Social-economic Attributes Research Articles

Non-linear relationship between built environment and active travel: A hybrid model considering spatial heterogeneity

Exploring the mechanisms between the built environment and active travel is crucial for promoting green transportation and fostering the sustainable development of cities. Existing literature focuses on the correlation between the built environment and active travel in urban centers, with limited studies on suburban environments, ignoring the implications of spatial heterogeneity. This paper conducts a comprehensive spatio-temporal analysis of suburban residents’ active travel behavior and explores the nonlinear relationship between suburban built environment and active travel. A novel hybrid modeling framework that combines eXtreme Gradient Boosting and Multi-scale Geographic Weighted Regression is introduced to forecast suburban active travel demand with the consideration of the nonlinearity and spatial heterogeneity in parameter estimates. Shapley additive explanation is used to elucidate the non-linear relationship between built environment variables and active travel. The results indicate that the proposed hybrid model performs the best with high predictive power and interpretability. The density of tourist attractions, transportation facilities, and household properties emerges as the three most influential factors affecting residents’ active travel behavior. Social-economic attributes contribute 15.70% to the prediction, while three categories of built environment variables (accessibility, transportation facilities, and land use) contribute 7.31%, 5.13%, and 71.87%, respectively. The hybrid model appears to be effective in identifying the nonlinear relationship and threshold effects between built environment variables and active travel. Besides, the model provides actionable insights into designing sustainable and efficient suburban environments, supporting targeted planning and policy-making efforts.

A novel hybrid machine learning model for prediction of CO2 using socio-economic and energy attributes for climate change monitoring and mitigation policies

Industrial development has contributed to carbon emissions majorly, resulting in high concentrations of greenhouse gases (GHGs) in the environment leading to climate change phenomenon. Climate change threatens people in multiple ways: threatening food security, water scarcity, frequent and extreme weather events, the spread of diseases, economic losses and migration etc. The World Health Organization (WHO) declared that climate change is the greatest threat to global health in the 21st century. Since 1970, CO2 emission has increased by about 90 per cent, making it 78 per cent of the total greenhouse gas emission. Climate change impact, carbon emission factors and social-economic attributes make the prediction of GHG emission a very complex research problem having dynamic scenarios due to a large number of factors and impacting variables. Accurate prediction of carbon emission in such a scenario makes it one of the most important and challenging research works. Artificial intelligence and machine learning approaches are increasingly being used to study complex, dynamic environmental phenomenons with high variability of time, space and other factors. The research paper proposes hybrid machine learning models for the prediction of CO2 emissions using energy and social-economic variables. The work uses energy and socioeconomic variables from 1960 to 2018 to collate them to provide a new perspective on the application of machine learning approaches in the modelling and prediction of GHG emissions. The proposed hybrid model of principal component analysis (PCA) and machine learning approaches is compared on accuracy and efficiency and performs better than other machine learning and deep learning approaches such as Linear regression variants, random forest regression (RFR), support vector regression (SVR), recurrent neural network (RNN), long short term Memory (LSTM) and Tabnet etc. The proposed hybrid model reports MAE equal to 0.0307, RMSE equal to 0.0346, MAPE equals to 5.1447 and SMAPE equal to 5.2267. In terms of efficiency of computation, the proposed hybrid machine learning model PCA+LER took 12.4 ms better than other models. This proposed research work of correlation analysis and prediction can help policymakers and governments in the mitigation and management of carbon emissions.

Farmers’ perception and management of water scarcity in irrigated rice-based systems in dry climatic zones of West Africa

Water scarcity threatens irrigated agriculture in sub-Saharan Africa (SSA). Knowledge of farmers’ perceptions and drivers for decision-making in view of coping with water scarcity is so far lacking but needed to improve local technologies and frame policies fostering their adoption. Here, for the first time, we investigated farmers’ perception of water scarcity, key adaptation strategies, and the determinants of their adoption in irrigated rice schemes in dry climatic zones of West Africa. We surveyed 572 farming households and conducted expert interviews with key informants in four contrasting irrigated rice schemes in Burkina Faso between April 2018 and August 2019. Information was gathered on biophysical field characteristics, grain yields, agronomic and water management practices, farmers’ perception of water scarcity, their adaptive responses, and social-economic attributes of adopting households. Nearly 80% of the respondents reported having experienced water scarcity during the past 5 years. To cope with the adverse effect of water scarcity, farmers implemented seventeen different adaptation strategies that could be categorized into seven groups. Most popular among those were “water and soil conservation practices” (consisting mainly of field bunding and leveling), “no rice cultivation,” and “crop rotation.” Farmers in drier areas (Sudano-Sahelian zone) were less likely to adopt and implement several adaptation strategies to water scarcity compared to farmers in wetter areas (Sudanian zone). Belonging to farming associations increased the probability of implementing several strategies to alleviate water scarcity, while female-headed households tended to have a lower propensity to adopt and implement concomitantly several adaptation strategies in comparison with their male counterpart. The dissemination of scheme- and household-specific technology options could contribute to mitigating water scarcity in irrigated rice-based systems in the dry climatic zones of West Africa, thus contributing to rural livelihood and food security.

Visiting Peri-Urban Forestlands and Mountains during the COVID-19 Pandemic: Empirical Analysis on Effects of Land Use and Awareness of Visitors

This research analyzed the status of visiting peri-urban forestlands and mountains during the first COVID-19 emergency period in Japan using a large-scale online questionnaire-based survey. We identified and examined the factors that correlated with visits to such areas, including respondents’ social-economic attributes, environmental conditions (such as the land use patterns of their residential areas), and awareness of the functions of forestlands. The results suggest that environmental conditions are a major factor encouraging residents to visit peri-urban forestlands and mountains during the pandemic. Peri-urban areas with forestlands have such environmental conditions, and residents who visited peri-urban forestlands and mountains tended to live in peri-urban areas. Residents’ expectations regarding forest functions were also strong factors influencing them to visit those places. Those who visited forests and mountains expected these areas to have mental health and educational functions. Especially, female respondents tended to be aware of forestlands as spaces for mental and physical relaxation, and respondents who have one or more children tended to be aware of the educational functions of forests. These findings imply that policy should consider the role of environmental conditions, awareness, and expectations about the function of forests and mountains, and prior interactions with nature in encouraging residents to visit such places for their health during the pandemic. These factors could also play a role in addressing the social and environmental disparities that exist between residents of different socio-economic statuses regarding access to nature. In future research, the detailed relationships between residents’ environmental conditions and expectations/awareness of the functions of peri-urban forestlands and mountains need to be explored.

Understanding Adoption Intent and Behavioral Response to Shared Electric Bicycles: A Survey in Ningbo, China

As a new form of shared mobility similar to bike-sharing, there is an increasing trend of people starting to use shared electric bicycles (SEB) for travel in China. Understanding the psychosocial factors that affect residents’ intention to use SEB is essential for the implementation of policies to develop sustainable transportation. Most research focuses on bike-sharing, while research on SEB is relatively rare. As such, this study proposes a theoretical framework based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to explore the mechanisms that influence the acceptance of and intention to use SEB. Drawing on this, it further addresses the moderating role of group heterogeneity and the residual effect of bike-sharing. An online survey of 313 SEB users in China was conducted in 2021. The results are constructed by structural equation modeling (SEM) and multiple-indicator multiple-cause (MIMIC) model. The results show that the research model can well explain people’s intention to use SEB. Perceived usefulness, attitude, subjective norm, and perceived behavioral control have direct positive effects on the intention to use SEB. However, there is group heterogeneity between social-economic attributes and latent variables. Moreover, satisfaction with bike-sharing could moderate the relationship between perceived usefulness and intention toward SEB. Based on the findings, some policy insights from users, government, and enterprises are proposed to guide the development of SEB.

Social Economic Attributes and Tax Compliance by Individual Taxpayers in Kenya

Tax revenues are crucial financial resources for any economy and plays a key role in economic development. In Kenya tax revenues are the major source of government budgetary resources besides other sources such as grants and loans. The average GDP growth rate in Kenya from FY 2006/17 to FY 2016/17 has been 5.6 percent which is higher than the global GDP growth rate of 2.3 percent. Conversely, Kenya’s tax revenue has not grown at the same pace as the economic growth. Weaknesses in revenue performance have triggered fiscal pressures. Budget deficits have fueled external borrowing amid efforts for the country to capitalize on domestic resource mobilization. Social economic attributes are crucial with regards to tax compliance by individual taxpayers since individual taxpayers have to be aware as well as being sensitive to tax legislation for them to be tax compliant. This study was set out to ascertain effects of taxpayer knowledge, tax complexity and individual taxpayer`s characteristics on tax compliance by individual taxpayers in Kenya. The study further sought to ascertain the moderating effect of tax penalties on relationship between social economic attributes and tax compliance by individual taxpayers in Kenya. The study was anchored on Social Learning Theory, Economic Deterrence Theory and Theory of planned behavior. Cross sectional descriptive research design was adopted in this study. This study targeted the individual taxpayers who were registered with KRA. The study targeted on 4806 staff from five Public Universities in Nairobi City County. Researcher used primary data which was gathered through administering questionnaires to respondents. Data collected was analysed using descriptive statistics and multiple regression. The results postulated that tax knowledge had a significant effect on tax compliance by individual taxpayers in Kenya. The study also found that tax complexity had a significant effect on tax compliance by individual taxpayers in Kenya. In addition, individual taxpayer’s characteristics had a significant effect on tax compliance by individual taxpayers in Kenya. Further, the study found tax penalties had a significant moderating effect on the relationship between social economic attributes and tax compliance by individual taxpayers in Kenya. The study found that the taxpayers sought tax consultants to file their returns. The study moreover found that the taxpayers did not seek updates regarding changes in tax law and regulations and were not conversant with Income Tax Act CAP 470. The study therefore recommends that KRA should educate the public regarding to any changes in tax laws and also ameliorate the readability of the tax laws hence reduce the complexity of the tax law. For KRA to achieve their target compliance level, there is need to have taxpayers understand the reasons why need to pay tax on time. KRA should sensitize the taxpayers on the importance of paying tax via civic education.

Ecological carrying capacity and sustainability assessment for coastal zones: A novel framework based on spatial scene and three-dimensional ecological footprint model

The ecological carrying capacity (ECC) assessment in coastal zones is essential for sustainable coastal management, but there remains a lack of a more effective assessment method to be applied across broad contexts. In this study, we proposed the concept of spatial scene, a geographical unit with a coordinate position, and high unification in social-economic attributes, land cover, ecological function, and externalities, to substitute for the land use/land cover (LULC) in the traditional three-dimensional ecological footprint (EF3D) model, thereby establishing a novel framework for coastal ECC (CECC) assessment. The coastal zone of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was chosen to examine the applicability and reliability of our framework. Results showed that the CECC estimated by spatial scene in the study area reached 0.1877 gha per capita, totaled 3.99 million gha in 2019, and the scenes of marine capture and forest provided the largest CECC. The per capita ecological footprint size (EFsize), ecological footprint depth (EFdepth), and EF3D reached 0.1684 gha, 14.35, and 2.42 gha, respectively, representing unsustainable development in the GBA coastal zone. The EF3D mainly distributed in scenes of grassland, forest, industrial, marine capture, coastal intertidal and offshore (CIO) port-shipping, traffic station, dryland, and CIO industrial-urban, while only the scenes of services and CIO tourism-entertainment were within CECC and therefore sustainable. Hong Kong, Huizhou, and Dongguan had the largest per capita EF3D. Compared to our results, the CECC and EFsize estimated by the traditional EF3D model were respectively 18% and 6% lower, while their EFdepth and EF3D were respectively 21% and 13% higher, which should be attributed to the significant differences in classification standard and scale between spatial scene and LULC. Our results showed higher correlations and more significant relationships with total gross domestic product (GDP), marine GDP, and main energy EF than those based on the traditional LULC, indicating a better reflection of the economic development status, energy consumption structure, and marine economic development modes by our framework. It is recommended to accelerate the industrial transformation and upgrading, and strengthen the conservation of ecological, agricultural, and marine space, in order to promote the sustainable development of GBA coastal zone. Our study revealed that our framework is capable of serving as a more effective and accurate method for assessing CECC and sustainability.

Understanding travelers’ mobility decisions in response to customer incentives

Incentives can influence travelers' mobility decisions and thus be used to indirectly redistribute traffic demands in space and in time to reduce congestion. This study aims to understand travelers' mobility decisions in response to customer incentives. An online questionnaire survey was conducted to obtain travelers' opinions and preferences on customer incentives. The results suggest that incentive types and travelers’ personal and social-economic attributes are coupled to affect their acceptance and rejection decisions. This study sheds light on using incentives to switch near-by travelers to potential business customers during peak hours, which ultimately contributes to congestion relief and economic prosperity.

Historical dataset of administrative units with social-economic attributes for Austrian Silesia 1837\u20131910

Scientists from many disciplines need historical administrative boundaries in order to analyse socio-economic data in space and time. In this paper, we present a set of historical data consisting of administrative unit boundaries and exemplary socio-economic attributes for Austrian Silesia, an historical region located in modern Czechia and Poland. The dataset covers nearly 700 administrative unit boundaries on the level of cadastral or political communes and their subparts and was acquired through manual vectorisation of historical maps (1:28,800) from the period 1837–1841. The local-level units can be easily joined into higher-level divisions such as court or political districts for the period 1837–1910. The data can then be combined with statistical data collected approximately every 10 years for a similar period. Within the quality assessment, the relations between cartographic and census data and their credibility are analysed. The present dataset provides many possibilities for joining a wide range of historical statistical data to better understand various demographic and economic processes based on advanced analyses, e.g., by using GIS.

Study of Merchant Adoption in Mobile Payment System Based on Ensemble Learning

To explore the influencing factors of the adoption of mobile payment systems from the perspective of merchants, this study builds a data analysis model based on three different ensemble learning algorithms, Adaboost model, random forest and XGBoost model, where static social-economic attributes, dynamic trading behavior and clustering effect variables of merchants are used as independent variables. Moreover, this paper establishes the prediction models and analyzes the prediction accuracy of different models. The results of the study indicate that: 1) Merchants in the housing industry, health hospitals and retail industries are more willing to adopt mobile payment systems; 2) The average daily transaction volume and the average amount of each consumer significantly affect the merchant mobile payment adoption behavior; 3) The adoption of mobile payment systems by neighboring merchants significantly positively affected the adoption behavior of merchants; 4) On the basis of the social-economic attributes of merchants, the hit rate and accuracy of the prediction model were greatly improved after adding transaction data.

Spatial–Temporal snapshots of global natural disaster impacts Revealed from EM-DAT for 1900-2015

Natural disasters causing human, economic, or environmental losses include all types of geophysical, meteorological, hydrological, climatological, or biological events that disturb human and natural environments. Emergency preparedness for, response to, mitigation of, and recovery from natural disasters that are bases for sustainable development requires sound impact assessments at proper spatial and temporal scales. This research provides spatial–temporal views of world natural disasters recorded in the EM-DAT database for the period of 1900-2015. Views of natural disaster impacts in terms of human fatalities, injuries, affected, and property damages are summarized and ranked at the world, continent, and country levels and by decade and the whole period. Top 10, 20, and 30 out of 221 countries are highlighted and referenced with the world totals. Correlates of country disaster impacts with social-economic attributes are explored. While countries with significant disaster impacts are found in all continents, large developing countries (e.g. China, India) or developed countries (e.g. United States, Germany) lead the global natural disaster hotspots, which are small in numbers but large in total disaster losses. Such global inter-country views of natural disaster impacts provide useful insights for sustainability policy-making relevant to international and national humanitarian efforts towards natural disasters.

Uncovering Spatial Structures of Regional City Networks from Expressway Traffic Flow Data: A Case Study from Jiangsu Province, China

On the basis of the “space of flows” theory, city networks emphasizing factor connectivity and spatial connection have become a core perspective for regional spatial relationships. They provide a context for discussing the spatial structures of city networks and a scientific basis for making regional development policies. Relying on expressway traffic flow data from Jiangsu Province in 2014, this study describes macro-spatial patterns and hierarchical structures of city networks, and uses the Walktrap algorithm of community detection to execute space divisions and elucidate potential spatial connection structures in the city networks. In comparison with other algorithms, the Walktrap algorithm demonstrates significant adaptation and stability to the short expressway traffic flows with the characteristics of strong network density and sparse node distribution. The results indicate that the macro-spatial patterns of Jiangsu’s city networks have clear regional differences. The cities with relatively dense spatial linkages are distributed along the Yangtze River banks, and many different sub-network systems have also developed internally. Cities and linkage axles have clear hierarchical structures. City hierarchies have certain spatial couplings with the cities’ intrinsic social economic attributes and geographical locations. The linkage axles hierarchy has clear spatial interactions and superposition with geographical distance. The community detection algorithm identified six spatially connected “city communities”, the Suzhou-Wuxi-Changzhou Community (SWCC), the Nanjing-Zhenjiang-Yangzhou-Taizhou Community (NZYTC), the Nantong-Yancheng Community (NYC), the Lianyungang Community (LYGC), the Huai’an-Suqian Community (HSC), and the Xuzhou Community (XZC). The community spatial metaphors had four aspects. First, trans-prefectural linkages were formed through spatially integrated effects of metropolis regions. Second, some communities share the same boundaries with their prefecture level administrative units; this reveals that significant administrative regional economies still exist in contemporary Jiangsu. Third, several cities located in the marginal areas of prefectures and captured by the powerful center cities in neighboring prefectures have been absorbed into the communities of neighboring prefectures. Fourth, the two cities Jiangyin and Jingjiang, divided into different administrative districts, have switched status opposite city community divisions.

Perceptions of Different Stakeholders on Reclaimed Water Reuse: The Case of Beijing, China

Public involvement is critical to the successful implementation of reclaimed water reuse programs. Based on the participatory research method, we studied the attitudes of the stakeholders who are involved in reclaimed water reuse in Beijing, China. Results showed that the general public’s knowledge on water resources was poor, while their awareness on reclaimed water reuse was high. The general public showed a strong acceptance of non-contact and non-potable reclaimed water reuse, but their acceptance of the three major water reuse types of river water supplement, park water supplement, and agriculture irrigation was not high. The beneficial use of reclaimed water was admired by water resource managers, industrial sectors, and researchers, and these stakeholders strongly supported the advancement of reclaimed water reuse. However, some of the stakeholders showed concerns about the potential risks from reclaimed wastewater reuse. Among them, risks from waste water treatment facilities were the biggest concern. Stakeholders’ perception of reclaimed water was influenced by their social-economic attributes. This study will enrich the current survey findings on public perception of reclaimed water reuse, particularly in developing countries.

A data-driven approach to predict the success of bank telemarketing

We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features. We also compared four DM models: logistic regression, decision trees (DTs), neural network (NN) and support vector machine. Using two metrics, area of the receiver operating characteristic curve (AUC) and area of the LIFT cumulative curve (ALIFT), the four models were tested on an evaluation set, using the most recent data (after July 2012) and a rolling window scheme. The NN presented the best results (AUC=0.8 and ALIFT=0.7), allowing to reach 79% of the subscribers by selecting the half better classified clients. Also, two knowledge extraction methods, a sensitivity analysis and a DT, were applied to the NN model and revealed several key attributes (e.g., Euribor rate, direction of the call and bank agent experience). Such knowledge extraction confirmed the obtained model as credible and valuable for telemarketing campaign managers.

Speeding violations related to a driver’s social-economic demographics and the most frequent driving purpose in Taiwan’s male population

Speeding is the most common driving violation and a determinant factor contributing to both the number and severity of accidents. This study explored speeding violations based on a national cohort of 8129 adult male drivers in the one-year period of 2008. The analysis focused on the relationships between self-reported speeding violations and social-economic attributes: age, educational attainment, income level, yearly driving distance and the most frequent driving purpose (major driving purpose such as commuting and leisure or shopping). Of the 8129 observed drivers, 19.1% (1552 drivers) were ticketed at least once for a speeding violation. Drivers aged 40–49 possess the lowest speeding risk (speeding tickets per million kilometers), and both younger and older drivers have relatively higher speeding risk. The results of the logistic regression model revealed that speeding violations were significantly associated with the attributes of age, education, income, yearly driving distance and the most frequent driving purposes. The most frequent driving purposes for business (odds=1.930, p=0.016) and leisure (odds=1.566, marginally significant p=0.096) were associated with a higher speeding risk compared with the most frequent driving purpose for shopping.

Effects of Item Nonresponses on the Performance of Travel Mode Choice Models for Developing Cities: A Comparative Analysis

To enhance the quality of transportation planning and policy making, it is necessary to properly deal with the nonresponse issues in transport surveys. However, such nonresponse issues especially in developing countries have been ignored in literature. This paper first statistically identifies the missing patterns of item nonresponse (INR) in person trip survey data collected in developing cities and then analyzes the effects of INR on the performance of travel mode choice model (an aggregated multinomial logit model) based on expectation-maximization (EM) imputation method. As a case study, three developing cities representing three levels of INR are analyzed as follow. Firstly, the statistically significant social-economic attributes of trip makers and trip-context factors are identified with respect to INR in the missing pattern analysis by using Chi-Square test method. Secondly, EM imputation based on missing pattern analysis is applied to deal with missing data to obtain the unbiased data set as a benchmark. Thirdly, the null hypothesis that the model parameters estimated with and without imputation are equal is statistically tested using independent-sample T tests and further the internal validity performed in terms of R-squared coefficients is used to identify the discrepancy of model predictions between with and without imputations. Finally, one critical indicator – value of travel time (VOTT) is evaluated considering the effects of missing data. The results confirm that the respondents and non-respondents are quite different in terms of the social-economic background in the developing cities and further show that not only the missing rates but also the missing patterns greatly affect the performance of mode choice model in terms of model parameters and the prediction ability. The calculation of VOTT reveals that the VOTT affected by INR tends to be overestimated.

Incentives of Retirement Transition for Elderly Workers: An Analysis of Actual and Simulated Replacement Rates in Ireland

Retirement behaviours and elderly poverty issues have been the subject of much attention and discussion in recent years as most countries are facing a rapidly ageing society. Ireland enjoys a relatively young population compared with other European countries, but is also struggling with increasing fiscal pressures. This paper analyses the retirement pattern and the replacement rate observed in Ireland using the Living in Ireland panel dataset. Since traditional empirical estimations may have selection bias issues as people with low replacement rates may not choose to retire, the paper adopts a combined method with both synthetic household simulation and empirical estimates. The study reveals the social economic attributes patterns associated with the replacement rates and retirement behaviours, and explores the heterogeneities of replacement rates among retirees.

Small crack propagation threshold of carbon tool steel under combined stresses: Chen, J.Q. Acta Metall. Sin. (China) (Oct. 1993) 29(10), A461–A467 (in Chinese)

Speeding is the most common driving violation and a determinant factor contributing to both the number and severity of accidents. This study explored speeding violations based on a national cohort of 8129 adult male drivers in the one-year period of 2008. The analysis focused on the relationships between self-reported speeding violations and social-economic attributes: age, educational attainment, income level, yearly driving distance and the most frequent driving purpose (major driving purpose such as commuting and leisure or shopping). Of the 8129 observed drivers, 19.1% (1552 drivers) were ticketed at least once for a speeding violation. Drivers aged 40–49 possess the lowest speeding risk (speeding tickets per million kilometers), and both younger and older drivers have relatively higher speeding risk. The results of the logistic regression model revealed that speeding violations were significantly associated with the attributes of age, education, income, yearly driving distance and the most frequent driving purposes. The most frequent driving purposes for business (odds = 1.930, p = 0.016) and leisure (odds = 1.566, marginally significant p = 0.096) were associated with a higher speeding risk compared with the most frequent driving purpose for shopping.