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Urban parks, pivotal in fostering physical activity, mental well-being, and environmental stewardship, are integral to green infrastructure planning. Despite advances in georeferenced data applications, existing park classifications often overlook actual visitation patterns. This study reclassifies urban parks using over 5.9 million records from approximately 330 thousand visitors across 300 Tokyo parks, comparing with size-based park categorizations. We employed a range of analytical tools, including principal component analysis, Isolation Forest algorithm, various clustering algorithms, and the Gini index. Our findings first revealed four key visitation indicators, activity intensity, utilization efficiency, temporal occupancy, and revisit volume. These indicators uncovered parks with atypical visitation patterns within each size category, leading to three novel park classifications, everyday leisure parks, social destination parks, and seasonal activity parks. Moreover, we discovered notable disparities in distances traveled to parks, particularly during nights, weekends, and holidays, with pronounced inequalities in seasonal activity parks and smaller parks. The findings advocate for a nuanced park management strategy, prioritizing maintenance and amenity development aligned with observed visitation patterns to enhance recreational potential, thereby contributing insights to urban park research that support the advancement of green infrastructure planning and policy aimed at improving park utility and enjoyment. • Unveiled four principal visitation indicators, capturing nuanced spatio-temporal visitation behaviors. • Detected atypical visitation patterns across all size-based park categories, highlighting disparities in park usage. • Identified three distinct park categories based on visitation data, each with unique visitation patterns. • Significant trip distance disparities were found in smaller and seasonal activity parks. [ABSTRACT FROM AUTHOR]

This study examines the effects of subway extension on housing prices in affluent urban neighborhoods, focusing on the Q-line extension in Manhattan, New York City. Utilizing synthetic controls and treatment effects estimators, distinct pricing trends across property types are revealed, particularly condominiums. The observed pricing dynamics deviate from the assumption that increasing supplies are associated with price discounts. Moreover, the connection between price discounts and noise complaints is not observed, highlighting the significant role of demand-side factors, especially neighborhood characteristics, in shaping housing prices. An unexpected demographic shift is observed, suggesting that the Q-line extension may disproportionately benefit non-white and low-income groups, challenging the prevailing gentrification narrative. We use the term "inverse premiumization" to denote the phenomenon where anticipated price increases in affluent neighborhoods due to transit improvements fail to materialize. Furthermore, our analysis of speculative behavior reveals a spike in short-term growth during the public notice period, gradually slowing down during construction and operation phases. These findings offer the nuanced adverse effects of subway extension on housing prices, contributing to our understanding of short-to medium-term price premiums and discounts. These insights are key considerations for city planners and policymakers navigating urban development, housing market, and speculative behavior dynamics. • Examines the short-to medium-term impact of the Q-line extension on housing prices in affluent urban neighborhoods. • Reveals that increases in supply not necessarily lead to price discounts. • Uncovers unintended benefits of subway extension for non-whites and low-income groups. • Introduces the concept of "inverse premiumization" related to gentrification and speculative behavior. • Provides a comprehensive analysis of the relationship between subway extension and property values. [ABSTRACT FROM AUTHOR]

Previous research has explored the effect of the built environment on the spread of the coronavirus disease (COVID-19) pandemic. This study extends the existing literature by examining the relationship between pandemic prevalence and density, employment, and transit factors at the county level. Using multilinear spatial-lag regressions and time series clustering analyses on the Smart Location Database encompassing 3141 counties in the United States, our findings reveal the following: (1) Density, employment, and transit variables yield heterogeneous effects to infection rate, death rate, and mortality rate. (2) Pedestrian-oriented road density is positively correlated to the prevalence of COVID-19, every 0.011 miles/acre increase is associated with 1% increase in the infection rate. (3) A consistent negative correlation is observed between jobs per household and infection rate, while a decrease in unemployment rate leads to an increase in the death rate. (4) The results from time series analysis suggest that areas characterized by low auto-oriented intersection density but high pedestrian-oriented road density are more susceptible to the impacts of pandemics. This highlights the need to prioritize pandemic prevention efforts in the suburban and rural areas with low population density, as emphasized in existing literature emphasized. • Relating the spatial distribution of pandemic with build environment at the county level. • Density, employment, and transit variables yield heterogeneous effects on infection rate, death rate, and mortality rate. • Strong temporal variation of pandemic severity throughout the study period. • Pandemic prevention attention needed to be placed in the suburban and rural areas with low population density. [ABSTRACT FROM AUTHOR]

The relationship between dense urban development, often represented by high-rise buildings, and its location vis-à-vis metro stations reflects the connection between transportation infrastructure and land use intensity. Existing literature on high-rise buildings has focused either on developed countries or on cities where urban and public transit developments have occurred in an uncoordinated manner. This paper examines the following questions: What is the spatial proximity and spatial correlation between high-rise buildings and metro stations in different stages of development in various parts of the city? What were some of the factors that resulted in the observed patterns? The results suggest that buildings constructed after 2000 and buildings within the urban core/Shanghai Proper districts had a greater spatial proximity to the metro stations. However, the spatial correlation, measured by the number of high-rise buildings within a 500-m buffer from the nearest metro stations and the time-distance to these stations, is stronger in the outer districts than in the urban core. These differences can be accounted for by Shanghai's stages of urban development, the existence of metro infrastructure when high-rise development was undertaken, and the city's land use policies. This case study sheds light on the relationship between high-density developments and metro systems in other large cities in China and other developing countries where rapid urban development coincides with the establishment of a comprehensive public transit system. • Investigate the relationship between high-density development and its location vis-à-vis metro stations. • Understand the different spatial distribution patterns of high-rise buildings between suburban districts and urban core. • Application of the spatial proximity and spatial correlation measures between high-rise buildings and metro transit. [ABSTRACT FROM AUTHOR]

Highlights • People choose LCT provided high population density and sufficient access to public transit. • Land-use diversity alone was not found to be significant in affecting LCT mode choice. • The proliferation of gated communities reduces connectivity and has a negative effect on LCT. Abstract Developing less auto-dependent urban forms and promoting low-carbon transportation (LCT) are challenges facing our cities. Previous literature has supported the association between neighborhood form and low-carbon travel behaviour. Several studies have attempted to measure neighborhood forms focusing on physical built-environment factors such as population and employment density and socio-economic conditions such as income and race. We find that these characteristics may not be sufficiently fine-grained to differentiate between neighborhoods in Chinese cities. This research assesses characteristics of neighborhood spatial configuration that may influence the choice of LCT modes in the context of dense Chinese cities. Urban-form data from 40 neighborhoods in Chengdu, China, along with a travel behaviour survey of households conducted in 2016, were used to generate several measures of land use diversity and accessibility for each neighborhood. We use principle component analysis (PCA) to group these variables into dimensions that could be used to classify the neighborhoods. We then estimate regression models of low-carbon mode choices such as walking, bicycling, and transit to better understand the significance of these built-environment differences at the neighbourhood level. We find that, first, members of households do choose to walk or bike or take transit to work provided there is relatively high population density and sufficient access to public transit and jobs. Second, land-use diversity alone was not found to be significant in affecting LCT mode choice. Third, the proliferation of gated communities was found to reduce overall spatial connectivity within neighborhoods and had a negative effect on choice of LCT. [ABSTRACT FROM AUTHOR]

Previous studies have frequently taken urban density as a surrogate for economic efficiency and applied composite scores such as urban intensity to assess the existing physical forms of towns. However, measures of intensity might nuance crude calculations of density while considering how such measures relate to future plans and the process of transformations. This paper used open source geospatial data and regulatory detailed planning to measure urban intensity of the existing and the planned. Spatial analytical techniques were applied to compute accessibility to destinations, building density, compactness of development, and diversity of land use function of sixteen specialty towns in China. The results showed that the regulatory detailed planning of these cases might not improve town planning measured by intensity in terms of spatial arrangement in the time horizon of 15–20 years. We found that well-integrated monocentric forms with the finer street network could potentially provide a more suitable spatial distribution for mid-sized towns measured by urban intensity, no matter whether the urban form is linear or center-spreading. Moreover, the comparison between the existing and the planned implies that the poly-centric urban form for mid-sized towns in China could end up with a lower score of urban intensity. We outline a framework of urban intensity measures that include analytical consideration of what is there now, planned to what is forthcoming, and conceptual in terms of time and space. • Differentiating urban intensity from density • Using urban block as the spatial unit of study is more accurate than a synthetic spatial grid. • Comparing existing and planned conditions of medium-sized towns • Evaluation and intervention of town planning policies [ABSTRACT FROM AUTHOR]

Urban intensity, in this paper, is measured by four related concepts: compactness, diversity, density, and connectivity. Together they lead to a single idea when considering spatial distributions potentially in a virtuous manner with regard to resource consumption, economic opportunity, social integration and environmental performance. The methodologies applied here included Moran's I, Shannon's index entropy, and accessibility isotimelines, which were then applied to real case scenarios in 20 towns in Zhejiang Province, selected based on their economic performances, population sizes, and geographical locations. Further inspection discovered that density, an outcome of urban form, is highly correlated to compactness, leading to its elimination. The results showed that among the varying spatial arrangements of urban activities, building footprints and infrastructural elements characterized by monocentric centers of use inscribed with well-defined and relatively uniform grids of streets and related networks, alongside of relatively integrated zones of use, seemed to perform best with regard to urban intensity. At the other end of the morphological spectrum, towns with sharp separations of uses and zones of development, often resulting in overall bifurcation of a town's spatial layout, performed less well. Also, linear forms for small towns were less favorable. [ABSTRACT FROM AUTHOR]

The rise of social networking platforms provides opportunities to examine the relationship between public emotion and housing price. This study investigates frequency and places of visits, population sentiment, and housing price using 8.7 million tweets retrieved from Manhattan, New York City in 2019. We implemented kernel density estimation, Getis-Ord G i hot spot analysis, and spatial lagged hedonic pricing models to identify the location variation of sentiment levels. The results show: (1) the spatial clustering of tweets frequency was highly related to land use types in places such as parks, financial districts, and train stations; (2) high sentiment levels coincided with high frequency clusters and higher positive sentiment is associated with higher housing price; and (3) sentiment level was significantly associated with housing price and building structure, amenities, and proximity to landmarks all had significant influences on housing price. The study indicates that a population with higher concentration of happiness correlates to higher property value and provides an innovative perspective to understand public sentiment in relation to housing price using social media data, supplemented by housing transaction data. We demonstrate a feasible framework for researchers and stakeholders to utilize in future urban and spatial geographical research. • Identify spatial clustering of tweets frequency and land use types. • Assess spatiotemporal characteristic of public sentiment using Twitter data. • Evaluate the relationship of public sentiment level to housing price at a neighborhood scale. [ABSTRACT FROM AUTHOR]

The rapid adoption of on-demand mobility services has brought a challenge in accessing their equity impacts at a regional scale due to the absence of consistent trip data and efficient transportation models. This study shows the potential of integrating synthetic population data and a choice-based optimization model to support large-scale mobility service region design with equity concerns. We propose a decision support tool that specifies budget-constrained optimal service regions for new mobility services under one of several objectives such as minimizing consumer surplus insufficiency. We test using New York State synthetic data and illustrate its application by considering new ride-hailing and microtransit services. The "cost of equity" is measured: under budget level II, each vehicle requires a subsidy of $71.39/day to minimize disparity and $29.86/day to minimize insufficiency. Our findings contribute to the literature by quantifying trade-offs among different services, objectives, and budget levels, thereby supporting funding and resource allocation. [ABSTRACT FROM AUTHOR]

The growing recognition of the societal implications stemming from technological advancements highlights the need for innovative governance approaches, particularly in urban environments. Focused on connected and autonomous vehicles (CAVs), this study proposes an integrated analytical framework for a dedicated open data platform (ODP). By integrating thematic analysis with process landscape and stakeholder analysis methods, we present a holistic CAV-ODP model positioned to optimize benefits across industries, public sectors, and society as a whole. We argue that multiple stakeholders can enhance both supply and demand aspects of the CAV-ODP, catalyzing activities such as data access, management, transformation, incubation, coordination, education, and application. Importantly, it advocates for government-led integration of interests spanning private and public domains, fostering a collaborative social network for data application and innovation. This inquiry not only reveals the social benefits generated by open CAV data but also underlines the significance of the CAV-ODP platform as an innovative governance arrangement, serving as a catalyst for a culture of sharing, bolstering collaborative governance, and preserving public value amid disruptive technological changes. Overall, the concept of government leadership is universally applicable, with the tangible power dynamics among the government, private sectors, and society influencing the expeditious materialization of the proposed framework. • Pioneer in comprehensive CAV open data application. • Stakeholders should optimize both supply and demand sides through data access, management, and incubation. • Reveals social benefits of CAV open data and the significance of a CAV-ODP platform as a social network. • Efficient platform operation boosts sharing culture and fosters a robust social environment. • Government leadership is universally applicable, with tangible power dynamics among private sectors and society. [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]

As the world's largest CH 4 emitter, China's CH 4 emissions contribute to climate change more than the amount emitted by many developed countries combined. The rapid growth of China's coal demand has important implications for CH 4 emissions from coal mining or coal mine methane (CMM) emissions. This paper aims to present an overview of bottom-up estimation of China's CMM emissions, including the trend in the last four decades and the limitations of current understanding on CH 4 emissions. Although characterized by significant differences in inventory compilation, statistically, the total CMM emissions rose from 4.64 to 16.41 Tg with a peak of 21.48 Tg from 1980 to 2016. Large discrepancies of inventory results existed in previous studies, which were affected by the coverage of emission sources, emission factors and activity-level data. The disagreements can be largely attributable to the emission factors of underground mining, which contain substantial variances in both spatial and temporal dimensions. To develop more reliable CMM inventories and make targeted mitigation measures, more attention should be paid to the transparency of the estimated results, coal statistics, on-site CMM emission factors, and the emissions from abandoned coal mines. As the leading CH 4 emission source in China, the estimations of CMM emissions urgently need to overcome existing and emerging challenges for compiling a consistent and accurate inventory. Unlabelled Image • We perform an overview of current bottom-up estimation of China's CMM emissions. • CMM emissions over 1980–2016 rose from 4.64 to 16.41 Tg with a peak of 21.48 Tg. • Inventory results were improved along with large discrepancies and disagreements. • We discuss the types of emission source, emission factors and activity-level data. • The limits of current understanding and future research prospects are provided. [ABSTRACT FROM AUTHOR]

• The traditional mixed-use neighborhoods are in need of intensified urban retrofitting projects. • The work-unit could benefit from comprehensive plans rather than patching individual pieces. • The most pressing issue for gated communities is their obsolete spatial functions. • Resettlement housing should provide more opportunities to connect with the rest of the city. Rapid urban expansion in China has created both opportunities and challenges for promoting active transport in urban residential communities. Previous studies have shown that the urban form at the city scale has affected active transport in Chinese cities. However, there is less agreement about how the physical and social variations of neighborhood types should be addressed. This research investigates the four most representative neighborhood types found in Chinese cities: traditional mixed-use, slab block work-unit, gated community, and resettlement housing. Household travel diaries conducted in Chengdu in 2016 were analyzed using binary logistic regressions, supplemented by informal onsite interviews. The findings indicate significant variations in the use and accessibility of active transport in each neighborhood type for non-work trips. This suggests that each neighborhood type may need different strategies for promoting active transport: (1) the traditional mixed-use neighborhoods are in need of intensified urban retrofitting projects to reclaim public open space; (2) the work-unit could benefit from comprehensive plans rather than a patchwork of projects; (3) while opening up gated communities can improve porosity across neighborhoods and promote active transport, the more pressing issue may be their inability to keep up with the transportation needs of the residents; and (4) residents of resettlement housing should have better access to employment using transit and non-motorized modes. [ABSTRACT FROM AUTHOR]

Globalization drives anthropogenic methane (CH 4) emissions from upstream to downstream agents through global supply chains. However, previous studies have overlooked the indirect CH 4 emissions enabled by primary suppliers. In this study, we use the Ghosh Multi-Regional Input-Output (MRIO) model and Structural Decomposition Analysis (SDA) to explore the temporal changes in global income-based CH 4 emissions and decompose the socioeconomic drivers. Our results show that global income-based CH 4 emissions increased by 17.21% from 2001 to 2014, with per capita primary inputs and technology being the two key drivers. Specifically, in China and India, income-based CH 4 emissions were mainly from agriculture, and input structure was responsible for restraining the growth in CH 4 emissions from their agricultural activities. On the other hand, service activities dominated the United States and the European Union, and production output structure was accountable for their emission growth related to service activities. To achieve global CH 4 emission mitigation, key sectors of major economies with high income-based CH 4 emissions should optimize their primary input structures, production output structure, and production technologies from the supply-side. It is crucial to reduce enabled CH 4 emissions in emerging economies, particularly in Asia, through the adjustment of primary input activities. [Display omitted] • Per capita primary inputs and technology dominated global CH 4 emission changes. • Income-based emissions decreased in West & Central Europe and the United States. • Agriculture and mining contributed to the high emissions in most emerging economies. • Production output structure accounted for the growth in service-enabled emissions. • Spatial heterogeneity of drivers of income-based CH 4 emissions was discussed. [ABSTRACT FROM AUTHOR]

With the expansion of economic globalization and the growth of international trade, the pulling effect of household consumption to global anthropogenic CH 4 emissions related to production activities is becoming increasingly evident. This paper adopts a new perspective from the household-consumption side to investigate the CH 4 emissions of major economies in 2014 and compares it with the scenario under the final-demand-based perspective by combing the world input-output database and the latest emission data from the UNFCCC and EDGAR v5.0 database. Budgets of CH 4 emissions for 43 economies are established and trade connections & balances among major economies are explored. Results show that consumption-driven economies are allocated more CH 4 emissions in the household-consumption-based accounting (HCBA) framework compared to the final-demand-based accounting (FDBA) framework. The total trade-related transfer of CH 4 emissions is shown to sum up to 19% and 27% of the global total under the HCBA and FDBA frameworks, respectively. The household-consumption-based CH 4 emissions of China, India, Indonesia and Mexico are much lower than their final-demand-based CH 4 emissions, while the converse is true for the United States, Russia, Japan, the United Kingdom and Germany. The new accounting framework provides a new view to understand trade-related CH 4 emissions of major economies and to identify the role of household consumption in global supply chains, offering important implications for greenhouse gas emission mitigation. Unlabelled Image • Latest CH 4 emission inventories for major economies in 2014 are provided. • Household-consumption-based accounting of CH 4 emissions is firstly conducted. • The findings are compared with those under the final-demand-based accounting. • Consumption-oriented economies should take more responsibility for CH 4 reduction. [ABSTRACT FROM AUTHOR]

Ensuring energy, water and food security is a core challenge as well as an opportunity to national sustainable development. This paper aims to perform an in-depth analysis on demand-driven energy, water and land resource requirements by Chinese economy 2012 and corresponding energy-water-land nexus relationships in its supply chains by using the input-output analysis and the structural path analysis. Results show that Agriculture and Light industry, mainly related to household consumption, are critical aggregated sectors for water and land requirements, in contrast to Heavy industry and Construction , which are related to investment for energy requirements. 15.41% of the total energy requirement, 44.18% of water and 58.64% of land can be attributed to the top 20 supply chain paths. The energy-water-land nexuses are explored by industrial sector, supply chain path and final demand category from the perspective of embodiment. On the final demand side, Agriculture , Light industry , Construction and Service are critical nexus sectors. Major sectoral nexus paths in terms of energy-land, energy-water, water-land, and energy-water-land nexuses are further identified. The energy-water-land nexus in China's supply chains reflect a strong dependence relationship of energy, water and land resource requirements, which then demonstrate essential intervention points of demand-side resource management. • Demand-driven energy, water and land requirements in China 2012 are revealed. • Embodied energy, water and land uses via domestic supply chains are traced. • The energy-water-land nexus are explored from the perspective of embodiment. • Critical sectors and supply chain paths of energy-water-land nexus are identified. [ABSTRACT FROM AUTHOR]

Driving fatigue cost is a major component of vehicle drivers' travel costs in an intercity or regional network. The charging behavior of electric vehicle (EV) drivers, which is generally synchronized with drivers' resting behavior, can contribute to the mitigation of drivers' fatigue, especially after a prolonged driving period. Overlooking the impact of driving fatigue on the travel cost may overestimate the side-effects of the charging behavior for EV drivers, and result in biased flow and charging demand distribution. In this study, by considering the fatigue as part of the travel cost, we make the first attempt to investigate the impact of EV drivers' charging and resting behaviors on their fatigue cost, and thus their travel plans and resultant flow distribution across the network. To this end, a novel network equilibrium modeling framework is first developed to capture the interaction among EV drivers' travel plans, which specify the routing, recharging, and resting plans on a general intercity or regional network where charging stations and rest stops are deployed. When traveling between their origins and destinations, EV drivers are assumed to determine their travel plans to minimize their individual travel cost composed of driving time, rest time, charging cost, and fatigue cost, while preventing their batteries from being exhausted. The equilibrium model is then formulated as a variational inequality and transformed into a nonlinear optimization problem. An efficient solution algorithm integrating column generation and Benders decomposition approach is proposed to solve the established optimization problem. Numerical examples are presented to demonstrate the performance of the proposed models and solution algorithm. Numerical results validate that considering the impact of driving fatigue on the travel cost emphasizes the need for en-route charging for EV drivers with long-distance trips, and has an appreciable impact on the network flow and charging demand distribution. In addition, large-sized batteries and fast chargers may not necessarily reduce drivers' travel costs for long-distance travel since charging behavior can be synchronized with drivers' resting behavior and thus contribute to the mitigation of drivers' fatigue. • Driving fatigue cost is a major component of vehicle drivers' travel costs. • Charging time can contribute to the mitigation of electric vehicle drivers' fatigue. • Drivers' travel plan specifies their routing, recharging, and resting plans. • A novel network equilibrium model is developed to delineate drivers' travel plans. • An efficient solution algorithm is proposed. [ABSTRACT FROM AUTHOR]

Coal resource-based cities (CRBCs) represent a significant portion of resource-based cities in China, accounting for 40 % of prefecture-level cities. However, existing research on CRBCs primarily focuses on outcome-based sustainability analysis, neglecting production-based evaluation. To bridge this gap, our study investigates sustainability of CRBC using the inclusive wealth index (IWI), IWI per capita, and IWI density. We find substantial variations in CRBCs' inclusive wealth influenced by regional distribution and life stages. Regionally, sustainable development capacity follows the sequence "east > west > central > northeast", with the northern region benefiting from abundant non-renewable natural capital. In terms of life stage sustainable development, the sequence is "growing > regenerative > grown-up > recessionary". The decline in wealth performance from growing to recessionary types confirms the negative impact of the "resource curse" phenomenon, where resource dependence hampers growth. However, the regenerative type demonstrates the potential for transformation through sustainable development strategies. Our study presents practical strategies to enhance regional policies by adopting the IWI accounting indicator system proposed by the United Nations Environment Programme and localizing it to align with the characteristics of CRBCs in China. This innovative approach facilitates the evaluation of CRBCs' sustainable development performance. • Significant regional discrepancy exisits in Chinese CRBCs. • Implement policies in recessionary CRBCs for wealth and SDGs attainment. • Transform regenerative CRBCs via sustainable development strategies. • Offer guiance to CRBCs globally as a valuable reference. [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]

COVID-19 was announced by the World Health Organization as a pandemic on March 11, 2020. Not only has COVID-19 struck the economy and public health, but it also has deep influences on people's feelings. Twitter, as an active social media, is a great database where we can investigate people's sentiments during this pandemic. By conducting sentiment analysis on Tweets using advanced machine learning techniques, this study aims to investigate how public sentiments respond to the pandemic from March 2 to May 21, 2020 in New York City, Los Angeles, London, and another six global mega-cities. Results showed that across cities, negative and positive Tweet sentiment clustered around mid-March and early May, respectively. Furthermore, positive sentiments of Tweets from New York City and London were positively correlated with stricter quarantine measures, although this correlation was not significant in Los Angeles. Meanwhile, Tweet sentiments of all three cities did not exhibit a strong correlation with new cases and hospitalization. Last but not least, we provide a qualitative analysis of the reasons behind differences in correlations shown above, along with a discussion of the polarizing effect of public policies on Tweet sentiments. Thus, the results of this study imply that Tweet sentiment is more sensitive to quarantine orders than reported statistics of COVID-19, especially in populous megacities where public transportation is heavily relied upon, which calls for prompt and effective quarantine measures during contagious disease outbreaks. • Machine learning techniques analyze links between public sentiment and lockdown policies. • Tweet sentiment presents positive correlations between restricted policies and numbers of confirmed cases/hospitalization. • Quarantine policies could polarize sentiments presented on social media. [ABSTRACT FROM AUTHOR]

As "the world's factory", China's energy consumption and GHG emissions can be largely attributed to its manufacturing economy. This paper aims to examine energy-related methane (CH 4) and carbon dioxide (CO 2) emissions by Chinese economy from a consumption-based perspective, and to explore the energy-climate-manufacturing nexus relationship in its supply chains. Nearly three-quarters of China's energy-related GHG emissions in 2012 were associated with the manufacturing industry directly or indirectly. Among which, over two-fifths of the national CH 4 and CO 2 emissions were embodied in the final demand of manufacturing products, mainly driven by the exports and capital formation. Meanwhile, manufacturing sectors served as important intemediate transmission nodes of embodied emissions for other industries such as construction and services. More than 80% and 40% of the embodied emissions in the sectors of construction and services were related to the intermediate uses of manufacturing products, respectively. Critical supply chain paths for linking embodied GHG emissions with manufacturing sectors were extracted through the structural path analysis technique. The top 30 common paths were responsible for about one fifth of the total CH 4 and CO 2 emissions. Three main transmission nodes of embodied energy-related GHG emission flows were identified. While approximately half of the energy-related CH 4 emissions occurred at the fourth or higher production layers, the CO 2 emissions were distributed evenly across the production layers. Mitigating energy-related GHG emissions associated with manufacturing economy by adjusting critical industrial sectors and final demands provides new insights for understanding the transitions of China's manufacturing industries to a low-carbon economy. Unlabelled Image • 75% of China's energy-related GHG emissions were related to manufacturing sectors. • Over 40% of the emissions were embodied in final demand of manufacturing products. • Energy-climate-manufacturing nexus relationships and structural paths are explored. • The co-control of CH 4 and CO 2 emissions call for actions on entire supply chains. [ABSTRACT FROM AUTHOR]