. Lastly, based on empirical findings, significant policy implications are suggested for the achievement of SGDs.

How do humans learn about the reliability of automation?

In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans track automation reliability. We fitted several alternative cognitive models to a series of participants’ judgements of automation reliability observed in a maritime classification task in which participants were provided with automated advice. We examined three experiments including eight between-subjects conditions and 240 participants in total. Our results favoured a two-kernel delta-rule model of learning, which specifies that humans learn by prediction error, and respond according to a learning rate that is sensitive to environmental volatility. However, we found substantial heterogeneity in learning processes across participants. These outcomes speak to the learning processes underlying how humans estimate automation reliability and thus have implications for practice.

Evaluating the Influence of Musical and Monetary Rewards on Decision Making through Computational Modelling.

A central question in behavioural neuroscience is how different rewards modulate learning. While the role of monetary rewards is well-studied in decision-making research, the influence of abstract rewards like music remains poorly understood. This study investigated the dissociable effects of these two reward types on decision making. Forty participants completed two decision-making tasks, each characterised by probabilistic associations between stimuli and rewards, with probabilities changing over time to reflect environmental volatility. In each task, choices were reinforced either by monetary outcomes (win/lose) or by the endings of musical melodies (consonant/dissonant). We applied the Hierarchical Gaussian Filter, a validated hierarchical Bayesian framework, to model learning under these two conditions. Bayesian statistics provided evidence for similar learning patterns across both reward types, suggesting individuals' similar adaptability. However, within the musical task, individual preferences for consonance over dissonance explained some aspects of learning. Specifically, correlation analyses indicated that participants more tolerant of dissonance behaved more stochastically in their belief-to-response mappings and were less likely to choose the response associated with the current prediction for a consonant ending, driven by higher volatility estimates. By contrast, participants averse to dissonance showed increased tonic volatility, leading to larger updates in reward tendency beliefs.

Altered Perception of Environmental Volatility During Social Learning in Emerging Psychosis.

Paranoid delusions or unfounded beliefs that others intend to deliberately cause harm are a frequent and burdensome symptom in early psychosis, but their emergence and consolidation still remains opaque. Recent theories suggest that overly precise prediction errors lead to an unstable model of the world providing a breeding ground for delusions. Here, we employ a Bayesian approach to test for such an unstable model of the world and investigate the computational mechanisms underlying emerging paranoia. We modelled behaviour of 18 first-episode psychosis patients (FEP), 19 individuals at clinical high risk for psychosis (CHR-P), and 19 healthy controls (HC) during an advice-taking task designed to probe learning about others' changing intentions. We formulated competing hypotheses comparing the standard Hierarchical Gaussian Filter (HGF), a Bayesian belief updating scheme, with a mean-reverting HGF to model an altered perception of volatility. There was a significant group-by-volatility interaction on advice-taking suggesting that CHR-P and FEP displayed reduced adaptability to environmental volatility. Model comparison favored the standard HGF in HC, but the mean-reverting HGF in CHR-P and FEP in line with perceiving increased volatility, although model attributions in CHR-P were heterogeneous. We observed correlations between perceiving increased volatility and positive symptoms generally as well as with frequency of paranoid delusions specifically. Our results suggest that FEP are characterised by a different computational mechanism - perceiving the environment as increasingly volatile - in line with Bayesian accounts of psychosis. This approach may prove useful to investigate heterogeneity in CHR-P and identify vulnerability for transition to psychosis.

Mapping Digital Recruitment Tools Using Advanced Convergent Qualitative Meta-integration

Organizations are now more skill-driven due to global competition and environmental volatility. As a result, finding the right talent for the needed position has been less important than in recent times. Traditional recruiting methods must meet the changing demands of recruiting the best-matched candidates. Therefore, incorporating digital solutions in recruitment and selection is an urgent necessity to thrive in the market. Digital recruitment tools, such as psychometric testing, gamification, video interviewing and applicant tracking systems can improve the function of recruitment and selection. However, a synopsis of this is still undermined. Considering the availability of the research gap, a systematic review, using an advanced convergent qualitative meta-integration method, is conducted to map the digital recruitment tools. A significant 23 research studies published in different reputable journals published from 2006 to 2022 were selected. The meta-integration review enlightened future directions that may help the beneficiaries to analyse and improvize the recruitment process.

Does corporate social responsibility drive financial performance? Exploring the significance of green innovation, green dynamic capabilities, and perceived environmental volatility

AbstractEnvironmental success can only become a reality when the financial goals of firms are not compromised. Based on this proposition, the study aimed to investigate four relationships, including the effect of corporate social responsibility (CSR) on financial performance, mediation of green dynamic capabilities (GDCs) between CSR and green innovation (GI), mediation of GI between CSR and financial performance, and moderation of perceived environmental volatility in GI and financial performance nexus. A sample of 655 manufacturing firms was collected from Pakistan to test the proposed hypotheses, and structural equation modeling was conducted. The results demonstrate a positive significant influence of CSR on financial performance. In addition, the mediation of GDCs and GI has also been confirmed. Furthermore, the results demonstrate that high environmental volatility weakens the GI and financial performance nexus. The study results offer unique contributions to the literature and interesting suggestions for practicing emerging economy managers.

Anxiety associated with perceived uncontrollable stress enhances expectations of environmental volatility and impairs reward learning

Unavoidable stress can lead to perceived lack of control and learned helplessness, a risk factor for depression. Avoiding punishment and gaining rewards involve updating the values of actions based on experience. Such updating is however useful only if action values are sufficiently stable, something that a lack of control may impair. We examined whether self-reported stress uncontrollability during the first wave of the COVID-19 pandemic predicted impaired reward-learning. In a preregistered study during the first-wave of the COVID-19 pandemic, we used self-reported measures of depression, anxiety, uncontrollable stress, and COVID-19 risk from 427 online participants to predict performance in a three-armed-bandit probabilistic reward learning task. As hypothesised, uncontrollable stress predicted impaired learning, and a greater proportion of probabilistic errors following negative feedback for correct choices, an effect mediated by state anxiety. A parameter from the best-fitting hidden Markov model that estimates expected beliefs that the identity of the optimal choice will shift across images, mediated effects of state anxiety on probabilistic errors and learning deficits. Our findings show that following uncontrollable stress, anxiety promotes an overly volatile representation of the reward-structure of uncertain environments, impairing reward attainment, which is a potential path to anhedonia in depression.

Identification of KASP markers and candidate genes for drought tolerance in wheat using 90K SNP array genotyping of near-isogenic lines targeting a 4BS quantitative trait locus.

This study identified a novel SNP and developed a highly efficient KASP marker for drought tolerance in wheat by genotyping NILs targeting a major QTL for drought tolerance using an SNP array and validation with commercial varieties. Common wheat (Triticum aestivum L.) is an important winter crop worldwide and a typical allopolyploid with a large and complex genome. With global warming, the environmental volatility and incidence of drought in wheat-producing areas will increase. Molecular markers for drought tolerance are urgently needed to enhance drought tolerance breeding. Here, we genotyped four near-isogenic line (NIL) pairs targeting a major QTL qDSI.4B.1 on wheat chromosome arm 4BS for drought tolerance using the 90K SNP Illumina iSelect array and discovered a single nucleotide polymorphism (SNP) (Excalibur_c100336_106) with consistent genotype-phenotype associations among all four NIL pairs and their parents. Then, we converted the SNP into a Kompetitive Allele-Specific PCR (KASP) marker, with an accuracy of 100% for the four NIL pairs and their parents and as high as 81.8% for the 44 tested wheat lines with known phenotypes collected from Australia and China. Two genes near this SNP were suggested as candidate genes for drought tolerance in wheat after checking the Chinese Spring reference genome annotation version 1.1. One gene, TraesCS4B02G085300, encodes an F-box protein reportedly related to the ABA network, a main pathway for drought tolerance, and another gene, TraesCS4B02G085400, encodes a calcineurin-like metallophos-phoesterase transmembrane protein, which participates in Ca2+-dependent phosphorylation regulatory system. Based on this work and previous research on pre-harvest sprouting, we established a quick and efficient general SQV-based approach for KASP marker development, integrating genotyping by SNP arrays (S) using NILs targeting major QTL for a specific trait (Q) and validating them with commercial varieties (V). The identified SNP and developed KASP marker could be applied to marker-assisted selection in drought breeding, and further study of the candidate genes may improve our understanding of drought tolerance in wheat.

Subanaesthetic doses of ketamine reduce but do not eliminate predictive coding responses: implications for mechanisms of sensory disconnection

BackgroundSensory disconnection is a key feature of sleep and anaesthesia. We have proposed that predictive coding offers a framework for understanding the mechanisms of disconnection. Low doses of ketamine that do not induce disconnection should thus diminish predictive coding, but not abolish it. MethodsKetamine was administered to 14 participants up to a blood concentration of 0.3 μg ml−1 Participants were played a series of tones comprising a roving oddball sequence while electroencephalography evoked response potentials were recorded. We fit a Bayesian observer model to the tone sequence, correlating neural activity with the prediction errors generated by the model using linear mixed effects models and cluster-based statistics. ResultsKetamine modulated prediction errors associated with the transition of one tone to the next (transitional probability), but not how often tones changed (environmental volatility), of the system. Transitional probability was reduced when blood concentrations of ketamine were increased to 0.2–0.3 μg ml−1 (96–208 ms, P=0.003); however, correlates of prediction error were still evident in the electroencephalogram (124–168 ms, P=0.003). Prediction errors related to environmental volatility were associated with electroencephalographic activity before ketamine (224–284 ms, P=0.028) and during 0.2–0.3 μg ml−1 ketamine (108–248 ms, P=0.003). At this subanaesthetic dose, ketamine did not exert a dose-dependent modulation of prediction error. ConclusionsSubanaesthetic dosing of ketamine reduced correlates of predictive coding but did not eliminate them. Future studies should evaluate whether states of sensory disconnection, including anaesthetic doses of ketamine, are associated with a complete absence of predictive coding responses. Clinical trial registrationNCT03284307.

Balancing structural IT capabilities for organizational agility in digital transformation: a resource orchestration view

PurposeTo achieve digital transformation, organizations have continued to rely on integrating the capabilities of information technology (IT) to facilitate decision-making and developing their reconfiguration capability to enhance agile operations. The pressure imposed by digital transformation necessitates investigations on leveraging different IT capabilities to attain substantial organizational agility in an optimal configuration. This study aims to provide a new perspective on balancing IT structural capabilities and proposes a framework for evaluating their coalignment and complementary returns based on resource orchestration theory.Design/methodology/approachA multi-method approach is used to evaluate the research model. This study tests hypotheses and explores the potential coalignment and complementary returns of balance in structural models and response surface analysis. Then, it analyzes the qualitative data and provides complementary findings to corroborate and confirm complex relationships.FindingsBalanced structural IT capabilities facilitate organizational agility but cooperate differently with internal (e.g. IT proactive stance) and external (e.g. environmental volatility) environmental factors. Balance between IT integration and reconfiguration must be maintained from several approaches during search/selection and configuration/deployment.Originality/valueThis study theorizes and empirically investigates the interactive mechanisms of two IT capabilities in influencing organizational agility under different boundary conditions. It enriches the understanding of balancing capabilities for organizational agility in digital transformation.

Management Strategy for Seaports Aspiring to Green Logistical Goals of IMO: Technology and Policy Solutions

Abstract Recently, because of serious global challenges including the consumption of energy and climate change, there has been an increase in interest in the environmental effect of port operations and expansion. More interestingly, a strategic tendency in seaport advancement has been to manage the seaport system using a model which balances environmental volatility and economic development demands. An energy efficient management system is regarded as being vital for meeting the strict rules aimed at reducing the environmental pollution caused by port facility activities. Moreover, the enhanced supervision of port system operating methods and technical resolutions for energy utilisation also raise significant issues. In addition, low-carbon ports, as well as green port models, are becoming increasingly popular in seafaring nations. This study comprises a comprehensive assessment of operational methods, cutting-edge technologies for sustainable generation, storage, and transformation of energy, as well as systems of smart grid management, to develop a green seaport system, obtaining optimum operational efficiency and environmental protection. It is thought that using a holistic method and adaptive management, based on a framework of sustainable and green energy, could stimulate creative thinking, consensus building, and cooperation, as well as streamline the regulatory demands associated with port energy management. Although several aspects of sustainability and green energy could increase initial expenditure, they might result in significant life cycle savings due to decreased consumption of energy and output of emissions, as well as reduced operational and maintenance expenses.

Is environmental sustainability transmissible? Transportation-based environmental taxation spillovers for sustainable development.

Environmental sustainability investigation has been a hotly debated topic of the modern literature; however, past studies have primarily overlooked its transmissibility or spillover outreach across economies. Herein, we investigate the novel aspect of whether transportation-based environmental taxation spatially induces spillover impacts across Italy, Germany, and France over the 1994-2020 period by employing a simultaneous spatial equation with multi-country dynamic stochastic general equilibrium modeling (DSGEM) framework. Transportation-based environmental taxation of the domestic economy negatively impacts its own investment and consumption, while it impacts the economy of neighboring economies positively. Change in output and investment in the domestic economy can be well explained by the environmental volatility of the domestic economy, whereas the environmental volatility of neighboring economies does not contribute much to explain the change in investment and output of the domestic economy. Volatility in pollution discharge occurs more by environmental volatility in the neighboring economy than in the domestic economy, and validating that environmental sustainability is transmissible across regions and economies. It urgently calls for environmental protection policies integrated and coordinated across the countries and regions to spread and capitalize on environmentally and economically favorable and sustainable effects globally. Achieving the spatially transmitted positive environmental and economic outcomes would help strengthen the Sustainable Development Goals (SDG), with a particular focus on Climate Action (SDG13), Sustainable Production and Consumption (SDG12), and Affordable and Sustainable Energy for All (SDG7).

Corporate Environmental Information Disclosure and Earnings Management in China: Ethical Behaviour or Opportunism Motivation?

China has entered a new stage of high-quality economic development, which puts forward in-depth requirements for environmental protection. Companies in heavily polluting industries are required to disclose environmental information. Fulfilling environmental responsibilities and disclosing this information may be either a responsible moral act or an opportunistic act. Taking listed companies in China’s heavily polluting industries from 2009 to 2020 as a sample, this study examines the economic consequences of enterprise environmental information disclosure (EID) from the earnings management (EM) perspective, as an external representation of ‘ethical behaviour’ and ‘opportunism motivations’ of EID and considers the effects of internal management competency and operating environment volatility. Findings include: (1) EID can restrain EM and support the ‘ethical behaviour’ motivation of EID. (2) Compared with the ‘soft disclosure’ of environmental information, the effect of ‘hard disclosure’ on EM is more obvious. (3) Greater management competency can strengthen the EM governance role of EID, while greater environmental uncertainty weakens this mechanism. (4) EID in enterprises in a mature period, state-owned, western regions, or low public environmental concern areas show an inhibitory effect on EM. Furthermore, its moralistic tendencies are more obvious compared with enterprises in growth or recession periods, non-state-owned, eastern regions, and those with high public environmental concerns.

Anxiety and depression related abnormalities in socio-affective learning

Affective distress (as observed in anxiety and depression) has been observed to be related to insufficient sensitivity to changing reinforcement during operant learning. Whether such findings are specific to anxiety or depression is unclear given a wider literature relating negative affect to abnormal learning and the possibility that relationships are not consistent across incentive types (i.e., punishment and reward) and outcomes (i.e., positive or negative). In two separate samples (n1 = 100; n2 = 88), participants completed an operant learning task with positive or negative, and neutral socio-affective feedback, designed to assess adaptive responses to changing environmental volatility. Individual parameter estimates were generated with hierarchical Bayesian modelling. Effects of manipulations were modelled by decomposing parameters into a linear combination of effects on the logit scale. While effects tended to support prior work, neither general affective distress nor anxiety or depression were consistently related to a decrease in the adaptive adjustment of learning-rates in response to changing environmental volatility (Sample 1: βα:volatility = −0.01, 95 % HDI = −0.14, 0.13; Sample 2: βα:volatility = −0.15, 95 % HDI = −0.37, 0.05). Interaction effects in Sample 1 suggested that while distress was associated with decrements in adaptive learning under punishment-minimisation, it was associated with improvements under reward-maximisation. While our results are broadly consistent with prior work, they suggest that the role of anxiety or depression in volatility learning, if present, is subtle and difficult to detect. Inconsistencies between our samples, along with issues of parameter identifiability complicated interpretation.

Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environments.

Humans are able to adapt to the fast-changing world by estimating statistical regularities of the environment. Although fear can profoundly impact adaptive behaviors, the computational and neural mechanisms underlying this phenomenon remain elusive. Here, we conducted a behavioral experiment (n = 21) and a functional magnetic resonance imaging experiment (n = 37) with a novel cue-biased adaptation learning task, during which we simultaneously manipulated emotional valence (fearful/neutral expressions of the cue) and environmental volatility (frequent/infrequent reversals of reward probabilities). Across 2 experiments, computational modeling consistently revealed a higher learning rate for the environment with frequent versus infrequent reversals following neutral cues. In contrast, this flexible adjustment was absent in the environment with fearful cues, suggesting a suppressive role of fear in adaptation to environmental volatility. This suppressive effect was underpinned by activity of the ventral striatum, hippocampus, and dorsal anterior cingulate cortex (dACC) as well as increased functional connectivity between the dACC and temporal-parietal junction (TPJ) for fear with environmental volatility. Dynamic causal modeling identified that the driving effect was located in the TPJ and was associated with dACC activation, suggesting that the suppression of fear on adaptive behaviors occurs at the early stage of bottom-up processing. These findings provide a neuro-computational account of how fear interferes with adaptation to volatility during dynamic environments.