| Nowdays, as a bio-physical quantitative assessment tool to estimate the sustainable utilization of natural resources, ecological footprint (EF) method proposed by Rees and Wackernagel et al. has gained much attention from international scientists, world's governments and non-government organizations. The EF model has been regarded as a hot issue of research both sustainable development and ecological economics. There are some critiques on EF model such as its static state, lacking of forecast, and its singularity of results. Per capital EF and biocapcity (BC) in China 1949-2006 are calculated based on variable equivalence factors, invariable average yields, and invariable national yield factors. The main purpose of this paper is to overcome the above-mentioned limitations of EF model. Integrated and single components of EF model are studied with nonlinear science methods such as empirical mode decomposition (EMD), dynamic model, and autoregressive integrated moving average models (ARIMA) model in this paper. EF embodied in international trade is also calculated based on emergy analysis, and input-output analysis methods. Some specified measurements and conclusions are as follows:1. Dynamic changes of global sustainable ecosystem are assessed with long-term series from 1961 to 2003, and the ecosystem sustainability of 154 countries and regions in 1996, 1999, 2001, and 2003 are analyzed based on ecological footprint index (EFI). Over the last 42 years, the world's EFI has reduced sharply with fluctuation. The country with the highest EFI in 2003 is Gabon, and the lowest is Iraq. The highest EFI is Yemen, and the lowest is Greece in 2001. The highest is Gabon, the lowest is Kuwait in either 1999 or 1996. The fluctuant cycles of global per capita EF from 1961 to 2003 is decomposed based on EMD, and a nonlinear dynamic simulative model is presented with the cycles. Simulative numerical values of three global per capita EF scenarios are analyzed based on the simulative model.2. EFI has reduced sharply with fluctuation in China 1949-2006.The change of its ecological footprint efficiency (EFE) is very slowly before 1980, subsequently, is sharply increased. The fluctuant cycles of per capita EF and BC in China 1949-2006 are decomposed based on EMD method. Nonlinear dynamic prediction models are presented with the cycles. Over the last 57 years, the obvious undulation cycles of per capita EF in China are 4.4 years and 13.1 years, and the periods of per capita BC are 4.5 years and 6.4 years. The business-as-usual scenario looks at the consequence that per capita ED would be 8.380gha in China in 2050. The slow-shift scenario shows it would be 1.059gha in 2050 and 0.614gha in 2100. The rapid-reduce scenario shows it would be 0.788gha in 2050 and 0.452gha in 2100, respectively.3. Ecological footprint component index (EFCI) and bicapacity component index (BCCI) are proposed based on entropy method. ARIMA (2,1,1) prediction model of EFCI and ARIMA (1,1,1) prediction model of BCCI are constructed based on entropy method in China 1949-2006. The results show EFCI will be increased to 0.02930 in 2007 and then fall to 0.02804 in 2010, and BCCI will be decreased to 0.01290 in 2010. The partial least squares regression model of EFC1 shows that the main influencing factors are population, GDP, resident consumption and total value of imports and exports.4. The prediction models of the components of per capita energy EF, EF and BC in China are constructed. The proportions of coal, crops, forest footprint will be decreased in China 2007-2015, and those of crude oil, natural gas, hydropower, pasture, fisheries, energy will be increased consistently. The proportion of built-up land will be increased after it decreases a few years. The proportions of crops, forest, and pasture biocapacity will be increased, and those of fisheries and built-up biocapacity will be decreased. The main energy consumption sections are industry, traffic transportation and telecommunication, living consumption, nonmaterial production sectors, and agriculture. The annual average proportion of primary industry EF in China 1995-2005 is 56.72 percent, the one of secondary industry EF is 32.85 percent, and the one of tertiary industry EF is 10.43 percent.5. The fluctuation periods of annual China's per capita EEF growth rate and per capita EEF are analyzed with EMD method. The main timescales of the thirty-seven factors that affect the annual growth rate of EEF are also discussed based on EMD and factor analysis methods. The multiple scenarios prediction models are constructed based on EMD method. The analysis findings from the common synthesized factors of three timescales of 37 factors suggest that China's energy policy-makers should attach more importance to stabilizing of economic growth, optimizing industrial structure, regulating domestic petroleum exploitation, and improving transportation efficiency. The business-as-usual scenario looks at the consequence that per capita EEF would be 9.548gha in 2050. The slow-shift scenario shows it would be 0.729gha in 2050 and 0.543gha in 2100. The rapid-reduce scenario shows it would be 0.611gha in 2050 and 0.489gha in 2100, respectively.6. EF embodied in international trade is calculated based on input-output analysis and emergy analysis methods in China 1995-2005, and their differences with traditional method is compared. The dynamic prediction model of import and export EF in China is constructed based on the calculation of traditional method. Over last 10 years, EF embodied in international trade in China is constantly increased, and import EF is bigger than export EF in general. The import EF will be obviously bigger than export EF in China 2006-2016. The EF1 taking into account import-export trade shows import-export trade structure in China will be more rational than at present.These are unique that EMD mutiple timescales analysis, mutiple scenarios of danamic model, partial least-squares regression model, and conceptions of EFCI and BBCI based on EF model in this paper. This resrearch is useful to find feasible approaches to reducing human impact on environment, and to provide references to sustainable development for China's policy-makers. It could be also to enrich the assessment index system of sustainable development based on EF analysis method.
|
PDF Full Text Request