Huang Jiali
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Huang Jiali has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Hui Zhao
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences
1 shared publication - Liu Yunwei
School of Agricultural Sciences of Xichang College, Xichang, Sichuan 615000, China
1 shared publication Robert E. Ulanowicz
Chesapeake Biological Laboratory, University of Maryland, Solomons, Maryland, United States of America; Arthur R. Marshall Laboratory, University of Florida, Gainesville, Florida, United States of America
1 shared publication- Yan Zhang
School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China, China
1 shared publication
Latest Publications
- 1 September 2018
- Published by Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences in Journal of Resources and Ecology
- Vol. 9 (5) , 516-525
Assessing the Impact of the Sloping Land Conversion Program on Rural Household Income in the Upper Reaches of Minjiang River, China
One secondary target of the SLCP is poverty reduction. Therefore, the impact of the SLCP on household income is a subject of much research. This study was conducted to determine whether the SLCP has affected incomes of households at different income levels13 years after its implementation. Using survey data from 2012 on rural households' livelihoods in the upper reaches of the Minjiang River in Sichuan Province and using the non-participating households as a reference, the current impact of the SLCP on household income is calculated with a multiple linear regression model and a quantile regression model. The socio-geographic features of participating and non-participating households are also be compared. The results show no significant differences between the SLCP participating and non-participating households in many socio-demographic characteristics. Participating in the SLCP had no significant impact on household income at all income levels in the study area in 2012. This finding suggests tha...
- 1 September 2018
- Published by Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences in Journal of Resources and Ecology
- Vol. 9 (5) , 516-525
Assessing the Impact of the Sloping Land Conversion Program on Rural Household Income in the Upper Reaches of Minjiang River, China
One secondary target of the SLCP is poverty reduction. Therefore, the impact of the SLCP on household income is a subject of much research. This study was conducted to determine whether the SLCP has affected incomes of households at different income levels13 years after its implementation. Using survey data from 2012 on rural households' livelihoods in the upper reaches of the Minjiang River in Sichuan Province and using the non-participating households as a reference, the current impact of the SLCP on household income is calculated with a multiple linear regression model and a quantile regression model. The socio-geographic features of participating and non-participating households are also be compared. The results show no significant differences between the SLCP participating and non-participating households in many socio-demographic characteristics. Participating in the SLCP had no significant impact on household income at all income levels in the study area in 2012. This finding suggests tha...
- Open Access
- 30 June 2014
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 9 (6) , e100923
Ecological Network Analysis for Economic Systems: Growth and Development and Implications for Sustainable Development
The quantification of growth and development is an important issue in economics, because these phenomena are closely related to sustainability. We address growth and development from a network perspective in which economic systems are represented as flow networks and analyzed using ecological network analysis (ENA). The Beijing economic system is used as a case study and 11 input–output (I-O) tables for 1985–2010 are converted into currency networks. ENA is used to calculate system-level indices to quantify the growth and development of Beijing. The contributions of each direct flow toward growth and development in 2010 are calculated and their implications for sustainable development are discussed. The results show that during 1985–2010, growth was the main attribute of the Beijing economic system. Although the system grew exponentially, its development fluctuated within only a small range. The results suggest that system ascendency should be increased in order to favor more sustainable development. Ascendency can be augmented in two ways: (1) strengthen those pathways with positive contributions to increasing ascendency and (2) weaken those with negative effects.
- 30 June 2014
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 9 (6) , e100923
Open AccessEcological Network Analysis for Economic Systems: Growth and Development and Implications for Sustainable Development
The quantification of growth and development is an important issue in economics, because these phenomena are closely related to sustainability. We address growth and development from a network perspective in which economic systems are represented as flow networks and analyzed using ecological network analysis (ENA). The Beijing economic system is used as a case study and 11 input–output (I-O) tables for 1985–2010 are converted into currency networks. ENA is used to calculate system-level indices to quantify the growth and development of Beijing. The contributions of each direct flow toward growth and development in 2010 are calculated and their implications for sustainable development are discussed. The results show that during 1985–2010, growth was the main attribute of the Beijing economic system. Although the system grew exponentially, its development fluctuated within only a small range. The results suggest that system ascendency should be increased in order to favor more sustainable development. Ascendency can be augmented in two ways: (1) strengthen those pathways with positive contributions to increasing ascendency and (2) weaken those with negative effects.