Purpose This study examines how artificial intelligence (AI) adoption shapes firms' ambidextrous green innovation by inducing differential effects on exploratory and exploitative activities from an attention-based perspective. Design/methodology/approach Drawing on the attention-based view, we theorize that AI adoption reconfigures organizational attention and technological capabilities, leading to divergent effects on green innovation. Using panel data from Chinese listed firms, we employ regression analyses to test these arguments. Findings AI adoption exerts a double-edged effect on ambidextrous green innovation. It significantly promotes exploratory green innovation by redirecting managerial attention toward forward-looking strategic issues, while suppressing exploitative green innovation by institutionalizing routine processes and reducing sustained attention to incremental improvements. Furthermore, Confucian cultural embeddedness, negative media coverage and institutional ownership strengthen the positive effect of AI on exploratory green innovation and mitigate its negative effect on exploitative green innovation. Practical implications These findings suggest that managers should align AI adoption with balanced attention allocation to avoid overemphasizing exploratory innovation at the expense of incremental improvements. Originality/value This study advances the literature by conceptualizing AI as an attention-restructuring mechanism that systematically differentiates firms' exploratory and exploitative green innovation. It contributes to research on AI and organizational ambidexterity by uncovering the contingent role of institutional and social contexts.
Purpose Climate variation driven primarily by carbon emissions is a major driver of global warming, significantly raising Earth’s temperature. This study examines the influence of rising temperatures and carbon emissions on agricultural performance (AGP) in Africa from 1990 to 2022. The aim is to identify management policies that enhance sustainable agricultural resilience and moderate the adverse effects of climate change. Design/methodology/approach This research employs second-generation panel econometric techniques. The short- and long-run (LR) relationships between AGP, temperature, carbon emissions, sustainable finance and population growth are estimated using a Panel Autoregressive Distributed Lag (P-ARDL) model. Findings The results disclosed the fact that rising temperatures and carbon emissions significantly reduce AGP, highlighting the urgent need for proactive environmental management. Conversely, sustainable finance and population growth positively influence long-term AGP and food security. These variables emerge as crucial levers for climate adaptation and agricultural resilience across Africa. Practical implications Climate stress has made agricultural production more unpredictable and this threatens food security. The findings stress the need for integrated environmental and agricultural management policies. Hence, the study recommends that policymakers and financial institutions must prioritize investments in climate-smart agriculture and sustainable finance to address the challenges posed by climate variation, ensuring sustainable agricultural resilience and food security. Originality/value This study provides new evidence on the links between rising temperatures, carbon emissions, sustainable finance, population growth and AGP in Africa by jointly examining their short-run and LR dynamics within a panel framework, thereby offering policy-relevant insights for climate-resilient agricultural and financial strategies.
Purpose This study examines whether exposure to green tax incentives is associated with corporate tax avoidance in an emerging-market setting. Using Bangladesh's Renewable Energy Policy (REP) incentives 2008 as a natural setting, it examines the direct effect of green tax incentives (GTI) on tax avoidance (TA) and the mediating role of institutional ownership (INST_OWN) and moderating role of financial leverage (LEV_A), with board gender diversity (GENDIV) examined as a secondary governance mechanism. Design/methodology/approach An unbalanced panel of 42 Dhaka Stock Exchange-listed firms that adopted integrated reporting between 2015 and 2024 (295 firm-year observations) is analyzed using fixed-effects panel regression with year dummies and robust standard errors clustered at the firm level. Green tax incentive is identified via sector eligibility and sustainability disclosure quality. Mediation is tested using the KHB method; moderation is assessed using interaction terms and simple slope analyses. Findings Green tax incentives are associated with lower levels of corporate tax avoidance (β = −0.121, p < 0.05). Institutional ownership partially mediates this relationship (indirect effect = −0.016, p < 0.01), while the association weakens as financial leverage rises (interaction = +0.118) and is no longer statistically distinguishable from zero at high leverage. Board gender diversity shows a negative but statistically insignificant association with tax avoidance. Practical implications Because the compliance-related association is not observed among highly leveraged firms, leverage may warrant attention in the design of green tax incentives. As the sample period ends in 2024, this evidence precedes Bangladesh’s 2025 fifteen-year tax holiday and can inform expectations about that regime rather than evaluate it. Originality/value This study extends prior green tax research by integrating mediation (institutional ownership) and moderation (leverage) within a unified governance framework in an emerging-market renewable policy setting. It offers baseline evidence from the pre-2025 incentive regime and contributes to the sustainability, governance and taxation literature by showing that the association between a green fiscal instrument and tax compliance is conditional on firm-level financial pressure.
Global crises and disruptions increasingly affect energy systems in ways that extend beyond market outcomes to environmental quality pressures and household vulnerability. However, crisis-impact evidence remains fragmented, limiting its usability for integrated monitoring and prioritisation. This study proposes an adaptive, indicator-based framework to identify environmental quality and household energy vulnerability impact domains of global crises affecting the energy sector and demonstrates its application using Lithuania during the initial COVID-19 crisis phase. The framework integrates four domains (energy demand and energy efficiency; air pollution and emissions pressures; renewable energy development; household energy vulnerability) operationalised through 33 sub-indicators drawn from Eurostat, national statistics and a complementary household survey. Results indicate crisis-period reallocation patterns rather than uniform sectoral contraction: aggregate final energy consumption declined while residential electricity and gas use increased; NO2 concentrations and emissions from stationary sources decreased; renewable capacity and renewable shares increased, reflecting both continued deployment and demand-related shifts and EU-SILC energy poverty outcomes did not worsen in 2020 despite rising electricity prices and reported affordability constraints, suggesting time lags between pressure indicators and deprivation measures. The framework contributes by integrating environmental quality, energy-system and household vulnerability indicators within one monitoring structure, thereby enabling crisis-period signals to be interpreted jointly rather than as separate environmental, energy or social outcomes. Designed for use across crisis episodes and phases, the framework supports integrated monitoring and evidence-informed prioritisation under uncertainty in Lithuania and comparable EU contexts.
Purpose This study addresses the “last-mile” governance dilemma of rural household waste sorting in developing countries, where participation is high but effectiveness is low. Guided by public-goods, externality, and collective-action theories, it aims to investigate two main questions: (1) what are the multidimensional (environmental, health, economic and social) welfare effects of rural waste sorting and (2) how do these effects vary across public and private domains, challenging the assumption that the benefits of waste sorting are homogeneous. Design/methodology/approach The study utilizes survey data collected from 939 rural households across two distinct regions in China: Jiangsu and Gansu. To rigorous analyze the data while accounting for potential self-selection bias and unobserved heterogeneity among participants, the authors employ a sophisticated econometric approach. Specifically, they combine Propensity Score Matching (PSM) with an endogenous switching regression (ESR) model. This methodology ensures robust causal inference regarding the multidimensional welfare effects and allows for a detailed comparison of outcomes between public and private behavioral domains. Findings Waste sorting yields positive but uneven welfare effects, following a gradient of social > environmental > health > economic benefits. This indicates that current participation is primarily driven by social norms rather than economic or health returns. Crucially, situational context matters: informal normative pressure in the public domain inflates perceived environmental benefits. Robustness checks reveal a “numerical illusion,” showing that the environmental effects realized through externally mobilized participation may be overestimated by up to 25.4%, highlighting a gap between reported and actual governance success. Originality/value This study advances behavioral and environmental governance research by dismantling the assumption of homogeneous welfare effects in waste sorting. Academically, it is among the first to quantify domain-specific, multidimensional outcomes and expose a significant measurement bias (“numerical illusion”) in public interventions. For policymakers and managers, it provides a crucial diagnostic framework to shift rural governance from arbitrary, campaign-driven participation targets toward outcome-based, scenario-adapted incentives that tie behavioral outcomes to tangible economic and health returns.
