This page has only limited features, please log in for full access.

Unclaimed
Wenyang Huang
School of Economics and Management, Beihang University, Beijing, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Original contribution
Published: 01 December 2020 in EcoHealth
Reads 0
Downloads 0

Biodiversity loss is on the list of the most challenging issues the world sustainability faces. This study aims to examine the global illegal ivory trades, identify key hub countries and map the key smuggling routes in the worldwide illegal ivory trading network. A social network analysis (SNA) and a set of network indicators are used to investigate CITES’s (Convention on International Trade in Endangered Species of Wild Fauna and Flora) ivory trading data from 1975 to 2017. Several important conclusions are derived: (1) The social network of global ivory trading is closely connected, with an average path length of 2.643 and an average clustering coefficient of 0.463. An average of 45,410.384 kg of ivory products was trafficked from each of the 182 countries to an average of another 8.17 countries. The dynamic networks of global ivory trading show the pattern of high connectivity and high aggregation. (2) The USA, the UK, Zimbabwe, South Africa, China, Japan, Sudan, Belgium and Hong Kong are the most important hubs in the worldwide ivory trade according to degrees and centralities in the SNA. (3) According to trading weight density, three significant ivory trafficking routes are illustrated: 1. African countries (Sudan, Zimbabwe, South Africa, Central African Republic, the Republic of Congo, Somalia and Uganda) to Hong Kong; 2. Belgium to Hong Kong and Japan; 3. Mutual transactions between Japan and Hong Kong. The analytical framework in this study can also be useful for studying other illegal trading activities, like other animal trades, with respect to biodiversity conversation, and could serve as a reference for other network-based sustainability challenges, such as human migration, biological invasion, and waste smuggling and dumping.

ACS Style

Wenyang Huang; Huiwen Wang; Yigang Wei. Mapping the Illegal International Ivory Trading Network to Identify Key Hubs and Smuggling Routes. EcoHealth 2020, 17, 523 -539.

AMA Style

Wenyang Huang, Huiwen Wang, Yigang Wei. Mapping the Illegal International Ivory Trading Network to Identify Key Hubs and Smuggling Routes. EcoHealth. 2020; 17 (4):523-539.

Chicago/Turabian Style

Wenyang Huang; Huiwen Wang; Yigang Wei. 2020. "Mapping the Illegal International Ivory Trading Network to Identify Key Hubs and Smuggling Routes." EcoHealth 17, no. 4: 523-539.

Journal article
Published: 15 November 2018 in Sustainability
Reads 0
Downloads 0

China is experiencing severe environmental degradation, particularly air pollution. To explore whether air pollutants are spatially correlated (i.e., trans-boundary effects) and to analyse the main contributing factors, this research investigates the annual concentration of the Air Quality Index (AQI) and 13 polluting sectors in 30 provinces and autonomous regions across China. Factor analysis, the linear regression model and the spatial auto-regression (SAR) model are employed to analyse the latest data in 2014. Several important findings are derived. Firstly, the global Moran’s I test reveals that the AQI of China shows a distinct positive spatial correlation. The local Moran’s I test shows that significant high–high AQI agglomeration regions are found around the Beijing–Tianjin–Hebei area and the regions of low–low AQI agglomeration all locate in south China, including Yunnan, Guangxi and Fujian. Secondly, the effectiveness of the SAR model is much better than that of the linear regression model, with a significantly improved R-squared value from 0.287 to 0.705. A given region’s AQI will rise by 0.793% if the AQI of its ambient region increases by 1%. Thirdly, car ownership, steel output, coke output, coal consumption, built-up area, diesel consumption and electric power output contribute most to air pollution according to AQI, whereas fuel oil consumption, caustic soda output and crude oil consumption are inconsiderably accountable in raising AQI. Fourthly, the air quality in Beijing and Tianjin is under great exogenous influence from nearby regions, such as Hebei’s air pollution, and cross-boundary and joint efforts must be committed by the Beijing–Tianjin–Hebei region in order to control air pollution.

ACS Style

Wenyang Huang; Huiwen Wang; Yigang Wei. Endogenous or Exogenous? Examining Trans-Boundary Air Pollution by Using the Air Quality Index (AQI): A Case Study of 30 Provinces and Autonomous Regions in China. Sustainability 2018, 10, 4220 .

AMA Style

Wenyang Huang, Huiwen Wang, Yigang Wei. Endogenous or Exogenous? Examining Trans-Boundary Air Pollution by Using the Air Quality Index (AQI): A Case Study of 30 Provinces and Autonomous Regions in China. Sustainability. 2018; 10 (11):4220.

Chicago/Turabian Style

Wenyang Huang; Huiwen Wang; Yigang Wei. 2018. "Endogenous or Exogenous? Examining Trans-Boundary Air Pollution by Using the Air Quality Index (AQI): A Case Study of 30 Provinces and Autonomous Regions in China." Sustainability 10, no. 11: 4220.