Search Results (444)

This paper used pavement condition data collected by the Federal Highway Administration (FHWA) between 2001 and 2006 aggregated by U.S. states to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement conditions and preservation expenditure over time, time autocorrelation parameters were introduced in a spatial modeling scheme that accounted for spatial autocorrelation and heterogeneity. The proposed framework accommodates data aggregation in network-level pavement deterioration models. Because pavement roughness across different roadway classes is anticipated to be affected by different explanatory parameters, separate time–space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications revealed that different time–space models were appropriate for pavement performance modeling across the different roadway classes. Factors that were found to affect state-level pavement roughness in time and space included preservation expenditure, predominant soil type, and predominant climatic conditions. The results have the potential to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level. Full article

The rapid increase in population and economy, coupled with accelerated urbanization, is placing immense pressure on the water–energy–food (WEF) system. In this context, the water–energy–food nexus framework has emerged, recognizing the interdependencies and interactions among water, energy, and food systems, with the aim of optimizing resource management through cross-sectoral collaboration to promote sustainable development. Understanding the spatio-temporal differentiation patterns of the WEF nexus and elucidating the driving mechanisms behind changes in their coupling relationships is essential. This knowledge is crucial for ensuring the security of each subsystem and enhancing the overall sustainability of interconnected systems through coordinated efforts. To address these challenges, this study first established evaluation indicators for water, energy, and food security to quantify their levels and spatio-temporal dynamics. Subsequently, the degrees of coupling coordination within the WEF nexus were calculated. Finally, the WEF nexus’s spatial correlations were analyzed by using a spatial autocorrelation model. Spatial econometric models then identified key factors affecting its coordination. The results revealed significant spatial heterogeneity in water, energy, and food security across mainland China’s provinces. From 2002 to 2022, water security improved substantially in 87% of the provinces, while energy security began to improve in the eastern regions following a phase of high consumption. Food security saw significant enhancements, particularly in Inner Mongolia and the northeastern provinces. The overall coupling coordination of the WEF nexus improved across 30 provinces, progressing toward primary coordination. However, Henan and Anhui provinces experienced fluctuations in WEF nexus coordination. Spatial correlation analysis showed upward trends and increased clustering in WEF nexus coordination. Factors such as economic development and population positively influenced coordination, while economic agglomeration, education, and effective irrigation area had negative effects. This study elucidates the complex interconnections and key influencing factors within the WEF nexus, providing a reference framework and practical recommendations for equitable resource management. Full article

Greece is experiencing a steady population decline caused by the declining migratory and natural balance. This research investigates the spatial impact of socio-economic and demographic factors on the natural population balance in Greece for the spatial zoning of municipal administrative units. Using geographically weighted regression (GWR) on data from the 2011 Greek census, the research explores the local impacts of factors like housing repair permits, vacant housing, employment rates, population inflows, distance from regional centers, aging, gender ratios, and education levels. An initial ordinary least squares (OLS) regression was conducted, revealing significant spatial variation and emphasizing the necessity of spatial econometric methods. The GWR model proved to be more effective in accounting for the variance in the data, removing spatial autocorrelation and revealing high local variation. Results show the high negative impact of the aging index in Western Greece and the Ionian islands, the counterintuitive positive effect of the gender ratio in urban areas, and the positive influence of population inflows in high-migration regions like Northern Greece and Crete. The results of this study underline the need to utilize spatial econometric methods for a precise and detailed understanding of demographic trends and provide valuable insights for localized strategies to address demographic challenges. Full article

This study elaborates on a typology of demographic change and tests this definition at the lowest granular level (LAU2, municipality) with official data. This typology distinguishes between fragile and resilient municipalities based on population dynamics (in terms of duration and intensity) over 1991–2021. This study’s second aim is to elaborate a spatial autoregressive econometric model to evaluate to what extent and in which direction the rate of participation of potential beneficiaries of the Rural Development Programmes (RDPs) of 2014–2020 is affected by demographic change and other explanatory variables. Regression models compare the results of the OLS (aspatial) and spatial autoregressive models (SAR) of four types of participation rates (all RDP schemes; all LEADER schemes; sectoral schemes of RDP and LEADER; non-sectoral schemes of RDPs and LEADER). This comparison makes it possible to understand the differences between centralised and decentralised management and between sectoral and broader rural-targeted schemes. The results of the models appear attractive in interpreting the role of RDP instruments in different regions and local areas. First, the rate of participation is strongly dependent on macro-regional differences. Regarding the demographic factors at the local level, this study highlights that demographic fragility does not necessarily hamper the use of RDP measures. Conversely, the participation rate in RDP policy schemes seems particularly significant in very fragile areas, whereas significance has yet to be proved in other demographic typologies. This result holds particularly true for the policy uptake of non-sectoral schemes. Furthermore, LEADER decentralised interventions fit the fragile areas more than resilient and vital ones due to the territorially targeted approach followed by the Local Action Groups. Full article

The effective allocation of labor and cultivated land resources to ensure food security is a global concern. Understanding the relationship between rural labor off-farm employment and the eco-efficiency of cultivated land use (ECLU) is critical, yet current research in this area remains insufficient. This study explores the dynamics between off-farm employment and ECLU using the North China Plain as a case study, analyzing panel data from 2001 to 2020 through spatial econometric models. The findings reveal significant temporal expansion and spatial differentiation in off-farm employment, with growth rates gradually slowing and spatial disparities diminishing. The average ECLU initially declined from 2001 to 2003, followed by fluctuating increases, with a notable acceleration in growth after 2017. A “U-shaped” relationship between off-farm employment and ECLU was identified, with a turning point at an off-farm employment ratio of 40.73%, occurring around 2003–2004 based on regional averages. Before this threshold, off-farm employment negatively impacted ECLU, while beyond this point, the impact became positive. The study also observed significant spatial spillover effects of off-farm employment on ECLU in the North China Plain. These findings underscore the complex interplay between rural labor migration and agricultural productivity. To maximize the benefits of off-farm employment, policies should encourage the reinvestment of income into sustainable agricultural practices. Furthermore, the significant spatial spillover effects call for enhanced regional coordination and tailored policy interventions to optimize labor allocation and improve ECLU. Full article

Tourism eco-efficiency (TEE) is a pivotal metric for assessing tourism’s sustainability and the balance between human activities and the environment, significantly influencing regional economic growth (RGDP). This research utilizes a comprehensive analytical framework, combining the Super SBM-DEA model, the Malmquist index, and spatial econometric models, to analyze the spatial interplay between TEE and RGDP within the Yangtze River Economic Belt (YREB) from 2009 to 2021. The results show that (1) TEE in the YREB exhibits a generally upward trajectory with fluctuations, with upstream and downstream regions consistently outperforming the midstream areas in terms of efficiency; (2) technological progress is identified as the primary driver behind efficiency variations; (3) and there exists a symbiotic relationship between local TEE and RGDP, where the economic prosperity of adjacent regions exerts a competitive pull on local TEE, while the TEE of neighboring areas can slow down local economic growth. The study concludes with strategic recommendations aimed at fostering regional collaborative advancement, offering valuable insights for the sustainable development agenda of nations and regions. Full article

In the context of rapid urbanization in third-world countries, many cities adopt high-density development, effectively using land but limiting open space, especially for children, impacting their spatial rights. This study focused on the Dongcheng and Xicheng districts of Beijing. It employed methods such as variance inflation factor, multiple linear regression, spatial autocorrelation, and spatial econometric models to investigate the impact of various configuration factors on children’s satisfaction with outdoor activity space. The study also revealed the spillover effects of outdoor activity space configuration for children in high-density urban environments. The results showed that (1) children’s satisfaction was significantly influenced by the configuration elements. The variables that had the most significant impact on satisfaction were the number of outdoor spaces, facilities’ amusement, advertisements, and service management levels. (2) Using spatial econometric models, we determined that spatial dependency significantly enhances the model’s explanatory power. The quantity of outdoor space had the greatest effect on children’s outdoor activity space satisfaction, followed by facilities’ amusement and advertisement impact, and service management had the least impact, though all categories positively affected satisfaction. This study held significant value and importance in improving the rights of children in mega-cities in developing countries and promoting the physical and mental well-being of children. Full article

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century, has been considerably affecting short-term weather conditions, such as increasing weather variability and the incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, the spatial error model (SEM) and the spatial Durbin model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches, and then over that threshold, milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance. Full article

Carbon reduction in livestock is a necessary path for the green transformation of the livestock industry. Has the digital economy as an emerging productive force in recent years driven carbon reduction in the livestock sector? This paper employs dynamic panel regression models, mediation effect models, and spatial econometric models to investigate the impact mechanisms of the digital economy on carbon emission reduction in livestock husbandry. The results indicate the following: (1) The digital economy has a positive impact on carbon reduction in livestock farming by fostering technical innovation, enhancing the level of human capital, and transforming the mode of production. (2) From 2013 to 2021, the digital economy did not show a significant carbon reduction effect until 2018. It has had a substantial impact on carbon reduction in livestock husbandry in the northeastern and western regions, while its influence in the eastern and central regions has not been significant. The digital economy positively affects carbon emission reduction in non-dairy cows and pigs, negatively affects poultry, and shows no significant effect on dairy cows and sheep. (3) In terms of spatial effects, the digital economy is not only driving carbon emission reductions from livestock farming in the local region but is also significantly driving carbon emission reductions from livestock farming in the surrounding provinces. The findings of this article provide some insights into future policy formulation for low-carbon development in the livestock sector. Full article

In the context of green development, it is very important to explore the impact of investment in forestry resource management (IFRM) on atmospheric haze pollution and carbon emissions. Based on long time series data of 30 provincial administrative regions in China from 2008 to 2019, this study used ArcGIS spatial analysis and spatial econometric models to investigate the impact of IFRM on haze pollution and carbon emissions and its potential spatial spillover effects. The results show that areas with higher haze pollution concentrations in China were mainly distributed in the Sichuan Basin and the North China Plain; areas with high carbon emission intensity were mainly distributed in Beijing, Shanghai, Tianjin, Shandong, Hebei, etc. For every 1% increase in IFRM, haze pollution and carbon emissions decreased by 0.0655% and 0.1169%, respectively, indicating that IFRM has significantly improved the ecological environment. In addition, IFRM not only significantly reduced haze pollution and carbon emissions in local areas, but also had a strong negative effect on haze pollution in neighboring areas. This study provides important strategies for promoting forestry resource management and regional green development. Full article

This study extends the existing research on carbon trading policies from the perspective of mediating effects. Based on the difference-in-differences method, this study helps to understand the relationship between China’s carbon trading policies and resource allocation efficiency. The study finds that carbon trading policy promotes the optimization of capital allocation efficiency but does not promote the optimization of labor allocation efficiency. This conclusion has passed a series of robustness tests. Moreover, our analysis shows that carbon trading policies can influence resource allocation efficiency through per capita GDP, foreign direct investment, and innovation levels using multiple mediating models. Factors such as market size, the number of emission entities, and the behavior of market participants affect the resource allocation efficiency in the carbon trading process. Finally, the spatial spillover effect of the carbon trading policy is verified. This paper provides empirical evidence and policy implications for achieving the dual carbon goal and sustainable development. Full article

The effects of land transfer on agricultural carbon emissions and their underlying mechanisms must be investigated if we are to achieve sustainable development and environmentally friendly high-quality agricultural development. This research experimentally investigated the spatial impacts of land transfer on agricultural carbon emissions and their underlying causes using multiple econometric models based on provincial panel data covering the years 2010 to 2022. The results allow us to draw the following conclusions: (1) Land transfer significantly inhibits agricultural carbon emissions. This conclusion remained valid after various robustness checks, including a reduction in sample size, change model type, and adjustment of geographical regions. (2) Agricultural socialized services play a positive moderating role in the process of land transfer to curb agricultural carbon emissions. (3) Land transfer has a substantial spillover effect on agricultural carbon emissions, resulting in significantly reduced emissions in the immediate area and nearby regions. Full article

The continuous reduction in peri-urban agricultural land due to spatial urban expansion forces subsistence farmers to seek arable land through different land access strategies. Among these, land rental transactions are crucial for accessing arable land across different regions. This study aimed to examine factors affecting land rental transactions in the peri-urban areas of the East Gojjam Administrative Zone in Ethiopia. Data were collected from 353 household heads of peri-urban areas, who were affected by expropriation. A total of 350 valid responses were analyzed using descriptive and inferential statistics and an econometrics model. The results indicated that 58% of the respondents participated in both renting and renting out land, which underlines the importance of land rental transactions in the peri-urban areas. Specifically, 60% of female-headed households were engaged in land rental transactions, with 14% renting in and 46% renting out land. In contrast, 38% of the male-headed respondents rented land, while only 19% rented out land. The model result identified sex, landholding size, number of oxen, participation in off-farm activities, and extension service as significant determinant variables for renting land. Households made land rental agreements both orally and in written documents, with oral agreements being more prevalent. Transaction dues were conducted through sharecropping and fixed rents, with sharecropping being the most common method. Thus, land rental transactions play pivotal roles to support the livelihoods of peri-urban subsistence farmers. Full article

In recent years, the thorough implementation of China’s green development concept has compelled local governments to devote more attention to environmental issues. This study aimed to verify whether increased government environmental attention (GEA) can sustainably ensure the implementation of environmental governance, particularly air pollution control. Using government work reports (GWRs) from local governments, this study employed machine learning methods to identify and quantify the attitudes of government officials as expressed in policy texts. A weighted dictionary method was used to quantify GEA from 2011 to 2016. The results of spatial econometric models indicated that air pollution exhibited positive spatial clustering effects across different regions, with the Yangtze River Delta and the Beijing–Tianjin–Hebei region being classified as high–high areas, while the western regions were classified as low–low areas. Baseline regression results showed that increased GEA can improve the effectiveness of pollution control, but excessive attention leads to a decline in governance efficiency. Overall, this study helps explain the unsustainability of campaign-style environmental governance and provides guidance for local governments on the rational allocation of attention when addressing environmental issues. Full article

Air pollution has recently gained much attention from the general population. Despite pollution control being an issue in both urban and rural regions, most of the available research has concentrated on urban districts. Hence, investigations into how urban–rural transition affects PM_2.5 are warranted within the framework of urban–rural integration. Using the Yellow River Basin as a case study, this study employed the entropy method and Analytic Hierarchy Process (AHP) to uncover the extent of urban–rural transformation. It then used the spatial autocorrelation method to investigate the spatiotemporal features of PM_2.5 and the spatial econometric model to investigate the mechanisms that influence the relationship between urban–rural transformation and PM_2.5. The results are as follows: (1) The level of urban–rural transformation shows an obvious upward trend with time. The development has progressed from asymmetrical north-east and south-west elevations to a more balanced pattern of north-east, middle-east, and west-west elevations. (2) The PM_2.5 concentration increased steadily, then fluctuated, and finally decreased. Notably, the general pattern has not changed much, and it is high in the east and low in the west. (3) Different subsystems of the urban–rural transformation have different impacts on air pollution at different stages. The influence of industrial transformation (IT) on PM_2.5 showed an inverted “N-shaped” curve of negative–negative–changes, and the industrial structure played a leading role in the spatiotemporal evolution of PM_2.5. An inverted “U-shaped” curve forms the left side of the impact of population transition (PT) on PM_2.5. Land transformation (LT) has a “U-shaped” curve for its effect on PM_2.5. This study provides a new perspective on the topic of PM_2.5 and its connection to urban–rural integration, which is crucial to understanding the dynamics of this shift. To achieve the goal of high-quality development, this study supports regional initiatives to reduce PM_2.5 emissions in the Yellow River Basin. Moreover, the results of this study can provide a reference for decision-makers in the world’s densely populated areas that suffer from serious air pollution. Full article