Search

Bioenergy will play an intimate and critical role in energy supply and carbon mitigation in the future. In recent years, "customizing the development of bioenergy to local conditions" and "prioritizing distributed utilization" have been the two key principles that have been released by the Chinese government to promote the national‐ and provincial‐level development of bioenergy. While many recognize the importance of bioenergy in achieving low‐carbon transition, little is known about the high‐resolution distribution of usable biomass feedstock and technical bioenergy potential in China, which brings about uncertainties and additional challenges for creating localized utilization plans. We propose a new assessment framework that integrates crop growth models, a land suitability assessment, and the geographic information systems to address these knowledge gaps. Distributions of 11 types of usable biomass feedstock and three kinds of technical bioenergy potential are mapped out through specific transformation technologies at 1 km resolution. At the national level, the final technical biogas potential is 1.91 EJ. The technical bioethanol potential (0.04–0.96 EJ) from the energy crop can supply 0.13–3.12 times the bioethanol demand for the consumption of E10 gasoline in 2015. The technical heat potential (1.06 EJ) can meet 20% of the demand for heating in all provinces (5.38 EJ). Most of the 2020 bioenergy goals can be achieved, excluding that for bioethanol, which will need to require more cellulosic ethanol from residues. At the provincial level, Henan and Inner Mongolia have the potential to develop clean heating alternatives via the substitution of agroforestry residues for coal. The results can provide a systematic analysis of the distribution of biomass feedstocks and technical bioenergy potential in China. With economic factors taken into consideration in further research, it can also support national and provincial governments in making bioenergy development plans in an effective and timely manner. [ABSTRACT FROM AUTHOR]

Highlights • Yield per hectare of sweet sorghum are influence by temperature and precipitation. • Xinjiang and Inner Mongolia are suitable for planting sweet sorghum. • The 2020 and 2050 biofuel goal can be satisfied by certain scenarios in this paper. • Climate change and land-use change are critical to energy crop development plan. Abstract Biofuel from energy crops will play an important role in supplying carbon-neutral fuel for the transport sector. However, there are large uncertainties regarding energy crop development and the energy crop potential and distribution need to be evaluated more comprehensively and systematically. In this study, we propose an integrated approach using the AquaCrop model, a geographic information system approach, and a scenario analysis and we conduct a land availability assessment with multiple factors including climate change, land use, soil, topography, regional crop management, and technological improvement to shed light on the potential production, distribution, and temporal-spatial variation of land suitable for sweet sorghum production in China. We evaluate a full range of spatial scales from 1-km resolution to a national assessment in four representative concentration pathways from 2010 to 2100. The results show that most provinces can plant sweet sorghum. The most suitable provinces for sweet sorghum production are Xinjiang, Heilongjiang, Jilin, Inner Mongolia, Yunnan, and Sichuan. The biofuel potential of sweet sorghum in high-slope farmland (0.30–0.73 EJ) and marginal land (11.98–15.18 EJ) can satisfy the biofuel goal in 2020 (0.12 EJ) and 2050 (6.70–8.60 EJ). Climate change and land-use change are critical factors influencing for the current implementation and long-term planning of energy crops in China. [ABSTRACT FROM AUTHOR]

The targets of limiting global warming levels below 2°C or even 1.5°C set by Paris Agreement heavily rely on bioenergy with carbon capture and storage (BECCS), which can remove carbon dioxide in the atmosphere and achieve net zero greenhouse gas (GHG) emission. Biomass and coal co‐firing with CCS is one of BECCS technologies, as well as a pathway to achieve low carbon transformation and upgrading through retrofitting coal power plants. However, few studies have considered co‐firing ratio of biomass to coal based on each specific coal power plant's characteristic information such as location, installed capacity, resources allocation, and logistic transportation. Therefore, there is a need to understand whether it is worth retrofitting any individual coal power plant for the benefit of GHG emission reduction. It is also important to understand which power plant is suitable for retrofit and the associated co‐firing ratio. In order to fulfill this gap, this paper develops a framework to solve these questions, which mainly include three steps. First, biomass resources are assessed at 1 km spatial resolution with the help of the Geography Information Science method. Second, by setting biomass collection points and linear program model, resource allocation and supply chain for each power plants are complete. Third, is by assessing the life‐cycle emission for each power plant. In this study, Hubei Province in China is taken as the research area and study case. The main conclusions are as follows: (a) biomass co‐firing ratio for each CCS coal power plant to achieve carbon neutral is between 40% and 50%; (b) lower co‐firing ratio sometimes may obtain better carbon emission reduction benefits; (c) even the same installed capacity power plants should consider differentiated retrofit strategy according to their own characteristic. Based on the results and analysis above, retrofit suggestions for each power plant are made in the discussion. [ABSTRACT FROM AUTHOR]

High-quality training and validation samples are critical components of land-cover and land-use mapping tasks in remote sensing. For large area mapping it is much more difficult to build such sample sets due to the huge amount of work involved in sample collection and image processing. As more and more satellite data become available, a new trend emerges in land-cover mapping that takes advantage of images acquired beyond the greenest season. This has created the need for constructing sample sets that can be used in classifying images of multiple seasons. On the other hand, seasonal land-cover information is also becoming a new demand in land and climate change studies. Here we produce the first training and validation data sets with seasonal labels in order to support the production of seasonal land-cover data for entire Africa. Nonetheless, for the first time, two classification systems were created for the same set of samples. We adapted the finer resolution observation and monitoring of global land cover (FROM-GLC) and the Food and Agriculture Organization (FAO) Land Cover Classification System legends. Locations of training-sample units of FROM-GLC were repurposed here. Then we designed a process to enlarge the training-sample units to increase the density of samples in the feature space of spectral characteristics of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series and Landsat imagery. Finally, we obtained 15,799 training-sample units and 7430 validation-sample units. The land-cover type at each point was recorded at the time of maximum greenness in addition to four seasons in a year. Nearly half of the sample units were also suitable for 500 m resolution MODIS data. We analysed the representativeness of the training and validation sets and then provided some suggestions about their use in improving classification accuracies of Africa. [ABSTRACT FROM AUTHOR]

[Display omitted] • This study breaks the scale gap between energy model and natural water resources. • This study provides a method to assess water stress risk due to power plants. • 2℃/1.5℃ targets obviously affect China's power transition pathways. • Achieving the 1.5℃ target is a key to release the water stress risk in catchment scale. Coal power production is the second largest source of water demand in China. However, as coal power would undergo significant changes under ambitious climate goals (2℃ or 1.5℃), it's not clear how the low-carbon transition of the power sector made at the provincial level would affect the catchment-level water resources in the future. With a power system model (MESEIC) and a unit-level coal-fired power unit dataset, this study explores different power sector transition pathways from 2020 to 2050, and maps out the catchment-level water stress risk of China's coal power with Monte Carlo method. Results show that the future power supply mix varies much under the shared socio-economic pathways (SSPs) and carbon emission targets. Without carbon emission targets, coal power would continue to dominate the power supply mix advantage and would cause severe risk of water stress. Under SSP1-5, the national water withdrawal from coal power in 2050 would be 12.2–176.2 billion m3 under the reference scenario, but would decline to 10.7–59.2 billion m3 with 2℃ target and 0.11–35.5 billion m3 with 1.5℃ target. Compared with the 2℃ target, the catchment-level water stress risk generated by coal-fired power plants in north China would be significantly reduced under the stricter target of 1.5℃. However, the benefits would be reduced under SSP5 because of the application of carbon capture and storage. This study reveals the strong synergies between reducing carbon emissions and alleviating water stress risk in China's power sector, but regional risk should be noted while achieving the carbon reduction targets. [ABSTRACT FROM AUTHOR]

• Coal-to-biomass retrofitting costs US$18.3–73.0 for each ton of carbon reduced. • Coal-to-biomass retrofit costs US$21.6–806.5 for each kg of SO 2 reduced. • The unit's size, age, and transportation distance are main influencing factors. • Carbon or SO 2 reductions mainly depends on cost or benefit preference, respectively. To avoid the irreversible impact of global climate change on human society, many countries have recently put forward ambitious goals to accelerate the low-carbon transition of energy systems. Among low-carbon measures, retrofitting existing coal power plants with biomass co-firing is regarded as a promising cost-efficient option to mitigate greenhouse gas and air pollutant emissions. However, the life-cycle economic cost or environmental benefit of this coal-to-biomass retrofit is not identical for various types of power plants in different regions. To facilitate a more efficient biomass utilization strategy in an energy system, it is necessary to carry out a large-scale and high-resolution cost-benefit assessment for the co-firing of biomass and coal in retrofitted plants. Taking China as an example, this study utilized a bottom-up approach and geographic information system, combined with the latest available datasets, to develop a unit-level cost-benefit analysis framework for the coal-to-biomass transition. The results indicate that the coal-to-biomass retrofit costs US$18.3–73.0 for each ton of carbon reduction, and US$21.6–806.5 for each kg of SO 2 reduction, at a 25% blending ratio. Installed capacity, operating year, and transportation distance are important influencing factors of cost-benefit heterogeneity. The priority of power unit retrofitting in terms of carbon reductions mainly depends on the cost preference, while that for SO 2 reduction is mainly determined by benefit preference. The analytical framework proposed in this study can be used in other countries to formulate an efficient biomass development strategy. [ABSTRACT FROM AUTHOR]