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This study explores the spatial and temporal relationship between tourism activities and transportation-related carbon dioxide (CO₂) emissions in the United States (US) from 2003 to 2022 using advanced geospatial modeling techniques.

The research integrated geographic information systems (GIS) to map tourist attractions against high-resolution annual emissions data. The analysis covered 3,108 US counties, focusing on county-level attraction densities and annual on-road CO₂ emission patterns. Advanced spatial analysis techniques, including bivariate mapping and local bivariate relationship testing, were employed to assess potential correlations.

The findings reveal limited evidence of significant associations between tourism activities and transportation-based CO₂ emissions around major urban centers, with decreases observed in Eastern states and the Midwest, particularly in non-coastal areas, from 2003 to 2022. Most counties (86.03%) show no statistically significant relationship between changes in tourism density and on-road CO₂ emissions. However, 1.90% of counties show a positive linear relationship, 2.64% a negative linear relationship, 0.29% a concave relationship, 1.61% a convex relationship and 7.63% a complex, undefined relationship. Despite this, the 110% national growth in tourism output and resource consumption from 2003–2022 raises potential sustainability concerns.

To tackle sustainability issues in tourism, policymakers and stakeholders can integrate emissions accounting, climate modeling and sustainability governance. Effective interventions are vital for balancing tourism demands with climate resilience efforts promoting social equity and environmental justice.

This study’s innovative application of geospatial modeling and comprehensive spatial analysis provides new insights into the complex relationship between tourism activities and CO₂ emissions. The research highlights the challenges in isolating tourism’s specific impacts on emissions and underscores the need for more granular geographic assessments or comprehensive emission inventories to fully understand tourism’s environmental footprint.

The purpose of this study is to analyze the horizontal expansion of Burayu Town between 1990 and 2020. The study typically acts as a baseline for integrated spatial planning in small- and medium-sized towns, which will help to plan sustainable utilization of land.

Landsat5-TM, Landsat7 ETM+, Landsat5 TM and Landsat8 OLI were used in the study, along with other auxiliary data. The LULC map classifications were generated using the Random Forest Package from the Comprehensive R Archive Network. Post-classification, spatial metrics, and per capita land consumption rate were used to understand the manner and rate of expansion of Burayu Town. Focus group discussions and key informant interviews were also used to validate land use classes through triangulation.

The study found that the built-up area was the most dynamic LULC category (85.1%) as it increased by over 4,000 ha between 1990 and 2020. Furthermore, population increase did not result in density increase as per capita land consumption increased from 0.024 to 0.040 during the same period.

As a result of financial limitations, there were no high-resolution satellite images available, making it challenging to pinpoint the truth as it is on the ground. Including senior citizens in the study region allowed this study to overcome these restrictions and detect every type of land use and cover.

Data on urban growth are useful for planning land uses, estimating growth rates and advising the government on how best to use land. This can be achieved by monitoring and reviewing development plans using satellite imaging data and GIS tools.

The use of Random Forest for image classification and the employment of local knowledge to validate the accuracy of land cover classification is a novel approach to properly customize remote sensing applications.

Infrastructure financialization plays a critical role in infrastructure development and urban growth around the world. However, on the one hand, the existing research on the infrastructure financialization focuses on qualitative and lacks quantitative country-specific studies. On the other hand, the spatial heterogeneity and influencing factors of infrastructure financialization are ignored. This study takes China as a typical case to identify and analyze the spatial characteristics, development process and impact factors of infrastructure financialization.

To assess the development and characteristics of infrastructure financialization in China, this study constructs an evaluation index of infrastructure financialization based on the infrastructure financialization ratio (IFR). This study then analyzes the evolution process and spatial pattern of China's infrastructure financialization through the spatial analysis method. Furthermore, this study identifies and quantitatively analyzes the influencing factors of infrastructure financialization based on the spatial Dubin model. Finally, this study offers a policy suggestion as a governance response.

The results demonstrate that infrastructure financialization effectively promotes the development of infrastructure in China. Second, there are significant spatial differences in China’s infrastructure financialization. Third, many factors affect infrastructure financialization, with government participation having the greatest impact. In addition, over-financialization of infrastructure has the potential to lead to government debt risks, which is a critical challenge the Chinese Government must address. Finally, this study suggests that infrastructure financialization requires more detailed, tailored,and place-specific policy interventions by the government.

This study not only contributes to enriching the knowledge body of global financialization theory but also helps optimize infrastructure investment and financing policies in China and provides peer reference for other developing countries.

This study delves into 17th-century colonial port cities – Madras, Bombay, and Calcutta – examining the impact of British imperialism on urban sustainability and heritage conservation. It explores historical development, spatial organization, and connectivity.

This study intricately explores the interplay among urban sustainability, morphology, and heritage conservation using space syntax analysis. It focuses on examining White and Black Town dispersion during British imperialism.

The investigation reveals varying degrees of dispersion of White and Black Towns, with Calcutta exhibiting the most consistent distribution among the three cities. These findings underscore the profound influence of British imperialism on the spatial organization of colonial port cities, offering valuable insights into their historical evolution and layout.

While this study provides valuable insights, it is limited by its focus on the colonial period and the specific cities of Madras, Bombay, and Calcutta. The findings may not be directly generalizable to other contexts or time periods. Additionally, the study’s reliance on historical data sources may present data accuracy and completeness challenges.

This study contributes to understanding colonial port cities, guiding sustainable urban development, heritage preservation, and equitable resource access for global sustainability. By focusing on the historical impact of British imperialism, the research provides original insights into the spatial dynamics of these cities, contributing to the broader discourse on urban sustainability and heritage conservation.

Given the importance of growth-oriented entrepreneurship in the context of economic development and the need to understand how rural communities can be developed, the purpose of this research paper is to determine how the drivers of growth expectations differ between urban and rural settings.

The methodology is threefold: firstly, a descriptive analysis with non-parametric testing is conducted; then pooled regression model is used to analyse the predictors of growth expectations in both contexts, and finally, coarsened exact matching is used to identify possible self-selection bias.

In contrast to mainstream entrepreneurship theory, it is found that entrepreneurs’ intrinsic knowledge, skills and abilities are not significant in the rural-specific model. The only exception is entrepreneurs’ educational level, the importance of which is emphasised as a pivotal factor in increasing high-growth ventures in rural communities. Additionally, when self-selection is eliminated, rurality worsens growth intentions.

There is evidence that some growth-oriented entrepreneurs self-select into rural communities. Because the high-growth entrepreneurial dynamics in rural areas are unique, public policies should target purpose-driven entrepreneurial education. This includes encouraging “lifestyle entrepreneurship” (e.g. retirees returning to rural areas to become entrepreneurs), preventing entrepreneurial brain drain in rural areas and attracting highly educated urban entrepreneurs to exploit opportunities in rural areas.

This research attempts to contribute to the ongoing debate regarding the factors that drive high-growth entrepreneurs in rural areas by analysing rural entrepreneurs in the high-growth context of a developing economy. The focus is on Chile – a country that is rarely investigated compared to the USA or Europe – to extend the literature on high-growth ventures and entrepreneurial ecosystems.

This paper aims to explore the spatio-temporal patterns of Chinese foreign direct investment (FDI) since the inception of the Belt and Road Initiative (BRI) in 2013 as an extended version of geographically weighted regression.

The panel data are used to examine spatial and temporal dynamics of the magnitude and the direction of China's outward FDI stock and its flow from 2011 to 2015 at a country level. Using the geographically and temporally weighted regression (GTWR), spatio-temporal distribution of FDI is explained through Logistic Performance Index, the size of gross domestic product (GDP), Shipping Linear Connectivity Index and Container Port Throughput.

A comparative analysis between participating and non-participating countries in the BRI shows that the size of GDP and Container Port Throughput of the participating countries have a positive effect on the increases of China's outward FDI Stock to Asia especially after 2013, while non-participating countries, such as North America, Western Europe and Western Africa, have no significant effect on it before and after the implementation of the BRI.

The findings, however, will not necessarily provide insight into the needs of China's outward FDI in certain countries to develop their economy. The findings provide the evidence to inform policy making to help identify the winners and losers of the investment, scale and direction of investment and the key drivers that shape the distributive investment patterns globally.

The study provides the empirical evidence to inform investment policy and strategic realignment by quantifying scale, direction and drivers that shape the spatio-temporal shifts of China's FDI.

The analysis also guides the Chinese government improve bilateral trade, build infrastructure and business partnerships with preferential countries participating in the BRI.

There is an urgent need to adopt a new perspective to unfold the spatial temporal complexity of FDI that incorporates space and time dependencies, and the drivers of the situated context to model their effects on FDI. The model is based on GTWR and an extended geographically weighted regression (GWR) allowing the simultaneous analysis of spatial and temporal decencies of exploratory variables.

The climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.

The investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.

The yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.

This study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.

The success of public–private partnership (PPP) projects is highly correlated to the successful management of risks encountered during the operation phase. PPP projects are especially exposed to risk due to the long operation period over which revenues need to be generated to recoup substantial initial investment and operational running costs. Despite the critical impact of risk exposure, limited research has been specifically undertaken on the matter of operational risk management. This study seeks to address this oversight by identifying and evaluating operational risk management strategies for PPPs.

Vulnerability theory is the theoretical lens used, with context drawn from Chinese PPP projects. Based on the data collected from expert interviews and questionnaires, 28 operational risk management strategies are identified. A fuzzy synthetic method is employed to analyze the effectiveness of the 28 strategies.

The findings reveal that providing an exit mechanism clause into the contract, establishing a comprehensive performance evaluation mechanism and developing a clear compensation mechanism are the top three effective strategies. This study also reveals that risk mitigation approaches that reduce vulnerability prove more effective than attempts to reduce external threats. Specifically, strategies aimed at managing contract, political, technical and financial risk are the most effective.

The findings of this study extend current knowledge regarding the risk management of PPP projects. They also offer a reference by which practitioners may select effective operational risk management pathways and thereby, galvanize the sustainable development of PPPs.

Despite the economic growth in Ghana, the manufacturing industry faces numerous challenges in their supply chains. The study aims to investigate the mediated-moderated role of supply chain technological innovation (SCTI) in the relationship between supply chain resilience (SCR) and supply chain performance (SCP) of manufacturing firms. By exploring this relationship, the study seeks to provide insights that can help manufacturing firms overcome the challenges they face and improve their overall supply chain performance.

The quantitative research approach and explanatory research design were utilised. A sample of 345 manufacturing firms was drawn from a population of 2495 manufacturing firms in the Accra metropolis. Analysis of this study was performed using the Partial Least Squares Structural Equation Modelling (PLS-SEM).

It was revealed that SCTI positively mediates the nexus between SCR and SCP. However, we document that SCTI negatively moderates the nexus. It is instructive to advocate that a mere presence of a more enhanced SCTI is not enough to improve upon SCP of manufacturing firms, but should be a channel through which SCR can improve SCP.

This study highlights the need for managers of firms to prioritise investment in technological innovation as a means of enhancing SCR and ultimately improving supply chain performance. By understanding the SCTI mediated-moderated relationship between SCR and SCP, supply chain managers, logistics managers, operation managers, as well as procurement managers can develop more effective strategies to optimise their operations. This study provides valuable insights for managers and policymakers in developing and implementing supply chain resilience strategies that take into account the important role of SCTI.

The originality of the study lies in exploring the mediated-moderated effect of technological innovation on the nexus between resilience and performance of supply chains in developing economies, where firms often face unique challenges such as infrastructure limitations, political instability and economic uncertainty. By investigating the interplay of SCTI between SCR and SCP, researchers can develop new insights and strategies to help navigate these challenges and achieve success.

Assembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.

In this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.

The method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.

The author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.