Asia-Pacific Journal of Regional Science最新文献

The purpose of this study was to examine factors based on extrinsic quality attributes that affect local rice preferences or consumption in urban areas of the Benin Republic (Benin). Benin has one of the highest rice import rates in Western Africa. Over the past decade, the level of rice supplied to the market has increased as compared with locally produced rice. To cope with this situation, rice institutions provide significant support to local production areas with an emphasis on intrinsic qualities. Consequently, consumers have become more willing to pay for locally produced rice. This trend is especially noticeable in production areas. On the other hand, imported branded rice continues to be preferred by urban consumers. This study examined consumer preferences in urban areas and rice producer marketing organizations using a Likert-scale question format. Surveys were conducted in four urban prefectures, and results were obtained from 200 urban consumers and 50 producers. A binomial logit model was used to examine the influence of the nine extrinsic factors. The results showed that the probability of respondent preference for local rice brands and the predicted coefficient for the variables “Packaging,” “Advertisement,” “Brand,” “Proximity,” and “Certification” of local rice brands that need to be improved in the urban area were positive and statistically significant. The study also revealed that consumers in urban areas preferred rice with good presentation (packaging); consumers were not informed about the availability of local rice brands (advertisement); consumers preferred to buy rice with a brand name as it helped them differentiate their favorite quality from others (brand); available evidence revealed that neighboring shops and urban open markets are where the consumers mostly buy (proximity). Finally, local rice producers affirmed that they ensure their products are controlled by a safety certification institution (certification). Lastly, based on these results, we also discuss various supportive measures to increase local rice production in Benin.

A bottom-up approach was adopted to conceptualise a smart sustainable city through the perspective of stakeholders living in Sunway City Kuala Lumpur, Malaysia, touted as a smart sustainable city. The group concept mapping method allows the collection of the city stakeholder views and translation of these views into a framework through five steps: create statements, sort statements, multidimensional scaling, hierarchical cluster analysis and label clusters. This process generated 80 statements that compose a smart sustainable city, creating a conceptual framework of eight dimensions: green environment, township planning, community-friendly township, utilities management, waste management, smart transportation, digitalisation and technology. A validation stage using a confirmatory composite analysis with a reduced statement list of 40 items and 297 participants was also conducted. These results demonstrated that the stakeholders prioritised non-technical features, such as clean air, clean water and community activities over the need to adopt digital features. New features such as urban agriculture, co-living and others were rated less important, implying that new concepts may require strong community participation and support for implementation from the city authorities. The main contribution of this study is the bottom-up approach using group concept mapping, contrary to the traditional top-down approach, offering a novel method for the conceptualising process.

Yield predictions prior to harvesting crops is significant for agricultural decision-making. This study aimed to predict rice yield at the stage prior to harvesting using crops and soil phenological properties in the Pathein District of Myanmar. Remote sensing imagery data derived from Sentinel-2A satellite imageries during the month of November at the stage prior to harvest of rice fields were collected and analyzed from 2016 to 2021. Four vegetation indices (VIs): (i) normalized difference vegetation index (NDVI), (ii) normalized difference water index (NDWI), (iii) soil-adjusted vegetation index (SAVI), and (iv) rice growth vegetation index (RGVI) were specified as independent variables for a rice yield prediction model, after which simple and multiple linear regression models were estimated and validated. The accuracy of the estimated models was assessed using observed data from 1790 ground reference points (GRPs) in rice-yielding croplands. The average observed rice yield over 6 years was 1.57 tons per acre, and the average rice yield predictions over 6 years were 1.28, 1.48, 1.28, and 1.17 per acre with simple linear regression models from NDVI, NDWI, SAVI and RGVI, respectively. On the other hand, THE observed rice yield was 1.49 tons per acre with a multiple regression model. This indicates that prediction by the multiple regression model with four vegetation indices is superior to predictions by all other linear regression models. The early predicted yield data is useful for rice-growing farmers to compare expenses against losses after any extreme climatic event.

In Asia and the Pacific, the effects of climate change have become severe; specifically, South Asia is the most vulnerable region in the world. The human and financial effects could be substantially worse if floods, droughts, cyclones, and other extreme climatic events take place in this region frequently. In this special issue, the authors reported climate change indicators such as sea-level rise, flood, drought, geographical, geopolitical and geological conditions. National and regional food security has also been threatened during the last decade due to the adverse effects of climate change on crop production. To overcome such challenges of climate change, introducing climate-smart agriculture, adaptation and mitigation policies to tackle the effect of climate change are required from regional perspectives. Therefore, this special issue solely highlights those climate change points with clarity to refer to the SDGs, mitigation of risk and adoption of new policies and proposes solutions for the adverse effects of climate change and its vulnerability to agricultural livelihoods.

Climate change has impacted forest ecosystems and their associated benefits/services that play an important role in global ecological balance and support the livelihood of the rural poor, especially in the forest fringe villages. A higher dependency on natural resources, lack of other livelihood options and disconnectedness from mainstream development make these villages more vulnerable to climate change. At the same time, forests in the protected areas face increased anthropogenic pressure due to tourism, settlements and unsustainable extraction of forest produce and fuel wood, which in turn, degrade the forest resources and threaten the associated livelihoods. Therefore, the present study aimed to assess the vulnerability of forest fringe villages of Kanha Tiger Reserve (KTR) and suggest suitable adaptation strategies based on identified drivers of vulnerability. Scores of the vulnerability index were computed using an indicator-based approach, on data collected from 196 households spread over 17 villages across the buffer region of KTR. The major drivers of the vulnerability identified using principal component analysis are: socioeconomic status, enabling facilities, resource extraction, resource adjacency and economic assets. Furthermore, economic conditions, access to electricity, and diversity of occupants in a household were identified as the most important drivers of vulnerability in the region. Based on these findings, we suggest a need for enhancing the income of the tribal population through livelihood diversification, skill development for government-related programs, improvement of agricultural practices, development of health facilities, improved market linkages, CSR activities and involvement of local villagers in Tiger Reserve related activities. Though many activities are implemented within the KTR, these activities can be further strengthened based on the identified indicators of vulnerability. This study provides implications for formulating adaptation and forest management strategies and policies. Additionally, such findings can serve as a benchmark for designing suitable site-specific adaptation strategies based on the major drivers of vulnerability to reduce the vulnerability of forest-dependent villages in other protected areas of the country.

Vietnam recently introduced a policy to promote climate-smart agricultural technologies (CSATs) to enhance farmer resilience and adaptation to climate change. This study sought to identify factors influencing the adoption and the continuation of CSATs adoption among smallholder farmers. The study surveyed 215 farmers in My Loi Village, Ha Tinh Province in North-Central Vietnam, where CSATs have been adopted and practiced since 2014. Logistic and ordinary least square regression models were applied to analyze the data. The results showed that attendance to training on CSATs, presence of a fellow farmer as a source of information, rice cultivation, farming experience and number of crops grown significantly influenced the adoption of CSATs. Farmer adoptions of CSATs, in contrast, were negatively influenced by more working men in the family and membership in a farming organization. The continuous adoption of CSATs was promoted by training, support from agriculture extension officers, upward mobility of farmers, farm ownership and the number of crops grown. Meanwhile, families with a larger number of male workers were less likely to continuously adopt CSATs. Policy-related recommendations were proposed to encourage farmers to adopt CSATs in the region. They included: (i) raising public awareness on CSATs through provision of high-quality information and training; (ii) enhancing technical assistance through the agricultural extension staff to all farmers, especially women; (iii) considering local context and smallholder farmer socioeconomic factors when developing climate-smart actions and programs.

This article reviews the book by Akita and Kataoka (Regional inequality and development: Measurement and applications in Indonesia, 2022). The book first provides an overview of various measurement methods of regional inequality. Next, it presents four case studies that deepen our understanding of regional inequality in the context of the development challenges of Indonesia: decentralization, premature deindustrialization, financial crisis, low labor productivity, among others. Overall, this book provides an excellent introduction and application of inequality decomposition methods in the context of regional disparities and structural change.

This study examined the impact of climate change on yield, production and cropped areas of rice, wheat, gram and maize in Himachal Pradesh (Province), India, from 1970 to 2019 using the Cobb–Douglas production function approach (CDPFA). Euler’s theorem and marginal impact analytical technique (MIAT) were applied to determine the nature and degree of homogeneity and projected values of the selected crop yields, production and cropped areas. The results revealed that climate change significantly affects the yield, production and cropped areas of Himachal Pradesh. However, the impact of climatic factors significantly varied according to the crops. Based on Euler’s theorem, the findings revealed a decreasing return to scale for these crops’ yield, production and cropped area function. The projected estimates showed that rice, wheat and gram production and yields are expected to decline significantly by the 2040s, 2060s, 2080s and 2100s. The projected cropped area of rice and wheat may increase by the 2040s, 2060s and 2080s due to climate change, but after that, the state may experience a declining trend in both crops. On the other hand, the projected cropped areas of maize have shown an upward trend over the years. In conclusion, agricultural production in the state is at an alarming stage due to climate change and requires significant policy intervention. Farmers should use appropriate agricultural technologies, mixed cropping patterns, advanced irrigation facilities and crop insurance policies to reduce the negative consequences of climate change in the agricultural sector of Himachal Pradesh.

Valuing air quality can help governments better evaluate the economic benefits of policies related to air pollution. However, many studies ignore heterogeneity and spatial effects within cities, which may render the results inaccurate. To fill these gaps, this study attempted to examine individuals’ marginal willingness to pay (MWTP) for air quality across locations and buyers in Beijing by utilizing hedonic price, spatial regression and quantile regression models. The results showed: (1) a concentration of PM2.5 is significantly negatively correlated with housing prices. Specifically, the value of the MWTP for a 1% improvement in PM2.5 is US$327, and this figure is US$177 after considering the spatial effects. (2) The MWTP for air quality is heterogeneous across locations. MWTP for air quality is lower the farther away the location is from the central business district (CBD) and the nearest employment center, the lower the MWTP for air quality. (3) Buyers of high-priced housing display a higher MWTP for air quality. These findings show that developing countries facing environmental issues should re-examine the traditional development model of “sacrificing the environment for economic growth” and develop a sustainable model. Moreover, further joining of air pollution control and a differential, location-specific scheme coupled with an individual-specific scheme for developing new communities is necessary.

Rapid satellite-based flash flood inundation mapping and the delivery of flash flood inundation maps during a flash flood event for wetland communities can provide valuable information for decision-makers to put relief measures and emergency responses in place without delay. With remote sensing techniques, flash flood mapping of large areas, basically wetlands, can be done quickly with a high level of precision through different water indices. This study developed an algorithm for rapid flash flood inundation mapping for crisis management through the demarcation of the most flash flood-inundated areas in the Haor Basin (wetlands) of Bangladesh by utilizing high-resolution Sentinel-2 remotely sensed data. The algorithm applied here involves near-infrared (NIR) spectral band-derived indices, namely, a normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) to develop a rapid flash flood water detection technique integrating three year (2017–2019) datasets before and after flash floods. A simple threshold method was created to cluster the data and identify the flash flood pixels in the imagery using a density slicing technique followed by natural break analysis. Calculations were then made to estimate the flash flood (inundated), mixed pixels and non-inundated pixels for each year and three combinations. NDVI and NDWI, as well as their combinations (NDVI-NDWI), were remarkably effective for extracting inundation, non-inundation and mixed pixels. Additionally, highly consistent results were obtained for all inundation classes in the studied areas, confirming that NIR-derived indices can effectively detect water pixels. However, a higher inundation pixel value was observed in the Tahirpur Subdistrict compared with the other two study areas (Gowainghat and Kulaura). The developed NIR band-derived water indices algorithm produced more than 80.0% accuracy to detect water-related pixels when verified with ground reference points. As shown by these results, the developed NIR band-derived water indices were capable of effectively detecting flash flood water turbidity in wetland areas. Therefore, these NIR band-derived water indices can be applied for rapid flash flood inundation mapping just after a flash flood occurrence for immediate decisions to support affected farmers.