Abstract
Developing biomass energy, seen as the most important renewable energy, is becoming a prospective solution in attempting to deal with the world’s sustainability-related challenges, such as climate change, energy crisis, and carbon emission reduction. As one of the most promising second-generation energy crops, giant silvergrass (Miscanthus × giganteus) is highly valued for its high potential for biomass production and low maintenance requirements. Mapping the potential global distribution of marginal land suitable for giant silvergrass is an essential prerequisite for the development of giant silvergrass-based biomass energy. In this study, a boosting regression tree was used to identify the marginal land resources for giant silvergrass cultivation using influencing factors, which include climate conditions, soil conditions, topography conditions, and land use. The results indicate that there are 3068.25 million hectares of land resources worldwide suitable for giant silvergrass cultivation, which are mainly located in Africa (902.05 million hectares), Asia (620.32 million hectares), South America (547.60 million hectares), and North America (529.26 million hectares). Among them, countries with the most land resources, Russia and Brazil, have the first- and second-highest amounts of suitable marginal land for giant silvergrass, with areas of 373.35 and 332.37 million hectares, respectively. Our results also rank the involved factors by their contribution. Climatic conditions have the greatest influence on the spatial distribution of giant silvergrass, with an average contribution of 74.38%, followed by land use, with a contribution of 17.38%. The contribution of the soil conditions is 7.26%. The results of this study provide instructive support for future biomass energy policy development.
Highlights
Climate change is a global issue that will severely threaten human security without proper response to it [1]
For the simulation of globally potential land resources done with the boosting regression trees (BRTs) model, we assembled a database of geolocated sites where giant silvergrass occurs worldwide, containing a total of 1839 records
We used both the 10-fold cross-validation and standard deviation values that quantify the uncertainty of the spatial prediction to evaluate the performance of the BRT model for simulating the potential land resources for growing giant silvergrass
Summary
Climate change is a global issue that will severely threaten human security without proper response to it [1]. Many international organizations have set up policies to cope with climate change. Climate Change Conference (COP21) set a goal of guaranteeing global average temperature increases below 2 ◦ C above pre-industrial levels and, if possible, putting further efforts to limit the temperature increase to 1.5 ◦ C [2]. Emissions of greenhouse gases must be reduced if we want to avoid further global warming [6]. A great deal of effort has been put into mitigating global warming, and developing renewable resources turns out to be one of the most effective means that is conducive to carbon emission reduction [7,8]. As one of the reliable and renewable energy sources, bioenergy occupies
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