Background COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. Results The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion. In contrast, the impacts of interpersonal and trade globalisation are enhanced over time, indicating that human mobility may best explain sustained disease diffusion. Conclusions Model results confirm that globalisation, settlement and population characteristics, and variables tied to high human mobility lead to greater reported disease diffusion. These outcomes serve to inform suppression strategies, particularly as they are related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. It is likely that many of these processes are replicated at smaller geographical scales both within countries and within regions. Epidemiological strategies must therefore be tailored according to human mobility patterns, as well as countries’ settlement and population characteristics. We suggest that limiting human mobility to the greatest extent practical will best restrain COVID-19 diffusion, which in the absence of widespread vaccination may be one of the best lines of epidemiological defense.

Background: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of the COVID-19 transmission, there remains a gap in knowledge regarding the drivers of geographical diffusion between places. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of COVID-19 diffusion over a six-week period in March and April 2020. Results: The quantile regression model suggest that globalisation and settlement population characteristics related to high human mobility predict disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total confirmed cases per million whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total confirmed cases per million. Conclusions: The analysis confirms that globalisation, settlement and population characteristics lead to greater disease diffusion, and primarily variables tied to high human mobility. These outcomes serve to inform policies around ‘flattening the curve’, particularly as they related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. Epidemiological strategies must be tailored to suit the range of human mobility patterns, as well as the variety of settlement and population characteristics.

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In this article, by drawing on empirical evidence from twelve case studies from nine countries from across the Global South and North, we ask how radical grassroots social innovations that are part of social movements and struggles can offer pathways for tackling socio-spatial and socio-environmental inequality and for reinventing the commons. We define radical grassroots social innovations as a set of practices initiated by formal or informal community-led initiatives or/and social movements which aim to generate novel, democratic, socially, spatially and environmentally just solutions to address social needs that are otherwise ignored or marginalised. To address our research questions, we draw on the work of Cindi Katz to explore how grassroots innovations relate to practices of resilience, reworking and resistance. We identify possibilities and limitations as well as patterns of spatial practices and pathways of re-scaling and radical praxis, uncovering broadly-shared resemblances across different places. Through this analysis we aim to make a twofold contribution to political ecology and human geography scholarship on grassroots radical activism, social innovation and the spatialities of resistance. First, to reveal the connections between social-environmental struggles, emerging grassroots innovations and broader structural factors that cause, enable or limit them. Second, to explore how grassroots radical innovations stemming from place-based community struggles can relate to resistance practices that would not only successfully oppose inequality and the withering of the commons in the short-term, but would also open long-term pathways to alternative modes of social organization, and a new commons, based on social needs and social rights that are currently unaddressed.

Background: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of the COVID-19 transmission, there remains a gap in knowledge regarding the drivers of geographical diffusion between places. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of COVID-19 diffusion over a six-week period in March and April 2020.Results: The quantile regression model suggest that globalisation and settlement population characteristics related to high human mobility predict disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total confirmed cases per million whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total confirmed cases per million. Conclusions: The analysis confirms that globalisation, settlement and population characteristics lead to greater disease diffusion, and primarily variables tied to high human mobility. These outcomes serve to inform policies around ‘flattening the curve’, particularly as they related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. Epidemiological strategies must be tailored to suit the range of human mobility patterns, as well as the variety of settlement and population characteristics.

Global warming is a critical crisis threatening human survival and development. International organizations and countries worldwide are introducing policies and practices to achieve carbon neutrality. In China, numerous carbon neutrality policies have been established; however, a systematic understanding of the underlying policy logic is lacking. Using the institutional analysis and development (IAD) framework, this paper analyzes selected carbon neutrality policies in China. We conducted a bibliometric visualization analysis of the texts of 20 policies and matched their logic to the elements of the IAD framework. We established 90 keywords with occurrences of no less than 10 times in China’s carbon neutrality policies. The network visualization analysis identified six clusters. We discuss implementation challenges of China’s carbon neutrality policies, address the policy implementation, and finally outline impacts on China’s carbon neutrality governance. This study responds to the global concern over China’s carbon neutrality commitments by clarifying the institutional logic of China’s policies and actions. This study could provide a reference for countries worldwide that are designing and introducing carbon neutrality policies.

Non-technical summary Mining regions are affected by climate change. Supplies of energy and water are required, and operations become hazardous during adverse weather events. Adapting to climate change takes three forms: incrementally improving the resilience of mining operations; transitioning to more inclusive governance through institutional and policy innovations; and more profound transformations that shift the balance of power, including profit-sharing, localized control or cessation of mining entirely. Clarifying adaptation pathways helps to identify priorities and inform policies for a fairer and more sustainable future for mining and the regions where it takes place.

Globalisation continuously produces novel economic relationships mediated by flows of goods, services, capital, and information between countries. The activity of multinational corporations (MNCs) has become a primary driver of globalisation, shaping these relationships through vast networks of firms and their subsidiaries. Extensive empirical research has suggested that globalisation is not a singular process, and that variation in the intensity of international economic interactions can be captured by ‘multiple globalisations’, however how this differs across industry sectors has remained unclear. This paper analyses how sectoral variation in the ‘structural architecture’ of international economic relations can be understood using a combination of social network analysis (SNA) measures based on firm-subsidiary ownership linkages. Applying an approach that combines network-level measures (Density, Clustering, Degree, Assortativity) in ways yet to be explored in the spatial networks literature, a typology of four idealised international network structures is presented to allow for comparison between sectors. All sectoral networks were found to be disassortative, indicating that international networks based on intraorganisational ties are characterised by a core-periphery structure, with professional services sectors such as Banks and Insurance being the most hierarchically differentiated. Retail sector networks, including Food & Staples Retailing, are the least clustered while the two most clustered networks—Materials and Capital Goods—have also the highest average degree, evidence of their extensive globalisations. Our findings suggest that the multiple globalisations characterising international economic interactions can be better understood through the ‘structural architecture’ of sectoral variation, which result from the advantages conferred by cross-border activity within each.