Spatial characteristics of leisure tourism resources are essential for human life, the urban economy, and tourism planning. This article presents a novel framework to explore these characteristics based on multisource data, such as points of interest, OpenStreetMap roads, Sentinel-2 multispectral instrument images, and other data, and proposes a new tourism area identification method by integrating the attractiveness of attractions with term frequency–inverse document frequency. The roles of the influencing factors were measured by using the geodetector and related statistical analyses. The results showed that the resources were centered on Jiaozhou Bay, and their axial direction was “northeast to southwest.” The distribution of the overall resources was characterized by “one cluster with multiple core points,” and different types of resources had different aggregation distributions. The recreational recreation and cultural leisure zones were more likely to be distributed in and near the center of each district, and their numbers were high, while the shopping leisure and natural recreation (NR) zones were the opposite. The distribution of each type of resource was the result of a combination of factors working together, except for NR resources, which were mainly influenced by natural factors, while others were mainly affected by socioeconomic factors. The study findings are instructive for tourism planning.

Although it has been emphasized for years, there remains insufficient knowledge on how to decompose the driving forces of carbon emissions that fluctuate with time, especially when considering the major natural or socio‐economic events. To bridge the knowledge gap, we use the structural decomposition analysis method and select Japan for a case study. The results show that such events were followed by temporary emission increases due to economic recovery, but the general decreasing trend of carbon emissions has been unchanged in a long run. In the case of Japan, during the financial crisis period (2005–2011), emissions increased due to production structure (136.2 Mt‐CO2) and consumption structure (61.8 Mt‐CO2). However, the intensity change and per capita consumption effect were the main driving forces that offset this increasing trend, which reduced by 209.7 Mt‐CO2 in total. After the 2011 earthquake, a significant gain in upgraded production structure led to a 78.2 Mt‐CO2 reduction until 2015, partially offset the 126.4 Mt‐CO2 increase caused by per capita consumption and export volume. In conclusion, although post‐event economic recovery has led to an increasing trend of carbon emissions, continuously optimized emission intensity and production structure are the major driving forces for repressing carbon emission rebound brought by unexpected events.

Abstract Since the 2008 financial crisis, China has been undergoing an economic transition consisting of prioritizing green economic and sustainable development instead of rapid growth driven by large‐scale investment. However, there is still a lack of fine print on how subregional effort can contribute to national or full supply chain mitigation plans, especially downscaling to the city level. To bridge this knowledge gap, we selected Jing‐Jin‐Ji urban agglomeration, one of the economic centers but also featured by intensive emission for decades, to analyze the emission variance and driving forces from 2012 to 2015 as a case study. Based on the consumption accounting framework, the carbon emissions of Jing‐Jin‐Ji have decreased by 11.7 Mt CO2 in total over the study period, and most cities showed the similar descending trend. The driving forces show that the emission intensity and production structure have largely reduced Jing‐Jin‐Ji's total due to measurements of economic transition. For instance, Beijing has decreased by 28.7 Mt of emission reduction which led by declined emission intensity. By contrast, per capita demands and growth of its population were the primary forces to increase emissions. To conclude, although the mitigation achievement is undeniable, we should also note that the economic transition has not changed the uneven pattern of selected urban agglomeration so far.

Global production fragmentation generates indirect socioeconomic and environmental impacts throughout its expanded supply chains. The multi-regional input-output model (MRIO) is a tool commonly used to trace the supply chain and understand spillover effects across regions, but often cannot be applied due to data unavailability, especially at the sub-national level. Here, we present MRIO tables for 2012, 2015, and 2017 for 31 provinces of mainland China in 42 economic sectors. We employ hybrid methods to construct the MRIO tables according to the available data for each year. The dataset is the consistent China MRIO table collection to reveal the evolution of regional supply chains in China's recent economic transition. The dataset illustrates the consistent evolution of China's regional supply chain and its economic structure before the 2018 US-Sino trade war. The dataset can be further applied as a benchmark in a wide range of in-depth studies of production and consumption structures across industries and regions.

Countries around the world have sought to stop the spread of the 2019 novel coronavirus (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the economic footprint of such “lockdowns” using detailed datasets of global supply chains and a set of pandemic scenarios. We find that COVID-related economic losses are largely dependent on the number of countries imposing lockdowns, and that losses are more sensitive to the duration of a lockdown that its strictness—suggesting that more severe restrictions can reduce economic damages if they successfully shorten the duration of a lockdown. Our results also highlight several key vulnerabilities in global supply chains: Even countries that are not directly affected by COVID-19 can experience large losses (e.g., >20% of their GDP)—with such cascading impacts often occurring in low- and middle-income countries. Open and highly-specialized economies suffer particularly large losses (e.g., energy-exporting Central Asian countries or tourism-focused Caribbean countries). Supply bottlenecks and declines in consumer demand lead to especially large losses in globalized sectors such as electronics (production decreases of 13-53% across our scenarios) and automobiles (2-49%). Although retrospective analyses will undoubtedly provide further policy-relevant insights, our findings already imply that earlier, stricter, and thus shorter lockdowns are likely to minimize overall economic damages, and that global supply chains will magnify economic losses in some countries and industry sectors regardless of direct effects of the coronavirus.

Countries have sought to stop the spread of coronavirus disease 2019 (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the supply-chain effects of a set of idealized lockdown scenarios, using the latest global trade modelling framework. We find that supply-chain losses that are related to initial COVID-19 lockdowns are largely dependent on the number of countries imposing restrictions and that losses are more sensitive to the duration of a lockdown than its strictness. However, a longer containment that can eradicate the disease imposes a smaller loss than shorter ones. Earlier, stricter and shorter lockdowns can minimize overall losses. A ‘go-slow’ approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns. Regardless of the strategy, the complexity of global supply chains will magnify losses beyond the direct effects of COVID-19. Thus, pandemic control is a public good that requires collective efforts and support to lower-capacity countries. Guan et al. analyse the impacts of COVID-19 restrictions on global supply chains. Earlier, stricter and shorter lockdowns can minimize overall losses. A ‘go-slow’ approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns.