Search

Self-perceived health is an important factor for assessing urban residents' satisfaction and quality of life. However, few have comprehensively investigated the impact of demographics, lifestyle and health awareness, indoor environment characteristics, and neighborhood features on self-perceived health. To fill this gap, we designed a framework using multivariable regressions to derive odd rations and to analyze the determinants of self-rated health, stratified into different sub-groups divided by gender, age, and neighborhood types. The study area is Chengdu, one of the most populous cities in western China. The results show that: (1) female respondents reported worse health, with household income level and marital status significantly affecting self-rated health; (2) elderly people reported the worst health, while unique factors affected only younger people (18–29 years old), such as gender, smoking, and indoor environment characteristics; and (3) different types of neighborhoods influence their residents' perception of health differently due to historical establishment, current population composition, and housing conditions. Our study provides new observations on neighborhood types, while agreeing with previous studies on the influences of gender and age. We contribute to the field by providing a more complex understanding of the mechanism by which people rate their own health, which is important for understanding the satisfaction of urban residents and the built environment in which they live. [ABSTRACT FROM AUTHOR]

In an effort to gauge the global pandemic's impact on social thoughts and behavior, it is important to answer the following questions: (1) What kinds of topics are individuals and groups vocalizing in relation to the pandemic? (2) Are there any noticeable topic trends and if so how do these topics change over time and in response to major events? In this paper, through the advanced Sequential Latent Dirichlet Allocation model, we identified twelve of the most popular topics present in a Twitter dataset collected over the period spanning April 3rd to April 13th, 2020 in the United States and discussed their growth and changes over time. These topics were both robust, in that they covered specific domains, not simply events, and dynamic, in that they were able to change over time in response to rising trends in our dataset. They spanned politics, healthcare, community, and the economy, and experienced macro-level growth over time, while also exhibiting micro-level changes in topic composition. Our approach differentiated itself in both scale and scope to study the emerging topics concerning COVID-19 at a scale that few works have been able to achieve. We contributed to the cross-sectional field of urban studies and big data. Whereas we are optimistic towards the future, we also understand that this is an unprecedented time that will have lasting impacts on individuals and society at large, impacting not only the economy or geo-politics, but human behavior and psychology. Therefore, in more ways than one, this research is just beginning to scratch the surface of what will be a concerted research effort into studying the history and repercussions of COVID-19. [ABSTRACT FROM AUTHOR]

The rationality and efficiency of the spatial structure of an urban park system are critical in building a livable urban environment. Fractal theory is currently treated as the frontier theory for exploring the law of complex systems; however, it has rarely been applied to urban park systems. This study applied the aggregation, grid and correlation dimension models of fractal theory in Fuzhou, China. The spatial structure and driving factors of the urban park system were analyzed and an innovative model was proposed. The evidence shows that the spatial structure of the park system has fractal characteristics, although self-organization and optimization have not yet been fully formed, revealing a multi-core nesting pattern. Moreover, the core is cluster of four popular parks with weakening adsorption, and the emerging Baima River Park is located at the geometric center, which is likely to be further developed. The system structure is primarily driven by geographical conditions, planning policies, and transportation networks. Against this backdrop, an innovative model for the park system was proposed. The central park has heterogeneity and synergistic development, relying on the kinds of flow which can lead to the formation of a park city, a variation of a garden city. At the regional scale, relying on the geographical lines, the formation of a regional park zone could be realized. These findings provide new perspectives to reveal the spatial structure of urban park systems. The information derived can assist policy makers and planners in formulating more scientific plans, and may contribute to building a balanced and efficient urban park system. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic affected how people interact with the built environment and ways of human habitation are facing significant challenges. However, the existing literature has not adequately addressed how the built environment affected the early prevalence of the pandemic. This research aims to extend the existing literature by relating the initial stage pandemic conditions with more comprehensive measures of the built environment including density, diversity, road network, and accessibility at the county level across the United States and conducting bi-weekly comparisons. We collected infection, death, and mortality data in 3141 counties between 1 March to 8 June 2020 and collected seventeen built environment attributes. Our results show that: (1) Road density and street intersection density were significantly associated with the infection rate; (2) Population density only maintained a positive correlation to the prevalence of COVID-19 during the first two weeks, after which the relationship became negative; and (3) Transit accessibility also contributed significantly to the pandemic and the accessibility of transit-oriented jobs was highly correlated to the infection rate in the first two weeks. The study provides valuable insights for policymakers and stakeholders to adopt resource allocation strategies for context-specific conditions. [ABSTRACT FROM AUTHOR]

As part of the climate policy to meet the 2 °C target, actions in all economic sectors, including agriculture, are required to mitigate global greenhouse gas emissions. While there has been an ever‐increasing focus on agricultural greenhouse gas (AGHG) emissions, limited attention has been paid to their economic drivers in the globalized world economy and related mitigation potentials. This paper makes a first attempt to trace AGHG emissions via global trade networks using a multiregional input‐output model and a complex network model. Over one third of global AGHG emissions in 2012 can be linked with products traded internationally, of which intermediate trade and final trade contribute 64.2% and 35.8%, respectively. Japan, the United States, Germany, the United Kingdom, and Hong Kong are the world's five largest net importers of embodied emissions, while Ethiopia, Australia, Pakistan, India, and Argentina are the five largest net exporters. Some hunger‐afflicted developing countries in Asia and Africa are important embodied emission exporters, due to their large‐scale exports of agricultural products. Trade‐related virtual AGHG emission transfers shape a highly heterogenous network, due to the coexistence of numerous peripheral economies and a few highly connected hub economies. The network clustering structure is revealed by the regional integration of several trading communities, while hub economies are collectors and distributors in the global trade network, with important implications for emission mitigation. Achieving AGHG emission reduction calls for a combination of supply‐ and demand‐side policies covering the global trade network. Key Points: Over one third of global AGHG emissions can be linked with intermediate trade (64.2%) and final trade (35.8%)Trade‐related virtual AGHG emission transfers shape a highly heterogenous networkThe hub economies are of great importance on global AGHG emission mitigation [ABSTRACT FROM AUTHOR]

China is facing both serious water scarcity and climate change challenges simultaneously. There is an urgent need to identify the synergies between water conservation and greenhouse gas (GHG) emission mitigation. This paper aims to trace the embodied water uses and GHG emissions (i.e., CO 2 , CH 4 and N 2 O) via China's domestic supply chains in 2012 and to explore their common important paths for driving the water withdrawals and the emissions of GHGs using the environmentally-extended input-output analysis and the structural path analysis. The results show a significant coupling between the embodied water uses and the embodied emissions of CO 2 , CH 4 and N 2 O in the supply chains on a national scale. The water-GHG nexus relationships from the consumption-side are systematically revealed in terms of industrial sectors, final demands and supply chain paths. The top 20 nexus paths ranked by water together accounted for 37.40% of the national total water withdrawals. Ranked by GHGs, the proportions were 22.46% of the total GHG emissions. Synergies in saving water resources and mitigating the emissions of CO 2 , CH 4 and N 2 O can be achieved through controlling important supply chain paths and critical transmission sectors. The highly interlinked nature of water and climate change issues requires comprehensive solutions to reduce future freshwater and GHG emission needs to maintain sustained economic growth in China. Image 1 • We trace embodied water uses and GHG emissions in 2012 Chinese supply chains. • We develop Sankey diagrams to illustrate the water-GHG nexus relationships. • The top 20 paths ranked by water contributed 37.40% of the total water uses. • The top 20 paths ranked by GHG contributed 22.46% of the total GHG emissions. • Critical nexus sectors and paths are potential water and climate policy targets. [ABSTRACT FROM AUTHOR]

This study investigated the urban growth dynamics of urban regions. The study area was the Marmara Region, one of the most densely populated and ecologically diverse areas in Turkey. Using CORINE land cover data for 2006, 2012, and 2018, the study utilized multiple correspondence analyses and cluster analyses, to analyze land cover changes. The resulting maps, visualized in GIS, revealed the rapid urban transformation of the regional structure, formerly comprised of four distinct areas, into a more complex structure, in which densification and sprawl occur simultaneously. Our findings demonstrated a dissonance between the spatial dynamics of the Marmara Region during the study period, and the capacity and scope of the simultaneously initiated regional policies and mega-projects. This uncoordinated approach has endangered the region's sustainable development. The paper, therefore, discusses the importance of land use planning and transboundary collaboration for sustainable regional development. Beyond the local case, the results contribute to critical theories in regional planning by linking theory and practice. [ABSTRACT FROM AUTHOR]

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide. [ABSTRACT FROM AUTHOR]

Abstract Agriculture is one of the most important sectors for global anthropogenic methane (CH 4) and nitrous oxide (N 2 O) emissions. While much attention has been paid to production-side agricultural non-CO 2 greenhouse gas (ANGHG) emissions, less is known about the emissions from the consumption-based perspective. This paper aims to explore the characteristics of agricultural CH 4 and N 2 O emissions of global major economies by using the latest emission data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and the recently available global multi-regional input-output model from the World Input-Output Database (WIOD). The results show that in 2014, the 42 major economies together accounted for 60.7% and 65.0% of global total direct and embodied ANGHG emissions, respectively. The consumption-based ANGHG emissions in the US, Japan, and the EU were much higher than their production-based emissions, while the converse was true for Brazil, Australia, and India. The global-average embodied ANGHG emissions per capita was 0.7 t CO 2 -eq, but major developing countries such as China, India, Indonesia and Mexico were all below this average value. We find that the total transfer of embodied ANGHG emissions via international trade was 622.4 Mt CO 2 -eq, 11.9% of the global total. China was the largest exporter of embodied ANGHG emissions, while the US was the largest importer. Most developed economies were net importers of embodied emissions. Mexico-US, China-US, China-EU, China-Japan, China-Russia, Brazil-EU, India-EU and India-US formed the main bilateral trading pairs of embodied emission flows. Examining consumption-based inventories can be useful for understanding the impacts of final demand and international trade on agricultural GHG emissions and identifying appropriate mitigation potentials along global supply chains. Highlights • We explore agricultural CH 4 and N 2 O emission characteristics of 42 major economies. • Impacts of final demand and international trade on ANGHG emissions are evaluated. • 622.4 Mt CO 2 eq CH 4 and N 2 O emissions in 2014 were embodied in international trade. • Developed economies as net importers had high per capita emission requirements. [ABSTRACT FROM AUTHOR]