Asian Journal of Applied Economics

Foreign Direct Investment, Education, and Gender Inequality in Middle-Income Nations: An Empirical Analysis Using a Panel Autoregressive Distributed Lag Approach

Tuan Nhat Pham — 2026-07-01

Background and Objectives: Gender equality is a cornerstone of the United Nations Sustainable Development Goals (SDG 5). Despite rapid economic transformation, persistent disparities continue to characterize many middle-income nations. Over the past two decades, these economies have become primary recipients of global foreign direct investment (FDI) while achieving significant progress in educational attainment, particularly in female primary enrollment. However, these advancements have not translated proportionately into reduced gender inequality, suggesting deeper structural constraints. This study examines the dynamic and potentially non-linear relationships between FDI, education, and gender inequality in middle-income countries. It specifically investigates whether FDI and education exert diverging short-run and long-run effects and explores the mechanisms through which these factors interact during the transition toward skill-intensive economic structures.

Methodology: The analysis utilizes a balanced panel dataset of 18 middle-income countries from 2000 to 2023, totaling 432 annual observations. Gender inequality is measured via the Gender Inequality Index, capturing disparities in reproductive health, empowerment, and labor market participation. Key explanatory variables include FDI inflows (as a percentage of GDP) and female primary school enrollment as a proxy for education, alongside controls for female labor force participation, life expectancy, and political rights. To capture both short-run dynamics and long-run equilibrium, the study employs the Pooled Mean Group (PMG) estimator within a panel Autoregressive Distributed Lag (ARDL) framework. This approach accommodates mixed orders of integration and allows for heterogeneous short-run adjustments while imposing long-run homogeneity. The robustness of the specification is confirmed through unit root and panel cointegration tests, followed by panel Granger causality tests to determine the directionality of the relationships.

Key Findings: The results reveal a clear divergence between short-run and long-run effects. In the short run, neither FDI nor education has a statistically significant impact on gender inequality, indicating that structural changes require a longer gestation period. The short-run coefficient for education is positive but insignificant, consistent with an "opportunity cost" mechanism where increased school enrollment temporarily reduces female labor force participation. Similarly, FDI inflows do not yield immediate effects, reflecting time lags in labor market transmission. Conversely, long-run estimates show that both FDI and education significantly reduce gender inequality. FDI facilitates improved outcomes through formal employment creation and the diffusion of inclusive labor practices, while education enhances human capital and decision-making capacity. Female life expectancy also emerges as a pivotal factor in reducing disparities. Sub-sample analysis indicates that the equalizing impact of FDI is more pronounced in upper-middle-income countries, highlighting the role of institutional quality. Finally, bidirectional Granger causality confirms a mutually reinforcing relationship between gender equality, FDI, and education.

Policy Implications: The findings suggest that FDI and education are not immediate policy levers but long-term structural drivers requiring supportive institutional frameworks. First, policymakers should implement targeted interventions to mitigate the short-run opportunity costs of education, such as scholarships and conditional cash transfers for female students. Second, governments should adopt a "quality-oriented" FDI strategy, prioritizing sectors that promote formal employment and gender-inclusive practices rather than focusing solely on capital volume. Third, sustained investment in women’s health is essential for long-term empowerment and labor market participation. Finally, given the bidirectional causality, gender equality should be treated as a strategic economic asset; enhancing gender parity can attract higher-quality investment and further reinforce human capital development. Achieving progress requires a coordinated policy approach that integrates education, investment, and social development within a long-term framework.

Digitalization and Bank Stability: Challenges and Opportunities for the Banking Industry Across Different Levels of Financial Development

Thy Le-Bao — 2026-07-01

Background and Objectives: Digitalization has fundamentally transformed the global banking sector by reshaping financial intermediation, operational efficiency, and risk management practices. The rapid expansion of digital technologies—such as mobile banking platforms, big data analytics, and automated credit scoring—has created new opportunities for financial inclusion and cost reduction. However, its implications for financial stability remain ambiguous and highly context dependent. While digitalization can improve information processing and enhance credit assessment, it may also introduce new vulnerabilities, including cybersecurity threats, model risk, and operational disruptions, particularly in data-constrained environments. This study aims to examine the complex relationship between digitalization and two critical dimensions of banking performance: credit risk, measured by non-performing loans (NPLs), and overall bank stability, proxied by the Z-score. In addition, the study investigates how these relationships vary across different levels of financial development and assesses the role of bank-specific and macroeconomic factors in shaping these outcomes. By adopting a broad cross-country perspective, the study seeks to provide policy-relevant insights into whether digital transformation strengthens or undermines banking sector resilience.

Methodology: The analysis is based on an unbalanced panel dataset covering 143 economies over the period 2010–2021, allowing for comprehensive cross-country comparisons across diverse institutional and financial environments. To address potential endogeneity issues—including reverse causality between digitalization and bank stability—as well as unobserved heterogeneity, the study employs the Dynamic System Generalized Method of Moments (DGMM) estimator. This approach is well-suited for dynamic panel models with persistent dependent variables and endogenous regressors. The empirical specification incorporates lagged dependent variables to capture dynamic adjustments in bank stability and credit risk, alongside a set of control variables including bank size, profitability, capital adequacy, cost efficiency, and key macroeconomic indicators. Furthermore, the analysis conducts sub-sample estimations by grouping countries according to their level of financial development, enabling a more nuanced evaluation of heterogeneous effects. A series of robustness checks is also performed to confirm the consistency and reliability of the empirical findings.

Key Findings: The results indicate that digitalization exerts a statistically significant and heterogeneous effects on bank stability and credit risk. In the full sample, digitalization is associated with a decline in bank stability (Z-score) and an increase in non-performing loans, suggesting that the costs and risks linked to digital adoption—such as high initial investment, cybersecurity exposure, and limitations of automated credit models in data-poor settings—may outweigh its benefits in the short to medium term. However, the effects vary considerably across different levels of financial development. In financially advanced economies, digitalization contributes positively to banking sector performance by reducing credit risk and enhancing stability, reflecting stronger institutional frameworks, better data infrastructure, and more effective regulatory oversight. In contrast, in less developed financial systems, digitalization improves borrower screening and loan quality to some extent, but the associated implementation costs and structural constraints tend to weaken banks’ capital buffers and overall stability. The findings also highlight the importance of control variables, with profitability consistently supporting bank stability, while bank size, cost efficiency, and capital investment exhibit varying effects depending on the financial development context. Overall, the evidence supports the view that digitalization functions as a “double-edged sword,” with its net impact determined by the maturity of the financial ecosystem.

Policy Implications: The findings underscore the need for a differentiated policy approach to digital transformation in the banking sector. In developing economies, policymakers should prioritize investments in digital infrastructure, data systems, and regulatory capacity, particularly in areas related to cybersecurity and digital risk supervision. Targeted support measures—such as subsidies, public–private partnerships, and phased implementation strategies—may be necessary to mitigate the high upfront costs associated with digital adoption and to prevent adverse effects on bank stability. In more advanced financial systems, regulatory attention should focus on managing systemic risks linked to technological concentration, including “too-big-to-fail” concerns arising from dominant digital financial institutions. Strengthening prudential oversight, promoting responsible innovation, and ensuring competition in digital financial services are essential to maintaining long-term stability. Overall, aligning digitalization strategies with institutional readiness and financial development levels is crucial to maximizing benefits while minimizing risks in the evolving banking landscape.

Inflation Unpacked: Breaking Down the Key Components Using a Neural Phillips Curve

Joan Christine Allon-Pineda — 2026-07-01

Background and Objectives: Effective monetary policymaking requires a clear understanding of the underlying drivers of inflation. For central banks such as the Bangko Sentral ng Pilipinas (BSP), this task is challenging because inflation reflects the interaction of demand-side pressures, supply-side shocks, and expectations. Key components of inflation dynamics, including the output gap and inflation expectations, are unobserved and are often difficult to measure accurately. Traditional Phillips curve models typically rely on strong assumptions, filtering methods, or survey-based proxies, which may be sensitive to misspecification, measurement error, and nonlinearities. This study applies a deep learning approach to examine inflation dynamics in the Philippines and disentangle the contributions of real activity, inflation expectations, and commodity prices within a New Keynesian Phillips Curve framework.

Methodology: The study employs a Hemisphere Neural Network (HNN), a deep neural network architecture designed to improve interpretability by grouping input variables into separate “hemispheres.” Each hemisphere corresponds to a key component of inflation: real activity, inflation expectations, and commodity prices. This structure allows the model to extract latent indicators that can be interpreted as macroeconomic states and to decompose realized inflation into component-specific contributions. Unlike standard neural networks, which often operate as black boxes, the HNN imposes an economically meaningful structure on the final layer, making the estimated components more transparent. The model is estimated using Philippine quarterly macroeconomic data and is assessed based on its ability to generate meaningful inflation forecasts, align with historical macroeconomic events, and produce interpretable indicators of the output gap and inflation expectations.

Key Findings: The results show that Philippine inflation can be decomposed into distinct contributions from real activity, inflation expectations, and commodity prices. Long-run inflation expectations remained relatively stable at around 3.5–4.5 percent over the sample period, broadly consistent with the BSP’s inflation target range of 2–4 percent. This stability suggests that expectations have remained relatively well anchored. Commodity prices account for much of the short-term volatility in inflation, particularly during episodes such as the 2014–2015 oil supply glut and the 2021–2022 global supply chain disruptions. By contrast, real activity appears to play a more important role in medium-term inflation movements, especially during the pandemic and post-pandemic recovery periods. The results also indicate that the relationship between inflation and economic activity in the Philippines remains relevant, contrary to claims in some strands of the literature that the Phillips curve has weakened or disappeared. The HNN-derived real activity gap broadly reflects domestic economic conditions and can be interpreted as an additional measure of inflationary pressure. Similarly, the model-based measure of inflation expectations aligns with short- to medium-term expectations from businesses and professional forecasters, suggesting that it can serve as a useful supplementary indicator when survey-based measures are limited.

Policy Implications: The findings highlight the usefulness of interpretable deep learning methods for monetary policy analysis. By distinguishing between inflation driven by demand-side pressures and inflation arising from temporary supply shocks, the HNN can help policymakers assess whether inflation movements warrant monetary tightening, easing, or a more cautious policy response. Inflation driven by real activity or expectations may call for a stronger monetary policy response, while inflation caused mainly by temporary commodity price shocks may justify a more measured approach. The model’s estimates of the output gap and inflation expectations can also complement existing indicators used by the BSP to assess domestic demand conditions, expectation formation, and the credibility of the inflation target. Overall, the study demonstrates that theory-guided machine learning can strengthen macroeconomic monitoring and support more targeted, evidence-based monetary policy decisions.

Unpacking Macroeconomic Performance in Pakistan: An Index Construction Approach

Sana Jamil — 2026-07-01

Background and Objectives: Pakistan’s macroeconomic trajectory has remained highly volatile since independence, characterized by persistent fiscal deficits, elevated inflation, monetary and external imbalances, rising unemployment, and vulnerability to global shocks. In the early 1970s, post-partition debt pressures and oil shocks contributed to inflation and fiscal deficits hovering around 7–8%. Subsequent decades were marked by recurring cycles of reforms and crises. During the 1980s, relative macroeconomic stability under non-democratic regimes was followed by the liberalization challenges of the 1990s, compounded by nuclear tests and sanctions; inflation reached 12.37% in 1994. The 2008 global financial crisis further exacerbated fiscal deficits and public debt. More recently, IMF-supported programs initiated in 2019 and 2023 sought to address balance-of-payments pressures, yet inflation surged to 30.78% in 2023, unemployment remained in the 4–6% range, and external sector performance remained constrained. Existing research often focuses on one or a few indicators of macroeconomic performance, which can overlook the complex interrelationships among fiscal, monetary, and external variables. Composite indices—such as those developed by the Organisation for Economic Co-operation and Development (OECD) and the International Monetary Fund (IMF)—offer a more comprehensive representation of macroeconomic conditions. Against this backdrop, this study aims to develop a composite macroeconomic performance index (MPI) for Pakistan using annual data for 1972–2024.

Methodology: A composite MPI for Pakistan is constructed using two complementary approaches: Principal Components Analysis (PCA) and Sarma’s (2008) distance-based (normative) method. The index integrates eight key indicators of macroeconomic performance: fiscal balance, public debt, inflation rate, trade openness, unemployment rate, broad money supply, interest rate, and the real exchange rate. Using both approaches allows assessment of whether alternative aggregation frameworks yield consistent signals regarding Pakistan’s macroeconomic performance and its evolution over time. For the PCA-based MPI, component weights are derived from eigenvalues, and the first three principal components are retained because they capture the maximum variance. These three components account for 54.6% of the total variation over 1972–2024 and provide the most robust summary of the underlying macroeconomic environment within the PCA framework. In contrast, the distance-based index (UNDP-style) normalizes the same eight indicators using min–max scaling and evaluates each year’s proximity to a predefined “ideal” macroeconomic stance, characterized by low inflation, a moderate increase in broad money supply and interest rates, manageable budget deficits and public debt, a stable exchange rate, and higher trade openness. Together, these methods provide a structured way to synthesize multiple macroeconomic dimensions into a single index while retaining sensitivity to both data-driven variance (PCA) and normative benchmarks (distance-based method).

Key Findings: The MPI dynamics reveal substantial shifts in Pakistan’s macroeconomic performance during 1972–2024, with alternating periods of relative stability and instability. Notable instability is observed in the early 1970s, during 2006–2012, and again from 2021 onward, whereas the 1980s, the 1990s, the early 2000s, and 2016 appear relatively more stable. Taken together, the index patterns underscore that macroeconomic performance in Pakistan has not followed a linear improvement path; instead, it has been shaped by repeated shocks and policy adjustment episodes. The use of two index construction methods is intended to provide a more robust depiction of these shifts, by examining whether the broad timing of stable versus unstable periods is consistently reflected across alternative aggregation strategies.

Policy Implications: The results highlight the fundamental role of public debt management, price stability, fiscal discipline, stable exchange rate conditions, monetary management, and trade openness in shaping macroeconomic performance in Pakistan. Importantly, improvements in individual indicators may not fully reflect overall macroeconomic conditions when other dimensions deteriorate. By offering an integrated measure of performance, the MPI developed in this study can support more holistic policy assessment and monitoring. Achieving sustainable improvements in macroeconomic performance requires strengthening structural reforms, improving domestic revenue systems, promoting export diversification beyond textiles, and reducing reliance on temporary or short-term stabilization measures.

Assessing Energy Security in South Asia: A Composite Index Approach Incorporating Governance, Sustainability, and Socioeconomic Dimensions

Munazza Akhtar — 2026-07-01

Background and Objectives: Energy security extends beyond resource availability and affordability to encompass governance, environmental sustainability, and socioeconomic dimensions that are critical for sustainable development. In South Asia, rising energy demand, rapid population growth, and urbanization are intensifying pressure on limited energy resources. A comprehensive assessment of energy security helps identify structural weaknesses, including import dependence, inefficient energy use, and unequal access to modern energy services across countries such as Pakistan, India, Bangladesh, Nepal, Bhutan, and Sri Lanka. It also provides insights into institutional effectiveness, environmental performance, and the long-term resilience of energy systems. Accordingly, this study develops a multifaceted energy security index for South Asian countries over the period 2001–2022.

Methodology: Data suitability is assessed using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity. A KMO value above 0.05 indicates adequate sampling, while a significant Bartlett’s test (p < 0.05) confirms sufficient correlations for factor analysis. Internal consistency among indicators is evaluated using Cronbach’s alpha (acceptable range: 0.7–0.9). Principal Component Analysis (PCA) is then applied to reduce dimensionality and extract latent components. Component weights are derived from eigenvalues, reflecting their contribution to total variance. The Min–Max normalization method is used to standardize variables, which are subsequently aggregated into a composite index based on weighted components.

Key Findings: The results indicate strong internal consistency (Cronbach’s alpha = 0.733) and high sampling adequacy (KMO = 0.863), with Bartlett’s test confirming significant inter-item correlations. The PCA yields a Multifaceted Energy Security Index (MFESI) comprising eight dimensions: energy sustainability, governance and efficiency, socio-environmental sustainability, affordability, energy access, human resilience, energy transition, and labor market dynamics. The index rankings show that India leads in overall energy security, followed by Pakistan, Bangladesh, Bhutan, Sri Lanka, and Nepal. Classification into four categories, Poor (1–2.5), Fair (2.5–5.0), Good (5.0–7.5), and Excellent (7.5–10) reveals that most countries fall within the “Fair” range. Dimension-wise analysis highlights India’s strength in sustainability and renewable energy, Bhutan’s strong governance performance but weak sustainability, and Pakistan’s strengths in socio-environmental sustainability alongside affordability challenges. Nepal consistently ranks lowest in governance and resilience, indicating structural constraints.

Policy Implications: The findings highlight significant regional disparities in energy security and underscore the need for coordinated policy responses. Strengthening regional cooperation through cross-border electricity trade and renewable energy collaboration is essential. Policy priorities include improving affordability through targeted subsidies and tariff reforms, enhancing climate-resilient energy infrastructure, and promoting institutional reforms. Country-specific strategies suggest improving governance and affordability in Pakistan, enhancing human resilience and sustainability in India, expanding energy access in Bangladesh, diversifying energy sources in Bhutan and Nepal, and strengthening governance and renewable energy development in Sri Lanka. These measures are critical for achieving Sustainable Development Goals (SDGs) 7, 13, and 16.

Social Security System Differences and Population Mobility: Evidence from the Guangdong–Hong Kong–Macao Greater Bay Area

Yingying Wang — 2026-07-01

Background and Objectives: Population mobility plays a critical role in promoting regional economic integration, labor market efficiency, and balanced development. In the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), one of China’s most dynamic economic regions, population movement has intensified alongside rapid urbanization and cross-border economic activity. However, institutional differences—particularly in social security systems—remain a significant barrier to the free and efficient movement of labor. Due to the “one country, two systems” framework and the coexistence of multiple administrative, legal, and welfare regimes, substantial disparities exist in social security coverage, benefit structures, contribution rates, and financing mechanisms across Guangdong, Hong Kong, and Macao. These differences generate institutional costs that may distort migration decisions and hinder optimal allocation of human resources. Against this backdrop, this study aims to examine how differences in social security systems within the GBA affect population mobility, both theoretically and empirically, and to identify the mechanisms through which these institutional costs influence migration patterns.

Methodology: The study adopts a mixed theoretical–empirical approach. First, it develops a conceptual framework grounded in new institutional economics and migration cost–benefit theory, conceptualizing differences in social security systems as institutional costs that influence mobility decisions. These costs are categorized into three components: public costs (government fiscal expenditures on social security), enterprise costs (employer contributions), and individual costs (employee contributions). Second, the study employs panel data from 11 cities in the Guangdong–Hong Kong–Macao Greater Bay Area over the period 2018–2021, a timeframe selected based on key policy changes enabling cross-border participation in mainland social insurance systems. A two-way fixed effects model is estimated to control for both regional heterogeneity and time effects. Population mobility is measured using comparable indicators across jurisdictions, while social security cost variables are constructed based on harmonized financial and institutional data. The analysis further incorporates control variables including wage income, economic development level, unemployment rate, and fiscal expenditure. Robustness checks are conducted using alternative dependent variables, extended sample periods, and additional controls, including pandemic-related factors and structural characteristics of the labor force.

Key Findings: The empirical results reveal three main findings. First, differences in social security systems significantly hinder population mobility in the GBA. Higher total social security costs—arising from institutional fragmentation, benefit non-portability, and transfer inefficiencies—are associated with lower levels of population inflows. This finding confirms the hypothesis that institutional costs act as barriers to labor mobility, even in economically integrated regions. Second, the effects of social security costs vary across responsible agents. Public costs and enterprise costs exert a statistically significant negative impact on population mobility, reflecting fiscal pressure, congestion effects in public services, and increased labor costs for firms. In contrast, individual costs exhibit a positive relationship with mobility, suggesting that higher personal contributions may signal better employment conditions and stronger social protection, thereby attracting labor. Third, the impact of social security costs is heterogeneous across city types. Non-core cities and those within the Pearl River Delta are more sensitive to increases in social security costs, while core cities and special administrative regions show weaker or statistically insignificant effects. Additional robustness tests confirm the stability of these findings and highlight the role of external shocks, such as the COVID-19 pandemic, in reinforcing the stabilizing function of social security systems.

Policy Implications: The findings underscore the importance of institutional coordination in promoting efficient population mobility within the GBA. First, policymakers should prioritize the harmonization and interoperability of social security systems across jurisdictions, focusing on improving benefit portability, transfer continuity, and mutual recognition mechanisms rather than pursuing full institutional unification. Establishing a regional coordination platform and expanding pilot integration zones could help reduce institutional barriers. Second, optimizing the cost-sharing structure among governments, enterprises, and individuals is essential to balance fiscal sustainability with labor market efficiency. Reducing excessive enterprise burdens and improving fund management can enhance labor demand and economic vitality. Third, reforms should address the non-portability of welfare benefits and rigid eligibility conditions that disproportionately affect mobile populations. Measures such as cross-regional settlement systems, pension portability frameworks, and unified service platforms can significantly lower migration costs. Overall, strengthening institutional convergence while maintaining regional flexibility is key to fostering a more integrated and dynamic labor market in the Greater Bay Area.

Climate Change, Global Uncertainty, and Operational Costs: Evidence from Indonesian SMEs

Syaiful Hasani — 2026-07-01

Background and Objectives: Small and Medium Enterprises (SMEs) play a crucial role in economic development and employment generation, particularly in emerging economies. In Indonesia, SMEs represent more than 99% of business entities, contribute approximately 61% of national GDP, and employ about 97% of the workforce. Despite their economic importance, SMEs are increasingly exposed to environmental and macroeconomic pressures that threaten operational sustainability. Climate change disrupts production through extreme weather events, energy price volatility, and instability in raw material supply chains. At the same time, global economic uncertainty reflected in inflationary pressures, exchange-rate volatility, and geopolitical tensions creates additional operational risks that may increase firms’ cost structures. These challenges are particularly relevant for SMEs in the garment and clothing sector, which is characterized by labor intensive production and strong supply chain dependence. Although previous studies have examined environmental uncertainty and firm performance, limited research has explored the mechanisms through which climate change and global uncertainty influence SMEs’ operational costs. In particular, digital technology adoption and digital financial literacy may function as adaptive capabilities that help SMEs respond to environmental turbulence, although such investments may also generate short-term adjustment costs. In addition, the role of financial inclusion in shaping these cost dynamics remains underexplored. Therefore, this study examines how climate change and global economic uncertainty influence SMEs’ operational costs through the mediating roles of digital technology adoption and digital financial literacy, as well as the moderating role of financial inclusion.

Methodology: This study employs a quantitative research design using cross-sectional survey data collected from 270 owners and managers of SMEs operating in Indonesia’s garment and clothing sector. Respondents were selected through purposive sampling to ensure their involvement in business operations, digital technology adoption, and financial decision-making. Data were collected using a structured questionnaire with Likert-scale indicators adapted from established studies. The main constructs include climate change exposure, global uncertainty, digital technology adoption, digital financial literacy, financial inclusion, and operational costs. The analysis also includes control variables, namely firm size, firm age, sub-sector, regional characteristics, and input utilization. The empirical analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM), which is suitable for examining complex mediation and moderation relationships.

Key Findings: The empirical results reveal several important findings. First, climate change has a positive and significant effect on SMEs’ operational costs, indicating that environmental disruptions increase production, energy, and logistics expenses. Second, both climate change and global economic uncertainty significantly stimulate the development of adaptive capabilities, particularly through increased digital technology adoption and improved digital financial literacy. Third, digital technology adoption and digital financial literacy significantly increase operational costs in the short term. This finding reflects adjustment costs associated with digital transformation, including investments in digital infrastructure, employee training, and system integration. Fourth, digital capabilities play a significant mediating role, suggesting that environmental and macroeconomic pressures influence operational costs indirectly through capability development. Finally, the moderating effect of financial inclusion is not statistically significant, indicating that access to financial services alone does not substantially alter how digital capability investments affect SMEs’ operational cost outcomes.

Policy Implications: The findings offer several policy implications. First, policymakers should strengthen SME resilience by improving logistics infrastructure, promoting energy-efficient production technologies, and stabilizing supply chain systems to reduce climate-related cost pressures. Second, governments should expand SME digital transformation initiatives, including training programs in e-commerce management, digital accounting systems, and digital supply chain platforms. Third, because digital capability development requires substantial initial investment, financial policy support is needed to reduce SMEs’ transitional cost burdens. Policy instruments such as tax incentives for digital technology adoption, subsidized digital infrastructure, and low-interest financing programs can facilitate SME digitalization. Strengthening digital capabilities and digital financial literacy can ultimately enhance SME resilience and support sustainable economic development under climate-related risks and global economic uncertainty.

Risk Governance and Stakeholder Analysis of Radioactive Waste Management in Small Modular Reactor Deployment: Evidence from Thailand

Cheema Soralump — 2026-07-01

Background and Objectives: Thailand is currently considering the integration of Small Modular Reactor (SMR) technology into its national energy portfolio under the Power Development Plan (PDP 2025–2037) as part of its transition toward low-carbon energy and climate commitments. As a newcomer nuclear country without upstream fuel-cycle capabilities—such as uranium mining, enrichment, or fuel fabrication—Thailand operates as a back-end nuclear system, where the primary responsibility lies in managing spent fuel and radioactive waste. This creates significant challenges across technical, financial, regulatory, and social dimensions, particularly given the absence of established infrastructure and institutional arrangements for long-term waste management. Against this backdrop, a systematic understanding of risks associated with the SMR back-end cycle is essential to ensure that nuclear adoption is economically viable, environmentally sustainable, and socially acceptable. This study therefore aims to (1) identify key stakeholders across four stages of the SMR back-end cycle—waste generation, interim storage, transportation, and final disposal—and (2) evaluate the associated risk factors using a structured risk-governance framework.

Methodology: The study applies a qualitative risk-governance framework grounded in ISO 31000:2018 and IEC/ISO 31010:2019 standards, complemented by relevant International Atomic Energy Agency (IAEA) Safety Standards. The analytical process follows four steps: context establishment, risk identification, risk analysis, and risk evaluation. Risk identification is conducted across the four stages of the SMR back-end cycle, yielding 21 risks classified into four dimensions: technical, financial, policy/regulatory, and social/environmental. A qualitative likelihood–consequence matrix—combining four likelihood levels (L1–L4) and five consequence levels (C1–C5)—is employed to categorize risks into low, medium, high, and extreme levels, thereby enabling a systematic assessment of both probability and impact across the waste management lifecycle.

Key Findings: The results show that the overall risk profile of Thailand’s SMR back-end cycle is driven primarily by non-technical factors, despite the presence of advanced engineering safety features. Technical risks are generally assessed as low to medium, reflecting the reliability of passive safety systems and established international standards governing radioactive material management. In contrast, financial, regulatory, and social risks emerge as the principal constraints. Financial risks are assessed as high, particularly those associated with long-term storage, maintenance, and repository development, due to their persistent and intergenerational cost burden in the absence of a dedicated national funding mechanism. Policy and regulatory risks are predominantly medium, reflecting uncertainties in SMR integration into national energy planning, licensing processes, and the absence of a clearly defined long-term disposal strategy, indicating fragmented institutional arrangements. Social risks constitute the most critical challenge, with community opposition identified as the only “extreme” risk, driven by concerns over radiation safety, limited institutional trust, and potential conflicts in stakeholder engagement, all of which affect the social license to operate.

Policy Implications: The findings suggest that the successful deployment of SMR technology in Thailand depends more on strengthening governance and institutional capacity than on addressing technical constraints alone. First, establishing a long-term financing mechanism—such as a national radioactive waste management fund—is essential to ensure fiscal sustainability, with a hybrid model combining short-term cost recovery, operator contributions, and long-term statutory funding recommended to address intergenerational liabilities. Second, improving regulatory coherence and institutional coordination is critical to reducing policy uncertainty, including clarifying agency roles, streamlining licensing procedures, and establishing a centralized coordination mechanism for radioactive waste management, alongside early investment in interim storage and final disposal planning. Third, enhancing public trust through transparent communication, continuous stakeholder engagement, and environmental monitoring is vital, with nuclear literacy programs and participatory frameworks aligned with international best practices playing a key supporting role. Overall, strengthening financial, institutional, and social foundations will be crucial to ensuring that SMR adoption contributes effectively to Thailand’s long-term energy transition.

Economic Activity and Provincial Solid Waste Generation in Thailand: Evidence from Satellite-Derived Spatial Analysis

Ronnakron Kitipacharadechatron — 2026-07-01

Background and Objectives: Rapid urbanization, economic growth, and changing consumption patterns have increased provincial solid waste generation in Thailand, while waste-management capacity remains uneven across provinces. Existing studies often rely on aggregate statistics or localized case studies, limiting understanding of the spatial interdependence underlying waste dynamics. Recent advances in geospatial technologies allow satellite-derived indicators, such as nighttime lights, land-surface temperature, urbanization measures, and precipitation, to be used as spatial proxies for economic activity, environmental conditions, and human pressure. This study investigates the spatial interconnections between satellite-derived indicators and provincial solid waste generation across Thailand.

Methodology: The study uses provincial solid waste data from the 2024 Annual Report of the Ministry of Natural Resources and Environment and satellite-derived indicators processed through Google Earth Engine. Spatial analysis, including quartile mapping, spatial autocorrelation, and spatial clustering, is applied to examine spatial patterns and dependence. A structural equation model is then used to assess the influence of satellite-derived indices on provincial solid waste generation.

Key Findings: The results show that provincial solid waste generation in Thailand is spatially clustered and strongly shaped by geographic context. Satellite-derived indicators, particularly socioeconomic activity, urbanization, and daytime land-surface temperature are positively associated with solid waste generation. In contrast, precipitation shows a modest negative association, suggesting that rainfall may reflect broader climatic and geographic conditions rather than directly intensifying waste generation. The structural equation model indicates that satellite-derived indices collectively explain a substantial share of provincial variation in solid waste generation.

Policy Implications: The findings support the use of spatially differentiated waste-management policies that prioritize provinces with high urban, economic, and thermal intensity. Satellite-based monitoring systems can help improve real-time decision-making, infrastructure planning, and provincial waste-management strategies. Policymakers should integrate urban planning, industrial zoning, and climate-responsive approaches to mitigate solid-waste pressures in rapidly developing areas.