China's rapid expansion of coal-fired power plants, coupled with the urgent need to reduce CO2 emissions, presents a significant challenge. Many of these plants risk becoming stranded assets unless mitigation strategies are implemented. Bioenergy with carbon capture and storage (BECCS) is a promising technology that can produce energy while simultaneously removing carbon from the atmosphere, thereby aligning with the Paris Agreement's climate stabilization targets. However, concerns exist about the feasibility of large-scale BECCS due to potential negative impacts on broader sustainability issues. This study aims to address these concerns by providing a comprehensive, spatially explicit analysis of BECCS potential in China. The study's primary hypothesis is that BECCS can be successfully implemented by integrating biomass utilization, coal-fired power plant retrofitting for biomass co-firing and CCS, and CO2 transportation to geological storage sites. The existing literature on BECCS feasibility has focused on regional estimates or lacked the spatial detail necessary to fully capture the complexity of the Chinese energy landscape. This gap in knowledge underscores the need for a data-rich, spatially explicit approach that considers the geographical constraints on biomass supply, power plant locations, and geological storage sites throughout China. Such an analysis will be essential in determining the economic viability and practical implementation of BECCS in China. The research also explores the challenges of deploying BECCS at a national scale including land and biomass availability, costs associated with biomass acquisition and pretreatment, water and fertilizer requirements, associated GHG emissions, investment needed for power plant retrofitting, CCS infrastructure requirements, CO2 transport costs, reduced electricity generation efficiency and inertia of the energy system. This comprehensive evaluation will provide critical insights into the potential and limitations of using BECCS to achieve carbon neutrality in the power sector within China.

Previous studies have explored the potential of BECCS at regional scales or for specific countries, but often lacked the granular spatial data necessary for a comprehensive national-level assessment, especially for a large and geographically diverse country like China. While some research estimated bioenergy potential from agricultural and forestry residues, there's significant variation in these estimations due to differing methodologies and data sources. Studies on the economics of BECCS also varied, largely due to differing assumptions on the cost of biomass acquisition, transport, and CCS implementation. A key gap identified was the lack of spatially explicit analysis that considered the interactions between biomass production, power plant locations, and geological storage sites. The current study addresses these limitations by building on these previous estimations and employing a spatially explicit model which integrates these elements, producing a more nuanced and accurate picture of BECCS potential in China.

The study employs a spatially explicit approach that integrates data on biomass feedstocks, power plant locations, and geological carbon storage capacity across China's 2836 counties. Biomass supply is estimated from 19 ligno-cellulosic feedstocks including agricultural and forestry residues and potential energy crops. The potential biomass yield is determined by combining statistical data and satellite land-use data with crop-specific straw-to-grain ratios. Electricity generation capacity of retrofitted coal-fired power plants is estimated from CO2 emission data. The spatial distribution of these power plants is incorporated alongside the locations of suitable geological repositories for CO2 storage, which includes deep saline aquifers, depleted oil and gas basins, and deep coal reserves. The model accounts for various constraints including biomass feedstock availability, power plant capacity, and geological storage capacity, each with a county-level resolution. A cost-minimization optimization model is utilized to determine the optimal biomass consumption in each county, considering different biomass transportation distances and CO2 transport to storage sites. The model calculates the marginal cost of emission reduction by varying the abatement target. This spatially explicit approach allows for the identification of the most cost-effective opportunities for BECCS deployment, considering the geographical distribution of resources and infrastructure. The study conducts a life-cycle analysis of emissions and costs, accounting for emissions associated with biomass production, pretreatment, transport, power plant retrofitting, CCS, and CO2 transport. Different scenarios are considered, varying biomass co-firing ratios, power plant technology (pulverized coal vs. IGCC), energy crop types, and electricity demand projections. The impact of various parameters on marginal costs is analyzed. The methodology also incorporates uncertainties through Monte Carlo simulations to estimate the range of possible outcomes. Specific methods used include: (1) data compilation for biomass feedstocks, power plants, and carbon storages; (2) estimation of the potential for growing dedicated energy crops using temperature, precipitation, and sunshine data; (3) optimization of biomass use in retrofitted power plants using linear programming to minimize cost under a given emission reduction target; (4) calculation of electricity generation efficiency in retrofitted plants under different biomass co-firing ratios; (5) calculation of unit costs and emissions of biomass utilization using a life-cycle analysis; (6) application of constraints on biomass supply, electricity generation, and carbon storage at the county level; (7) modeling biomass transport between counties; (8) parameterization of CO2 transport costs using pipeline construction costs; and (9) uncertainty analyses through Monte Carlo simulations. The study also assesses the role of BECCS in a low-carbon energy portfolio including water, wind, solar, and nuclear power, projecting future emissions under different scenarios. Soil implications, considering sustainable agricultural residue removal and soil remediation technologies, were also explored.

The study estimated that 3.04 Gt of dry matter per year of ligno-cellulosic biomass could theoretically be harvested from agricultural residues (0.79 Gt/year), forestry residues (0.31 Gt/year), and potential energy crops. This equates to 5.24 Gt CO2/year of carbon sequestration. With a 25.1% electricity generation efficiency in 90% biomass co-firing plants, co-firing all biomass could theoretically produce 4.03 PWh/year, exceeding China's 2015 electricity demand. However, the study's spatially explicit analysis reveals geographical mismatches between biomass production areas, power plant locations, and geological storage sites. Only 0.3 Gt/year of biomass could be burnt in power plants with CO2 captured and stored in the same counties, limiting the potential for local, self-sufficient BECCS. The marginal cost of BECCS to reduce net CO2 emissions increases with the abatement target, rising from $49 to $103 (t CO2-eq)-1 for the abatement of 0 to 2 Gt CO2-eq/year. The cost increases to much higher levels ($180, $292, and $309 (t CO2-eq)-1) for higher abatement targets. The study also analyzed different scenarios, including varying biomass co-firing ratios, power plant technologies, and energy crop types. Reducing the co-firing ratio from 90% to 30% reduces marginal cost, but significantly limits the emission reduction potential. Using IGCC plants instead of PC plants increases the marginal cost. The analysis of scenarios with only CCS or only bioenergy shows that CCS becomes a more cost-effective decarbonization option for deeper emission reductions compared to bioenergy alone. The decomposition of costs and emissions in BECCS deployment highlights significant contributions from biomass acquisition and pretreatment, CCS, power plant retrofitting, and biomass transportation. The study identifies key bioenergy production areas, primarily in rural regions, and economic cost distributions, showing that 11% of counties generate 50% of bioenergy when abating 1 Gt CO2-eq/year. The study assessed BECCS's role within a low-carbon energy portfolio with WWSN (water, wind, solar, and nuclear) power. Investing $2.3 trillion (1% of China's GDP) in BECCS from 2021–2030 could reduce emissions by 21 Gt CO2-eq, while a $7.4 trillion investment could reach carbon neutrality in the power sector by 2030. An investment of $1.1 trillion (0.5% of GDP) would be needed to meet more stringent emission reduction targets. The results emphasize the value of BECCS in meeting near- and long-term emission reduction targets in China. In consideration of sustainable agricultural residue removal and the implementation of soil remediation technologies, the marginal cost curve shifts to the left if 50% of agricultural residues can be used for bioenergy with minor adjustments if soil remediation is applied.

The findings of this study demonstrate significant potential for BECCS in decarbonizing China's power sector, but also highlight considerable logistical and economic challenges. The spatially explicit approach employed provides a more nuanced understanding than previous regional-level assessments, revealing the geographical constraints and mismatches between biomass resources, power plant locations, and CO2 storage sites. The increasing marginal cost of emission reduction with higher abatement targets suggests that the most cost-effective opportunities for BECCS deployment should be prioritized. The identification of key bioenergy production areas and associated economic cost distributions informs targeted policy interventions for optimized resource allocation and equitable benefit sharing. The integration of BECCS within a low-carbon energy portfolio with WWSN power provides valuable insights for national energy planning and the pursuit of carbon neutrality. The study underscores the importance of considering life-cycle emissions and the trade-offs between cost and environmental impact in BECCS development. While the study advances our understanding of BECCS potential in China, further research is needed to refine the estimations of biomass yield, transport costs, and the long-term impacts of sustainable agricultural residue removal on soil health. Moreover, additional investigations into technological advancements in CCS and the potential for enhanced electricity generation efficiency in BECCS facilities are warranted.

This research provides a spatially explicit, data-driven assessment of the potential and cost of using BECCS to decarbonize China's power sector. The findings highlight significant opportunities for emission reduction but emphasize the importance of addressing logistical challenges and ensuring sustainable biomass sourcing. Retrofitting existing coal plants for BECCS can offer a viable pathway towards carbon neutrality, but significant investment is needed. Future research should focus on refining cost and emission estimations, investigating technological improvements, and assessing the long-term sustainability implications of widespread BECCS adoption. The integrated methodology and detailed findings offer valuable insights for policymakers and researchers aiming to develop effective climate mitigation strategies in China and other regions.

The study's estimations rely on several data sets and assumptions, and uncertainties in the biomass supply, transport costs, and CO2 storage capacity may affect the results. The model assumes a 30-year lifetime for carbon storage reservoirs, which could influence the overall capacity estimations. The analysis focuses on the power sector and does not encompass the broader implications of BECCS on other sectors. Additionally, the model relies on existing power plant infrastructure and may not fully capture potential technological advancements in the future. The assumptions made regarding the sustainable harvesting of agricultural residues may impact the overall results, while the model's complexity and computational demands might require further simplifications for wider applicability.