Search Results (12,406)

Virtual influencers have emerged as an important marketing tool in social commerce and digital consumer engagement. However, limited research has examined how virtual influencer characteristics shape consumers’ green purchase intention through underlying emotional and relational mechanisms. Drawing upon Parasocial Interaction Theory and Affect-as-Information Theory, this study investigates the effects of perceived authenticity, social presence, and interactivity on consumers’ green purchase intention, with parasocial intimacy and emotional arousal serving as mediating variables. A mixed-method approach integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) was employed. Data were collected from 346 consumers who had prior experience interacting with virtual influencers and purchasing green products. The PLS-SEM results indicate that perceived authenticity, social presence, and interactivity significantly enhance parasocial intimacy and emotional arousal, which subsequently increase green purchase intention. Furthermore, the fsQCA findings reveal multiple configurational pathways leading to high green purchase intention, suggesting that different combinations of virtual influencer characteristics can generate similar consumer outcomes. This study contributes to the electronic commerce and influencer marketing literature by extending the understanding of virtual influencer effectiveness in sustainable consumption contexts. Methodologically, the integration of symmetrical and asymmetrical analytical approaches provides a more comprehensive explanation of consumer decision-making in virtual influencer marketing environments. The findings also offer practical implications for marketers seeking to leverage virtual influencers to promote green consumption in digital commerce settings. Full article

This study investigates the role of institutional policy support in enhancing climate adaptation strategies and maize productivity among smallholder farming households in Southwest Nigeria, a region critical for maize production yet vulnerable to climate variability. Employing a household-level data approach, data were collected from maize-based households across six states using questionnaires and interviews, supplemented by secondary climate and policy records. We employed a multivariate probit (MVP) regression with instrumental variable correction, addressing endogeneity in institutional support variables. Bayesian linear regression modeled maize yield as a function of institutional support, incorporating weakly informative priors and Markov Chain Monte Carlo (MCMC) sampling for posterior estimation. The Bayesian approach provides full posterior distributions and credible intervals, facilitates probabilistic interpretation of policy effects, improves estimation stability in the presence of multicollinearity, and enables rigorous sensitivity. Model robustness was evaluated via Bayesian fit metrics and sensitivity analysis with bootstrap resampling across prior types. Multivariate probit regression identifies institutional support, credit, irrigation, market access, and road infrastructure as key drivers of adaptation strategy adoption, modulated by socioeconomic (gender, experience) and farm-specific factors (farm size). Bayesian linear regression confirms significant yield impacts from institutional variables. Subgroup analysis indicates greater benefits for large farms over small farms, with gender-neutral impacts. While institutional support significantly boosts adaptation and productivity, gaps in irrigation access, climate information, and smallholder targeting limit equitable outcomes. The findings advocate for enhanced infrastructure, financial incentives, and tailored policies to strengthen climate resilience and food security, aligning with Nigeria’s climate goals and the Sustainable Development Goals. Full article

Rapid urbanization, increasing demand variability, and digitalisation of urban infrastructure require intelligent decision-support systems that can improve sustainability, resilience, and operational efficiency in smart city operations. Artificial intelligence (AI) and data-driven optimization offer strong potential for adaptive urban services, including dynamic pricing, demand response, resource allocation, and energy-aware management. However, many reinforcement learning applications still focus mainly on short-term performance while giving limited attention to transparency, fairness, stability, and accountability. This study proposes a trustworthy reinforcement learning framework for AI-driven urban decision-making, using sustainable dynamic pricing and resource optimization as mechanisms for adaptive and responsible decision-making. A custom reinforcement learning environment was developed using historical e-commerce transactional data as a methodological proxy to simulate interactions among demand, resource or inventory availability, service categories, price elasticity, and changing market conditions. Three reinforcement learning algorithms, namely Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic, were evaluated under comparable experimental conditions. Performance was assessed using profitability, decision stability, fairness-oriented pricing behavior, decision consistency, and interpretability. To improve transparency, trajectory-based policy audits and SHapley Additive exPlanations were applied to identify the main factors influencing pricing decisions. The results show that the Deep Q-Network agent achieved the most balanced performance, increasing total profit by 12.58% while recording no unethical price increases under low-demand conditions. Explainability analysis showed that stock or resource levels, demand shifts, and price elasticity were the strongest positive drivers of pricing actions, whereas inventory hoarding and unfavorable price increases reduced decision quality. The findings indicate that reinforcement learning can support sustainable and resilient urban decision-making when optimization objectives are combined with trustworthy AI principles. The proposed framework provides a practical basis for accountable AI-based decision-support systems in smart city operations, including demand-responsive services, resource optimization, sustainable dynamic pricing, and energy-aware management. Full article

As critical minerals required for the production of new energy batteries, the demand forecasts and trends for Li, Co, Ni, and Mn are of crucial importance for ensuring the energy transition and China’s sustainable development. This study has developed a comprehensive analytical framework, combining dynamic material flow analysis, stock-driven forecasting, and scenario analysis to conduct a life-cycle assessment of the critical mineral resources (2010–2060). (1) Retrospective estimates show that in 2024, demand for Li, Co, Ni, and Mn reached 117 kt, 79 kt, 78 kt, and 45 kt, respectively. By 2060, the maximum demand for these minerals is projected to rise to 651 kt, 1090 kt, 1080 kt, and 757 kt, respectively. (2) Li is the mineral with the highest demand. Under different recovery scenarios, the lithium substitution rates reach 55.4%, 68.3%, and 85.3%, respectively. Given that the demand in cascade utilisation is far lower than the volume of retired new energy vehicle batteries, a cascade utilisation rate exceeding 23% would theoretically suffice to meet the entire relevant market demand. To secure the supply of critical minerals for energy batteries, this study suggests accelerating the construction of standardised recycling systems and implementing strict entry approvals to prevent overcapacity. Full article

Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Based on evidence synthesized from 152 studies and supporting geospatial and scenario analyses, results indicate that AI-optimized systems can recover 75–94% of prime-land yields, achieve carbon sequestration rates of 2.1–6.8 t CO₂e ha⁻¹ yr⁻¹, central estimate ≈ 7–9 Gt CO₂e yr⁻¹ at 35% adoption with moderate exclusions, and generate projected internal rates of return ranging from 8–22%, depending on feedstock type, regional conditions, and financing assumptions. Yield-recovery and carbon-sequestration ranges are drawn from synthesis of the reviewed literature; IRR, financial-leverage, and market-expansion figures are author-constructed scenario projections based on this evidence, not independently observed outcomes. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively projected, under scenario-based modeling, to reduce composite project risk scores by 30–45% and expand the investable universe of degraded-land biofuel projects by an estimated 340% relative to a no-AI, no-green-finance baseline; these figures represent author-constructed scenario estimates rather than direct empirical findings. We develop the AI-Biofuel-Land Restoration-Green Finance (ABLR-GF) conceptual framework (not yet empirically validated through field pilots or simulation) with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management. Full article

In the context of deepening data assetization, this paper explores the strategic role of data assets in sustaining corporate competitiveness. Utilizing a dataset of China’s A-share listed companies from 2010 to 2023, we examine the influence of data assets on continuous innovation from a synergistic perspective of robust anchoring and dynamic reconstruction. The findings indicate that data assets can significantly empower continuous innovation, with the effect being more pronounced in specific contexts defined by ownership, regional marketization, the intensity of intellectual property protection, and market structure. In further analysis, we also found that this effect extends to green continuous innovation, offering more direct evidence for the sustainability implications of data-driven innovation. Crucially, our mechanism analysis reveals that data assets facilitate innovation through two pathways: (1) a “de-financialization” pathway, which reduces reliance on financial speculation and consolidates the real economy; and (2) a capability-building pathway, which enhances firms’ dynamic capabilities. These findings offer practical implications for policymakers and managers seeking to promote sustainable corporate growth, as they demonstrate that embedding innovation continuity into core strategies serves as a foundational driver of long-term environmental, social, and economic resilience. Full article

This review provides an integrative synthesis of the R&D performance measurement literature and introduces the Beyond Traditional Metrics (BTM) framework as a multi-criteria reference model for R&D performance evaluation in the petroleum sector. Research and Development (R&D) constitutes a strategic pillar of the petroleum industry, where technological innovation supports competitiveness, operational efficiency, and the transition toward more sustainable energy systems. However, evaluating the performance of R&D projects remains a major challenge because their outcomes are often uncertain, intangible, long-term, and multidimensional. Commonly used Key Performance Indicators (KPIs)—such as cost, time, and number of deliverables—therefore provide only a partial representation of R&D effectiveness. R&D performance assessment must therefore consider the intrinsic diversity of innovation activities. Reverse engineering emphasizes replication and adaptation of existing technologies, while innovation-driven R&D seeks to create novel knowledge, technological capabilities, and strategic learning. Accordingly, the selection of performance indicators should be adapted according to project type, technological maturity, and strategic objectives. To avoid biased evaluation, the approach integrates principles derived from the Multi-Criteria Decision Analysis (MCDA) approach, enabling prioritization of criteria aligned with each project’s objectives, complexity, and organizational priorities. To move beyond simple cost and time metrics, this study revisits the meaning of “performance” in R&D and explores a multidimensional evaluation perspective capable of capturing both tangible and intangible forms of value creation by integrating five complementary dimensions: Knowledge Creation and Diffusion, Innovation Velocity, Dynamic Strategic Alignment, Team and Organizational Health, and Resilience and Robustness under technological, regulatory, operational, and market uncertainty. The framework is illustrated through an exploratory application to a hypothetical portfolio of petroleum-sector R&D projects, demonstrating its potential usefulness for benchmarking, portfolio prioritization, and multidimensional innovation assessment under conditions of uncertainty. Full article

The rapid expansion of embodied intelligent agricultural machinery has not been accompanied by a commensurate increase in market adoption, highlighting a growing disconnect between technological supply and downstream demand. This study develops a dynamic general equilibrium model to examine how government fiscal support and labor-market frictions shape the adoption of embodied intelligent agricultural machinery and structural transformation in agricultural production. Structural parameters are estimated using agricultural machinery purchase data and listed-company recruitment records for 2021–2025, and the estimated parameters are incorporated into dynamic simulations. The results show that the scale and composition of fiscal support operate through distinct mechanisms. An expansion in the scale of fiscal support primarily raises final agricultural output by promoting the accumulation of R&D outcomes and technological knowledge and thereby improving productivity, while exerting only a limited direct effect on the demand structure for embodied intelligent machinery. Changes in the composition of fiscal support, by contrast, affect sectoral output allocation through shifts in government demand. Lower labor-market frictions increase the nominal demand share of embodied intelligent agricultural machinery and induce a reallocation of capital, labor, and output toward the embodied intelligent sector, with relatively limited effects on aggregate agricultural output. Reallocating fiscal resources from current government purchases toward government savings generates an intertemporal trade-off between current support and long-run productivity accumulation. The labor-market-friction mechanism remains robust across alternative elasticities of substitution. Overall, the findings indicate that sustained technology adoption depends on the effective transmission of technological supply into market demand through coordinated fiscal support and improved labor-market allocation. Full article

This study examines the relationship between financial inclusion and economic growth, focusing specifically on the role of financial sector development in South Asia. The research encompasses annual data from five developing economies in South Asia: Bangladesh, India, Pakistan, Nepal, and Sri Lanka, during the period from 2000 to 2024. Multidimensional indicators of financial inclusion, economic growth, and financial sector development were generated using principal component analysis, while the panel autoregressive distributed lag–pooled mean group technique was applied to evaluate both long-run and short-run dynamics. The findings indicate that growth of the financial sector contributes significantly to economic development, providing its position as a major force behind economic expansion. The results suggest that financial sector development may represent a potential transmission channel linking financial inclusion and economic growth. The study highlights the importance of strengthening inclusive financial systems and financial sector institutions to achieve sustainable economic growth in South Asia, and offers policy recommendations for enhancing financial access, financial literacy, innovation, and financial market efficiency. Full article

The growing importance of sustainable digital commerce has increased the need to understand how Environmental, Social, and Governance (ESG)-related information communicated through digitally mediated environments influences consumer decision-making and value creation. This study investigates the effects of corporate ESG information and public ESG information on consumer awareness and subsequent behavioral responses, including purchase intention, investment intention, advocacy, and positive perceptions toward ESG-oriented companies. Drawing on Stakeholder Theory and the theory of planned behavior, the study examines how different sources of ESG information shape consumer evaluations and intentions in an increasingly information-driven marketplace. Using partial least squares structural equation modeling (PLS-SEM), data from 1836 respondents obtained from the Korea Consumer Agency’s national consumer survey were analyzed. The results indicate that corporate ESG information significantly enhanced consumer awareness, whereas public ESG information did not have a significant effect on consumer awareness. Public ESG information significantly strengthened positive perceptions but did not significantly influence advocacy. Consumer awareness emerged as the strongest predictor of purchase intention, investment intention, advocacy, and positive perception. It significantly mediated the relationships between corporate ESG information and consumer responses, whereas no significant indirect effects were observed for public ESG information. However, corporate ESG information did not directly increase purchase intention or investment intention, suggesting that awareness represents the primary mechanism through which ESG communication influences consumer responses. These findings contribute to the literature on sustainable digital commerce by demonstrating that ESG information functions as a strategic market signal that promotes consumer engagement and sustainable value creation through awareness. The study further provides practical implications for firms and policymakers seeking to develop credible ESG communication strategies that support the green transition and foster sustainable consumer decision-making. Full article

African leafy vegetables (ALVs) are increasingly recognised for their nutritional, phytochemical, and socioeconomic importance, particularly in resource-limited communities. This narrative review synthesises current evidence on the effects of preservation methods on nutritional composition, phytochemical stability, microbial quality, sensory attributes, consumer acceptability, and their contribution to sustainable food systems in South Africa, supported by evidence from across Africa and the broader scientific literature. Evidence indicates that ALVs are rich sources of essential nutrients and bioactive compounds with important health-promoting properties. Preservation methods such as sun drying, blanching, freezing and solar drying are widely used to extend shelf life and improve year-round availability. However, their effects on nutrient retention, phytochemical stability, microbial safety, and sensory quality vary considerably. The review further highlights persistent challenges within ALV value chains, including inadequate processing and storage infrastructure, limited value addition, weak market integration, and low consumer awareness. Collectively, these constraints restrict the wider utilisation and commercialisation of ALVs despite their potential to strengthen food security and improve dietary quality. Standardised preservation approaches, investment in appropriate processing infrastructure, stronger value chains, improved consumer awareness, and supportive institutional and policy frameworks are required to enhance the adoption and sustainable utilisation of ALVs. Full article

Against the backdrop of increasing global supply chain disruption risks, supplier diversification has emerged as a critical strategy for enhancing economic resilience and sustainable trade performance. Using matched supplier–customer transaction data for Chinese listed firms and Chinese Customs data from 2009 to 2016, this study examines the impact of supplier diversification on firms’ export product scope and its underlying mechanisms. The results show that supplier diversification significantly expands firms’ export product scope by mitigating resource and technological lock-in effects associated with concentrated supply chains. Specifically, a 0.1-unit increase in supplier diversification is associated with an average increase of approximately 0.074 distinct HS 6-digit product categories exported by a firm. Mechanism analyses indicate that this effect operates primarily through promoting technological innovation and reducing production costs. The positive effect is more pronounced among larger firms, firms facing weaker financial constraints, and firms operating in highly competitive or technology-intensive industries. Further analyses show that supplier diversification facilitates the entry of new products into export markets and supports the continued export of existing products, while having no significant effect on product exit. These findings identify supplier diversification as an important micro-level supply-side determinant of firms’ export product scope and provide implications for supplier portfolio design, supply chain resilience, and sustainable export strategies. Full article

With the global low-carbon transition and increasing wind and photovoltaic penetration, virtual power plant (VPP) scheduling increasingly requires coordinated low-carbon operation, resource management, and long-term stability. Existing studies often treat demand response (DR) resources as passive adjustable capacity, but insufficiently consider how satisfaction variation, fatigue accumulation, and scheduling experience under continuous dispatch feed back into subsequent response behavior. Meanwhile, the coordination among electricity, gas, carbon, and green certificate markets and market incentives reflecting the low-carbon value of power-to-gas (P2G) remain underexplored. To address these issues, this paper introduces a response-willingness feedback mechanism that feeds the satisfaction and fatigue accumulation of the demand response aggregator (DRA) back into subsequent deliverable DR capacity and scheduling behavior. A virtual power plant operator (VPPO)–DRA bi-level optimal scheduling model is then formulated under a Carbon–Green Certificate Coordinated P2G Incentive Mechanism, characterizing the Stackelberg interaction between the VPPO and the DRA. A hierarchical solution framework integrating CMA-ES, dynamic programming, and Gurobi is developed. Deterministic comparisons showed that the model maintained VPP profitability while improving DR sustainability and renewable accommodation. Under ex post stress testing, the fixed nominal day-ahead VPPO strategy maintained feasibility in all 200 cases and positive VPP profit in 94.0%. Full article

Carbon emissions trading market is an important institutional innovation to promote green and low-carbon transformation of economic development and sustainable economic and social development. With the quasi-natural experiment of China’s carbon emissions trading pilot policy since 2013, this paper constructs a multi-period double-difference model based on the panel data of 30 provinces and systematically evaluates the effectiveness as well as the heterogeneous performance of the carbon emissions trading policy on carbon emissions. The results show that the implementation of carbon emissions trading policy significantly reduces regional carbon emissions, with a significant impact coefficient of −0.1701 at the 1% level, and the finding passes a series of robustness tests. Heterogeneity analysis shows that the impact effect of carbon emissions trading policies is more significant in the eastern and central regions and more significant in regions with high levels of human capital. Mechanism analysis indicates that the carbon emissions trading policies achieve carbon emission reduction through the market mechanism and government intervention mechanism, and promote regional investment in scientific and technological innovation, reduce the total amount of energy consumption, and optimize the structure of energy consumption. Further analysis indicates that the carbon trading policy exerts a significant spatial spillover effect on carbon emission reduction. Full article

Microbiological bioproducts constitute a rapidly growing group of preparations used in modern agriculture and play a significant role in achieving the goals of sustainable crop production. Because they contain live microorganisms or their metabolites, they promote plant growth and development, increase nutrient availability, improve soil properties, and suppress pathogen development, thereby representing a promising alternative to conventional fertilizers and chemical plant protection products. The aim of this study was to analyze the current state of knowledge on the application of microorganisms in agriculture, with a particular focus on their mechanisms of action, bioproduct formulation principles, and the legal requirements for their registration and marketing in Poland and the European Union. The paper presents a classification of microbiological bioproducts and compares their properties with those of chemical products. The stages of product formulation, including strain selection and identification, mass production, the choice of appropriate carriers, and stabilization methods, are discussed. Furthermore, the primary mechanisms of microbial action on plants are characterized, including biological nitrogen fixation, phosphate solubilization, the production of phytohormones and siderophores, and pathogen biocontrol mechanisms. In addition, the current legal regulations governing the classification, registration, and quality and safety requirements of agricultural microbiological products are outlined. The analysis indicates that microbiological bioproducts have significant potential to support sustainable crop production and mitigate the negative environmental impacts of agriculture. However, their widespread adoption requires further advancements in formulation technologies, quality standardization, and the improvement and harmonization of existing legal frameworks. Full article