This page has only limited features, please log in for full access.
Cuiling Zhang; Wenli Qiang; Shuwen Niu; Rui Wang; He Zhang; Shengkui Cheng; Fan Li. Options of Chinese dietary pattern based on multi-objective optimization. 资源科学 2021, 43, 1140 -1152.
AMA StyleCuiling Zhang, Wenli Qiang, Shuwen Niu, Rui Wang, He Zhang, Shengkui Cheng, Fan Li. Options of Chinese dietary pattern based on multi-objective optimization. 资源科学. 2021; 43 (6):1140-1152.
Chicago/Turabian StyleCuiling Zhang; Wenli Qiang; Shuwen Niu; Rui Wang; He Zhang; Shengkui Cheng; Fan Li. 2021. "Options of Chinese dietary pattern based on multi-objective optimization." 资源科学 43, no. 6: 1140-1152.
Wenli Qiang; Cuiling Zhang; Aimin Liu; Shengkui Cheng; Xiang Wang; Fan Li. Evolution of global virtual land flow related to agricultural trade and driving factors. 资源科学 2020, 42, 1704 -1714.
AMA StyleWenli Qiang, Cuiling Zhang, Aimin Liu, Shengkui Cheng, Xiang Wang, Fan Li. Evolution of global virtual land flow related to agricultural trade and driving factors. 资源科学. 2020; 42 (9):1704-1714.
Chicago/Turabian StyleWenli Qiang; Cuiling Zhang; Aimin Liu; Shengkui Cheng; Xiang Wang; Fan Li. 2020. "Evolution of global virtual land flow related to agricultural trade and driving factors." 资源科学 42, no. 9: 1704-1714.
Global agricultural trade plays an essential role in balancing supply and demand regarding agricultural products worldwide. Based on complex network theory, two types of agricultural trade networks weighted by the physical quantity and monetary value were built. In both networks, eight groups of agricultural products showed diverse variation in time and space. During 1986 to 2016, the total physical trade increased by 2.55 times with a gradual growth process, and total monetary value increased 1.98 times with fluctuation. The cumulative distribution of node degree and strength followed power-law distribution. Scale expansion and structure complexity of both networks reflected heterogeneity between nodes and the trend of agricultural economic globalization. Meeting demand and seeking greater returns are the main drivers of global agricultural trade development. Mainly developed countries occupied the important positions in the global agricultural trade network, but some emerging economies such as China, Brazil, and India became important sources of demand and supply. China not only needs to fully use international resources to meet demand for agricultural products, but also needs to ensure its own food security through multiple countermeasures.
Wenli Qiang; Shuwen Niu; Xiang Wang; Cuiling Zhang; Aimin Liu; Shengkui Cheng. Evolution of the Global Agricultural Trade Network and Policy Implications for China. Sustainability 2019, 12, 192 .
AMA StyleWenli Qiang, Shuwen Niu, Xiang Wang, Cuiling Zhang, Aimin Liu, Shengkui Cheng. Evolution of the Global Agricultural Trade Network and Policy Implications for China. Sustainability. 2019; 12 (1):192.
Chicago/Turabian StyleWenli Qiang; Shuwen Niu; Xiang Wang; Cuiling Zhang; Aimin Liu; Shengkui Cheng. 2019. "Evolution of the Global Agricultural Trade Network and Policy Implications for China." Sustainability 12, no. 1: 192.
祥 王; Wang Xiang; 文丽 强; 叔文 牛; 爱民 刘; 升魁 成; 真 李; Qiang Wen-Li; Niu Shu-Wen; Liu Ai-Ming; Cheng Sheng-Kui; Li Zhen. 全球农产品贸易网络及其演化分析. JOURNAL OF NATURAL RESOURCES 2018, 33, 940 -953.
AMA Style祥 王, Wang Xiang, 文丽 强, 叔文 牛, 爱民 刘, 升魁 成, 真 李, Qiang Wen-Li, Niu Shu-Wen, Liu Ai-Ming, Cheng Sheng-Kui, Li Zhen. 全球农产品贸易网络及其演化分析. JOURNAL OF NATURAL RESOURCES. 2018; 33 (6):940-953.
Chicago/Turabian Style祥 王; Wang Xiang; 文丽 强; 叔文 牛; 爱民 刘; 升魁 成; 真 李; Qiang Wen-Li; Niu Shu-Wen; Liu Ai-Ming; Cheng Sheng-Kui; Li Zhen. 2018. "全球农产品贸易网络及其演化分析." JOURNAL OF NATURAL RESOURCES 33, no. 6: 940-953.
What factors determine the spatial heterogeneity of household energy consumption (HEC) in China? Can the impacts of these factors be quantified? What are the trends and characteristics of the spatial differences? To date, these issues are still unclear. Based on the STIRPAT model and panel dataset for 30 provinces in China over the period 1997–2013, this paper investigated influences of the income per capita, urbanization level and annual average temperature on HEC, and revealed the spatial effects of these influencing factors. The results show that the income level is the main influencing factor, followed by the annual average temperature. There exists a diminishing marginal contribution with increasing income. The influence of urbanization level varies according to income level. In addition, from the eastern region to western region of China, variances largely depend upon economic level at the provincial level. From the northern region to southern region, change is mainly caused by temperature. The urbanization level has more significant impact on the structure and efficiency of household energy consumption than on its quantity. These results could provide reference for policy making and energy planning.
Yongxia Ding; Wei Qu; Shuwen Niu; Man Liang; Wenli Qiang; Zhenguo Hong. Factors Influencing the Spatial Difference in Household Energy Consumption in China. Sustainability 2016, 8, 1285 .
AMA StyleYongxia Ding, Wei Qu, Shuwen Niu, Man Liang, Wenli Qiang, Zhenguo Hong. Factors Influencing the Spatial Difference in Household Energy Consumption in China. Sustainability. 2016; 8 (12):1285.
Chicago/Turabian StyleYongxia Ding; Wei Qu; Shuwen Niu; Man Liang; Wenli Qiang; Zhenguo Hong. 2016. "Factors Influencing the Spatial Difference in Household Energy Consumption in China." Sustainability 8, no. 12: 1285.