Search Results (78)

Land use change significantly affects regional carbon emissions and ecosystem service value (ESV). Under China’s Dual Carbon Goals, this study takes Beijing-Tianjin-Hebei, experiencing rapid land use change, as the study area and counties as the study unit. This study employs a combination of methods, including carbon emission coefficients, equivalent-factor methods, bivariate spatial autocorrelation, and a multinomial logit model. These were used to explore the spatial relationship between land use carbon emissions and ESV, and to identify their key driving factors. These insights are essential for promoting sustainable regional development. Results indicate the following: (1) Total land use carbon emissions increased from 2000 to 2015, then declined until 2020; emissions were high in municipal centers; carbon sinks were in northwestern ecological zones. Construction land was the primary contributor. (2) ESV declined from 2000 to 2010 but increased from 2010 to 2020, driven by forest land and water bodies. High-ESV clusters appeared in northwestern and eastern coastal zones. (3) A significant negative spatial correlation was found between carbon emissions and ESV, with dominant Low-High clustering in the north and Low-Low clustering in central and southern regions. Over time, clustering dispersed, suggesting improved spatial balance. (4) Population density and cultivated land reclamation rate were core drivers of carbon–ESV clustering patterns, while average precipitation, average temperature, NDVI, and per capita GDP showed varied effects. To promote low-carbon and ecological development, this study puts forward several policy recommendations. These include implementing differentiated land use governance and enhancing regional compensation mechanisms. In addition, optimizing demographic and industrial structures is essential to reduce emissions and improve ESV across the study area. Full article

The increasing environmental concerns of plastic waste have encouraged more interest in environmentally friendly packaging, but consumer willingness to pay (WTP) for green alternatives in emerging markets such as Saudi Arabia is not fully explored. This research explores the relationship between awareness of sustainability and price sensitivity in determining WTP for green packaging in the Saudi retail market. The study utilizing a mixed method included both a Contingent Valuation Method (CVM) and a Discrete Choice Modeling (DCM). In it, data was gathered and analyzed using a sample of 424 urban consumers in Saudi Arabia’s major cities. The findings of OLS regression indicated awareness of sustainability had a significant, positive effect on WTP, whereas price sensitivity had a negative effect. There was a marginal interaction effect indicating that awareness could overcome price aversion. Logistic regression supported awareness as a dominant factor in binary product choice, although price sensitivity was not significant in the said model. The multinomial logit model also showed that the type of package, environmental labels (more so the “100% recyclable” type), and price had significant effects on consumer preferences. These results indicate that there is acceptance of sustainable packaging by consumers in Saudi Arabia if the product is communicated effectively and priced competitively. Full article

This study examines travel mode choice behavior within the context of Thailand’s emerging high-speed rail (HSR) development. It conducts a comparative assessment of predictive capabilities between the conventional Multinomial Logit (MNL) framework and advanced data-driven methodologies, including gradient boosting algorithms (Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting) and neural network architectures (Deep Neural Network, Convolutional Neural Network). The analysis leverages stated preference (SP) data and employs Bayesian optimization in conjunction with a stratified 10-fold cross-validation scheme to ensure model robustness. CatBoost emerges as the top-performing model (area under the curve = 0.9113; accuracy = 0.7557), highlighting travel cost, service frequency, and waiting time as the most influential determinants. These findings underscore the effectiveness of machine learning approaches in capturing complex behavioral patterns, providing empirical evidence to guide high-speed rail policy development in low- and middle-income countries. Practical implications include optimizing fare structures, enhancing service quality, and improving station accessibility to support sustainable adoption. Full article

With the rise of omnichannel (OC) retailing in food e-retail, understanding how OC retailing and product quality attributes influence consumer purchasing behaviour and value perceptions is crucial for developing e-retail strategies and enhancing consumer services. This study examined their impacts on Chinese consumers’ purchases of Australian beef (brisket) through a discrete choice experiment in Beijing, Shanghai, Guangzhou and Shenzhen and analysed 872 valid responses using multinomial logit, random parameter logit, and latent class models. Our findings reveal that Chinese consumers prefer buying Australian brisket via OC apps and offline stores, paying approx. 44% and 134% more per 500 g, respectively, compared to self-operated e-commerce stores. Brand, manufacturer and origin traceability are key quality attributes, with additional paid for brisket manufactured and packaged in Australia (under Australian brands) and featuring the MLA “True Aussie Beef” label over QR codes. This study also identified four distinct consumer clusters: (i) premium shoppers, (ii) channel and traceability-oriented shoppers, (iii) omnichannel and price-oriented shoppers and (iv) tech-savvy and discerning shoppers, highlighting varying sensitivities to e-retail channels and product attributes. These findings offer strategic and actionable insights for Australian beef exporters and OC retailers seeking to optimise consumer engagement and value creation in China’s evolving e-retail landscape. Full article

Rational parking allocation criteria are critical in addressing urban parking challenges and promoting the sustainable development of urban transportation. It has been common practice to reduce parking allocation for buildings according to the extent of the public transportation accessibility; however, the calculation of reduction coefficients lacks scientific rigor. This research proposes an estimation approach for reduction coefficients of parking allocation according to public transportation accessibility. First, public transportation accessibility is analyzed using Javier Gutiérrez’s weighted-average travel time model, optimizing the existing parking zoning scheme. Second, a multinomial logit model is built based on residents’ trip survey data to assess the cross-elasticity of the public transportation accessibility (travel time) with the probability of car travel. Third, the reduction in the private-car-travel probability is approximated as a parking reduction, and the reduction in the public transportation accessibility is translated to a reduction in the parking allotment, using cross-elasticity as a bridge. Finally, an empirical study is conducted on Nanjing’s central urban area, analyzing the reduction ratios within different parking zones around metro stations within specific distances and the interaction effects of these two scenarios to verify the rationality of the calculated reduction coefficients. According to the study, parking allocation standards in Zones I and II can be reduced by 10.6% and 7.5%, respectively, based on Zone III standards, while parking allocation standards within 100 m, 300 m, and 500 m of metro stations can be reduced by 17%, 12%, and 8%, respectively, based on the original standards. This paper can serve as a reference for the development of parking standard policies for public buildings. Full article

With increasing concerns over climate change and air pollution, sustainable transportation has become a critical component of modern city planning. Bike-sharing systems have emerged as an eco-friendly alternative to motorized transport, contributing to energy conservation and emission reduction. To elaborate on bike-sharing’s contribution to urban sustainable development, this study conducts a quantitative analysis of its environmental benefits through a case study of the Bluebikes program in the Boston area, using a longitudinal dataset of 20.07 million bike trips from January 2015 to December 2024, with data between January 2020 and December 2021 excluded. A combination of Scheiner’s model and Multinomial Logit model was adopted to evaluate the substitution of Bluebikes trips, an optimized Seasonal Autoregressive Integrated Moving Average (SARIMA) model was employed to predict future usage, while energy savings were calculated by estimating reductions in gasoline and diesel consumption. The findings reveal that during the analyzed period, Bluebikes trips saved 2616.44 tons of oil equivalent and reduced CO₂ and NO_X emissions by 7614.96 and 16.43 tons, respectively. Furthermore, based on the historical trends, it is forecasted that the Bluebikes program will annually save an average of 723.66 tons of oil equivalent and decrease CO₂ and NO_X emissions by 2422.65 and 4.52 tons between 2025 and 2027. The results highlight the substantial environmental impact of Bluebikes and support policies that encourage their usage. Full article

This study aims to improve the predictive accuracy of metropolitan planning organizations’ (MPOs’) travel demand models (TDM) by unraveling the factors influencing transportation mode choices. By exploring the interplay between trip characteristics, socioeconomics, built environment features, and regional conditions, we aim to address existing gaps in MPOs’ TDMs which revolve around the need to also integrate non-motorized modes and a more comprehensive array of features. Additionally, our objective is to develop a more robust predictive model compared to the current nested logit (NL) and multinomial logit (MNL) models commonly employed by MPOs. We apply a one-vs-rest random forest (RF) model to predict mode choices (Home-based-Work, Home-Based-Other, and non-home-based) for over 800,000 trips by 80,000 households across 29 US regions. Validation results demonstrate the RF model’s superior performance compared to conventional NL/MNL models. Key findings highlight that increased travel time and distance are associated with more auto trips, while household vehicle ownership significantly affects car and transit choices. Built environment features, such as activity density, transit density, and intersection density, also play crucial roles in mode preferences. This study offers a more robust predictive framework that can be directly applied in MPO TDMs, contributing to more accurate and inclusive transportation planning. Full article

This study investigates the impact of high-speed rail (HSR) on Thailand’s public transportation market and evaluates the effectiveness of machine learning techniques in predicting travel mode choices. A stated preference survey was conducted with 3200 respondents across 16 provinces, simulating travel scenarios involving buses, trains, airplanes, and HSR. The dataset, consisting of 38,400 observations, was analyzed using the CatBoost model and the multinomial logit (MNL) model. CatBoost demonstrated superior predictive performance, achieving an accuracy of 0.853 and an AUC of 0.948, compared to MNL’s accuracy of 0.749 and AUC of 0.879. Shapley additive explanations (SHAP) analysis identified key factors influencing travel behavior, including cost, service frequency, waiting time, travel time, and station access time. The results predict that HSR will capture 88.91% of the intercity travel market, significantly reducing market shares for buses (4.76%), trains (5.11%), and airplanes (1.22%). The findings highlight the transformative role of HSR in reshaping travel patterns and offer policy insights for optimizing pricing, service frequency, and accessibility. Machine learning enhances predictive accuracy and enables a deeper understanding of mode choice behavior, providing a robust analytical framework for transportation planning. Full article

Despite steady economic and social development in Bangladesh, women are in an underprivileged situation in many ways. This study explores the association between the use of information and communication technology (ICT) and women’s empowerment in Bangladesh. This study employs ordinary least squares regression, the ordered logit model, and the multinomial logit model, using Financial Inclusion Insights (2017) Survey data, to investigate the relationship between mobile phone use and women’s empowerment. The study’s main result indicates that mobile phone use facilitates women’s empowerment in general, but the impact needs to be considered for different groups of women. Housewives who are restricted within the household are impacted less than non-housewives by mobile phone use, contrary to our expectations. Also heads of households and spouses of heads of households, who are in very different positions in a patriarchal family structure, are similarly impacted by mobile phone use. This result implies that ICT only affects the empowerment of women who are not restricted to the household, and we conclude that ICT access alone is not enough to ensure women’s awareness about their rights. Full article

This study identified the main factors affecting car selection decisions through discrete choice experiments based on a large dataset collected in four European countries in 2023 using stated choice questionnaires. The choice set includes six current and popular car powertrains with factors related to vehicle features, user characteristics, and specific geographical contexts, which can influence the adoption of vehicles with electrified powertrains. An easily applicable multinomial logit model was first proposed to explore the effects of selected attributes and the model’s ability to reproduce user preferences with different incentive policies, geographical contexts, and energy prices. A mixed logit model and a segmented multinomial logit model were introduced to consider the sample’s heterogeneity. The first captures the preference dispersion among respondents related to incentives and operational costs. The second, which specifically classifies users based on car market segments, showed a greater variation in factors related to the purchase cost and battery range. The models estimate the weight of nine factors, offering support for targeted policy recommendations. Cost-related factors confirm their relevance in choices, and the analysis shows that users who want to enhance their vehicle range by 1 km are willing to pay approximately EUR 80. Full article

The Eastern Cape Province of South Africa is largely rural, with agriculture as the main livelihood for many households. However, the province has a semi-arid climate and reliance on rain-fed agriculture, which makes the region highly vulnerable to climate-related risks, particularly droughts. Smallholder farmers, who play a crucial role in food production and local economies, face significant challenges due to limited access to resources like irrigation, modern technology, and financial support, exacerbating their vulnerability to climate variability. These droughts cause severe losses in agricultural productivity, threatening food security, increasing poverty, and driving rural migration. Despite the potential benefits of adaptation strategies, many farmers lack the tools and knowledge to effectively cope with the increasing frequency and severity of droughts. This study examines how smallholder farmers in South Africa’s Eastern Cape Province perceive, experience, and cope with drought risk and its food and nutrition security implications. Using structured questionnaires, the authors collected data from 160 smallholder farmers and employed statistical analysis techniques, including a multinomial logit model. The study found that smallholder farmers understand drought risk but continue to face high vulnerability to adverse effects such as water scarcity, crop failure, increased disease incidence, and rising temperatures. Socioeconomic and institutional factors significantly influenced the adaptation strategies chosen by smallholder farmers. This study suggests that a combination of localized and modern adaptation approaches, supported by various institutional, policy, and technological interventions, is essential to enhance the resilience of these farmers. Specifically, localized strategies such as using traditional water management systems and crop diversification were effective in addressing region-specific climate challenges. At the same time, modern approaches like climate-resilient crop varieties and early-warning systems provided broader, more scalable solutions. Institutional support measures, such as access to climate information and extension services, policy interventions promoting sustainable practices, and technological advancements in irrigation and seed technologies, were key factors in improving adaptive capacity and reducing vulnerability. There is an urgent need to provide comprehensive support and empower farmers to adapt effectively, protect their livelihoods, and contribute to broader food security and poverty reduction efforts. This study highlights the importance of understanding and addressing the perceptions and experiences of smallholder farmers facing drought risk to ensure sustainable food production, income generation, and poverty alleviation. Full article

Urban green spaces provide recreation opportunities that contribute to physical wellbeing, health, and social wellbeing. However, managing green spaces to promote access and use for recreation and at the same time meet the preferences of visitors is often challenging, especially in developing countries. Using Port Moresby Nature Park (PMNP) in Papua New Guinea as a case study, the objective of this study was to examine visitors’ perceptions of how to manage the park to improve its use for recreation, perceptions of acceptable user fees and preferences for nature types and recreation amenity alternatives. Data were obtained using interviews with 295 visitors to PMNP, of which 291 responses was valid for this study. The data were analysed using descriptive statistics and a multinomial logit regression marginal effect model. The results showed that PMNP can be improved by constructing more toilets, providing more benches at strategic positions, providing water fountains, expanding the children’s playgrounds and training more PMNP staff in customer care. A picnic area was the most preferred and an area containing the Papuan hornbill was the least preferred. On average, the visitors would pay 35% more than the park user fee. A recreation amenity associated with reptiles and birds of paradise was the most preferred and an amenity with only reptiles was the least preferred. Multinomial logit regression model results revealed that preferences for recreation amenity alternatives were influenced by demographic characteristics, the nature type visited, recreation activities, the level of the park user fee, and the time spent at and distance of the interviewees’ dwelling to PMNP. The most important explanatory variables associated with the choice of each of the recreation amenities as reflected by marginal effects include the use of a children’s playground for recreation, grilling and partying during recreation, engagement in walking in natural areas during recreation, the use of animal-dominated areas during recreation and the use of picnic areas during recreation. These findings will assist park managers in making informed decisions by considering visitors’ preferences, the affordability of the park user fee and how to improve an urban green space in a sustainable manner. Full article

In this study, we model the rate or proportion of a specific phenomenon using a set of known covariates. To fit the regression model, which explains the phenomenon within the intervals $(0,1)$, $[0,1)$, $(0,1]$, or $[0,1]$, we employ a logit link function. This approach ensures that the model’s predictions remain within the appropriate range of zero to one. In cases of inflation at zero, one, or both, the logit link function is similarly applied to model the dichotomous Bernoulli-type variable with a multinomial response. The findings demonstrate that the model yields a non-singular information matrix, ensuring valid statistical inference. This ensures the invertibility of the information matrix, allowing for hypothesis testing based on likelihood statistics regarding the parameters in the model. This is not possible with other asymmetric models, such as those derived from the skew-normal distribution, which has a singular information matrix at the boundary of the skewness parameter. Finally, empirical results show the model’s effectiveness in analyzing proportion data with inflation at zero and one, proving its robustness and practicality for analyzing bounded data in various fields of research. Full article

Using survey data from three dwelling types in Jakarta, we examine how dwelling type, socioeconomic identity, and commuting distance affect women’s travel-mode choices and motivations behind women’s choices for nearby and distant non-working trips. We compared the performance of the multinomial logit (MNL) model with two machine-learning classifiers, random forest (RF) and XGBoost, using Shapley additive explanations (SHAP) for interpretation. The models’ efficacy varies across different datasets, with XGBoost mostly outperforming other models. The women’s preferred commuting modes varied by dwelling type and trip purpose, but their motives for choosing the nearest activity were similar. Over half of the women rely on private motorized vehicles, with women living in the gated community heavily relying on private cars. For nearby shopping trips, low income and young age discourage women in urban villages (kampungs) and apartment complexes from walking. Women living in gated communities often choose private cars to fulfill household responsibilities, enabling them to access distant options. For nearby leisure, longer commutes discourage walking except for residents of apartment complexes. Car ownership and household responsibilities increase private car use for distant options. SHAP analysis offers practitioners insights into identifying key variables affecting travel-mode choice to design effective targeted interventions that address women’s mobility needs. Full article

The government of China has implemented the Southern Shaanxi Disaster Resettlement program since 2011, which aims to address the problems of reduced livelihood resilience, increased livelihood risks, and single-risk management strategies caused by the frequent occurrence of natural disasters. This study considers the specific situation of disaster resettlement in Ankang Prefecture, southern Shaanxi Province, and draws on Quandt’s measurement idea to quantify livelihood resilience at the household scale in terms of five types of capital assets: natural, physical, human, financial, and social. A coarsened exact matching model was used to control confounding factors in the observational data to reduce sample selection bias, and then multinomial logit regression models were used to examine how livelihood resilience affects risk management strategies; moreover, the effects of different indicators of livelihood resilience, relocation characteristics, and follow-up support measures on risk management strategies were analyzed. Results show that livelihood resilience is higher among new-stage relocation, voluntary relocation, and centralized resettlement households, and working outside of the home accounts for the largest proportion of risk management strategies chosen by the sample households. In addition, livelihood resilience and its dimensions and indicators, relocation characteristics, and follow-up support measures have different impacts on risk management strategies. These results have considerable significance in guiding research on risk management strategies at the household scale and can serve as a reference for disaster resettlement in other developing nations and regions. Full article