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Large digital platforms face intense scrutiny over self-preferencing, which involves a platform provider favoring its own offers over those of competitors. In online marketplaces, also called retail or e-commerce platforms, much of the academic and regulatory debate focuses on determining whether the marketplace provider gives preference to its own private labels, such as “Amazon Basics” or Walmart’s “Great Value” products. However, we outline, both conceptually and empirically, that self-preferencing can also occur through other dimensions of vertical integration – namely, retailing and fulfillment.

This article contributes by conceptualizing three dimensions of vertical integration in online marketplaces – private labels, retailing and fulfillment. Then, two studies empirically assess (1) which of the 20 most-visited global online marketplaces vertically integrates which dimension and (2) which share of 600 m available offers is vertically integrated to which degree in eleven international Amazon marketplaces.

The majority of the leading marketplaces vertically integrate all three dimensions, implying ample opportunities for self-preferencing. Across international Amazon marketplaces, only 0.02% of available offers consist of an Amazon private-label product. However, Amazon is a retailer for around 31% and fulfills around 38% of all available offers in its marketplaces. Hence, self-preferencing on Amazon can occur most frequently through retailing and fulfillment but comparatively infrequently through private-label offers. Still, these shares differ substantially by country – every second offer is vertically integrated in the USA, but only one in ten in India.

Most of the self-preferencing debate often focuses on private-label products. Instead, we present large-scale empirical results showing that self-preferencing on Amazon could occur most often through retailing and fulfillment because these channels affect much larger shares of offers. We also measure the variation of these shares across countries and relate them to regulatory environments.

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

The purpose of this paper is to quantitatively measure the vulnerability level of the whole rural social-ecological system in Yunnan Province and to analyze the spatial differences of the vulnerability in different regions.

Based on the “exposure-sensitivity-adaptability” vulnerability assessment framework, this paper establishes the index system of rural social-ecological system vulnerability to climate change. Combined with the questionnaire survey and meteorological data, the entropy method was used to measure and analyze the vulnerability level and influencing factors of the overall rural social-ecological system in Yunnan Province. At the same time, the vulnerability level of social-ecological system in Yunnan Province is divided into five levels, and the spatial differences of vulnerability level of 16 states (cities) in Yunnan Province are analyzed.

The results show that: the social-ecological system has high exposure to climate change (0.809), strong sensitivity (0.729), moderate adaptability (0.297) and overall system vulnerability is at a medium level (0.373). Yunnan Province is divided into five levels of social-ecological system vulnerable areas. The areas of extreme, severe, moderate, mild and slight vulnerability account for 21.45%, 24.65%, 36.82%, 13.18% and 3.90% of the whole province, respectively. The geographical division and vulnerability division of Yunnan Province are basically consistent in space.

Comprehensive evaluation of the vulnerability of the social-ecological system of Yunnan Province to climate change is the scientific basis for the country to formulate countermeasures against climate change, and it is also the need to improve the adaptability of the social and economic system of the fragile area, reduce the vulnerability and realize the sustainable development of national social economy. The research results can provide a basis for decision-making of climate adaptation in Yunnan and other regions and provide methods and indicators for the assessment of social-ecological system vulnerability under the background of climate change.