Asia-Pacific Journal of Regional Science最新文献

Human development index (HDI) was estimated based on an alternative and modified methodology. We considered five components to be relatively more rational and representative of the HDI: income, education, health, social governance, and technological adaptation. Reviews suggested for the formulation of hypotheses that the present HDI fails to address many factors included in SDGs and hence is insufficient in its representativeness. In addition, the components need not necessarily be equally weighted. The validity of HDI was improved by inclusion of social governance and technological adaptation. The method of mapping between SDGs and HDI components was used along with the Laplace rule of probability to determine the component weights, and HDI was estimated by weighted geometric mean. The modified methodology was applied by conducting a sample household survey in Bagmati Province, Nepal, in 2023, based on three-stage stratified random sampling that covered mountain to Terai regions of the province including 17 rural and urban municipalities with a sample size of 569 households. The estimated weights of the components differed notably (0.16–0.26), which implied varied levels of importance and could be crucial in development planning. Survey results quantified sub-indices of HDI as income = 0.341 (95% CI 0.337, 0.345), education = 0.650 (95% CI 0.645, 0.655), health = 0.807 (95% CI 0.806, 0.807), social governance = 0.678 (95% CI 0.674, 0.681), and technological adaptation = 0.462 (95% CI 0.454, 0.469). These figures suggest a high priority for economic progress and technological support for the people of Bagmati Province. Finally, the estimated HDI of the province was found to be 0.559 (95% CI 0.555, 0.564), which is substantially lower than the current UNDP-estimated HDI value (0.661) and warrants more focused development policies than those based on the UNDP HDI value. Moreover, the inequality adjusted HDI was found to be substantially lower by 13.4% compared to HDI and demonstrates the existence of considerable inequalities in the province. Overall, the comprehensively modified HDI is useful to enhance policy implications, particularly in developing countries like Nepal and results suggested that Bagmati Province would suffer from inappropriate development policies due to overestimated UNDP-adopted HDI.

Improving productivity performance is essential for achieving regional economic sustainability. With the advent of the network society, regional economic agents have benefited not only from local within-region interactions but also from external out-of-region economies. However, it remains unclear whether localized external economies or spillovers of externalities through inter-regional networks are more significant in improving productivity performance. Therefore, this study examined the relationships between productivity performance and geographical externalities from an industry perspective using regional economic data for Japan and employed a dynamic total factor productivity function approach. It also assessed the impact of high-quality transportation infrastructure improvements on productivity catch-up. The results revealed the long-run effects of inter-regional networks on the productivity performance of industries. Furthermore, it revealed that, as a geographical externality, the “borrowed size” effect contributed significantly to productivity improvements in both manufacturing and non-manufacturing industries. These findings suggest that enhancing both high-quality transportation infrastructure and inter-regional transportation networks is likely to improve industry productivity performance.

Currently, economies around the world are striving for sustainable development with commitments to ensure environmental quality, while simultaneously experiencing rapid growth in the global financial market. The primary objective of this study was to investigate how financial globalization has influenced CO2 emissions in Asian countries. We utilized secondary data from 39 Asian countries spanning the years 2000 to 2020. Through cross-dependence tests, unit root tests, and panel cointegration analysis, we found that the variables exhibited a random pattern rather than a direct interconnection. Therefore, regression techniques such as POLS (Pooled Ordinary Least Squares), FEM (Fixed Effects Model), and REM (Random Effects Model) were utilized for analysis. The findings revealed an inverted U-shaped relationship between financial globalization and CO2 emissions, suggesting an initial increase in emissions followed by a decline as financial globalization progresses beyond a certain threshold. The Environmental Kuznets Curve (EKC) hypothesis was confirmed, and positive correlations were found between trade openness, urbanization, energy intensity and CO2 emissions. These findings underscore the importance of sustainable financial globalization policies, emphasizing the need for Asian countries, including Vietnam, to balance economic growth with environmental protection by promoting green investments, adopting cleaner technologies and transitioning to renewable energy sources.

Little is known about how e-commerce impacts a firm’s performance and hiring decisions. The aim of this study was to empirically assess the impact of e-commerce on employment, labor demand and the skill composition of labor in a developing economy. We used Chile as a case study of a developing economy in Latin America that has seen e-commerce sales increase tenfold in the last decade. Data on e-commerce and employment for around 6,000 firms from the Chilean Ministry of Economy's Longitudinal Survey of Companies (ELE) for 2017 were used. Our identification technique was based on a matching procedure between firms that use e-commerce and those that do not. The fundamental aim was to compare labor demand among similar firms from multiple aspects, with the exception of e-commerce usage. Our results showed that e-commerce has a positive effect not only on total employment but also on hiring in Chilean firms by boosting the demands for both high and medium-skilled labor.

Carbon emissions embodied in trade account for 23% of total global emissions. This phenomenon will exacerbate as global trade flourishes, impeding carbon reduction. To clarify the evolutionary characteristics of global embodied carbon network, we constructed a multiregional input–output model spanning 2001 to 2021, encompassing China (CHN), the Regional Comprehensive Economic Partnership (RCEP), the European Union (EU), and the United States-Mexico-Canada Agreement (USMCA), which represent 76% of global GDP. Combining industry correlation analysis and scenario simulation, we further explored embodied carbon emissions (ECE) mitigation potential through trade restructuring and carbon emissions intensity reducing in China. Results revealed an 89% increase in global ECE over the past two decades, with 70% contributed by intermediate trade. China emerged as the only country with net carbon inflow, but it experienced a significant decrease in net inflow in 2021 (32% lower than the peak in 2011). Conversely, the other three economic entities exhibited net carbon outflow in aggregate terms. Notably, RCEP (except CHN) experienced a transition in the direction of carbon flows in final trade, shifting from accounting for 10% of net global carbon outflows to 11% of inflows. In addition, dual control of emission intensity and trade scale, particularly in manufacturing industries, can reduce net inflows to China by up to 19% and contribute a 3.42% decrease in global ECE. Promoting trade to final products and transforming key industries to low-carbon, high value-added sectors were vital for carbon mitigation. These results can facilitate policy-making in shaping green and low-carbon foreign trade patterns.

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This study highlights the urgent need for sustainable pulse production solutions in Bangladesh, given the nutritional and economic importance of pulses and the increasing challenges posed by climate change. Data from 1972 to 2020 were analyzed using the autoregressive distributed lag (ARDL) approach to investigate the long-run and short-run dynamics affecting pulse production. Granger causality tests was also employed to explore causal relationships and significant influences among the variables. The results revealed that average annual rainfall significantly enhances long-term pulse production, while temperature and humidity had insignificant negative effects. Conversely, carbon dioxide emissions showed a substantial negative long-term impact on pulse production. Non-climatic factors, such as pulse area, total population, and fertilizer use, showed significant positive effects in the long run, whereas energy consumption in agriculture remained statistically insignificant. In the short-run, pulse area, total population, and fertilizer use notably boosted production, while energy consumption continued to be insignificant. Granger causality tests identified causal links between CO₂ emissions and total population, as well as two-way relationships between rainfall, pulse area, and production. These findings provide critical insights for stakeholders and policymakers in developing sustainable pulse production strategies.

Internationally, it has been repeatedly pointed out that the proportion of female members in the Diet and the local assemblies is extremely low in Japan. This is called a political gender gap. We analyzed the relationships between socioeconomic indicators and this political gender gap, and underlying causes using several spatial regression models. Prefectures are geographically connected; so, they can influence each other. Therefore, we incorporated geographical relationships between prefectures into the model. We found that a high proportion of women in a particular prefectural assembly was associated with a low proportion of women in adjacent prefectural assemblies. Thus, competition and repulsion exist between the adjacent prefectural assemblies. Furthermore, several series of analyses using spatial regression models showed that the impact of the political gender gap in a particular prefecture spread to adjacent prefectures, but not further. We found that considering prefectural spatial autocorrelations was important while examining the political gender gap, and previous studies that did not consider spatial autocorrelation might have been biased. However, here we were able to better explain the background of real-world phenomena occurring in the real world by applying spatial regression models.

Pro-environmental behavior (PEB) addresses environmental challenges such as climate change, pollution, habitat loss, and resource depletion. This research aimed to identify the most influential factors shaping farmers’ pro-environmental behavior. The study was conducted in Biral and Kaharol upazila of Dinajpur District in Bangladesh. One hundred and thirty-eight (138) farmers participating in the Climate Smart Agriculture and Water Management Project (CSAWMP) were selected as samples through a simple random sampling procedure. The pro-environmental behavior of the farmers was measured using a five-point rating scale with 24 items under four dimensions. Internal consistency, composite reliability, convergent validity, and discriminant validity of the constructs were ensured before data collection. The data were collected through face-to-face interviews using a structured questionnaire from September 2023 to February 2024. Statistical analyses, viz. correlation analysis, regression analysis (multiple linear and stepwise), and path analysis, were performed using SPSS, SPSS AMOS, and SmartPLS 4 software. Most farmers exhibited moderate pro-environmental behavior and adopted practices under the ‘social environmentalism’ dimension (mean = 4.5). Overall, the socio-psychological variables had the greatest influence (62.9%) on farmers’ pro-environmental behavior, where environmental knowledge was the sole contributor of approximately 48.8%, followed by environmental attitude (ΔR² = 10%). According to the path analysis, environmental knowledge had the greatest direct effect (0.488), and ‘environmental self-efficacy’ had the greatest indirect effect (0.430) (in the positive direction) on the dependent variable. In contrast, age had the greatest indirect effect (−0.461) in the negative direction. Interventions such as community education programs, policies addressing psychological barriers and socio-economic disparities, and incentivized green technologies should be employed to ensure environmental sustainability.

Trends in fast foods and green e-marketing are increasing around the world including Pakistan. Drawing on unique survey data of the year 2023 for urban Pakistan, this study aimed to identify the principal determinants of consumer satisfaction toward green e-marketing of international fast food chains using service quality (SERVQUAL) dimensions and the conventional economics of happiness approach. The overall sample size of 400 respondents was determined by the Yamane method. Applying ordered probit regression analyses, we found that the tangibility dimension in terms of eco-labeling of fast food packaging and the empathy dimension in terms of employees’ understanding of customer needs and preferences according to their age or gender were the most significant and important determinants for consumer satisfaction. Interestingly, monthly expenditures on green consumption fell hand in hand with consumer satisfaction, and positively and significantly related to consumer satisfaction. Using independent sample T test and ANOVA test for gender difference and different working age groups, respectively, we found significant differences between male and female consumers in their mean online expenditures on green fast food products. At least one of the working age groups showed different mean online expenditures toward the green consumption of international fast food products from the rest of the working age groups. Therefore, policy makers and stakeholders should be advised to support eco-labeling and consider consumer preferences according to their age or gender.

The objective weighting methods recently introduced in the literature include method based on the removal effects of criteria (MEREC), logarithmic percentage change-driven objective weighting (LOPCOW), and modified preference selection index method (MPSI). The aim of this study was to analyze the effects of the above methods on criteria weights and rank in decision problems involving over 20 alternatives. Based on the quality of life index (QLI), the ranking of Asian countries was carried out by combining the above weighting methods with the multi-attributive ideal-real comparative analysis (MAIRCA). Combination of these methods has not been used before in this research area. Capitals of Asian countries were selected and evaluated with eight different criteria. MEREC, LOPCOW, and MPSI methods were used to weight the criteria. The MAIRCA method was used to rank the alternatives. These results were compared with Numbeo rankings. The MPSI method showed the closest ranking to the Numbeo ranking, exhibiting the highest Spearman rank correlation and lowest Euclidean distance. Given its straightforward applicability, the MPSI method is preferred among the aforementioned objective weighting methods, followed by the MEREC and then the LOPCOW methods. In addition, we also examined the applicability of the preference selection index (PSI) method to the data sets. The results indicate the PSI method may not calculate the criteria weights in decision-making problems where the number of alternatives is high.