Asia-Pacific Journal of Regional Science最新文献

The core areas of the large cities in developing countries have reached saturation points. Therefore, the economic activities in cities spill over into surrounding rural areas, giving birth to census towns (CTs) that belong to small towns. In the case of India, CTs are responsible for 32% of urban growth between 2001 and 2011. Therefore, haphazard, unregulated and autonomous expansion of urban activities in the form of CTs must be prepared for greater economic development. In this context, the present study considers 18 CTs located in the Pune District and close to Pune City to examine factors affecting household income. Several important factors, including infrastructure, environmental benefits, agglomeration effects, distance to power and markets, governance structure, local employment and important household-level characteristics were considered for the analysis. The principal component analysis (PCA) method was used to construct infrastructure and environmental indexes. The regression results showed that infrastructure and agglomeration variables have a mixed effect on the household income of the CTs. The environmental benefits and governance structure positively affect household income. Local employment scenarios and distance to power and the market have a negative effect. Finally, several policy measures are suggested to boost the productivity of the CTs and create jobs and business activity. This will serve as a catalyst to bring about a better quality of urbanization in CTs, creating new avenues for development and complying with select Sustainable Development Goal 11.

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.

In the analysis of migration flows, the gravity model is used as a standard framework. However, the analysis of interregional migration within a country by age and gender group using the gravity model is relatively new. This study nonparametrically estimated the relations between the number of inter-municipal migrations and origin–destination distance by age–sex groups in Japan. We assumed the number of inter-municipal migrations is proportional to the origin and destination populations. Poisson local constant estimates were used. The resulting estimator was the weighted average of the migration preference index, i.e., the ratio of the number of interregional moves to the product of the origin and destination populations, so that the interpretation of estimation results was easy and estimation computationally light. Partitioning estimate was used to reduce downward bias caused by a negative relation between the number of inter-municipal migrations and the origin–destination distance. The relation between the number of inter-municipal migrations and the origin–destination distance was fairly smooth, and the elasticity of the number of inter-municipal migrations with respect to distance somewhat larger in absolute value in shorter distances. In addition, age group 90-, especially men, were found to move frequently when compared to age groups 15–19 and 20–24 years.

In developing countries, cities play a significant role in stimulating economic growth, employment opportunities and transformative change. At the same time, uneven urbanisation—driven by poverty and different levels of socioeconomic development—often contribute to wider income disparities. Issues of inequality in India have received significant attention from academia, policies and practice. Yet, there is a dearth of comprehensive understanding on the relations between inequality in urban population distribution and income inequality in India. The main objective of this study was to analyse the impact of inequality in population concentration from different classes of cities/towns on urban income inequality and the total state-level (urban + rural) income inequality in India between 1991 and 2011. This study used unit-level data on ‘Consumer Expenditures’ from the National Sample Survey and the most recently updated 35 states. The findings indicate that the inequality in urban population concentration between different classes of cities and towns exerts a significant influence on urban income inequality and total state-level income inequality in India. Additionally, the inequality in concentration of urban population in large cities increases state-level inequality, whereas in small towns, it decreases. State-level control variables such as infant mortality, literacy rate, and per-capita net state domestic product exhibit a positive and statistically significant effect on both urban and total inequality in India. The results of this research suggest that the attainment of inclusive and progressive spatial development in India necessitates the pursuit of a more equitable process of urbanisation, characterised by a gradual socio-spatial transformation in smaller towns. This serves as a guide for other developing nations seeking to achieve sustainable and equitable urbanisation.

Himachal Pradesh offers many tourist experiences to Indian and foreign visitors. The mountainous state experiences a high degree of tourism seasonality, interrupting the efficient operation of the tourism infrastructure. Fundamental requirements for tourism planning are accurate projections of tourist demands. However, estimating future demands becomes challenging because of the high degree of seasonality. The compound and annual growth rate methods are used in state-level tourism research to forecast future growth. Since these models cannot manage seasonality and trends in data series, they inaccurately predict future demand. In this context, forecasting models such as the Decomposition, Box–Jenkins, and Holt–Winters exponential smoothing methods were used to forecast the seasonal tourism demands in the study area. The dataset utilized for the analysis was the monthly Indian and foreign tourist arrivals from 2008 to 2018. Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Theil’s U1 coefficients validated that the forecast models produced accurate results. The Box–Jenkins model accurately forecasted the tourist arrivals (2019 to 2031) as reflected by the lowest error metrics (Indian: RMSE 36833.8, MAPE 3.0, Theil's U1 0.023; Foreign: RMSE 6907.59, MAPE 15.51, Theil's U1 0.10). This approach outperformed the traditional seasonal data series forecasting techniques and contributed to the literature on univariate tourist demand forecasting for hilly areas experiencing a high degree of seasonality. Estimating maximum tourist arrivals is crucial in long-term strategic planning for tourism expansion to withstand maximum loads and ensure efficient business flow, higher investment, enhanced economic growth, and environmental protection in the state. This study is a pioneer in examining tourism demands for stakeholders, tourism operators, and planners to plan the fluctuating tourism in Himachal Pradesh while adhering to sustainability principles. Furthermore, it provides inputs for effective planning and policy formulations specific to the tourism industry based on future demand assessments.

Coastal areas are not conducive to cultivating a variety of crops due to the occurrence of natural disasters. Therefore, homestead gardening has the potential to increase food production and consumption in coastal households, thereby enhancing climate resilience. Adopting homestead gardening as an adaptation measure for climate change can improve food security in climate-vulnerable areas. This study identified factors associated with the adoption of homestead gardening and their impacts on dietary diversity in coastal areas of Bangladesh. A total of 750 coastal households were surveyed. To analyze the data, descriptive statistics, household dietary diversity score, endogenous switching regression model, and propensity score matching method were applied. According to the results, only 14.26% of coastal households adopted homestead gardening. The likelihood of adoption was increased by factors, such as age, spousal education, own cultivable land size, salinity perception, and mobile phone ownership. Results of the endogenous switching regression model indicated that the adopters of homestead gardens had a 13-point greater mean probability of a higher household dietary diversity score than the non-adopters. Results of the propensity score matching also showed a nearly identical finding. A strong extension service that uses modern information technology is required to disseminate homestead gardening technology in coastal areas. Climate change awareness initiatives are recommended to raise households cognizance on the negative effects of climate change, which in turn helps increase the adoption of homestead gardening as an adaptation measure.

Smong is an indigenous knowledge of the Simeuluean people related to tsunamis, which proved to be successful in saving lives during the 2004 Indian Ocean Tsunami. This study investigates the cross-generational development of Smong narratives among the Simeuluean people by exploring their resilience to tsunamis. The data collected through in-depth interviews in 2016, 2017, 2022, and 2023 unraveled the intricate dynamics of Smong narratives. This study captures the perspectives of 37 interviews of 18 participants selected based on age, gender, and availability. Older participants who received the Smong story from someone who experienced the 1907 tsunami and the 2004 tsunami played a vital role in preserving Smong narratives, emphasizing the enduring integration of Smong within the cultural repertoire. Younger participants, especially youth/middle-aged individuals, showed innovative reinterpretations, intertwining indigenous knowledge with new modes of understanding. Gender-diverse perspectives revealed the emotional depth embedded in Smong’s narratives. Temporal shifts in Smong narratives emerged, reflecting the uncertain role of Smong in contemporary times. The findings illustrate a complex interplay of generational perspectives, cultural adaptation, gender dynamics, and temporal shifts within the Simeuluean people. Participant narratives provide nuanced insights into lived experiences, contributing to the local context of Smong narratives. This study enhances our understanding of the evolving nature of Smong narratives, emphasizing the importance of preserving the cultural format while developing contemporary applications.

The roles of institutional quality’s impact on firm performance are becoming increasingly prominent in the literature. This is true in the Global North and South. Vietnam has seen less research on this topic than other developing countries, so this paper seeks to rectify this by examining whether or not institutional quality influences firm performance, as measured by total factor productivity (TFP). This paper also digs deeper into the sub-components to see which institutions are the most influential. We applied the General Method of Moments (GMM) approach to a firm-level panel dataset covering the 2010–2020 period to examine institutional quality's impact on firm TFP. Results are explored by firm size and by ownership type (domestic private, foreign and state-owned). Using rich datasets covering institutional quality at the provincial level in Vietnam and also individual firm performance from 2010 to 2020, we found that Time cost (how long it takes firms to deal with the government on various issues) and Labor policy (how easy it is to hire good quality labor) are the most important of the 10 institutions studied. Additionally, while not all institutions influence TFP, institutional quality overall (all 10 institutions combined) clearly has a positive influence on TFP. This study fills a research gap by examining the relationship between institutional quality and firm performance in Vietnam. The findings emphasize the significance of Time cost and Labor policy as influential institutions and highlight the positive overall impact of institutional quality on TFP. The policy recommendations offered provide valuable insights for the government to further enhance firm productivity through targeted measures.