| 摘 要： |
Given the limited reserves of conventional fossil fuels and their negative impact on global climate, governments are focused on finding alternatives to polluting energy from renewable energy sources. Bioenergy from energy plants is considered one of the most promising potential alternative energy sources recently. There are many energy plants around the world that are underutilized, given that the knowledge of their quantity and distribution remains a lacuna to be filled. In this study, we proposed a marginal land identification method that integrated a machine learning algorithm, natural occurrence records of energy plants, and multiple influencing factors. We took Jatropha curcas L.-based biodiesel as an example, which is one of the most promising alternatives of conventional diesel fuel, to simulate the global marginal land distribution with this method. From the results, we find 1588.46 million hectares of marginal land resources around the world, 49.34% of which are from Africa (783.77 million hectares). The remaining half is comprised of, in descending order, South America (464.73 million hectares), Oceania (134.78 million hectares), Asia (106.62 million hectares), North America (97.50 million hectares), and Europe (1.05 million hectares). In addition, we find the marginal land resources abound with savannas (732.60 million hectares) and woody savannas (584.14 million hectares), which together account for 82.89% of the total. Also, Our results of the variables indicate the most significant impact of water-related factors (93.37% of the total contribution) on the global distribution for Jatropha curcas L. Among all the variables, mean annual water vapor pressure (85.29%), soil water content (5.97%), and mean annual temperature (4.30%) are the most important ones to predicting potential land resources. These results can provide data support for the large-scale development of Jatropha curcas L.-based biodiesel. Besides, the method in this study can be applied to identify marginal lands for other bioenergy species. |
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