Asia-Pacific Journal of Regional Science最新文献

Renewable energy technologies, e.g., solar photovoltaics, electric vehicles and wind turbines, are instrumental in producing renewable energy and promoting environmental safety. We examined how these technologies enhance environmental safety, considering regulatory governance and public awareness from the perspectives of residents in the Hail Region of Saudi Arabia. We administered a questionnaire survey using a purposive sampling technique with 275 respondents and analyzed the data using a PLS-SEM approach. Wind energy technology significantly improved environmental safety, moderated by regulatory governance and public awareness. In contrast, the roles of solar energy and electric vehicle technologies in environmental safety were found to be ineffective, even when moderated by both regulatory governance and public awareness. While the age of respondents emerged as a relevant factor, gender, occupation and education level did not significantly impact the improvement of environmental health. We conclude that advocating for the establishment of robust regulatory governance can enhance the functionality of solar and electric vehicle technologies and contribute to the environmental safety of Hail, Saudi Arabia.

Vietnam receives large amounts of foreign direct investment (FDI), yet these inflows are uneven across provinces. This raises a practical question: under what conditions does FDI increase income inequality within provinces, and when does it help reduce it? Using data for all 63 provinces (2018–2023), we linked the GINI coefficient compiled by Vietnam’s General Statistics Office (GSO) with registered FDI from Vietnam’s Ministry of Planning and Investment (MPI) and estimated fixed-effects models with province-level cluster-robust, heteroskedasticity-consistent Type-1 (MacKinnon-White; HC1) standard errors and Driscoll-Kraay errors, allowed for nonlinearity of log(FDI + 1), included a one-year lag, and implemented Hansen’s panel-threshold test based on the Provincial Competitiveness Index (PCI). We found an inverted-U effect in the contemporaneous relationship: inequality increased with FDI up to a turning region of about USD 41.5–48.5 million, after which additional FDI did not increase inequality. Dynamically, a one-log-point increase in last year’s FDI (roughly doubling) was linked to an approximate 0.003 decrease in the GINI, suggesting short-run benefits through employment and supplier linkages. A governance breakpoint near a PCI score of approximately 62.4 separated two regimes: when PCI was below this level, the inverted-U was salient; when PCI was above it, the FDI terms were jointly insignificant, and the average FDI×PCI interaction was negative but imprecise. These findings represent short-run, within-province associations and suggest that provincial policymakers and national ministries should focus on improving governance in low-PCI provinces and emphasize diffusion tools rather than broad concessions in high-PCI provinces.

This paper evaluates the efficiency of 63 Vietnamese provinces over the 2018–2023 period by employing a slacks-based directional distance function (SBM-DDF) with bootstrap bias correction and spatial autocorrelation analysis. Using inputs of labor, public expenditure, and capital investment alongside desirable outputs—GRDP, HDI, public revenue and poverty reduction—and explicitly treating undesirable outcomes, the study uncovers three distinct phases of efficiency dynamics: an initial slowdown in 2019, resilience during the COVID-19 period, and a strong post-pandemic recovery by 2023. The average efficiency score rose from 0.843 in 2018 to 0.893 in 2023, with persistent bimodal distributions indicating structural heterogeneity. Spatial analysis confirms significant positive autocorrelation, with Global Moran’s I demonstrating that provincial efficiency is spatially dependent. Local Moran’s I identifies four configurations: High–High clusters in the Southeast and Mekong Delta reflecting agglomeration spillovers; Low–Low clusters in the Central Highlands signaling spatial poverty traps; High–Low efficiency islands achieving superior performance in weak regions; and Low–High provinces failing to capture neighboring spillovers. These findings highlight that infrastructure, institutions and human capital jointly condition efficiency through spatial mechanisms. COVID-19-induced disruptions reveal conditional spatial resilience. Policy implications emphasize differentiated, region-based coordination—strengthening High–High networks, addressing Low–Low constraints, enhancing absorptive capacity in Low–High provinces, and replicating High–Low success models—to foster balanced and inclusive growth.

As urbanization accelerates and environmental concerns intensify, electric motorcycles are gaining attention as an eco-friendly and sustainable mode of transportation. However, research on their acceptability and market penetration in Southeast Asia remains limited. This study analyzes the key factors that influence the adoption of electric motorcycles in major Southeast Asian countries. Using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT) as a framework, we examined the relationships between six key factors: performance expectancy, social influence, government intervention, environmental concern, performance risk, and individual innovativeness, and behavioral intention to adopt electric motorcycles. A survey was conducted, collecting 313 responses, and structural equation modeling and multi-group analysis were employed to assess moderating effects based on country, gender, education level, and motorcycle ownership. The results revealed that social influence and government intervention positively influenced adoption intention, while environmental concern had a negative impact. Additionally, the multi-group analysis demonstrated that the significance of adoption factors varied across gender, country, and education level, with differences also observed between motorcycle owners and non-owners. These findings highlight the importance of country-specific policy design and technological advancements to enhance the acceptability and market growth of electric motorcycles. By offering practical insights, this study contributes to expanding the electric motorcycle market and addressing environmental challenges in Southeast Asia.

Green innovation has received increased attention recently due to its contribution to resource conservation and environmental preservation. This study examined the financial impact of green innovation by empirically investigating the relationship between green innovation and stock liquidity of global energy firms. We found that green innovation is positively associated with stock liquidity of these firms. The findings were robust to an alternative proxy of stock liquidity, modified model specification and endogeneity control. Moreover, this positive relationship was more pronounced for firms with greater visibility and financially healthier firms. Additionally, our subsample analysis showed that the positive relationship between green innovation and stock liquidity only holds for firms operating in countries with lower levels of institutional quality. The findings of this study offer important implications for energy firms, policymakers, and investors regarding the adoption of green innovation to improve stock liquidity of firms in the capital market.

This paper examines the impact of common ownership on the two-stage green innovation efficiency (GIE) of listed companies in China. The results show that common ownership significantly improves both green R&D efficiency (GRDE) and green achievement transformation efficiency (GATE). Further heterogeneity analysis reveals that the positive effect of common ownership is more pronounced in firms operating in highly competitive markets and in regions with a higher level of green finance development. In addition, the effect on GRDE is more substantial for state-owned enterprises, whereas the effect on GATE is more pronounced for non-state-owned enterprises. Mechanism analysis suggests that common ownership enhances GIE through two main channels: internal governance (by alleviating principal-agent problems, fostering executives’ innovation consciousness, and promoting the greening of corporate strategies) and external coordination (by enhancing joint innovation capacity, easing financial constraints, and increasing market recognition). Further analysis indicates that robust university-industry collaboration and strong environmental pollution control intensity are critical factors enabling enterprises to upgrade their green innovation efficiency from lower to higher tiers. These findings offer important guidance for China and other emerging economies in fully leveraging capital markets to support corporate sustainable development.

This paper evaluates the performance of Sustainable Development Goals (SDGs) and their relevant sub-indicators in Türkiye, a country classified as upper-middle-income and exemplifies the middle-income trap phenomenon. In this context, methods that analyze the interactions between SDGs are applied, specifically in conjunction with changes in income performance, including the human development income index (HDII). Monitoring approaches and principal component analysis (PCA) are applied to evaluate the performance and interactions among the 17 goals and their relevant sub-indicators for the period from 2000 to 2023. The Evenness Index Score (EIS ) is calculated for the 17 SDGs to examine the distribution of performance among the goals within each year. Additionally, the interactions between the SDG index and four separate samples of sub-targets of the SDGs are analyzed. Five SDG principles (people, prosperity, planet, peace, and partnership) and their relevance to Türkiye’s broader objectives in addressing the challenges of achieving the SDGs are considered in the classification of sub-indicators. The findings highlight structural deficiencies, showing that growth in Gross National Income (GNI) per capita has not automatically translated into comprehensive SDG advancement. Despite relative improvements in income and SDG performance, the results indicate competing interests on the path toward middle-income growth and across the range of sustainable goals. Nevertheless, inclusiveness, environmental sustainability, and the quality of growth and institutions remain as important challenges. Targeting specific policies and focusing on particular goals (namely Goals 2, 5, 4, 8, 16, and 13) are likely to have a positive impact on achieving the SDGs.

Rural depopulation and migration to urban areas pose critical challenges for regional labor markets and balanced development. This study examined the causal effect of attending a local college on the likelihood of working in one’s hometown, addressing a gap in the literature on spatial labor mobility and human capital retention. Using rich individual-level data from Taiwan—including birthplace, college location, current job location, and migration history—we employed an instrumental variable approach to account for the endogeneity of college location choice. Our findings showed that attending a local college significantly increases the probability of hometown employment by approximately 41%. This effect was particularly pronounced in rural areas, where local colleges appear to play a vital role in retaining labor within less urbanized regions. These outcomes are potentially driven by the accumulation of local social capital and stronger community networks fostered through place-based education. This paper contributes to regional science by providing rigorous micro-level evidence linking educational location to spatial labor market outcomes. Our methodological approach and detailed dataset enable a robust identification strategy that complements prior aggregate-level research on regional disparities and migration. The findings offer new insights into the mechanisms behind regional labor retention and highlight the strategic importance of local higher educational institutions in shaping spatial employment patterns. These results are broadly relevant to policymakers seeking sustainable and regionally balanced development in Taiwan and other Asia-Pacific economies facing similar demographic and spatial challenges.

The current paper introduces a new methodology to capture the complex relationships between energy-mix (renewable and non-renewable energy ratio) and variables such as economic growth, urbanization, manufacturing activities, innovation and trade in low-carbon technology (LCT) for the Brazil, Russia, India, China and South Africa (BRICS) group of countries. The study adopted the extended Lotka–Volterra model and the conventional econometric model for annual panel data of BRICS from 2000 to 2023. The results showed that predatory variables such as economic growth, urbanization and manufacturing activity negatively impact the energy-mix ratio (prey). Innovation in renewable energy and trade in low-carbon technology served as protectors and positively impacted the energy-mix ratio. The estimates highlighted that in the short run, the effect of predatory forces is more pronounced than in the long run. BRICS countries are at an important stage in their energy transition journeys and must take prompt action in the short-run. Policy interventions are solicited to counter the negative impact of energy transition. This study contributes to the current literature by providing a new methodology that compliments conventional econometric models for estimating nonlinear relations, thereby enhancing the understanding of energy economics and sustainability.

Small towns are the engine of economic growth if they are appropriately managed. However, they are neglected in more extensive discussions on urban development policies in developing countries. In this study, we asked the following questions: How can we measure the level of deprivation in small towns, whether small towns are deprived, and what are the spatial locational patterns of these towns? We answered these questions by considering 3755 small towns in India in 2011. The deprivation index was calculated by considering eighteen variables, focusing mainly on the availability of basic services, housing conditions, health facilities and educational facilities using Principal Component Analysis (PCA). To determine factors that affect their level of deprivation, we employed Ordinary Least Squares (OLS) and two-stage least squares (2SLS) regression models, considering variables related to agglomeration shadows, agglomeration economies, infrastructure availability, and accessibility to markets and political power. Results indicated that the levels of deprivation in small towns differ from one another. While the least deprived small towns were usually found in high-income group states, the most deprived ones were in poorer and middle-income states. Regression results showed that though there is little evidence of agglomeration shadows, small towns “borrow” the agglomeration benefits from their large neighbors. Therefore, agglomeration shadows do not necessarily deprive small towns. The agglomeration economies reduce the deprivation while the distance to political power and market potential increase it. Finally, we present several policy options to make small towns more productive, and drive higher and more sustainable growth in India in the future.