Search Results (360)

The co-pyrolysis of waste sawdust and non-recyclable polypropylene at 500 °C was investigated for low-cost adsorbent production and solid waste valorization in rural Thailand. Sustainability was evaluated through techno-economic analysis, gate-to-gate environmental screening, CO₂ emission accounting, adsorption cost analysis, and social return on investment assessment. The sawdust–polypropylene biochar produced at 500 °C production system requires a total capital expenditure of 46,000 THB and achieves a unit production cost of 316 THB kg⁻¹ at a 35% w/w biochar yield, 7–14 times lower than commercial granular-activated carbon and powdered-activated carbon. Based on a hypothetical community-scale deployment scenario, the estimated capital expenditure payback period under in-house granular-activated carbon substitution falls below six months. All annual techno-economic and SROI results presented in this study represent scenario-based screening estimates and should not be interpreted as demonstrated community-scale performance. Gate-to-gate environmental screening estimated gross production emissions of 8.771 kg CO₂e kg⁻¹ SPB-500. A consequential waste-diversion scenario incorporating carbon sequestration and avoided-disposal credits yielded a hybrid scenario-based net greenhouse-gas balance of +3.082 kg CO₂e kg⁻¹ SPB-500, supporting its potential application under the evaluated scenario of approximately 0.4–5.9 kg CO₂e kg⁻¹ relative to commercial-activated carbon benchmarks. Social return on investment analysis yields a base–case ratio of 1.39:1 (five-year total present value: 6,025,841 THB; minimum across sensitivity scenarios: 1.13:1), with water quality improvement (SDG 6; 46.1%) and health risk reduction (20.7%) jointly accounting for 66.8% of monetized outcomes, confirming investment justification from public health benefits alone. Quantifiable alignment is demonstrated across five UN Sustainable Development Goals. Collectively, these findings suggest that SPB-500 co-pyrolysis has the potential to be an economically accessible and socially beneficial waste-valorization technology under the evaluated scenario, supporting its potential application in decentralized heavy-metal remediation in resource-constrained communities. Full article

This paper develops and evaluates a predictive analytics framework for influencer marketing return on investment (ROI), integrating hybrid deep learning architectures with trust-aware modelling to address the dual purpose of (a) developing a rigorous evaluation framework for influencer campaign performance and (b) examining the effectiveness of influencer marketing predictors. The concept of influencer marketing has quickly grown to be one of the most effective mediums within the contemporary digital advertising landscape. Due to the growing number of brands dedicating huge amounts of budgets to social media partnerships, the importance of data-driven approaches that can predict the outcomes of campaigns and, consequently, ensure the best possible return on investment (ROI) has become urgent. This paper introduces a machine learning system that can be used to forecast the sales of products promoted by influencer marketing campaigns based on campaign-level features, including type of platform, influencer type, type of campaign, time of the year, number of engagements, estimated reach, and campaign duration. A publicly available influencer marketing ROI dataset was trained and tested on an XGBoost regression model with a coefficient of determination ($R^2$) of 0.95 indicating high predictive power and generalization. The results show that engagement metrics and estimated reach are some of the most impactful factors in sales performance, and additional contextual factors like platform selection, type of campaign, and timing of the year also moderate results. In addition to predictive modelling, this paper explains how artificial intelligence (AI) can be strategically integrated throughout the influencer marketing lifecycle. With the inclusion of AI-based analytics, marketers will be able to leverage their intuitive decision-making processes with quantifiable and replicable measures and approaches that can lead to true consumer trust and lasting brand resonance. The framework proposed can provide practitioners and researchers with a scalable basis for implementing intelligent systems in the context of influencer marketing. Recent computer science research further demonstrates that AI-driven frameworks spanning generative content modelling, AI-powered CRM architectures for understanding consumer preferences on social media, and parasocial-trust models of influencer engagement provide strong methodological complements to the predictive approach developed here, while governance and project management considerations for deploying such systems are increasingly addressed in the literature. Concurrently, a growing body of influencer marketing research examines how platform affordances shape information-seeking and trust, how influencer attributes and social satisfaction mediate purchase intention, how influencer marketing drives sustainable consumption, and how social media measurably shapes health-related behaviours all of which motivate the predictive and trust-modelling objectives of this work. Full article

Brownfield redevelopment increasingly requires sustainability-oriented frameworks integrating Life Cycle Assessment (LCA), Life Cycle Costing (LCC), Net Present Value (NPV), Social Life Cycle Assessment (S-LCA), and Social Return on Investment (SROI) to evaluate conventional remediation benchmarks and nature-based solutions (NBS) for the restoration of the Vieux-Charmont brownfield (France) into a public ecological park. Eight remediation scenarios were assessed, including combinations of excavation, soil treatment, landfill disposal, soil reuse, and phyto-management. The results demonstrated substantial differences among restoration pathways. The conventional landfill-oriented benchmark generated the highest environmental burdens, whereas the best-performing phyto-management scenario achieved the lowest impacts, reducing climate change and land use impacts by 91.6% and 75.6%, respectively. Scenarios integrating reduced excavation intensity and treated soil reuse consistently improved environmental performance and long-term economic viability. The best-performing phyto-management configuration also achieved the highest NPV after 20 years (1.38 million Euros, 2026). The social assessment results indicated improved socio-economic performance for phyto-management systems within the adopted S-LCA and SROI framework. Overall, the findings demonstrated that remediation strategies should not be evaluated solely according to contaminant removal efficiency or direct operational costs. Instead, integrated sustainability frameworks combining environmental, economic, and social dimensions provide a more robust basis for supporting sustainable brownfield restoration and circular land management strategies. Full article

Environmental, Social, and Governance (ESG) performance has become a central criterion for evaluating corporate sustainability. Yet the empirical relationship between ESG and financial performance remains contested, especially in emerging markets where institutions are evolving. This study examines how ESG performance relates to corporate financial performance among Chinese A-share listed companies and tests whether corporate innovation functions as a transmission mechanism. Using a balanced panel (2015–2023), we combine System Generalized Method of Moments (System GMM) with a non-parametric Bootstrap mediation procedure. We decompose ESG into environmental, social, and governance dimensions and distinguish between innovation input (R&D investment) and innovation output (patent generation). The results indicate a positive directional association between overall ESG performance and return on assets (ROA), but the direct financial effect is primarily driven by the governance dimension. Environmental and social performance do not show robust direct effects. However, ESG significantly promotes corporate innovation, especially patent output. Bootstrap mediation results confirm that patents represent a robust and universal channel through which ESG contributes to financial performance, while the R&D pathway is more conditional. The findings also indicate ownership heterogeneity between state-owned and private enterprises. By distinguishing between innovation input and output, this study explains how ESG practices may be translated into economic value in an emerging market context distinct from advanced economies. Full article

Green hydrogen—hydrogen produced from renewable electricity—is central to global decarbonization strategies. However, despite their fragile governance, damaged infrastructure, water scarcity, and limited investment security, conflict-affected developing economies remain largely absent from hydrogen research. This study addresses that gap by developing and validating a multi-evidence strategic framework for green-hydrogen (GH₂) adoption in fragile institutional environments, using Palestine as a challenging test case. Methodologically speaking, the framework integrates four evidence streams—barrier prioritization by 45 Palestinian experts using the Analytic Hierarchy Process (AHP); structural modeling of barrier–adoption–sustainability relationships using partial least squares structural equation modeling (PLS-SEM); strategic-pathway ranking using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS); and an original Sustainable Development Goal (SDG) Contribution Index—externally validated by an independent panel of 120 energy experts across 18 Middle East and North Africa (MENA) countries. Three findings stand out. Firstly, expert perception and structural evidence diverge: technical barriers receive the highest expert weight (56.2%) yet show the weakest structural effect on adoption (β = −0.230), whereas social barriers, weighted lowest by experts (4.8%), rank second in predictive power (β = −0.310). Secondly, Small-Scale Community Production is the most robust deployment pathway, ranked first under every weighting scenario tested. Thirdly, government policy quality acts as a governance multiplier, raising the sustainability returns of adoption by 20.2%, with benefits concentrated in SDGs 7, 13, 8, and 9. Practically speaking, the framework yields seven strategic goals and a phased 2026–2040 roadmap for fragile developing economies. Full article

This study examines the relationship between environmental, social, and governance (ESG) criteria and profitability in the global insurance sector from two distinct perspectives. The System GMM analysis measures the associations between ESG criteria and asset profitability. The analysis, conducted using the CRADIS method and weighted by the CRISUS, MAXC, and NMV methods, determines the companies’ multidimensional performance rankings. Thus, the financial outcomes of companies’ sustainability investments are comprehensively revealed. According to the System GMM estimation results, environmental and social variables are negatively associated with asset profitability, whereas the governance variable and return on equity are positively associated with asset profitability. The leverage ratio and firm size are negatively associated with profitability. While asset profitability and return on equity stand out as the most significant factors compared with environmental, social, and governance variables, environmental and social variables have become increasingly prominent in decision-making processes since 2020. According to the NMV method, return on equity is the decisive criterion, whereas the CRISUS-MAXC integrated method identifies return on assets as the decisive criterion; in both methods, the leverage ratio remains variable and has the lowest impact. According to the CRADIS method rankings, Admiral Group and Zurich Insurance were identified as having the highest performance and the lowest volatility. CNA Financial, Great Eastern, and Hanwha Corp were identified as the lowest-performing companies. Sensitivity analysis results indicate that the NMV-CRADIS method is more resilient to changes in weights. Full article

This paper develops a dynamic signaling framework to analyze how educational investment evolves under imperfect information and how the informational value of credentials changes over time. It addresses a central question: under what conditions do signaling equilibria become fragile, and how does this fragility generate educational overinvestment and credential inflation in equilibrium? The model features heterogeneous productivity groups and endogenous educational choices, in which education plays both a signaling and a productive role. Informational frictions and wage-setting mechanisms jointly determine equilibrium configurations, allowing for separation, pooling, and mixed equilibria. The analysis shows that separating equilibria are inherently fragile: when signaling costs decline or when the share of lower-productivity workers becomes sufficiently small, incentives for imitation intensify, progressively eroding informational differentiation. This fragility gives rise to a cascade mechanism of overinvestment, whereby individuals increase educational attainment beyond efficient levels to preserve relative positioning. As a result, signaling distortions propagate across educational levels, generating persistent credential inflation and weakening the informational content of degrees. The framework also identifies conditions under which mixed equilibria may dominate separating equilibria in terms of aggregate welfare, particularly when the proportion of low-productivity workers is limited. By incorporating a productive dimension of education, the model distinguishes between pure signaling rents and genuine productivity gains, providing a unified interpretation of overeducation, declining returns to credentials, and persistent wage dispersion. Finally, the analysis characterizes an optimal taxation scheme that eliminates inefficient signaling rents while preserving incentives for productivity-enhancing investment. Taken together, the results highlight how equilibrium fragility, informational distortions, and strategic educational measures provide a unified explanation for diploma inflation, equilibrium segmentation, and persistent deviations from socially optimal investment levels. Full article

Urban regeneration is critical for addressing contemporary urban challenges, yet its complexity arises from the dynamic interactions among different participants’ preference and strategic behavior factors, making it a multi-agent system driven by strategic behaviors. This study, based on a Chinese urban regeneration case, develops a dynamic evolutionary game model for industrial park renewal to explore the strategic interactions among three key stakeholders: government, social capital, and property owners. The findings reveal three insights: Firstly, the probabilities of social capital participation and property owner cooperation exhibit opposing trends, highlighting conflicting incentives. Secondly, social capital participation follows an inverted U-shaped trajectory with investment ratios, reflecting a strategic trade-off between risk and control; further robustness checks incorporating time delays and phased investments confirm that the curvature of this trajectory is highly sensitive to the project’s development cycle. Thirdly, lower land repayment costs, higher rental income, greater project returns, and a higher profit-sharing ratio promote cooperative strategies among property owners, though this effect remains marginal. The study further demonstrates that non-cooperative behavior among property owners results in a single evolutionary stable strategy (1, 1, 0) where the government repurchases land property rights, and social capital acquires these rights for redevelopment. The findings suggest that this conclusion applies specifically to industrial park renewal in urban centers held by property owners in cities, where it is government-led facilitation, with property owners exiting and social capital entering simultaneously, thereby ensuring alignment of multi-agent strategic behavior in China. Full article

The “Digital Countryside” initiative is profoundly reshaping rural China, transforming traditional villages into complex hybrids of physical realities and virtual networks. However, current research often treats rural space statically and overlooks the dynamic interplay between spatial dimensions in developing regions. Drawing on Henri Lefebvre’s spatial triad theory, this study proposes a novel framework to examine how the restructuring of physical, social, and digital spaces influences grassroots governance effectiveness. Empirically, this study is based on a dataset covering 108 villages across Jiangsu Province, with 210 valid questionnaires collected from village cadres and representatives. Each questionnaire is linked to a specific village, forming a village-referenced individual-level dataset. The analysis primarily focuses on Northern Jiangsu as a representative developing region, while retaining inter-regional variation for robustness. Using K-Means clustering and Partial Least Squares Structural Equation Modeling (PLS-SEM), the results reveal significant spatial heterogeneity, identifying distinct village configurations with uneven developmental paths. Crucially, structural analysis indicates a “saturation effect” where traditional physical infrastructure no longer directly drives governance improvements. Instead, Digital Space has emerged as the dominant engine. However, this digital impact is not automatic; it relies on a critical mediation pathway through “Technological Embedding” and the fostering of multi-actor “Subject Synergy.” Furthermore, avoiding governance “involution” ultimately depends on an institutional imperative: synergy alone cannot directly drive governance efficacy without flexible “Institutional Environment Adaptation.” Most critically, Spatial Justice Perception is identified as a decisive boundary condition; low perceived fairness acts as a “justice trap” that significantly dampens the positive returns of digital investment, underscoring that breaking this trap is essential for promoting sustainable rural development and long-term governance effectiveness in the digital era. Full article

Economic evaluation plays a pivotal role in investment decision-making for mining projects, especially under volatile market conditions. In this study, a risk-based decision-support framework is developed to assess the economic sustainability of an open-pit gold mining operation by integrating sensitivity analysis with Monte Carlo simulation, where Net Present Value (NPV) is used as the primary performance indicator. The proposed approach provides a flexible and practical computational framework for evaluating investment risk under uncertainty. A case study from an open-pit gold mine in Kyrgyzstan is used to compare two scenarios: continuation of the current operation and an alternative option involving a $30 million investment to improve mill processing performance. The sensitivity analysis shows that gold price, mining cost, and recovery rate are the most influential parameters affecting project outcomes, while Monte Carlo simulation is used to capture uncertainty in these variables and to generate a distribution of possible NPV results. The results indicate that gold price and recovery rate have a dominant influence on project value, and that improving mill performance leads to higher recovery and increased economic returns. The simulation results show a median NPV of approximately 220 million USD with a probability of negative NPV (17.52%), while the enhanced scenario achieves an IRR of approximately 13%, indicating improved financial performance. In addition, the findings suggest that accounting for uncertainty provides more reliable support for investment decisions and contributes to a more efficient use of mineral resources. In this context, the proposed framework contributes to sustainability assessment tools by supporting economically sustainable resource utilization through risk-based evaluation of recovery improvement under uncertainty. While the present study focuses on the economic pillar of sustainability, the framework can provide a basis for future integration of environmental and social indicators. Full article

Social media engagement drives both individual behavior and content dissemination, yet traditional analytics often reduce interactions to simple counts, obscuring the complex structures underlying user activity. In the highly competitive digital landscape, understanding how users interact with content is crucial for businesses aiming to optimize social media campaigns and maximize return on investment (ROI). Traditional engagement metrics, such as likes and shares, fail to capture the underlying structure and dynamics of user behavior. This study investigates the latent patterns of engagement by combining topological data analysis (TDA) with behavioral clustering across 100,000 posts on multiple platforms. Using persistent homology and k-nearest neighbour graphs, we reveal a primary bifurcation between Active (validation-focused) and Passive (consumption/propagation) users, nested four-strain substructures, and over 650 significant $H_1$ loops indicating recurring feedback cycles. Active users exhibit strong cluster cohesion and high engagement rates, while Passive users contribute broadly to content diffusion with slightly higher loop counts, highlighting distinct functional roles in social media dynamics. These findings provide a principled framework for targeting content, reinforcing feedback loops, and leveraging hub posts to amplify engagement. By linking topological structure to behavioral patterns, this work advances both the theoretical understanding of digital interaction and the practical design of more effective social media campaigns. Full article

Climate change has emerged as a defining global crisis, with the frequency and intensity of climate-induced disasters rising sharply and imposing disproportionate costs on developing economies and small island states. This article examines the role of climate-resilient infrastructure as a central pillar of climate-smart growth, integrating mitigation, adaptation, and long-term development objectives. It situates climate-resilient infrastructure within a planetary health setting, emphasizing the interdependence between human well-being, ecological systems, and infrastructure resilience. Climate-resilient infrastructure, not merely seen as an engineering solution but as a public good that generates significant positive externalities, reduces systemic macroeconomic risk and delivers welfare gains that exceed private financial returns. It discusses the cross-country heterogeneities in resilience outcomes, driven by differences in geographic exposure, economic capacity, institutional quality, and political economy constraints. Building on this, the study advances a welfare-based approach to infrastructure prioritization that incorporates service disruptions, distributional impacts, and fiscal risk, rather than asset values alone. It further outlines policy and financing strategies to bridge the gap between social and private returns, including public investment, concessional finance, blended instruments, and nature-based solutions. By embedding infrastructure within a planetary health lens, the paper argues that resilient systems are critical not only for safeguarding lives and livelihoods, but also for sustaining ecological stability, reducing health risks, and enabling inclusive, sustainable, and climate-smart economic growth. Full article

The present study provides a high-frequency empirical assessment of the financial performance, volatility, and market integration of thematic sustainability-oriented equity funds, focusing on clean energy and environmental innovation indices. Specifically, the study compares the financial performance of representative thematic green equity funds, such as ICLN and QCLN, and an emerging-market benchmark (ECON) with conventional developed-market indices (SPY, QQQ, GSPC, and XLE) using daily stock prices from 2010 to 2025. The analysis employs a transparent and replicable framework based on daily logarithmic and cumulative returns and incorporates the compound annual growth rate (CAGR), Sharpe and Sortino ratios, beta estimation, correlation analysis, and maximum drawdown. The research frequency is appropriate for a thorough analysis of short-term market structures and performance. The results indicate that sustainability-oriented equity indices exhibit higher volatility, deeper drawdowns, and greater sensitivity to broad market movements than conventional benchmarks. Sustainability-focused equity indices that emphasize clean energy exhibit higher market sensitivity (betas above 1) and strong correlations with traditional equity indices. Correlation and beta estimates suggest a high degree of integration with traditional equity markets, implying limited diversification benefits within an equity-only framework. Periods of relative outperformance appear to be associated with favorable policy conditions and energy market dynamics, but are not consistently sustained over the sample period. In addition, the overall results suggest that sustainability investments generate substantial environmental and social externalities. Risk-adjusted performance measures suggest weaker historical performance over the sample period relative to conventional benchmarks. These findings should be interpreted as a comparative historical assessment rather than a structural risk model. From a policy perspective, the findings suggest that stable and credible regulatory frameworks, including long-term climate policy support and investment-enabling institutions, may be important for improving the financial resilience and long-term viability of green equity instruments. From a sustainability transition perspective, the observed volatility and market dependence of sustainability-oriented equity indices may constrain their effectiveness as standalone market-based financing mechanisms without complementary institutional and policy support. Full article

Transportation asset management (TAM) has traditionally centered on technical performance and economic efficiency. In recent years, however, there has been increasing recognition of the environmental and social impacts of maintenance and rehabilitation (M&R) activities. This paper presents a systematic review of how Environmental, Social, and Governance (ESG) metrics are being incorporated into TAM. Using PRISMA 2020, four major databases were searched, identifying 75 studies since 2010. Environmental metrics were the most developed, especially those measuring emissions, energy use, and material consumption. Social metrics appeared less frequently and are typically used descriptively, including indicators of income inequality, user costs, and equity-focused metrics such as the Benefit Distribution Ratio and Social Return on Investment. Governance was the least explored pillar and is generally addressed through fiscal transparency, risk management, or institutional practices rather than explicit measurable indicators. Overall, the review shows growing interest in integrating ESG into TAM, but the adoption of social and governance metrics remains limited. In particular, governance indicators are rarely operationalized as measurable variables within TAM decision-making, highlighting a critical gap in the literature. This study synthesizes ESG-related indicators used in TAM and provides a structured foundation for future research and more comprehensive sustainability-oriented decision frameworks. Full article

The harms associated with substance use continue to disproportionately affect marginalized populations. This study presents a retrospective program evaluation of a mobile health unit that delivers integrated clinical and harm reduction services to marginalized populations in Eastern Puerto Rico. Methods: A secondary data analysis was conducted using administrative data from a mobile health unit, capturing client encounters, service utilization (e.g., mental health support, health screenings, safe injection counseling, and case management), visit frequency, and demographic characteristics. This study is framed as an implementation-focused program evaluation. Descriptive and exploratory analyses were conducted to assess service delivery, program reach, utilization patterns, and selected program outcomes over a 1.5-year period. Results: Between January 2022 and October 2023, the mobile health unit served 279 participants across eight municipalities. Participants exhibited higher rates of intravenous drug use, mental health disorders, homelessness, and incarceration history compared with previously published estimates for the general Puerto Rican population, although these comparisons are indirect. The program delivered multidisciplinary services and facilitated referrals addressing key social determinants of health, including housing, nutritional assistance, identification services, in-patient treatment, and medication-assisted treatment. Model-based estimates using the Mobile Health Map Impact Tracker tool suggest that, in 2023, mobile health screenings may be associated with a return on investment of approximately 6:1, 259 avoided emergency department visits, 29 life-years saved, and approximately USD 2.4 million in healthcare cost savings. Conclusions: This evaluation demonstrates the feasibility of a mobile health model integrating harm reduction and clinical services to reach highly marginalized populations and facilitate connections to health and social services. Findings reflect program implementation, service reach, and engagement rather than causal effectiveness. Mobile health approaches may represent a feasible and potentially beneficial strategy for expanding access to care, although further research incorporating patient-level outcomes is needed to assess effectiveness. Full article