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Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China.

ABSTRACT: As China ramped-up coal power capacities rapidly while CO₂ emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizing the power sector in China with BECCS. We identify a potential of 222 GW of power capacities in 2836 counties generated by co-firing 0.9 Gt of biomass from the same county, with half being agricultural residues. Our spatially explicit method helps to reduce uncertainty in the economic costs and emissions of BECCS, identify the best opportunities for bioenergy and show the limitations by logistical challenges to achieve carbon neutrality in the power sector with large-scale BECCS in China.

SUBMITTER: Xing X

PROVIDER: S-EPMC8154910 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China.

Xing Xiaofan X Wang Rong R Bauer Nico N Ciais Philippe P Cao Junji J Chen Jianmin J Tang Xu X Wang Lin L Yang Xin X Boucher Olivier O Goll Daniel D Peñuelas Josep J Janssens Ivan A IA Balkanski Yves Y Clark James J Ma Jianmin J Pan Bo B Zhang Shicheng S Ye Xingnan X Wang Yutao Y Li Qing Q Luo Gang G Shen Guofeng G Li Wei W Yang Yechen Y Xu Siqing S

Nature communications 20210526 1

As China ramped-up coal power capacities rapidly while CO<sub>2</sub> emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizi ...[more]

PMID: 34039971

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Project description:Information about how animal abundance varies across landscapes is needed to inform management action but is costly and time-consuming to obtain; surveys of a single population distributed over a large area can take years to complete. Surveys employing small, spatially replicated sampling units improve efficiency, but statistical estimators rely on assumptions that constrain survey design or become less reasonable as larger areas are sampled. Efficient methods that avoid assumptions about similarity of detectability or density among replicates are therefore appealing. Using simulations and data from >3500 black bears sampled on 73 independent study areas in Ontario, Canada, we (1) quantified bias induced by unmodeled spatial heterogeneity in detectability and density; (2) evaluated novel, design-based estimators of average density across replicate study areas; and (3) evaluated two estimators of the variance of average density across study areas: an analytic estimator that assumed an underlying homogeneous spatial Poisson point process for the distribution of animals' activity centers, and an empirical estimator of variance across study areas. In simulations where detectability varied in space, assuming spatially constant detectability yielded density estimates that were negatively biased by 20% to 30%; estimating local detectability and density from local data and treating study areas as independent, equal replicates when estimating average density across study areas using the design-based estimator yielded unbiased estimates at local and landscape scales. Similarly, detectability of black bears varied among study areas and estimates of bear density at landscape scales were higher when no information was shared across study areas when estimating detectability. This approach also maximized precision (relative SEs of estimates of average black bear density ranged from 7% to 18%) and computational efficiency. In simulations, the analytic variance estimator was robust to threefold variation in local densities but the empirical estimator performed poorly. Conducting multiple, similar SECR surveys and treating them as independent replicates during analyses allowed us to efficiently estimate density at multiple scales and extents while avoiding biases caused by pooling spatially heterogeneous data. This approach enables researchers to address a wide range of ecological or management-related questions and is applicable with most types of SECR data.

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Dataset Information

Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China.

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Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China.

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