• Energy Research
• 15. Life on land close
• Energies close
• Chinese Academy of Sciences close

This study focuses on the potential impacts of large-scale land use and land cover changes (LUCC) on surface temperature from a global perspective. As important types of LUCC, urbanization, deforestation, cultivated land reclamation, and grassland degradation have effects on the climate, the potential changes of the surface temperature caused by these four types of large-scale LUCC from 2010 to 2050 are downscaled, and this issue analyzed worldwide along with Representative Concentration Pathways (RCPs) of the Intergovernmental Panel on Climate Change (IPCC). The first case study presents some evidence of the effects of future urbanization on surface temperature in the Northeast megalopolis of the United States of America (USA). In order to understand the potential climatological variability caused by future forest deforestation and vulnerability, we chose Brazilian Amazon region as the second case study. The third selected region in India as a typical region of cultivated land reclamation where the possible climatic impacts are explored. In the fourth case study, we simulate the surface temperature changes caused by future grassland degradation in Mongolia. Results show that the temperature in built-up area would increase obviously throughout the four land types. In addition, the effects of all four large-scale LUCC on monthly average temperature change would vary from month to month with obviously spatial heterogeneity.

Microalgal biofuel research in China has made noticeable progress, and algae cultivation for biofuel production is considered to be an important contribution to Greenhouse Gas (GHG) mitigation and energy security. In this paper, the algal biofuel potentiality in China was reviewed from the points of view of algal biodiversity, algal culture collection, GHGs (especially CO2) mitigation, and the availability of the required sunlight, wastewater and land resources. The cultivation of microalgae utilizing power plants gas with large amounts of CO2 and wastewaters from urban households, industry and animal husbandry are suitable for large scale production in China. Land is hardly a limitation for algae cultivation.

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.

Forestland dynamics can affect the ecological security of a country and even the global environment, and therefore it is of great practical significance to understand the characteristics of temporal and spatial variations of forestland. Taking Jiangxi Province as the study area, this study first explored the driving mechanism of the natural environment and social economy on deforestation and afforestation using a simultaneous equation model. The results indicate that population size, topographic and geomorphologic factors, climate, and location play leading roles in influencing forestland density fluctuations. Specifically, the population size, economic development level, gross value of forestry production, climate conditions, and government policies are key influencing factors of afforestation. Deforestation is mainly influenced by agricultural population, non-agricultural economy, forestry production, forestry density, location, transportation, and climate. In addition, this study simulated the spatial distribution of land use and analyzed the spatial characteristics and variation trends of forestland area and quality under the Representative Concentration Pathways (RCPs) climate scenarios from 2010 to 2030 using the Conversion of Land Use and its Effects (CLUE) model. The results indicate that forestland declines under the Asia-Pacific integrated model (AIM) climate scenario. The environment tends to be heavily damaged under this kind of scenarios, and measures should be taken in order to protect the environment. Although the model for energy supply strategy alternatives and their general environmental impact (MESSAGE) scenario is to some extent better than the AIM scenario, destruction of the environment will still occur, and it is necessary to restrain deforestation and convert shrub land into forestland or garden land. These results can provide significant information for environmental protection, forest resource exploitation, and utilization in the areas experiencing deforestation and afforestation.

Ecosystem services are tightly coupled with availability of solar energy and its partition into energy fluxes, and biomass accumulation, which represents the energy flux in ecosystems, is a key aspect of ecosystem services. This study analyzed the effects of climate change and land use and land cover change (LUCC) on the biomass accumulation change in the Lower Heihe River Basin during 2001–2010. Biomass accumulation was represented with net primary productivity (NPP), which was estimated with the C-Fix model, and scenario analysis was carried out to investigate effects of climate change and LUCC on biomass accumulation change in a spatially explicit way. Results suggested climate change had an overall positive effect on biomass accumulation, mainly owning to changes in CO2 concentration and temperature. LUCC accounted for 70.61% of biomass accumulation change, but primarily owning to fractional vegetation change (FVCC) rather than land conversion, and there is a negative interactive effect of FVCC and climate change on biomass accumulation, indicating FVCC resulting from water diversion played a dominant in influencing biomass accumulation. These results can provide valuable decision support information for the local ecosystem managers and decision makers to guarantee sustainable provision of essential ecosystem services.

Great attention has been paid to carbon bio-sequestration due to increasing concerns over global warming. Understanding the relationship between carbon bio-sequestration and its influencing factors is of great significance for formulating appropriate management measures for global warming mitigation. Since change in carbon bio-sequestration is a complex process, it is difficult to take into account all of its influencing factors, while the panel data model may provide an effective way to measure their subtle effects. In this paper, decomposition analysis is applied to further analyze these influencing factors. The results indicate that climatic, demographic and geographical variables play important roles in explaining the spatial heterogeneity of carbon bio-sequestration in China, which is consistent with previous researches. Meanwhile, the irrigation rate is found to be the most critical factor influencing carbon bio-sequestration changes, followed by climatic and economic factors. These results may provide decision makers in China with important scientific reference information for formulating regional carbon bio-sequestration management policies, which are of great significance to alleviating and adapting to global warming.

Downward surface shortwave radiation (DSSR) plays an important role in the energy balance of the earth’s surface. Accurate estimate of DSSR is of great significance for the rational and effective use of solar energy. Some parameterization schemes were proposed to estimate DSSR using meteorological measurements given ground-based radiation observation sites are scare and uneven. With the development of remote sensing technique, remotely sensed data can be applied to obtain continuous DSSR in space. Commonly, the spatial resolution of most radiation products is relatively low and cannot meet the needs of certain fields. Moreover, some retrieval algorithms based on the radiation transfer models are complicated for non-professionals. In this study, a back-propagation (BP) neural network method with Levenberg–Marquardt (LM) algorithm (hereafter referred to as LM-BP) was applied to predict DSSR by building the relationship between measured DSSR and high-resolution remote sensing data from the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER). The DSSR observations from the four-component radiation sensor installed at the land covered by vegetable, village, maize, orchard, Gobi, sandy desert, desert steppe, and wetland were used to validate the model estimates. The results showed that the estimates of DSSR from LM-BP agreed well with the site measurements, with the root mean square error (RMSE) and the mean bias error (MBE) values of 27.34 W/m2 and −1.59 W/m2, respectively. This indicates that by combining the LM-BP network model and ASTER images can obtain precise DSSR in heterogenous surface. The DSSR results of this study can provide accurate high-spatial resolution input data for hydrological, evapotranspiration, and crop models.