Increasing impacts of China's oil and gas demands on global CH4 emissions.

The energy sector stands out as a main contributor to increasing global methane (CH4) emissions. Given China's heavy dependence on energy imports, a closer examination of its oil and gas-related CH4 emissions becomes imperative. This study conducts an in-depth analysis of China's contribution to global CH4 emissions stemming from its consumption of crude oil and natural gas since 2000. The results indicate that CH4 emissions from crude oil and natural gas imports rose from 614 Gg in 2000 to 7692 Gg in 2019. When considering domestic production, the demand-induced CH4 emissions in 2019 increased to approximately 10754 Gg (equivalent to 320 Mt CO2-eq and 887 Mt CO2-eq based on global warming potential (GWP) values at the 100-year and 20-year time period), of which 72 % were related to crude oil and natural gas imports. The primary contributor to this increase in CH4 emissions was the expansion of the trade scale. The growth trend of crude oil imports-induced CH4 emissions was also positively influenced by emission intensity and trade structure, but these two drivers had a negative impact on the growth of natural gas imports-induced CH4 emissions. The virtual transfer of CH4 emissions via international oil and gas trade requires urgent policy attention. In collaboration with its trading partners, China should take aggressive actions to achieve meaningful mitigation in CH4 emissions associated with the oil and gas trade.

How do density, employment and transit affect the prevalence of COVID-19 pandemic? A study of 3,141 counties across the United States.

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.

Dynamic topic modeling of twitter data during the COVID-19 pandemic.

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.

Global temporal evolution of CH4 emissions via geo-economic integration.

Globalization has characterized geo-economic integrations of world regions via South-South, North-North, and South-North trades, which play a critical role in displacing global greenhouse gas emissions. Based on the global CH4 emission inventories from the EDGAR database and the global multi-region input-output accounts from the EORA database, this study explores the trade-induced CH4 emission transfers of 20 geographical regions of the world from 1990 to 2015. Global total CH4 emissions increased by 19.13% in 2015 compared to 1990, while trade-related emissions increased by 46.28% over the same period. Western Europe, the USA, Japan, Other East Asia, and Mainland China were the largest five importers of embodied CH4 emissions, while Sub-Saharan Africa, Russia, Middle East, Mainland China, and Southeast Asia were the largest five export regions of embodied CH4 emissions. Substantial agriculture- and energy-related CH4 emissions were transferred from developed regions to developing regions. The trade between economies from the global south and the global north had undergone positive changes, with the trade structure and transfer path showing a trend of divergence. Among the total CH4 emissions embodied in international trade, the CH4 emissions embodied in South-North trade accounted for more than half (55.94-62.72 Tg, 71%-55%) from 1990 to 2015. The CH4 emissions embodied in the South-South trade accounted for 19%-34% (14.62-39.46 Tg), and the proportion of CH4 emissions embodied in the North-North trade appeared to be relatively small (10%-11%, 8.05-12.82 Tg) during the period. It is imperative to strengthen South-South, South-North, and North-North cooperation in multilateral trade to jointly cut down the CH4 emissions among world regions.

Why are methane emissions from China's oil & natural gas systems still unclear? A review of current bottom-up inventories.

There is growing awareness and concern on methane (CH4) emissions from China's oil and natural gas (ONG) systems owing to the carbon neutral target. This paper aims to present a comprehensive review on the bottom-up inventories of the CH4 emissions from the perspective of the ONG systems in China. The trend and magnitude of total emissions in the last four decades were revealed and limitations of current estimations were explored. Previous studies showed that the average CH4 emissions from China's ONG systems have almost tripled from 1980 (760 Gg) to 2015 (2180 Gg) with a trend of steady increase. However, the estimated values varied by an order-of-magnitude with the largest discrepancy of 2700 Gg. This discrepancy was unlikely caused mainly by the incompleteness of estimation, since dominant emission sources were all covered by representative studies. Moreover, the differences of activity-level data were within ±10%, which ruled out the possibility that it was the main contributor to the large discrepancies. The emissions estimate has huge variation in large part because of differences in assumed emission factors (EFs) that vary by an order of magnitude. The difficulty was to determine which of the EFs were accurate due to measurement-based data availability. Thus, the large discrepancies stem from the scarcity of publicly available data, which enlarged the impact from various methods adopted by previous studies. For better understanding of CH4 emissions from the ONG systems in China, the measurements of facility-level emissions and statistics on the ONG infrastructure are required urgently. Due to the high cost and experience-oriented measurement work, international cooperation and communications are critical prerequisites for future CH4 emission estimates and effective mitigation strategies.

Global trade network and CH4 emission outsourcing.

The intensifying globalization contributes to the anthropogenic methane (CH4) emissions outsourcing, a strong greenhouse gas and harmful air pollutant, through the increasingly complex global trade network. However, the CH4 flow patterns embodied in global traded goods and services have not been interpreted from the perspective of a complex network. In this paper, we integrate global CH4 emission inventory from the EDGAR (the Emission Database for Global Atmospheric Research) databases, global multi-regional input-output model from the GTAP database, and complex network analysis to reveal the structural characteristics of the global CH4 flow network (GCFN). In the GCFN, more than one quarter of the global anthropogenic CH4 emissions in 2014 are associated with international trade. The top 20 economies contribute to about 70% of the total embodied CH4 emission flows. The GCFNs mainly consist of tripartite patterns centered on China, the USA and Russia. Some emerging countries, such as Thailand and Brazil, also exhibit dominated positions in different kinds of GCFNs. Moreover, the core-periphery structure of the GCFN confirms the existence of a few hub economies associated with a large amount of CH4 emissions. The results emphasize the multinational cooperation on global CH4 emission mitigation, and well-focused mitigation policies should be implemented on some key economies.

Mitigation potential of global ammonia emissions and related health impacts in the trade network.

Ammonia (NH3) emissions, mainly from agricultural sources, generate substantial health damage due to the adverse effects on air quality. NH3 emission reduction strategies are still far from being effective. In particular, a growing trade network in this era of globalization offers untapped emission mitigation potential that has been overlooked. Here we show that about one-fourth of global agricultural NH3 emissions in 2012 are trade-related. Globally they induce 61 thousand PM2.5-related premature mortalities, with 25 thousand deaths associated with crop cultivation and 36 thousand deaths with livestock production. The trade-related health damage network is regionally integrated and can be characterized by three trading communities. Thus, effective cooperation within trade-dependent communities will achieve considerable NH3 emission reductions allowed by technological advancements and trade structure adjustments. Identification of regional communities from network analysis offers a new perspective on addressing NH3 emissions and is also applicable to agricultural greenhouse gas emissions mitigation.

The impacts of the built environment on the incidence rate of COVID-19: A case study of King County, Washington.

With COVID-19 prevalent worldwide, current studies have focused on the factors influencing the epidemic. In particular, the built environment deserves immediate attention to produce place-specific strategies to prevent the further spread of coronavirus. This research assessed the impact of the built environment on the incidence rate in King County, US and explored methods of researching infectious diseases in urban areas. Using principal component analysis and the Pearson correlation coefficient to process the data, we built multiple linear regression and geographically weighted regression models at the ZIP code scale. Results indicated that although socioeconomic indicators were the primary factors influencing COVID-19, the built environment affected COVID-19 cases from different aspects. Built environment density was positively associated with incidence rates. Specifically, increased open space was conducive to reducing incidence rates. Within each community, overcrowded households led to an increase in incidence rates. This study confirmed previous research into the importance of socioeconomic variables and extended the discussion on spatial and temporal variation in the impacts of urban density on the spread of COVID, effectively guiding sustainable urban development.

Methane emissions of major economies in 2014: A household-consumption-based perspective.

With the expansion of economic globalization and the growth of international trade, the pulling effect of household consumption to global anthropogenic CH4 emissions related to production activities is becoming increasingly evident. This paper adopts a new perspective from the household-consumption side to investigate the CH4 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 CH4 emissions for 43 economies are established and trade connections & balances among major economies are explored. Results show that consumption-driven economies are allocated more CH4 emissions in the household-consumption-based accounting (HCBA) framework compared to the final-demand-based accounting (FDBA) framework. The total trade-related transfer of CH4 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 CH4 emissions of China, India, Indonesia and Mexico are much lower than their final-demand-based CH4 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 CH4 emissions of major economies and to identify the role of household consumption in global supply chains, offering important implications for greenhouse gas emission mitigation.

Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19.

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.

How the manufacturing economy impacts China's energy-related GHG emissions: Insights from structural path analysis.

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 (CH4) and carbon dioxide (CO2) 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 CH4 and CO2 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 CH4 and CO2 emissions. Three main transmission nodes of embodied energy-related GHG emission flows were identified. While approximately half of the energy-related CH4 emissions occurred at the fourth or higher production layers, the CO2 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.

China's CH4 emissions from coal mining: A review of current bottom-up inventories.

As the world's largest CH4 emitter, China's CH4 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 CH4 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 CH4 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 CH4 emission source in China, the estimations of CMM emissions urgently need to overcome existing and emerging challenges for compiling a consistent and accurate inventory.

Socioeconomic determinants of China's growing CH4 emissions.

Reducing CH4 emissions is a major global challenge, owing to the world-wide rise in emissions and concentration of CH4 in the atmosphere, especially in the past decade. China has been the greatest contributor to global anthropogenic CH4 emissions for a long time, but current understanding towards its growing emissions is insufficient. This paper aims to link China's CH4 emissions during 2005-2012 to their socioeconomic determinants by combining input-output models with structural decomposition analysis from both the consumption and income perspectives. Results show that changes in household consumption and income were the leading drivers of the CH4 growth in China, while changes in efficiency remained the strongest factor offsetting CH4 emissions. After 2007, with the global financial crisis and economic stimulus plans, embodied emissions from exports plunged but those from capital formation increased rapidly. The enabled emissions in employee compensation increased steadily over time, whereas emissions induced from firms' net surplus decreased gradually, reflecting the reform on income distribution. In addition, at the sectoral level, consumption and capital formation respectively were the greatest drivers of embodied CH4 emission changes from agriculture and manufacturing, while employee compensation largely determined the enabled emission changes across all industrial sectors. The growth of CH4 emissions in China was profoundly affected by the macroeconomic situation and the changes of economic structure. Examining economic drivers of anthropogenic CH4 emissions can help formulate comprehensive mitigation policies and actions associated with economic production, supply and consumption.