Shih‐Yung Chiu

This study examines the sustainable competitiveness of Taiwan's 22 counties and cities between 2016 and 2020. We develop and apply a parallel dynamic two‐stage slacks‐based measure data envelopment analysis framework that integrates Shannon entropy to measure sustainability performance across social, economic, and environmental dimensions, and to evaluate how effectively these outcomes translate into residents' quality of life. Our approach consolidates multiple indicators into subsystem‐specific measures while directionally adjusting for undesirable outputs—such as pollution, waste, and crime—thus ensuring that efficiency estimates fully reflect these critical constraints. The results reveal that persistent inefficiency in the economic subsystem represents the main barrier to sustainable competitiveness. In contrast, environmental governance and social inclusion exhibit a stronger, more direct relationship with well‐being. Spatial regression models further indicate that differences in quality of life are primarily explained by local governance capacity, with limited spillover effects from neighboring regions. A striking finding is that smaller, less industrialized jurisdictions frequently outperform major metropolitan areas, indicating that sustainable competitiveness stems not from administrative scale, but from balanced advancement across all sustainability domains. This study provides an integrated and methodologically consistent framework that helps clarify the linkages between sustainability performance and well‐being. The findings provide empirically grounded insights that can help policymakers promote urban sustainability and contribute to achieving the UN Sustainable Development Goals.

This study examines the effectiveness of human capital as a driver of sustainable urban well‐being across 21 jurisdictions in Taiwan. Using a four‐stage parallel dynamic slacks‐based measure data envelopment analysis framework, we examine the transmission of educational inputs through economic, social, and environmental sustainability dimensions. The analysis identifies a critical governance challenge: although substantial institutional investment has been made in education, the education stage is the primary structural constraint, exhibiting a limited capacity to translate into improvements in urban happiness. We determine that this ‘education‐to‐economy’ friction is associated with structural mismatch, in which a persistently low fertility rate and credential inflation may weaken the alignment between human capital formation and regional industrial requirements. Total factor efficiency trajectories further reveal substantial spatial disparities, with southern and peripheral regions exhibiting a ‘high‐input, low‐absorption’ pattern. These results suggest that Taiwan's pursuit of urban happiness is constrained not by fiscal limitations but by challenges in aligning educational outputs with localised industrial upgrading and energy requirements. The present study offers a localised perspective on the global challenge of aligning human capital investment with the demands of a postindustrial and demographically shrinking society.

Many Asian economies undergo rapid energy transition and industrial restructuring, which has become increasingly necessary to better understand how changes in energy structure and environmental constraints jointly influence industrial efficiency. Although the availability and quality of energy‐related data have steadily improved, most studies rely on static or single‐stage approaches, offering limited insight into the dynamic, multistage mechanisms linking energy development and industrial outcomes. This study investigates the impact of energy development on industrial efficiency using data from 18 Asian economies and regions. The analysis covers the period 2015–2020, a critical stage characterized by accelerated energy transition and climate policy adjustments in Asia. Methodologically, this study employs a two‐stage dynamic nonoriented directional distance function model to evaluate efficiency changes at the stage and overall levels. By integrating intertemporal links and undesirable outputs into a multistage framework, the model extends conventional efficiency analysis and provides a more comprehensive assessment of energy–industry interactions. Furthermore, total factor productivity indices are applied to measure productivity changes, and Tobit regression is used to analyze how the energy intensity, renewable energy share, energy supply structure, and carbon emissions influence overall efficiency. The findings reveal significant regional disparities in efficiency across countries. They indicate that improving energy efficiency and accelerating the transition to cleaner and more diversified energy systems are essential for enhancing industrial performance under carbon constraints. These results provide empirical support for policymakers to design differentiated energy and industrial policies that promote sustainable development and low‐carbon economic transformation in Asia.

This study examines the efficiency and productivity with which 17 Asia‐Pacific countries (hereafter referred to as the APAC17 countries) transform economic and public resources into human development outcomes over the period 2015–2022, using a dynamic two‐stage framework based on the dynamic network‐directional distance function (DN‐DDF). The first stage treats labour force and gross capital formation as core economic inputs, with gross domestic product (GDP) specified as an intermediate linking variable and PM2.5 air pollution incorporated as an undesirable output to capture environmental externalities associated with economic development. The second stage evaluates government expenditures on health and education for their efficiency in generating human development outputs, including the Human Development Index (HDI) and its related indicators. The results indicate that economic efficiency is highly uneven: China, Indonesia and the Philippines have persistently exhibited low efficiency, whereas Australia, New Zealand and Armenia have maintained high efficiency levels. In contrast, human development efficiency is generally close to full efficiency. This study provides an analytical framework that integrates DN‐DDF to address gaps in prior research and emphasises the importance of institutional reform and public expenditure efficiency in advancing the sustainable development goals (SDGs).

This study examines how key social, economic, and environmental factors affect Delayed Childhood Development, measured by the prevalence of stunting among children under five. Drawing on panel data from 47 countries between 2015 and 2019, we employ a dynamic network directional distance function (DN‐DDF) model to assess efficiency across four interconnected dimensions: social development, economic development, environmental performance, and sustainable outcomes. Stunting is treated as an undesirable output in the final stage, serving as a proxy for long‐term developmental risk. The findings reveal that African countries exhibit the lowest efficiency performance in both economic and environmental aspects, with Asia ranking highest and Africa lowest in overall efficiency. Further verification using a Tobit regression model shows that official development assistance (ODA), urban population, and the Human Development Index (HDI) are positively associated with efficiency. In particular, the strong correlation between urbanization and HDI suggests that structural demographic factors may play a key role in shaping national development efficiency. These results underscore the need for integrated policy strategies to address systemic inefficiencies and promote child development in vulnerable regions.

The semiconductor industry is central to global technological progress but increasingly faces the challenge of balancing financial performance with rising environmental, social, and governance (ESG) expectations. While ESG and artificial intelligence (AI) are widely regarded as strategic priorities, limited empirical research has investigated their combined influence on firm‐level efficiency in high‐tech, capital‐intensive sectors. This study addresses this gap by applying a three‐stage Data Envelopment Analysis (DEA) to evaluate the performance of 33 publicly listed semiconductor firms in Taiwan across profitability, sustainability, and market value. The model reflects the sequential value‐creation process: the Profit stage measures how R&D and labor are transformed into innovation output (intangible assets and income); the Sustainability stage assesses how governance and environmental inputs shape ESG scores; and the Market stage examines how ESG performance and capital investment affect market valuation. Stage‐specific efficiency is measured using ESG and structural variables aligned with the semiconductor sector's vertically disintegrated and interdependent value chain. AI‐driven innovation is proxied by patent activity. Results reveal performance disparities across stages: Market stage efficiency ranks highest (0.9664), followed by Profit stage, while Sustainability stage lags—highlighting an ESG implementation gap in otherwise competitive firms. Tobit regression analysis shows that AI‐driven innovation enhances sustainability efficiency, whereas firm size and leverage are more closely linked to profitability. This study proposes a multi‐stage framework integrating ESG and AI to evaluate sustainable innovation in high‐tech sectors, offering insights into how firms can balance competitiveness with long‐term sustainability.

This study investigates the challenges of achieving social sustainability efficiency in European countries, especially in health and well‐being, under sustainable development goal 3 amid demographic aging pressures. Using an innovative modified circular dynamic three‐stage systematic network data envelopment analysis model, this research evaluates the influence of social protection investments on healthcare and well‐being systems. The findings reveal that Denmark, Estonia, France, and Germany demonstrate optimal efficiency, while Lithuania, Luxembourg, the Netherlands, and Slovenia require strategic improvements, particularly in healthcare efficiency. Policy recommendations underscore the need for enhanced healthcare technology, occupational safety, and adaptive strategies to support social sustainability across 24 countries from 2016 to 2020. This approach offers a comprehensive efficiency framework and actionable insights to guide future policy development to address European healthcare efficiency gaps.

Climate change has directly impacted coffee‐producing regions in Africa. This study uses the two stage Dynamic Network Directional Distance Function (DN‐DDF) and Malmquist Productivity Index (MPI) methods to evaluate the efficiency and productivity changes of agricultural production and sustainable development in 19 African coffee‐producing countries from 2014 to 2018. According to the empirical results, Central African Republic, Ethiopia, Liberia, Uganda, and Zambia have the highest overall efficiency. This study has three main contributions: (1) using a two‐stage model to evaluate the impact of agricultural production efficiency on sustainable development efficiency, (2) using a dynamic model with forest area as a cross‐period variable to reflect the real‐world phenomenon of coffee relying on forest mixed planting, and (3) objectively evaluating the impact of extreme weather on agricultural production by using rainfall as an exogenous variable to address previous research shortcomings. The results of this study can promote the improvement of agricultural production efficiency in African coffee‐producing countries and provide important reference value for sustainable development goals.