A Modified Model And Its Case Study For Assessing Energy Ecological Footprint Based On Net Primary Productivity

Quantitatively assessing the sustainability has always been the frontier and focus in ecological economics. As an effective bio-physical tool, ecological footprint (EF) has gained much attention and positive response from the scientific community and become the most widely used method to estimate the sustainable utilization of natural resources in the world by now. Despite the success of the EF in the policy world and wide public the method is still riddled with problems, in particular energy ecological footprint (EEF). EEF has been the most important and disputed subject in EF analysis, as it is both the dominant share on the total EF and the major cause of ecological deficit. In this case, EEF is one of the key research priorities for improving not only EF accounting but the regional planning; therefore, it is of great importance to develop a new EEF model that can be used to quantify the impact of human energy consumption on the ecosystems.A major source for criticism is what EEF actually measures, exclusion of some useful information and excessive pessimism in evaluation results. We review relevant literature, summarize the progress in this field, and raise the main shortcomings of EEF. This requires a full understanding of the appropriate use and limitations of the traditional model. The four main shortcomings are established as follows:(1) the assumption that different types of biologically productive land are mutually exclusive in space goes against the objective fact; (2) the parameter'energy factor'that represents the calorific values per area of land appropriation for each energy source is hard to determine; (3) the lack of correlation between land and EEF obscures the distinction between sustainable and unsustainable land use; and (4) the methodological inconsistency between EEF and biological EF produces incomparable results, as well as between fossil energy footprint and electricity footprint. To overcome these shortcomings mentioned above, we suggest some underlying solutions, including:(1) to consider land other than forest that is available to absorb anthropogenic CO2 emissions; (2) to consider net primary productivity (NPP) as carbon sequestration rate rather than the energy factor; (3) to take land use change especially land degradation into account; and (4) to convert carbon sequestration into various types of land at regional scales.Firstly, some improvements are made to modify the traditional EEF model in terms of definition, parameters and accounting methodology. We use the modified model that takes NPP as the basis to assess the EEF of Jilin Province. The results demonstrate the followings:during 1994 and 2008, (1) Jilin witnesses an obvious change in land use that there is a slight increase in the area of cropland, forest and built-land and a sharp decrease in grassland area; (2) regional NPP fluctuates dramatically in the first 10 years, but recently it remains stable approximately at 5.268 t/hm2·a; (3) total fossil-fuel CO2 emissions reduce with fluctuation from 9107.527×104 t to 8675.138×104 t; (4) total EEF soars from 573.449×104 hm2 to 1419.534×104 hm2; (5) EEF per capita ascends from 0.228 hm2 to 0.524 hm2, representing a heavier environmental pressure due to energy consumption.Secondly, the changes in the components of EEF per capita are analyzed. It is indicated that crude coal, crude oil and electricity occupy most of the total amount. All energy sources increase to some extent with the exception of fuel oil, natural gas and electricity. In terms of dynamics, there is a strongest fluctuation in kerosene and a weakest fluctuation in crude oil, while the most significant increase and decrease associated with diesel oil and fuel oil, respectively. When the EEF per capita is divided according to the land types as the sources of carbon sequestration, it indicates that grassland declines by 0.005 hm2, while cropland, forest, garden plot, built-land and water rise by 0.089,0.002,0.192,0.005,0.013 hm2, respectively. In particular, forest, cropland and grassland account for 94.02% to 95.05% of the total EEF per capita. In the meanwhile, the ecological effect index of land use is bigger than 1 and still appears to be growing in most years except between 1997 and 2002. As a whole, land use change has made great contribution to the increase in EEF. Thirdly, a comparison is made between the two models. Using the modified model, the EEF per capita is estimated to be only between 30.77% and 37.17% of that of the traditional method, mainly because some changes have been made to the modified model in terms of assumptions, parameters and accounting method. In spite of the differences in both annual change and distribution of EEF, some common features of dynamics are shared by the two EEFs, which shows that the modified model holds reasonably true.Fourthly, the EEF of modified model is further evaluated using complex analysis and decomposition analysis. It is demonstrated that economic growth whose contribution to the change in total EEF amounts to 113.720×104 hm2 annually is a major driving force, while technological progress makes a negative contribution of -57.314×104 hm2 annually. As a whole, the economic effect is estimated to be twice as much as technological effect. However, despite the dominant impact of economic growth on EEF, technological progress has become the key to enhancing benefit in energy consumption.Fifthly, the impact of both GDP and land area on EEF per capita is analyzed from different angles. The results illustrate the followings:(1) GDP per capita shows highly significant correlations with EEF per capita, which is able to be expressed approximately as logistic curve; (2) during 1994 and 2008, the intensity of EEF decreases from 0.612×10-4 hm2/RMB to 0.221×10-4 hm2/yuan, while the elastic coefficient of EEF remains less than 1 with dramatic fluctuation; (3) land use change is closely related to EEF per capita; (4) the pressure of EEF doubles from 0.306 to 0.759 over the same period; and (5) the increase in EEF is mainly due to urban expansion and grassland degradation using correlation analysis.Finally, a prediction model is established to simulate the expected EEF under three given land use change scenarios. During 2009 and 2023, under the scenarios of low growth, medium growth and high growth, the average annual EEF per capita will increase by 6.36%,10.73% and 11.43%, respectively. Except for the economic growth, land use change that makes great contribution to the increase in EEF will become one of the main driving forces. Thus more efforts on protecting the grassland in western Jilin should be made in order to strengthen the land capacity for carbon sequestration. Furthermore, we suggest that promoting technological progress is also a promising way to restrain the increase in EEF.In conclusion, the significant features and innovations of this thesis are the following: (1) establishing a modified model for assessing EEF based on NPP combing CO2 emissions with carbon sequestration, which can be seen as a methodological improvement; and (2) converting EEF into a variety of real types of land, and simulating the expected EEF under given land use change scenarios, which expands the realm of temporal-spatial scales in EEF analysis. As a whole, our work is able to make more meaningful assessment of the impact of energy consumption on the ecosystems on the regional NPP. In particular, the modified model can be used to improve the validity and accuracy of the calculation results and compensate some of the shortcomings in the traditional model. It also can used to get a deeper understanding of the interaction between energy consumption and environmental protection, and look for new strategies for sustainable development that help us avoid the high CO2 emissions path.