This paper aims to provide a better understanding of the current situation of natural resources consumption in the world and its relationship with the level of social development. The Ecological Footprint concept is used to indicate human beings' environmental impact, and the Human Development Index (HDI) is used as a process for the social development level. Based on the dataset of 136 countries from the World Wildlife Fund (WWF), we calculate the Gini coefficients of the total Ecological Footprint per person (EF) and its sub items, i.e., Renewable Resources Footprint per person (REF) and Energy Footprint per person (EEF). The results indicate that significant inequality exists in natural resources consumption among the 136 countries from 1996 to 2005, and inequality of the EEF is the largest. We also calculate the Lorenz asymmetry coefficients of EF, REF and EEF, which are all greater than 1, indicating that the inequality results from some countries having extremely high resources consumption. The regression analyses of EF, REF and EEF with HDI, respectively, are made to show that there is a significant U-shaped relationship between natural resources consumption per person and the social development level rather than an inverted U-shaped relationship. Therefore, the Environmental Kuznets Curve (EKC) hypothesis is not supported by this research.

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It is necessary to use interval data to define terms or describe extreme behaviors because of the existence of uncertainty in many real-world problems. In this paper, a novel efficient interval twin support vector regression (ITSVR) is proposed to handle such interval data. This ITSVR employs two nonparallel functions to identify the upper and lower sides of the interval output data, respectively, in which the Hausdorff distance is incorporated into the Gaussian kernel as the interval kernel for interval input data. Compared with other support vector regression (SVR)-based interval regression methods, such as the interval support vector interval regression networks (ISVIRN), this ITSVR algorithm is more efficient since only two smaller-sized QPPs are solved, respectively. The experimental results on several artificial datasets and three stock index datasets show the validity of ITSVR.

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The ecological footprint (EF) can be used to investigate relationships between population, environment and development. In China, the per caput EF is estimated to have increased by 83% between 1981 (0.82 ha caput-1) and 2000 (1.49 ha caput-1), to about 1.31 times China's area (including its oceanic territory), while the ecological deficit increased from 0.066 ha caput-1 in 1981 to 0.735 ha caput-1 in 2000. Over this period, the proportions of six sub-footprint types have changed considerably: the percentages of arable, fossil energy and forest land decreased from 44.8%, 41.5% and 4.1% to 27.1%, 40.1% and 3.0%, respectively; while sea, pasture and built-up land percentages increased from 3.8%, 4.4% and 1.3% to 15.2%, 12.4% and 2.2%, respectively. The production coefficients of gross domestic product (GDP) to the EF of China increased from 584 RMB ha-1 in 1981, to 1522 RMB ha-1 in 2000, reflecting an increasing efficiency in resource use. The EF correlates positively with disposable income and expenditure, which can be described by income and expenditure elasticity. Some measures are suggested to decrease the Chinese ecological deficit on the road to sustainability.