ABSTRACT This study examines the evolution and determinants of corporate environmental disclosure practices through a systematic review of 75 peer‐reviewed articles published from 2017 to 2025 in the Scopus database. Using the PRISMA framework, a bibliometric analysis was conducted with R software, complemented by the Theory–Context–Characteristics–Method (TCCM) approach to map the intellectual structure, thematic development, and research gaps. The findings indicate significant growth in environmental disclosure research following ESG integration and Net‐Zero commitments, with China and emerging Asian economies leading due to regulatory pressures and governance reforms. Legitimacy and stakeholder theories dominate, while governance mechanisms, institutional enforcement, and managerial orientation drive disclosure practices. Thematic analysis identifies environmental reporting as a core theme, whereas disclosure quality, green innovation, and performance alignment remain underexplored. Future research should prioritize disclosure credibility, sector‐specific analysis, and innovation‐driven sustainability outcomes.

Doing domestic laundry is a social practice held together by social, material, and personal elements. Therefore, understanding the underlying dimensions behind laundry practices can reveal the drivers and inhibitors for change toward sustainable consumption and production. Researchers have mostly investigated household laundry practices in developed regions of the world. As a result, understanding the antecedents of these practices in contexts such as those of emerging Asian economies is limited. This study set out to answer the following questions for five megacities in Asia: Tokyo, Bangkok, Hanoi, Ho Chi Minh City (HCMC), and Metro Manila: How do laundering practices and associated resource consumption compare among the five cities? What are the critical material and social elements influencing resource consumption in laundering practices in each city? To answer these questions, we synthesized laundry-activity data from four independent studies and quantified energy and water use for household laundry. We also developed an analytical framework that draws on social practice theories to understand the broader social and material elements that shape laundry practices with their resource implications. The results showed high energy and water consumption in Metro Manila, related to lower incomes and the use of inefficient washing machines. HCMC and Bangkok had the lowest water and energy consumption, respectively, due to efficient front-loading washing machines and airdry practices. To drive sustainable laundry practices, we urge policymakers to encourage greater use of efficient front-loading washing machines and to expand access to shared laundry facilities and drying spaces.

The construction sector in many emerging Asian economies plays a critical role in generating employment but also faces significant socio-economic challenges, including the shortage of qualified personnel and limited access to formal skills development pathways. This study addresses socio-economic challenges for a self-reliant, employment-driven economy by examining how masonry training contributes to skills development, labor market access, employment opportunities, and the quality of employment in Nepal’s construction sector. Despite growing policy emphasis on Technical and Vocational Education and Training (TVET), empirical evidence remains limited on how such programs respond to socio-economic challenges by simultaneously improving labor market access, expanding employment opportunities, and strengthening employment quality outcomes in post-disaster and migration-affected contexts. The purpose of this study is to evaluate the contribution of masonry training to four interrelated employment dimensions: skills, access, opportunity, and quality. The analysis is based on primary survey data collected in 2020 from 134 trained masons in Bagmati Province, Nepal, who had completed certified masonry training and gained subsequent labor market experience. Data were gathered using a 20-item Likert-scale questionnaire covering four employment indicators, and the responses were analyzed using descriptive statistics (mean and standard deviation) in SPSS version 26 to assess perceived post-training changes. The results show that employment skills recorded a mean score of 3.69 (SD = 0.92), with market relevance of skills rated highest (M = 4.40), indicating strong alignment between training content and labor market demand. Employment access achieved the highest overall mean (M = 4.03, SD = 0.54), and respondents strongly agreed that the demand for the mason trade is high (M = 4.56), confirming favorable market absorption of trained workers. Employment opportunity also showed a moderately high mean (M = 3.96, SD = 0.69), with equal treatment in employment rated at 4.41. At the same time, entrepreneurship skills scored comparatively lower (M = 3.00), revealing uneven gains in business capability development. However, employment quality recorded a lower mean (M = 3.16, SD = 1.29); although income increased after training (M = 3.88), the ability to purchase health insurance remained limited (M = 2.69), indicating that improvements in earnings have not fully translated into broader social protection or long-term welfare gains. Overall, the aggregate mean score of 3.71 (SD = 0.78) demonstrates that masonry training positively contributes to employment outcomes, while also highlighting structural gaps in social protection and employment stability. These findings open new directions for research and policy to address socio-economic challenges through integrated TVET reforms that strengthen labor market access, promote employment opportunities, and improve the quality of employment in construction-driven economies.

Abstract Estimating output gaps is challenging for emerging Asian economies due to limited data availability and the potential effects of outliers. I apply the Beveridge-Nelson (BN) filter, comparing it with commonly used filters—Hodrick-Prescott (HP), Christiano-Fitzgerald (CF), and Hamilton—across four emerging Asian economies and argue that it provides more reliable and informative estimates than alternative methods. When benchmarked against narrower indicators of slack, BN filter output gap estimates provide a more informative indicator than capacity utilization and unemployment, given longer data coverage and controlling for long-run structural changes. I also document two systematic results for these economies. First, cyclical consumption is more volatile than the output gap. Second, decomposing GDP growth volatility shows that less than one-third of growth fluctuations is accounted for by movements in trend growth, with most variation attributed to the cyclical component. Taken together, these findings contrast with the interpretation in Aguiar and Gopinath (2007) that shocks to the trend are the primary driver of fluctuations in emerging economies and departs from their view that the “cycle is the trend.” Crucially, the BN filter estimates are also subject to smaller and less frequent revisions when faced with large changes in economic conditions, which benefits real-time policy decision-making.

This study investigates the effects of financial development and renewable energy consumption on environmental quality in the Emerging Asia group over the period 1999–2023 using panel data. Carbon dioxide emissions are used as a proxy for environmental quality, while broad money represents financial development. Renewable energy consumption, economic growth, and urbanization are included as control variables. Fixed-effects and random-effects models are estimated, and the Hausman test indicates that the random-effects specification is more appropriate. The results show that financial development, economic growth, and urbanization significantly increase carbon emissions, whereas renewable energy consumption improves environmental quality. These findings highlight the importance of promoting green finance and renewable energy policies to achieve sustainable environmental outcomes in Emerging Asian economies.

Emerging economies confront the dual challenge of accelerating digital transformation while simultaneously mitigating environmental degradation under conditions of institutional and governance heterogeneity. In this context, this study examines how artificial intelligence (AI) capability influences green innovation efficiency (GIE) in emerging Asian economies and investigates whether environmental, social, and governance (ESG) performance conditions this relationship. Using an unbalanced panel of 59,112 firm-year observations from 4926 publicly listed firms across 15 emerging Asian economies over the period 2011–2022, we employ a comprehensive panel-data econometric framework that accounts for unobserved heterogeneity, dynamic effects, endogeneity, and potential self-selection bias. The empirical results indicate that AI capability is positively and significantly associated with higher green innovation efficiency. More importantly, ESG performance strengthens this relationship, suggesting that robust governance frameworks enhance firms’ ability to translate digital intelligence into environmentally efficient innovation outcomes. These findings underscore that AI adoption alone is insufficient to generate sustainable value; rather, its environmental effectiveness depends critically on complementary governance structures that promote transparency, accountability, and responsible risk management. The results remain robust after correcting for endogeneity concerns, alternative model specifications, and extensive sensitivity and heterogeneity analyses. Overall, this study contributes to the literature on digital transformation and sustainability by providing large-scale, multi-country evidence that highlights the pivotal role of ESG in shaping the sustainability returns to AI adoption in emerging economies.

Ghana prioritizes the production of raw cocoa beans while engaging in trade for other commodities. Although cocoa holds significant economic importance for Ghana, there is a notable gap in research regarding the economic factors influencing cocoa exports, especially between European and Asian markets. The effects of Gross Domestic Product (GDP), trade taxes, and economic distance on trade flows, market accessibility, and sustainability in these regions remain ambiguous. This study utilized panel data from 2001 to 2023, employing the gravity model through Panel Ordinary Least Squares (POLS), Generalized Least Squares (GLS), and Poisson Pseudo-Maximization Likelihood (PPML) estimation methods. The findings indicate that Ghana's bilateral cocoa trade with European and Asian markets is significantly influenced by Ghana's GDP, the economies of its trading partners, international trade taxes, and economic distance. To enhance Ghana's bilateral cocoa trade, forming trade agreements with emerging Asian economies could reduce barriers and improve market access. Ghana should consider revising cocoa export duties to increase competitiveness and negotiate with trade partners to lower import tariffs on its cocoa products in Asian markets.

Purpose This study examines corporate disclosure of decent work practices under SDG 8 (DW_SDG8) in emerging Asian economies and investigates how national governance quality (NGQ) and its dimensions shape such disclosures. It further explores whether the relationship between governance and disclosure differs across countries with varying levels of socio-economic development and labour productivity. Design/methodology/approach Using 1,800 firm-year observations from five emerging Asian nations over 2017–2022, DW_SDG 8 disclosure scores are developed through content analysis of corporate reports, mapped against GRI indicators. A random-effects Tobit model is used as the main estimator, with robustness checks via instrumental variable two-stage least squares and fixed-effects models. Additional analyses split the sample by Human Development Index (HDI) and labour productivity rate (LPR). Findings Results show a steady rise in DW_SDG8 disclosures since 2017, with target 8.6 (youth employment) receiving the greatest attention. NGQ and its six components positively influence disclosure. However, the effects are stronger in high-HDI and high-LPR contexts, where institutional capacity and stakeholder expectations reinforce disclosure practices. Practical implications Findings suggest policymakers should strengthen governance systems and institutional capacity to promote credible reporting. Regulators should also issue target-level disclosure guidelines to ensure balanced attention across all DW_SDG8 goals, while accounting for country-level development and productivity contexts. Originality/value This study extends sustainability disclosure research by focusing specifically on DW_SDG8 and by highlighting how socioeconomic and productivity conditions moderate the governance-disclosure nexus in emerging Asia.

ABSTRACT The promotion of clean energy is critical to achieving sustainable development. This study investigates the factors that affect the exports, trade potential, and revealed comparative advantage (RCA) of clean energy products (CEPs) in the emerging Asian economies (EAEs). The export data of the CEPs was collected from UN Comtrade based on six‐digit HSN codes and for other variables, the world development indicators database was used for the period 2000–2020. The Augmented Gravity Model and Poisson Pseudo Maximum Likelihood Estimation (PPMLE) were applied for the analysis and the results show that exporting countries' economic growth is significantly promoting the exports of CEPs in all countries except Indonesia and Saudi Arabia. Surprisingly, it was found that distance is playing a positive role in export growth in most countries, a sign of a globalized world. However, these economies, especially India and Pakistan should resolve their border disputes and enhance regional cooperation to increase CEPs exports, which will help them achieve sustainable economic growth.

This article examines the dynamics of economic convergence between the Southern and Eastern Mediterranean Countries (SEMCs) and the Emerging Asian Economies (EAEs) over the period 2000–2022, with a specific focus on the role of human capital. Using an econometric approach based on the conditional convergence model and panel data, the results reveal the existence of a catch-up process reinforced by investment in human capital. Human capital, measured through a composite index of education and health developed using the Principal Component Analysis (PCA) method, stimulates economic growth and accelerates the reduction of development gaps. These findings highlight the strategic importance of education and health policies in promoting rapid and sustainable catch-up among developing countries. The study also opens avenues for integrating institutional and structural variables in future analyses of economic convergence.

Purpose This research examines the interplay between tax revenue, trade, and economic growth in Bangladesh, Indonesia, Malaysia, Vietnam, and Turkey from 1990 to 2024. It examines whether trade openness weakens growth, whether growth can be strengthened, and whether there is a nonlinear threshold at which excessive trade openness has a negative impact on growth. Methodology This study uses annual panel data from 1994 to 2024. Econometric techniques include the Fixed Effects Model (FEM), Generalized Least Squares (GLS), nonlinear estimation, and the System Generalized Method of Moments (GMM). These methods address heterogeneity and possible endogeneity. The models include interaction and squared terms to account for moderating and nonlinear effects of trade openness. Findings Tax revenue has a significantly positive impact on economic growth. This finding confirms the role of tax revenue in driving growth by improving fiscal capacity in emerging economies. The trade opening further enhances this relationship, suggesting that liberalization leads to greater efficiency, better allocation, and technological spillovers. Nonlinear analysis reveals no benefits when trade openness becomes excessively high. Hyper-globalization can reduce fiscal space and amplify external vulnerabilities. Control variables indicate that FDI has a positive impact on growth, while inflation, population growth, and high consumption have negative effects. Conclusion This paper presents a new analysis by jointly examining taxation and trade openness using a nonlinear panel model, providing specific evidence for emerging Asian economies. Future Research Directions. Further studies could include digital tax reforms, quality of institutions and environmental taxation or expand the analysis to other wider regional panels to test the cross-country heterogeneity. Practical Implications Policymakers should coordinate domestic tax reforms and maintain moderate levels of trade openness to support sustainable and inclusive growth in emerging economies.

Green accounting has evolved from a niche concept into a strategic mechanism for integrating environmental considerations into business decision-making, particularly in emerging Asian economies facing rapid industrialisation and rising ecological pressures. This study conducts a systematic literature review (SLR) of 1,370 peer-reviewed articles published between 2015 and 2025 to synthesise current knowledge on green accounting adoption, measurement frameworks, and technological enablers across diverse organisational and institutional contexts. The review identifies four thematic domains: (1) the influence of green accounting on firm performance and financial outcomes, (2) organisational drivers and barriers shaping adoption, (3) the development and fragmentation of measurement and policy frameworks, and (4) the role of green innovation and digital technologies in strengthening environmental reporting. Findings reveal that green accounting enhances operational efficiency, risk management, and market valuation when embedded within broader sustainability strategies, yet adoption remains uneven due to capability gaps, inconsistent indicators, and resource constraints. Measurement systems are expanding toward multidimensional carbon and resource accounting models, but lack alignment, limiting comparability and policy integration. Technological readiness emerges as a critical enabler, with digital tools and clean technologies improving data accuracy and transparency, though benefits depend on organisational capacity and governance structures. The review concludes with recommendations for sector-wide measurement templates, staged capability-building routines, and workflow-integrated data collection to improve reliability and practical relevance. Future research should address SME adoption pathways, comparative analysis of frameworks, and the integration of low-cost digital solutions into routine operations. This synthesis provides a comprehensive foundation for advancing green accounting as a driver of sustainable business performance in Asia.

Moderating Role of Financial Development in the ESG Performance—Stock Liquidity Nexus: Evidence from Emerging Asian Economies

Circular economy (CE) practices have become a central issue in management and entrepreneurship research, particularly in emerging economies where micro and small enterprises (MSEs) dominate the business landscape. This study explores how green transformational leadership impacts financial performance through the mediation of corporate entrepreneurship practices, while also examining the moderating role of environmental uncertainty. Data was collected using a two-phase, time-lagged survey design involving 353 Indonesian MSEs. Partial least squares structural equation modeling (PLS-SEM) was employed to test the model. The analysis reveals that GTL has a significant positive effect on CE practices ( β = 0.37, p < 0.01) and CE practices strongly enhance financial performance ( β = 0.44, p < 0.01). The findings also confirm that CE practices mediate the relationship between green transformational leadership and financial performance ( β = 0.16, p < 0.01). Moreover, environmental uncertainty was found to significantly weaken the positive relationship between GTL and CE practices ( β = −0.21, p < 0.01). However, it did not have a significant moderating effect on the CE practices-financial performance relationship. This study offers valuable insights for MSE owners and policymakers in emerging Asian economies on leveraging leadership strategies to balance sustainability and financial goals.

ABSTRACT This study investigates the impact of green entrepreneurial innovations, energy diversification, economic complexity, economic growth, energy intensity, and government spending on income inequality in seven emerging Asian economies from 1998 to 2022. Using advanced methods such as the quantile‐on‐quantile, cross quantilogram, and wavelet‐quantile regression, we reveal that eco‐innovation and energy diversification play crucial roles in reducing inequality. Results show that process eco‐innovations and climate change mitigation patents significantly reduce inequality, particularly at higher Gini quantiles. Likewise, GDP growth lowers inequality in high‐inequality contexts, with negative coefficients reaching −0.24. Energy diversification is found to decrease inequality at low and medium quantiles. Policy implications suggest integrating diverse energy sources, expanding access to affordable energy, and subsidizing renewable adoption for low‐income households and businesses to mitigate inequality and promote inclusive, sustainable growth.

This study examined the role of Artificial Intelligence (AI) as a catalyst for economic and financial development in emerging Asian economies using a dual-method framework that combined econometric estimation with machine-learning prediction. Drawing on longitudinal data from 2000 to 2024, the research analyzed how AI adoption—measured through innovation intensity, patents, digital infrastructure, and investment—shaped GDP growth, total factor productivity, and financial inclusion. Panel cointegration results confirmed stable long-run relationships between AI and key macroeconomic indicators, while FMOLS and DOLS estimations demonstrated that AI adoption exerted strong and positive long-run effects on growth, productivity, and digital financial access. Granger causality tests indicated bidirectional causality between AI and financial development, highlighting the centrality of fintech-enabled inclusion channels. Complementing econometric results, machine-learning models (Random Forest, Gradient Boosting, LSTM) revealed high predictive accuracy, with LSTM emerging as the strongest performer (R² = 0.89). Feature-importance analysis showed that digital infrastructure, fintech usage, and institutional quality were the most influential predictors of economic outcomes. The findings suggested that AI reshaped development pathways by enhancing forecasting precision, improving decision-making efficiency, and enabling broader access to financial services. However, disparities in digital readiness and governance limited the uniform diffusion of benefits across countries. The study concluded that AI represents a transformative driver of structural growth in emerging Asia, provided that complementary investments in digital infrastructure, regulatory modernization, and human capital development are strengthened.

Purpose In the last few years, marketers have focused more on repurchase intention than on traditionally targeted purchase intention. This study examines the various determinants of online repurchase intention in this context, building upon the theory of planned behavior (TPB) and its extensions. Risk is a crucial factor in online purchases, and its impact on the proposed framework is also assessed. Design/methodology/approach Customer experience, customer satisfaction and online purchasing behavior were considered as key determinants. Three types of risks – product, financial and privacy – were considered moderators. About 504 online shoppers from two emerging digital economies participated in the survey. The structural equation modeling approach was used to assess the proposed model. Findings Findings indicate that online customer experience and online purchase behavior positively influence online repurchase intention. Additionally, online purchase behavior mediates the relationship between online customer experience, customer satisfaction and online repurchase intention. However, financial risk dampens the positive relationship between online purchase behavior and online repurchase intention. Originality/value The study encompasses two emerging Asian economies with thriving e-commerce sectors that can provide guidance for other aspiring nations. It thoroughly studies the impact of risks in digital marketplaces.

This study develops a binary logistic regression-based early warning model to predict financial distress among small and medium-sized enterprises (SMEs) from China. Drawing on a sample of 90 firms listed on the Chinese SME board, including 45 special treatment firms from 2017 to 2022 and 45 matched non-distressed counterparts as a control group, this study analyses 24 financial indicators covering solvency, operational efficiency, profitability and growth capacity. Principal component analysis identified six core variables, which were subsequently incorporated into the logistic regression framework. The model achieved high predictive accuracy, exceeding 80% in the year of distress (T) and maintaining robust forecasting capability up to three years prior (T-1, T-2, T-3). These findings confirm the effectiveness of the model in offering early identification of financial vulnerabilities. This study advances financial risk management literature by developing a targeted, multi-dimensional early warning system for SMEs, providing actionable insights for managers, investors and policymakers to enhance financial resilience. Empirically validated financial early warning models should emphasize targeted monitoring, proactive intervention and informed decision-making to enhance financial stability among Chinese SMEs.

ABSTRACT The work examines the relationship between environmental, social, and governance (ESG) scores and corporate financial performance (CFP) in hard‐to‐abate sectors (HTAS) in emerging Asian economies. Employing a robust quantitative methodology, the study critically analyzes the data of 2425 firms from HTAS. The CFP and ESG scores data were collected from the Refinitiv Eikon (REE) database for 21 emerging Asian economies from 2019 to 2024. The study considers the independent variable ESG scores and the dependent variables Current Ratio (CR) and Net Profit Margin (NPM). To explore the relationship, descriptive statistics, Pearson correlation, robust pooled ordinary least square regression (POLS), random effect model (REM), fixed effect model (FEM), and robust REM were explored in detail. The study revealed that the ESG score has a ‐ve correlation with CR (−0.088**) and NPM (−0.055*). The empirical models show that no significant relationship exists between ESG score and CFP (CR; NPM) of HTAS in emerging Asian economies. The comparative analysis of models highlights that the robust REM is an effective and reliable model. The work contributes to the stakeholder theory and shows the relationship between ESG score and CFP, specifically CR and NPM. The finding highlights that policy and decision‐makers in emerging Asian economies must pay significant attention to ESG and reassess ESG disclosure. The work also provides holistic policy and managerial insights for researchers, academicians, corporates, investors, and regulators.

ABSTRACT This study investigates the relationship between the forecasting power of web-based financial news sentiments and market capitalisation by analysing large, mid, and small-cap Indian stocks at both the index and sectoral levels. The analysis is further extended to two emerging Asian economies, Malaysia, and Vietnam. Given the dominant contribution of the finance sector, the study analyses the impact of financial news sentiments within the financial sector across each market cap. For this, 1,54,448 news headlines are extracted from an online news website, where financial news is identified using a TFIDF-GRU model and a keyword search with 187 financial terms. Sentiments are computed using a hybrid Doc2Vec-TFIDF feature extraction technique and an SVM classifier. The study employs a hybrid BiLSTM-GRU model incorporating web-based financial news sentiments alongside technical and macroeconomic indicators such as 10-year bond yield, exchange rate, gold price, crude oil price, and S&P500 closing price. Findings reveal that the forecasting power of web-based financial news sentiments varies significantly with market cap, with a strong impact on large-cap and mid-cap stocks. The study holds significant economic and policy implications, offering actionable insights for stakeholders across financial markets, regulatory bodies, and government.