Sustained High Performance Research Articles

Decoding Sustainability Reporting: Insights from Text Mining and Classification Models for SDGs

Objective: This study aims to investigate the signaling effects of Sustainability Reporting (SR) in listed Singapore firms, particularly in demonstrating firm capitalization and sustainability performance. The research focuses on identifying distinct SR signals using advanced text mining and classification techniques, and their alignment with the United Nations' Sustainable Development Goals (SDGs). Theoretical Framework: The study is grounded in signaling theory, which posits that firms communicate observable characteristics (SR content) to signal unobservable attributes (firm capabilities and sustainability performance). By applying this framework, we analyze how SR may act as a signaling mechanism for large-cap firms and sustainability leaders. Method: This research collects 1,844 sustainability reports from listed Singapore firms between 2016 and 2022. Using a combination of Structural Topic Modeling (STM) and Random Forest (RF) classification models, we extracted and analyzed the key SR signals that differentiate firms by size and sustainability performance. Results: The results indicate that SR signals such as community engagement, climate risk, and compliance are effective in distinguishing between large firms and firms with strong sustainability performance. The Random Forest models achieved high accuracy in identifying these groups. Community engagement, compliance with the Global Reporting Initiative (GRI), and sustainable construction were among the most impactful topics, with large firms focusing more on scope and governance, while sustainability leaders emphasized environmental risks and compliance. Research Implications: The findings provide insights into the practical and theoretical implications of SR as a signaling tool. Key SR topics are identified as critical signals for firms with large capitalization and high sustainability performance, guiding firms in utilizing SR in the context of SDGs. Originality/Value: The findings show that SR topics such as community engagement, climate risk, and compliance serve as key signals for differentiating firms by size and sustainability performance. Firms can strategically emphasize these areas to enhance their SR practices while contributing to broader SDGs.The findings provide insights into the practical and theoretical implications of SR as a signaling tool. Key SR topics are identified as critical signals for firms with large capitalization and high sustainability performance, guiding firms in enhancing their SR strategies.

Sustainable Plastics with High Performance and Convenient Processibility.

Designing and making sustainable plastics is especially urgent to reduce their ecological and environmental impacts. However, it remains challenging to construct plastics with simultaneous high sustainability and outstanding comprehensive performance. Here, a composite strategy of in situ polymerizing a petroleum-based monomer with the presence of an industrialized bio-derived polymer in a quasi-solvent-free system is introduced, affording the plastic with excellent mechanical robustness, impressive thermal and solvent stability, as well as low energy, consumes during production, processing, and recycling. Particularly, the plastic can be easily processed into diverse shapes through 3D printing, injection molding, etc. during polymerization and further reprocessed into other complex structures via eco-friendly hydrosetting. In addition, the plastic is mechanically robust with Young's modulus of up to 3.7GPa and tensile breaking strength of up to 150.2MPa, superior to many commercially available plastics and other sustainable plastics. It is revealed that hierarchical hydrogen bonds in plastic predominate the well-balanced sustainability and performance. This work provides a new path for fabricating high-performance sustainable plastic toward practical applications, contributing to the circular economy.

A multi-level action coupling reinforcement learning approach for online two-stage flexible assembly flow shop scheduling

Multi-product centralized delivery and kitting assembly present significant challenges to hierarchical co-processing in multi-stage manufacturing systems. The combinations of priority dispatching rules at each level are transiently adaptive, and the performance in online scheduling deteriorates rapidly with changing environment. This paper investigates the selection of rule combinations for sustained high-performance responsive scheduling in two-stage flexible assembly flow shop scheduling problem with asynchronous execution and complex decision correlation. A Multi-Level Action Coupling Deep Q-Network (MALC-DQN) approach is proposed for adaptive integrated scheduling in hybrid processing and assembly shops. Firstly, the problem is skillfully established as an event-triggered integrated decision markov decision process. The prioritized batch experience replay mechanism is employed to retain the complete correlation information of key decision sequences. Then, coupling and sequence feature extraction modules are developed to enhance the agent’s ability to perceive execution process and the environment. Furthermore, the multi-level wait-limit mechanism and efficient action filtering mechanism are designed to mitigate ineffective waiting waste and action space explosion during learning. Finally, a series of sophisticated experiments are conducted to validate the effectiveness of the proposed methodology. In 20 actual instances of different sizes, MLAC-DQN outperformed its closest competitor, with a 26.6% improvement in average tardiness. Moreover, extraordinary robustness is demonstrated in 16 sets of experiments involving different configurations of resources, orders, and arrival concentration levels.

DIII-D research to provide solutions for ITER and fusion energy

The DIII-D tokamak has elucidated crucial physics and developed projectable solutions for ITER and fusion power plants in the key areas of core performance, boundary heat and particle transport, and integrated scenario operation, with closing the core-edge integration knowledge gap being the overarching mission. New experimental validation of high-fidelity, multi-channel, non-linear gyrokinetic turbulent transport models for ITER provides strong confidence it will achieve Q ⩾ 10 operation. Experiments identify options for easing H-mode access in hydrogen, and give new insight into the isotopic dependence of transport and confinement. Analysis of 2,1 islands in unoptimized low-torque IBS demonstration discharges suggests their onset time occurs randomly in the constant β phase, most often triggered by non-linear 3-wave coupling, thus identifying an NTM seeding mechanism to avoid. Pure deuterium SPI for disruption mitigation is shown to provide favorable slow cooling, but poor core assimilation, suggesting paths for improved SPI on ITER. At the boundary, measured neutral density and ionization source fluxes are strongly poloidally asymmetric, implying a 2D treatment is needed to model pedestal fuelling. Detailed measurements of pedestal and SOL quantities and impurity charge state radiation in detached divertors has validated edge fluid modelling and new self-consistent ‘pedestal-to-divertor’ integrated modeling that can be used to optimize reactors. New feedback adaptive ELM control minimizes confinement reduction, and RMP ELM suppression with sustained high core performance was obtained for the first time with the outer strike point in a W-coated, compact and unpumped small-angle slot divertor. Advances have been made in integrated operational scenarios for ITER and power plants. Wide pedestal intrinsically ELM-free QH-modes are produced with more reactor-relevant conditions, Low torque IBS with W-equivalent radiators can exhibit predator-prey oscillations in T e and radiation which need control. High-β P scenarios with q min > 2, q 95–7.9, β N > 4, β T–3.3% and H 98y2 > 1.5 are sustained with high density ( n¯ = 7E19 m−3, f G–1) for 6 τ E, improving confidence in steady-state tokamak reactors. Diverted NT plasmas achieve high core performance with a non-ELMing edge, offering a possible highly attractive core-edge integration solution for reactors.

Clustering‐Resistant Cu Single Atoms on Porous Au Nanoparticles Supported by TiO2 for Sustainable Photoconversion of CO2 into CH4

AbstractPhotocatalysts based on single atoms (SAs) modification can lead to unprecedented reactivity with recent advances. However, the deactivation of SAs‐modified photocatalysts remains a critical challenge in the field of photocatalytic CO2 reduction. In this study, we unveil the detrimental effect of CO intermediates on Cu single atoms (Cu‐SAs) during photocatalytic CO2 reduction, leading to clustering and deactivation on TiO2. To address this, we developed a novel Cu‐SAs anchored on Au porous nanoparticles (CuAu‐SAPNPs‐TiO2) via a vectored etching approach. This system not only enhances CH4 production with a rate of 748.8 μmol ⋅ g−1 ⋅ h−1 and 93.1 % selectivity but also mitigates Cu‐SAs clustering, maintaining stability over 7 days. This sustained high performance, despite the exceptionally high efficiency and selectivity in CH4 production, highlights the CuAu‐SAPNPs‐TiO2 overarching superior photocatalytic properties. Consequently, this work underscores the potential of tailored SAs‐based systems for efficient and durable CO2 reduction by reshaping surface adsorption dynamics and optimizing the thermodynamic behavior of the SAs.

Clustering-Resistant Cu Single Atoms on Porous Au Nanoparticles Supported by TiO2 for Sustainable Photoconversion of CO2 into CH4.

Photocatalysts based on single atoms (SAs) modification can lead to unprecedented reactivity with recent advances. However, the deactivation of SAs-modified photocatalysts remains a critical challenge in the field of photocatalytic CO2 reduction. In this study, we unveil the detrimental effect of CO intermediates on Cu single atoms (Cu-SAs) during photocatalytic CO2 reduction, leading to clustering and deactivation on TiO2. To address this, we developed a novel Cu-SAs anchored on Au porous nanoparticles (CuAu-SAPNPs-TiO2) via a vectored etching approach. This system not only enhances CH4 production with a rate of 748.8 μmol ⋅ g-1 ⋅ h-1 and 93.1 % selectivity but also mitigates Cu-SAs clustering, maintaining stability over 7 days. This sustained high performance, despite the exceptionally high efficiency and selectivity in CH4 production, highlights the CuAu-SAPNPs-TiO2 overarching superior photocatalytic properties. Consequently, this work underscores the potential of tailored SAs-based systems for efficient and durable CO2 reduction by reshaping surface adsorption dynamics and optimizing the thermodynamic behavior of the SAs.

Fabrication of Porous Adsorbents Templated from Capillary Foam Stabilized with Chlorella for Highly Efficient Removal of Cationic Dyes.

The thermodynamic instability of conventional aqueous foam-stabilized surfactants is a critical bottleneck in the construction of porous materials. Herein, a novel strategy is proposed for preparing a capillary foam based on Chlorella and utilizing it as a template for constructing porous materials with high-efficiency adsorption. The capillary foam was stabilized by Chlorella particles enclosed within a gel network of oil bridges connecting the particles (capillary suspension). Chlorella particles, which act as stable particles, form oil bridges and are distributed at the phase interface of the capillary foam. These particles exhibited resistance to shear forces, allowing the formation of a long-term stable capillary foam. Using this foam as a template, a porous material with outstanding adsorption performance for Methylene Blue (MB) and Brilliant Green (BG) dyes was successfully constructed. Additionally, the material exhibited a sustained high adsorption performance even after five thermal regeneration-adsorption cycles. In conclusion, this study presents a green and straightforward method for constructing capillary foams with high stability, which is a promising approach for developing porous materials with exceptional adsorption and regeneration properties for dyes.

Pulsed laser-modified zinc anode with improved dendrite and corrosion resistance for sustainable high performance zinc ion hybrid supercapacitors

This research explores the development and evaluation of zinc ion hybrid supercapacitors (ZIHSCs) with a focus on the enhanced performance and low corrosion attributes of pulsed laser-modified zinc anodes compared to their non-modified counterparts. These anodes, paired with cathodes crafted from activated carbon derived from jute sticks on graphite foil, were immersed in a 2 M zinc sulfate electrolyte. A pivotal aspect of the study is improving the electrochemical properties and corrosion resistance of zinc anodes achieved through precise pulsed laser modification. Extensive electrochemical testing was conducted to assess the capacitive behavior, energy storage capabilities, and durability of the supercapacitors during prolonged cycling. Hence, using the laser modified anode, we attain remarkable stability in Zn||Zn symmetric cells for 1000 h at high current density of 10 mA cm−2 with a capacity of 1 mAh cm−2, and ZIHSCs achieving specific capicitance up to 287 F/g at a current density of 0.1 A/g, and reaching an energy density of 102 Wh/kg while sustaining a power density of 80 W/kg. After 10,000 galvanostatic charge-discharge cycles, these supercapacitors maintained high capacitance values, underscoring their resilience. Notably, charge transfer resistance reduction, improved ion diffusion and enhanced corrosion resistance contributes to the supercapacitors' longevity and efficiency, which can be attributed to these anodes laser-induced surface alterations. This study highlights the crucial role of pulsed laser-modified zinc anodes in advancing ZIHSCs performance, marking a significant step towards achieving efficient and sustainable energy storage solutions.

Exploring the Psychological Health and Work Efficiency of Enterprise Employees under the EAP Perspective

Against the backdrop of rapid global economic advancement, intensifying corporate competition has made employee mental health a pivotal factor affecting both organizational performance and employee well-being. Employee Assistance Programs (EAPs), as structured mental health support services, have garnered increasing attention from corporate entities and academia alike. EAPs not only aim to address employees' psychological issues but also strive to enhance their work efficiency and overall satisfaction. By offering psychological counseling, stress management, and other comprehensive services, EAPs effectively alleviate workplace stress, promote mental well-being, and consequently elevate corporate productivity and competitiveness. Research underscores a significant positive correlation between mental health and work efficiency. Maintaining a healthy psychological state facilitates sustained high performance among employees, mitigating absenteeism and turnover stemming from psychological issues. As businesses increasingly prioritize employee mental health, the global adoption of EAPs is becoming more widespread. However, enterprises face various challenges in implementation, such as safeguarding employee privacy, low participation rates, and resource constraints. Thus, exploring the impact of EAPs on employee mental health and work efficiency, analyzing challenges encountered during implementation, and devising corresponding strategies are crucial for optimizing management practices and enhancing operational efficiency in enterprises.

The Employee Engagement and Job Performance Nexus in a Top Autodealership Company

This study explored the relationship between employee engagement and job performance within a leading Philippine auto dealership company. A quantitative approach was employed through a survey distributed to 351 employees across various locations. The findings revealed a youthful workforce with near-equal gender distribution and a high proportion holding college degrees. A slight majority reported being single, potentially indicating a preference for flexibility. Furthermore, the largest group had a tenure of 1-5 years, suggesting a sizeable cohort of newer employees. Employee engagement was perceived to be high indicating a generally motivated and enthusiastic workforce. A significant positive correlation was identified between engagement and job performance, suggesting that more engaged employees perceived themselves as performing at a higher level. Based on these results, the study proposes actionable recommendations to enhance engagement. These include targeted strategies for the younger demographic, skill development programs for all employees, exploration of flexible work arrangements and well-being initiatives, and fostering a culture of recognition and alignment with the company's mission. By implementing these strategies, the dealership can cultivate a more engaged workforce, leverage its diverse strengths, and achieve sustained high performance.

Development and evaluation of interpretable machine learning regressors for predicting femoral neck bone mineral density in elderly men using NHANES data.

Osteoporotic femoral neck fractures (OFNFs) pose a significant orthopedic challenge in the elderly population, accounting for up to 40% of all osteoporotic fractures and leading to considerable health deterioration and increased mortality. In addressing the critical need for early identification of osteoporosis through routine screening of femoral neck bone mineral density (FNBMD), this study developed a user-friendly prediction model aimed at men aged 50 years and older, a demographic often overlooked in osteoporosis screening. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), the study involved outlier detection and handling, missing value imputation via the K nearest neighbor (KNN) algorithm, and data normalization and encoding. The dataset was split into training and test sets with a 7:3 ratio, followed by feature screening through the least absolute shrinkage and selection operator (LASSO) and the Boruta algorithm. Eight different machine learning algorithms were then employed to construct predictive models, with their performance evaluated through a comprehensive metric suite. The random forest regressor (RFR) emerged as the most effective model, characterized by key predictors such as age, body mass index (BMI), poverty income ratio (PIR), serum calcium, and race, achieving a coefficient of determination (R²) of 0.218 and maintaining robustness in sensitivity analyses. Notably, excluding race from the model resulted in sustained high performance, underscoring the model's adaptability. Interpretations using Shapley additive explanations (SHAP) highlighted the influence of each feature on FNBMD. These findings indicate that our predictive model effectively aids in the early detection of osteoporosis, potentially reducing the incidence of OFNFs in this high-risk population.

Optimizing strength behavior of sustainable ultra high performance green concrete with minimum cement content using response surface method

Optimizing strength behavior of sustainable ultra high performance green concrete with minimum cement content using response surface method

Conceptual underpinnings: theory selection in strategic management research

PurposeTheories are crucial for addressing research questions and advancing the boundaries of knowledge. However, in the field of strategic management, the existence of diverse schools of thought from various disciplines, including economics, politics, and sociology, poses significant challenges for researchers seeking to develop theories for argumentation and theorization. In this study, we have conceptualized a novel approach to selecting an appropriate theory for addressing specific research questions.Design/methodology/approachThought experiment, disciplined imagination, and a conceptual examination of a diverse set of theories.FindingsBecause the central focus in the field of strategic management revolves around how firms achieve sustainable high performance, a research question should initially clarify the fundamental phenomenological issues it aims to address. Subsequently, the process of problematization should identify the ontological assumptions and premises that establish a connection between the research question and existing theories. Finally, the identification and abstraction of rhetorical concepts derived from these assumptions and premises lead to theory selection criteria, namely connectedness, reliability, parsimoniousness, and falsifiability.Research limitations/implicationsAlthough we believe that our model for theory selection is generalizable to a wide range of management disciplines, we have primarily focused on its application in the field of strategic management. Future work could validate and further explore the applicability and effectiveness of this model in selecting appropriate theories for conceptual development in other domains.Originality/valueWhile many researchers have proposed methods for writing theoretical papers, few have provided suggestions specifically focused on theory selection. This paper stands out as one of the few that not only attempts to address this gap but successfully develops a comprehensive model for theory selection.

Cyclic compressive behavior of high-performance hybrid fiber reinforced recycled aggregate concrete: Degradation law and acoustic emission features

To tackle the issues of inadequate mechanical properties of recycled aggregate concrete and its limited application in load-bearing structural components, a high-performance hybrid fiber reinforced recycled aggregate concrete (HP-HFRAC) was developed in this study. Sixteen groups of HP-HFRAC specimens fabricated with varying replacement rates of recycled coarse aggregate and different fiber parameters were subjected to uniaxial compression, as well as acoustic emission (AE) tests, to investigate the cyclic behavior and uncover the underlying mechanisms. The findings demonstrated a strong correlation between the AE signals and the macroscopic phenomenon throughout the whole compression process. The AE amplitude distribution was found to be capable of identifying damage mechanisms, including matrix cracking (40–50 dB), fiber sliding (50–80 dB), and fiber pull-out (more than 80 dB). Additionally, the inclusion of hybrid fibers contributed to mitigating the degradation of stiffness and the accumulation of plastic strain, resulting in the ductile failure of the specimens and significantly enhancing the energy dissipation capacity. Specifically, the peak stress of specimens with 1.5% steel fibers increased by 19% compared to that of specimens without steel fibers, compensating for the strength loss due to the presence of recycled coarse aggregate. Finally, constitutive equations governing the skeleton curves and the unloading and reloading curves of HP-HFRAC subjected to cyclic compression were formulated, which were validated using both the test results and the predicted results of the physics-based data-driven model previously published in our companion paper. The findings of this study might contribute to the development of sustainable high performance recycled aggregate concrete for reliable application in load-bearing components.

Cactus-inspired moisture harvesting for sustainable and efficient high salinity desalination

Water scarcity has become a global issue that needs to be addressed urgently. Solar steam generation (SSG) is a promising technology to alleviate the water crisis through desalination and condensation. However, the stability and efficiency of SSG can be seriously impacted by salt accumulation after prolonged operation. Common salt rejection schemes are only applicable for low-salinity (<10 wt%) desalination. In this work, inspired by the cactus's fog-capturing feature, the atmospheric water harvesting (AWH) function is integrated by an interlocked braided fibrous framework (IBFF) to absorb moisture for sustainable salt dilution during high salinity (20 wt%) desalination. The loop-pile density/height of IBFF is regulated to mimic the cactus roots/spines with different architectures, thus optimizing the water absorption/moisture harvesting performance. The excellent moisture-induced salt dilution ability ensures efficient and sustainable high salinity desalination performance over cyclic operation, with an exceptional evaporation rate of up to 3.11 kg m−2·h−1 under 1 sun radiation. Overall, this new approach offers a practical possibility for durable and competitive high salinity desalination with a continuous water/moisture supply.

Stock market response to natural disasters: Does corporate sustainability performance make difference?

This study aims to examine the short-term price reactions of the stocks listed on Borsa Istanbul (BIST) after the severe earthquake of February 6, 2023, that happened in the southeastern region of Türkiye covering 11 provinces and causing more than 50,000 fatalities. Using a sample of 450 companies listed on the BIST All Index, this study estimates the abnormal returns (ARs) of stocks by employing an event study methodology. Using BIST 100 companies as a sub-sample, this study also explores whether sustainability performance creates resilience against downside risk after the earthquake. The results of the market model and the mean-adjusted model show that on the event day, most of the sectors had negative and significant ARs, while only the non-metal mineral products had positive and significant ARs. Most of the sectors continued to decline in the following 10 days except basic metal and non-metal mineral products sectors. The telecommunications sector was the most negatively affected sector in both models after the event day as it has underperformed in ensuring uninterrupted access to communication. The findings also indicate that firms in the financial sector that have higher sustainability performance are more resilient in responding to the disaster.

Life cycle thinking-based analysis of diesel and electric-powered buses for Canadian transit systems

Increasing greenhouse gas emissions from the conventional fleet of diesel buses has made Canadian transit agencies explore low-emission alternative fuels. Despite electric buses showing great potential to reduce emissions during their operational phase, the transformation from diesel buses to electric buses would require in-depth analysis pertaining to their economic and social implications. Published literature highlights the importance of developing a comprehensive framework that considers multiple decision parameters over a life cycle perspective for analyzing different fuel options to replace the existing fleet of diesel buses. This paper assesses the triple-bottom-line sustainability of diesel and electric buses in different regions of Canada. Moreover, a framework is proposed to incorporate multiple decision criteria (life cycle environmental, economic, and social impacts) over different perspectives to make the best decisions for transitioning the diesel bus fleet. The results showed that the environmental performance of electric buses highly depended on the electricity grid mix. Despite diesel buses having a low cost of production compared to electric buses, most provinces showed a low life cycle operational cost for electric buses. Electric buses’ life cycle social impacts are high during their production stage, whereas diesel buses have the highest social impacts during their operational phase. Overall, electric buses have a high sustainability performance in all provinces and territories in Canada except Nunavut. The proposed framework and findings can aid policymakers and planners in implementing electric buses for public transit systems in Canada and beyond.

Selective furfural hydrodeoxygenation over Cu-Fe catalysts with In-Situ Cu@Fe3O4 formation

This study investigates Cu-Fe mixed oxide catalysts with varying Cu/Fe molar ratios (0.5 to 2) synthesized via a sol–gel method for vapor-phase FFR HDO. The catalysts were characterized using XRD, H2-TPR, NH3-TPD, N2 physisorption, FESEM and XPS to gain sights into their properties. The characterizations unveiled the critical role of Cu/Fe molar ratio in influencing the properties and performance of the catalysts in FFR HDO. Fe promoted the dispersion of CuO, especially at a Cu/Fe ratio of 1, revealing synergistic Cu-Fe interactions. Increasing Cu/Fe ratio enhanced the reducibility of Fe but diminished that of Cu. Extensive investigations were conducted to assess the impact of key parameters, including Cu/Fe ratio, temperature, contact time, reduction temperature and H2/FFR molar ratio. The Cu-Fe mixed oxide catalyst with Cu/Fe = 1 proved to the optimal catalyst, exhibiting > 99 % FFR conversion and 90 % 2-MeF selectivity at H2/FFR = 15, 230 °C, and 0.5 gFFR h−1 gcatalyst-1. The extended evaluation of catalytic activity demonstrated sustained high performance, with stable conversion and 2-MeF selectivity values of > 99 % and 90 % over a period of 12 h. Even after 24 h on stream, the conversion remained above 90 %. Subsequent regeneration study showed partial recovery of the activity, with the catalyst maintaining conversion and selectivity consistently at about 85 % and 86 %, respectively for 10 h. Catalyst deactivation was a result of the site and/or pore blockage caused by the adsorption of FFR and FAL as well as the formation of oligomeric compounds on the catalyst’s surface.

Programmable DNA Interphase Layers for High-Performance Anode-Free Lithium Metal Batteries.

Anode-free lithium (Li) metal batteries are promising candidates for advanced energy storage, attributed to their appealing characteristics such as high energy density, low cost, and convenient production. However, their major challenges lie in the poor cycling and rate performance owing to the inferior reversibility and kinetics of Li plating and stripping, which significantly hinder their real-world applications. Here, it is demonstrated that deoxyribonucleic acid (DNA), the most important genetic material in nature, can serve as a highly programmable interphase layer for innovation of anode-free Li metal batteries. It is found that the abundant base pairs in DNA can contribute transient Li-N bonds that facilitate homogeneous Li+ flux, thus resulting in excellent Li plating/stripping kinetics and reversibility even at a harsh areal current of 15 mA cm-2. The anode-free LiFePO4 full batteries based on an ultrathin (0.12 µm) and ultralight (≈0.01 mg cm-2) DNA interphase layer show high CEs (≈99.1%) over 400 cycles, corresponding to an increase of ≈186% compared with bare copper (Cu) foil. These results shed light on the excellent programmability of DNA as a new family of interphase materials for anode-free batteries, and provide a new paradigm for future battery innovation toward high programmability, high sustainability, and remarkable electrochemical performance.

Do firms' sustainability practices pay off during global crises? Evidence from Asia and the Pacific

AbstractThis paper examines the relationship between firms' sustainability practices and their value during a crisis when overall trust and support are lacking in the economy. While a handful of studies explore this research agenda concentrating on the US context and a standalone crisis, we provide novel evidence on the Asia Pacific region considering three global crises. Applying difference‐in‐differences regressions, we find that high‐sustainability companies posted superior value during the global financial crisis and the European debt crisis. This result, however, does not hold for the COVID‐19 crisis. This key finding passes several endogeneity tests (firm‐fixed effect, propensity score matching, and post‐crisis interaction). As we explore potential mechanisms of firm resistance during crises and apply a triple difference approach, we find evidence that firm value was intensified during the crises due to sustainability activities coupled with high customer loyalty, high institutional ownership, and a low degree of information asymmetry. Finally, although high sustainability performance generally positively impacted firm value during crises, this relationship was negative (weaker) for the highly impacted industries (the emerging countries) included in the sample. Our key finding continues to hold for a set of robustness checks. The outcome of this study provides significant policy directions to regulators, corporate managers, and investment professionals.