1
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Xu Y, Smith P, Qin Z. Sustainable bioenergy contributes to cost-effective climate change mitigation in China. iScience 2024; 27:110232. [PMID: 39021785 PMCID: PMC11253528 DOI: 10.1016/j.isci.2024.110232] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/14/2024] [Accepted: 06/06/2024] [Indexed: 07/20/2024] Open
Abstract
Bioenergy development is critical for achieving carbon neutrality. Biomass residues from agriculture, forest, and livestock manure provide substantial bioenergy resources in China, but their availability, climate, and economic impacts have not been evaluated systematically. Here we assess biomass sustainability, bioenergy potential, greenhouse gas emissions (GHG) reduction, and cost-effectiveness using an integrated data-modeling approach. Nationally, only 27% of biomass can be used for sustainable bioenergy production, but can contribute to significant climate change mitigation with optimized regional utilization. The annual GHG reduction can reach 1.0 Gt CO2e for bioenergy, or 1.4 Gt CO2e for bioenergy with carbon capture and storage (BECCS), which is comparable to total terrestrial ecosystem carbon sinks in China. The abatement cost varies regionally but is lower than many other carbon removal technologies. Our findings reveal region-specific bioenergy pathways that contribute to carbon neutrality, and encourage future assessments to explore factors including technological advances and carbon markets.
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Affiliation(s)
- Yifan Xu
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, and Key Laboratory of Tropical Atmosphere-Ocean System (Ministry of Education), Sun Yat-sen University, Zhuhai 519000, China
| | - Pete Smith
- Institute of Biological and Environmental Sciences, University of Aberdeen, AB24 3UU Aberdeen, UK
| | - Zhangcai Qin
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, and Key Laboratory of Tropical Atmosphere-Ocean System (Ministry of Education), Sun Yat-sen University, Zhuhai 519000, China
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2
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Percy AJ, Edwin M. A comprehensive review on the production and enhancement techniques of gaseous biofuels and their applications in IC engines with special reference to the associated performance and emission characteristics. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 934:173087. [PMID: 38763185 DOI: 10.1016/j.scitotenv.2024.173087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/01/2024] [Accepted: 05/04/2024] [Indexed: 05/21/2024]
Abstract
The increasing global demand for energy, coupled with environmental concerns associated with fossil fuels, has led to the exploration of alternative fuel sources. Gaseous biofuels, derived from organic matter, have gained attention due to their renewable nature and clean combustion characteristics. The paper extensively explores production pathways for gaseous biofuels, including biogas, syngas, and hydrogen, providing insightful discussions on various sources and processes. The energy content, physical, and chemical properties of gaseous biofuels have been analysed, highlighting their potential as viable alternatives to conventional fuels. Distinctive properties of biogas, producer gas, and hydrogen that impact combustion characteristics and engine efficiency in IC engines are underscored. Furthermore, the review systematically reviews enhancement techniques for gaseous biofuels, encompassing strategies to augment quality, purity, and combustion efficiency. Various methods, ranging from substrate pretreatment for biogas to membrane separation for hydrogen, illustrate effective means of enhancing fuel performance. Rigorous examination of performance parameters such as brake thermal efficiency, specific fuel consumption and emissions characteristics such as NOx, CO, CO2, HC of gaseous biofuels in dual-fuel mode emphasizes efficiency and environmental impact, offering valuable insights into their feasibility as engine fuels. The findings of this review will serve as a valuable resource for researchers, engineers, and policymakers involved in alternative fuels and sustainable transportation, while also highlighting the need for further research and development to fully unlock the potential of gaseous biofuels in IC engines.
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Affiliation(s)
- A Jemila Percy
- Department of Mechanical Engineering, University College of Engineering, Nagercoil, Anna University Constituent College, Nagercoil, Tamil Nadu, India
| | - M Edwin
- Department of Mechanical Engineering, University College of Engineering, Nagercoil, Anna University Constituent College, Nagercoil, Tamil Nadu, India.
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3
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Zhang D, Zhu Z, Chen S, Zhang C, Lu X, Zhang X, Zhang X, Davidson MR. Spatially resolved land and grid model of carbon neutrality in China. Proc Natl Acad Sci U S A 2024; 121:e2306517121. [PMID: 38408236 DOI: 10.1073/pnas.2306517121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 01/18/2024] [Indexed: 02/28/2024] Open
Abstract
China has committed to achieve net carbon neutrality by 2060 to combat global climate change, which will require unprecedented deployment of negative emissions technologies, renewable energies (RE), and complementary infrastructure. At terawatt-scale deployment, land use limitations interact with operational and economic features of power systems. To address this, we developed a spatially resolved resource assessment and power systems planning optimization that models a full year of power system operations, sub-provincial RE siting criteria, and transmission connections. Our modeling results show that wind and solar must be expanded to 2,000 to 3,900 GW each, with one plausible pathway leading to 300 GW/yr combined annual additions in 2046 to 2060, a three-fold increase from today. Over 80% of solar and 55% of wind is constructed within 100 km of major load centers when accounting for current policies regarding land use. Large-scale low-carbon systems must balance key trade-offs in land use, RE resource quality, grid integration, and costs. Under more restrictive RE siting policies, at least 740 GW of distributed solar would become economically feasible in regions with high demand, where utility-scale deployment is limited by competition with agricultural land. Effective planning and policy formulation are necessary to achieve China's climate goals.
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Affiliation(s)
- Da Zhang
- Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084, China
| | - Ziheng Zhu
- Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084, China
| | - Shi Chen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Chongyu Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Xi Lu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Xiliang Zhang
- Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Michael R Davidson
- Department of Mechanical and Aerospace Engineering, University of California San Diego, San Diego, CA 92093
- School of Global Policy and Strategy, University of California San Diego, San Diego, CA 92093
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4
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Zhang Y, Hong W. Spatial-temporal evolution of carbon emissions and spatial-temporal heterogeneity of influencing factors in the Bohai Rim Region, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:13897-13924. [PMID: 38265590 DOI: 10.1007/s11356-024-32057-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 01/14/2024] [Indexed: 01/25/2024]
Abstract
The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of "middle-high and low-outside." (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level.
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Affiliation(s)
- Yangyang Zhang
- School of Management Engineering, Qingdao University of Technology, Qingdao, 266520, China.
| | - Wenxia Hong
- School of Management Engineering, Qingdao University of Technology, Qingdao, 266520, China
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5
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Lei Q, Li L, Chen H, Wang X. Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17189-17200. [PMID: 37917731 DOI: 10.1021/acs.est.3c05305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
As the world grapples with the challenges of energy transition and industrial decarbonization, the development of carbon capture technologies presents a promising solution. The Scalable Modeling, Artificial Intelligence (AI), and Rapid Theoretical calculations, referred as SMART here, is an interdisciplinary approach that combines high-throughput calculation and data-driven modeling with expertise from chemical, materials, environmental, computer and data science and engineering, leading to the development of advanced capabilities in simulating and optimizing carbon capture processes. This perspective discusses the state-of-the-art material discovery research enabled by high-throughput calculation and data-driven modeling. Further, we propose a framework for material discovery, and illustrate the synergies among deep learning models, pretrained models, and comprehensive data sets, emerging as a robust framework for data-driven design and development in carbon capture. In essence, the adoption of the SMART approach promises a revolutionary impact on efforts in energy transition and industrial decarbonization.
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Affiliation(s)
- Qi Lei
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, P. R. China
| | - Lanyu Li
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, P. R. China
| | - Honghao Chen
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, P. R. China
| | - Xiaonan Wang
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, P. R. China
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6
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Singh U, Banerjee S, Hawkins TR. Implications of CO 2 Sourcing on the Life-Cycle Greenhouse Gas Emissions and Costs of Algae Biofuels. ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2023; 11:14435-14444. [PMID: 37799816 PMCID: PMC10548588 DOI: 10.1021/acssuschemeng.3c02082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/15/2023] [Indexed: 10/07/2023]
Abstract
Production of algal biomass and its conversion to biofuels are important technological platforms within the larger umbrella of CO2 capture and utilization. This analysis incorporates a life-cycle assessment (LCA) with respect to global warming potential and techno-economic assessment (TEA) of algae biofuels, focusing on the sourcing and delivery of CO2. This analysis evolves past work in this area to include high-purity biogenic CO2, industrial fossil fuel use, fossil power plants, and direct air capture, and uses a Sherwood plot approach to estimate the CO2 capture energy penalty. We also show that allocation or displacement facilitates a more intuitive distinction between biogenic and fossil sources of carbon. Thus, the LCA better reflects the influence of coproduct handling strategies as compared to previous works. The TEA is also strongly influenced by the CO2 concentration in the flue gas. Currently, when CO2 is sourced from large-point sources, the price of biofuels ($4.5-6.5/GGE) may become comparable to fossil diesel. However, as DAC systems become more economical, they may deliver competitive CO2 sources for biofuels in 2050 with a total cost of <$7/GGE. Based on the net emissions and costs, algae biofuels with CO2 sourced from biogenic sources are consistent with a decarbonized economy as of now, with substantial potential for DAC with decreasing costs.
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Affiliation(s)
- Udayan Singh
- Energy Systems Division, Argonne National
Laboratory, 9700 Cass Avenue, Lemont, Illinois 60439, United States
| | - Sudhanya Banerjee
- Energy Systems Division, Argonne National
Laboratory, 9700 Cass Avenue, Lemont, Illinois 60439, United States
| | - Troy R. Hawkins
- Energy Systems Division, Argonne National
Laboratory, 9700 Cass Avenue, Lemont, Illinois 60439, United States
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7
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Yun H, Dai J, Tan T, Bi X. Accelerate Large-Scale Biomass Residue Utilization via Cofiring to Help China Achieve Its 2030 Carbon-Peaking Goals. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37307511 DOI: 10.1021/acs.est.3c00453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cofiring biomass with coal for power generation is an affordable and ready-to-deploy technology to help reduce carbon emissions and resolve residual biomass. Cofiring has not been widely applied in China primarily because of some practical limitations, i.e., biomass accessibility, technological and economic constraints, and lack of policy support. We identified the benefits of cofiring with consideration of these practical limitations based on Integrated Assessment Models. We found that China produces 1.82 Bts/year of biomass residues, 45% of which is waste. 48% of the unused biomass can be utilized without fiscal intervention and 70% can be utilized with the subsidized Feed-in-Tariffs for biopower and carbon trading. The average Marginal Abatement Cost of cofiring is twice that of China's current carbon price. Cofiring can help China create 153 billion yuan of farmers' income annually and reduce 5.3 Bts of Committed Cumulative Carbon Emissions (CCCEs, 2023-2030), contributing to the needed CCCE mitigations to China's overall sector and the power sector by 32 and 86%, respectively. About 201 GW of coal-fired fleets are not compliant with China's 2030 carbon-peaking goals, and 127 GW can be saved by implementing cofiring, representing 9.6% of the total fleets in 2030.
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Affiliation(s)
- Huimin Yun
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, College of Chemical Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jianjun Dai
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, College of Chemical Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Tianwei Tan
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, College of Chemical Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Xiaotao Bi
- Clean Energy Research Centre and Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z3
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8
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Meng W, Zhu L, Liang Z, Xu H, Zhang W, Li J, Zhang Y, Luo Z, Shen G, Shen H, Chen Y, Cheng H, Ma J, Tao S. Significant but Inequitable Cost-Effective Benefits of a Clean Heating Campaign in Northern China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37256786 DOI: 10.1021/acs.est.2c07492] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Residential emissions significantly contribute to air pollution. To address this issue, a clean heating campaign was implemented to replace coal with electricity or natural gas among 13.9 million rural households in northern China. Despite great success, the cost-benefits and environmental equity of this campaign have never been fully investigated. Here, we modeled the environmental and health benefits, as well as the total costs of the campaign, and analyzed the inequality and inequity. We found that even though the campaign decreased only 1.1% of the total energy consumption, PM2.5 emissions and PM2.5 exposure experienced 20% and 36% reduction, respectively, revealing the amplification effects along the causal pathway. Furthermore, the number of premature deaths attributable to residential emissions reduced by 32%, suggesting that the campaign was highly beneficial. Governments and residents shared the cost of 2,520 RMB/household. However, the benefits and the costs were unevenly distributed, as the residents in mountainous areas were not only less benefited from the campaign but also paid more because of the higher costs, resulting in a notably lower cost-effectiveness. Moreover, villages in less developed areas tended to choose natural gas with a lower initial investment but a higher total cost (2,720 RMB/household) over electricity (2,190 RMB/household). With targeted investment and subsidies in less developed areas and the promotion of electricity and other less expensive alternatives, the multidevelopment goals of improved air quality, reduced health impacts, and reduced inequity in future clean heating interventions could be achieved.
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Affiliation(s)
- Wenjun Meng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Lei Zhu
- School of Economics and Management, Beihang University, Beijing 100191, P. R. China
| | - Zhuang Liang
- School of Economics and Management, Beihang University, Beijing 100191, P. R. China
| | - Haoran Xu
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Wenxiao Zhang
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Jin Li
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Yuanzheng Zhang
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Zhihan Luo
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
| | - Yilin Chen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
| | - Shu Tao
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, P. R. China
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P. R. China
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9
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Li K, Yang J, Wei Y. Impacts of carbon markets and subsidies on carbon capture and storage retrofitting of existing coal-fired units in China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 326:116824. [PMID: 36442336 DOI: 10.1016/j.jenvman.2022.116824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/13/2022] [Accepted: 11/16/2022] [Indexed: 06/16/2023]
Abstract
Carbon capture and storage (CCS) is a feasible technology option to reduce carbon emission in the power industry. However, the high cost of CCS deployment in power plants precludes its large-scale application. Carbon markets may act as an incentive for CCS, but the impact of auction and quota allocation mechanisms in carbon markets on CCS is unclear. In order to investigate the roles of the auction and quota allocation mechanism on the CCS retrofitting in coal-fired units, the life-cycle cost method was used to evaluate the CCS retrofitting cost of China's coal-fired units in the carbon market after supplementing the existing database. The impact of subsidies on stimulating CCS retrofitting was jointly considered. The results show that most units have a CCS retrofit Levelized additional cost of electricity (Lacoe) of $25.24/MWh to $64.57/MWh, making the CCS retrofitting burdensome, even for ultra-supercritical unit that has a low cost. The combination of grandfathering quota allocation mechanism and subsidy will effectively promote CCS retrofitting of coal-fired units, especially when the auction ratio is 30%-40%, about 400-540 GW units will be retrofitted under the carbon market using grandfathering and 12.05$/MWh-22.77$/MWh subsidies. Additionally, there are significant differences among provinces in terms of the lifetime costs of the CCS retrofitting of coal-fired units. Xinjiang, Guangdong, and Jiangsu, with retrofitting potentials of respectively 20.68 GW, 10.58 GW-43.00 GW and 15.00 GW-52.27 GW are best suited for the CCS retrofitting of coal-fired units.
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Affiliation(s)
- Kaiyuan Li
- School of Economics and Management, China University of Geosciences, Beijing, 100083, China
| | - Jin Yang
- School of Economics and Management, China University of Geosciences, Beijing, 100083, China; Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resource, Beijing 100083, China.
| | - Yanting Wei
- School of Economics and Management, China University of Geosciences, Beijing, 100083, China
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10
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Wang R, Li H, Cai W, Cui X, Zhang S, Li J, Weng Y, Song X, Cao B, Zhu L, Yu L, Li W, Huang L, Qi B, Ma W, Bian J, Zhang J, Nie Y, Fu J, Zhang J, Wang C. Alternative Pathway to Phase Down Coal Power and Achieve Negative Emission in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:16082-16093. [PMID: 36321829 DOI: 10.1021/acs.est.2c06004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Although widely recognized as the key to climate goals, coal "phase down" has long been argued for its side effects on energy security and social development. Retrofitting coal power units with biomass and coal co-firing with a carbon capture and storage approach provides an alternative way to avoid these side effects and make deep carbon dioxide emission cuts or even achieve negative emission. However, there is a lack of clear answers to how much the maximum emission reduction potential this approach can unlock, which is the key information to promote this technology on a large scale. Here, we focus on helping China's 4536 coal power units make differentiated retrofit choices based on unit-level heterogeneity information and resource spatial matching results. We found that China's coal power units have the potential to achieve 0.4 Gt of negative CO2 emission in 2025, and the cumulative negative CO2 emission would reach 10.32 Gt by 2060. To achieve negative CO2 emission, the biomass resource amount should be 1.65 times the existing agricultural and forestry residues, and the biomass and coal co-firing ratio should exceed 70%. Coal power units should grasp their time window; otherwise, the maximum negative potential would decrease at a rate of 0.35 Gt per year.
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Affiliation(s)
- Rui Wang
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Haoran Li
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
- China Electric Power Planning & Engineering Institute, Beijing100120, China
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing100084, China
| | - Wenjia Cai
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Xueqin Cui
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Shihui Zhang
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Jin Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing100084, China
| | - Yuwei Weng
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Xinke Song
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing100084, China
| | - Bowen Cao
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Lei Zhu
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Le Yu
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Wei Li
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Lin Huang
- Microsoft Research AI4Science, Beijing100080, China
| | - Binbin Qi
- State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing102249, China
| | - Weidong Ma
- Microsoft Research Asia, Beijing100080, China
| | - Jiang Bian
- Microsoft Research Asia, Beijing100080, China
| | - Jia Zhang
- Microsoft Research AI4Science, Beijing100080, China
| | - Yaoyu Nie
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China
| | - Jingying Fu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing100049, China
| | - Jiutian Zhang
- Green Development Institute, Beijing Normal University, Beijing100875, China
| | - Can Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing100084, China
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11
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Xu S, Wang R, Gasser T, Ciais P, Peñuelas J, Balkanski Y, Boucher O, Janssens IA, Sardans J, Clark JH, Cao J, Xing X, Chen J, Wang L, Tang X, Zhang R. Delayed use of bioenergy crops might threaten climate and food security. Nature 2022; 609:299-306. [PMID: 36071193 DOI: 10.1038/s41586-022-05055-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 06/29/2022] [Indexed: 11/09/2022]
Abstract
The potential of mitigation actions to limit global warming within 2 °C (ref. 1) might rely on the abundant supply of biomass for large-scale bioenergy with carbon capture and storage (BECCS) that is assumed to scale up markedly in the future2-5. However, the detrimental effects of climate change on crop yields may reduce the capacity of BECCS and threaten food security6-8, thus creating an unrecognized positive feedback loop on global warming. We quantified the strength of this feedback by implementing the responses of crop yields to increases in growing-season temperature, atmospheric CO2 concentration and intensity of nitrogen (N) fertilization in a compact Earth system model9. Exceeding a threshold of climate change would cause transformative changes in social-ecological systems by jeopardizing climate stability and threatening food security. If global mitigation alongside large-scale BECCS is delayed to 2060 when global warming exceeds about 2.5 °C, then the yields of agricultural residues for BECCS would be too low to meet the Paris goal of 2 °C by 2200. This risk of failure is amplified by the sustained demand for food, leading to an expansion of cropland or intensification of N fertilization to compensate for climate-induced yield losses. Our findings thereby reinforce the urgency of early mitigation, preferably by 2040, to avoid irreversible climate change and serious food crises unless other negative-emission technologies become available in the near future to compensate for the reduced capacity of BECCS.
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Affiliation(s)
- Siqing Xu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP³), Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Rong Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP³), Department of Environmental Science and Engineering, Fudan University, Shanghai, China. .,IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China. .,Institute of Atmospheric Sciences, Fudan University, Shanghai, China. .,Shanghai Frontiers Science Center of Atmosphere-Ocean Interaction, Shanghai, China. .,MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China. .,Institute of Eco-Chongming (IEC), Shanghai, China.
| | - Thomas Gasser
- International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, France.,Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Nicosia, Cyprus
| | - Josep Peñuelas
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain.,CREAF, Cerdanyola del Vallès, Spain
| | - Yves Balkanski
- Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, France
| | - Olivier Boucher
- Institut Pierre-Simon Laplace, Sorbonne Université/CNRS, Paris, France
| | - Ivan A Janssens
- Department of Biology, University of Antwerp, Wilrijk, Belgium
| | - Jordi Sardans
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain.,CREAF, Cerdanyola del Vallès, Spain
| | - James H Clark
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP³), Department of Environmental Science and Engineering, Fudan University, Shanghai, China.,Green Chemistry Centre of Excellence, University of York, York, UK
| | - Junji Cao
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Xiaofan Xing
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP³), Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Jianmin Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP³), Department of Environmental Science and Engineering, Fudan University, Shanghai, China.,IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China.,Institute of Atmospheric Sciences, Fudan University, Shanghai, China
| | - Lin Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP³), Department of Environmental Science and Engineering, Fudan University, Shanghai, China.,IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China.,Institute of Atmospheric Sciences, Fudan University, Shanghai, China
| | - Xu Tang
- IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China.,Institute of Atmospheric Sciences, Fudan University, Shanghai, China
| | - Renhe Zhang
- IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China.,Institute of Atmospheric Sciences, Fudan University, Shanghai, China.,Shanghai Frontiers Science Center of Atmosphere-Ocean Interaction, Shanghai, China.,MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China
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12
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A Critical Review on Decarbonizing Heating in China: Pathway Exploration for Technology with Multi-Sector Applications. ENERGIES 2022. [DOI: 10.3390/en15031183] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Coal-fired heating is the main method of heating in China, causing serious air pollution and large amounts of CO2 emissions. Decarbonizing heating is important to reduce carbon emissions, and choosing a suitable heating technical scheme is conducive to the early realization of carbon neutrality in China. Coal to gas and coal to electricity transformation projects were carried out in 2017 and achieved remarkable effects. This study compares the current domestic and international clean heating modes, where gas heating, electric heating, heat hump heating, biomass heating, and solar heating coupling system are taken into account. The heating technology potential and heating support aspects, including the industrial sector, building sector, carbon capture and storage (CCS) technology, and publicity are explored as well. Regarding the actual situation in China, a comparative analysis is also conducted on the different types of heat pumps, and then an optimal heating scheme for urban and rural areas is proposed. It is suggested that the urban area with centralized heating can install ground source heat pumps, and the rural area with distributed heating can apply a coupling system of solar photovoltaics to ground source heat pumps (PV-GSHP). Based on current policies and standards support, this study calculates the carbon emissions of this scheme in 2030 and provides a detailed analysis of relevant parameters. The feasibility and superiority of the scheme are confirmed by comparison and discussion with other studies. Moreover, specific measures in planning, subsidy, construction, and electricity are proposed to implement the heating scheme. This study provides a reference for the mode selection and technical scheme of heating decarbonation in China, and that could be also considered in other regions or countries.
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13
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Xing X, Wang R, Bauer N, Ciais P, Cao J, Chen J, Tang X, Wang L, Yang X, Boucher O, Goll D, Peñuelas J, Janssens IA, Balkanski Y, Clark J, Ma J, Pan B, Zhang S, Ye X, Wang Y, Li Q, Luo G, Shen G, Li W, Yang Y, Xu S. Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China. Nat Commun 2021; 12:3159. [PMID: 34039971 PMCID: PMC8154910 DOI: 10.1038/s41467-021-23282-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 04/19/2021] [Indexed: 11/08/2022] Open
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.
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Affiliation(s)
- Xiaofan Xing
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Rong Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China.
- IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health (WECEIPHE), Fudan University, Shanghai, China.
- Institute of Atmospheric Sciences, Fudan University, Shanghai, China.
- Center for Urban Eco-Planning and Design, Fudan University, Shanghai, China.
- Big Data Institute for Carbon Emission and Environmental Pollution, Fudan University, Shanghai, China.
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai, China.
| | - Nico Bauer
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, France
- Climate and Atmosphere Research Center (CARE-C) The Cyprus Institute 20 Konstantinou Kavafi Street, 2121, Nicosia, Cyprus
| | - Junji Cao
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Jianmin Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
- IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health (WECEIPHE), Fudan University, Shanghai, China
- Institute of Atmospheric Sciences, Fudan University, Shanghai, China
| | - Xu Tang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
- IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health (WECEIPHE), Fudan University, Shanghai, China
- Institute of Atmospheric Sciences, Fudan University, Shanghai, China
| | - Lin Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Xin Yang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Olivier Boucher
- Institut Pierre-Simon Laplace, Sorbonne Université/CNRS, Paris, France
| | - Daniel Goll
- Lehrstuhl für Physische Geographie mit Schwerpunkt Klimaforschung, Universität Augsburg, Augsburg, Germany
| | - Josep Peñuelas
- CREAF, Cerdanyola del Vallès, Catalonia, Spain
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Catalonia, Spain
| | - Ivan A Janssens
- Department of Biology, University of Antwerp, Wilrijk, Belgium
| | - Yves Balkanski
- Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, France
| | - James Clark
- Department of Chemistry, Green Chemistry Centre of Excellence, The University of York, York, UK
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Peking University, Beijing, China
| | - Bo Pan
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, China
| | - Shicheng Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Xingnan Ye
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Yutao Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Qing Li
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Gang Luo
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Peking University, Beijing, China
| | - Wei Li
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Yechen Yang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Siqing Xu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, China
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14
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Yang Q, Zhou H, Bartocci P, Fantozzi F, Mašek O, Agblevor FA, Wei Z, Yang H, Chen H, Lu X, Chen G, Zheng C, Nielsen CP, McElroy MB. Prospective contributions of biomass pyrolysis to China's 2050 carbon reduction and renewable energy goals. Nat Commun 2021; 12:1698. [PMID: 33727563 PMCID: PMC7966788 DOI: 10.1038/s41467-021-21868-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 02/15/2021] [Indexed: 11/20/2022] Open
Abstract
Recognizing that bioenergy with carbon capture and storage (BECCS) may still take years to mature, this study focuses on another photosynthesis-based, negative-carbon technology that is readier to implement in China: biomass intermediate pyrolysis poly-generation (BIPP). Here we find that a BIPP system can be profitable without subsidies, while its national deployment could contribute to a 61% reduction of carbon emissions per unit of gross domestic product in 2030 compared to 2005 and result additionally in a reduction in air pollutant emissions. With 73% of national crop residues used between 2020 and 2030, the cumulative greenhouse gas (GHG) reduction could reach up to 8620 Mt CO2-eq by 2050, contributing 13–31% of the global GHG emission reduction goal for BECCS, and nearly 4555 Mt more than that projected for BECCS alone in China. Thus, China’s BIPP deployment could have an important influence on achieving both national and global GHG emissions reduction targets. BIPP with biochar sequestration is a ready-to-implement negative emission technology in China. Here, the authors show that its national deployment could contribute to a 61% reduction of carbon emissions per GDP in 2030 compared to 2005, and contribute 13–31% of the global biomass-based negative emission goal by 2050.
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Affiliation(s)
- Qing Yang
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, PR China. .,John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA. .,Department of New Energy Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, PR China. .,China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, PR China.
| | - Hewen Zhou
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, PR China.,Department of New Energy Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, PR China
| | - Pietro Bartocci
- Department of Engineering, University of Perugia, Perugia, Italy
| | | | - Ondřej Mašek
- UK Biochar Research Centre, School of GeoSciences, University of Edinburgh, Edinburgh, UK
| | - Foster A Agblevor
- USTAR Bioenergy Center, Department of Biological Engineering, Utah State University, Logan, UT, USA
| | - Zhiyu Wei
- Department of New Energy Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, PR China.,China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, PR China
| | - Haiping Yang
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, PR China.,Department of New Energy Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, PR China.,China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, PR China
| | - Hanping Chen
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, PR China. .,Department of New Energy Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, PR China. .,China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, PR China.
| | - Xi Lu
- School of Environment and State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, PR China
| | - Guoqian Chen
- College of Engineering, Peking University, Beijing, PR China
| | - Chuguang Zheng
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, PR China.,Department of New Energy Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, PR China
| | - Chris P Nielsen
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Michael B McElroy
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
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15
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Song J, Li K, Ren J, Yang W, Liu X. Holistic suitability for regional biomass power generation development in China: An application of matter-element extension model. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2020; 276:111294. [PMID: 32896823 DOI: 10.1016/j.jenvman.2020.111294] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 08/13/2020] [Accepted: 08/20/2020] [Indexed: 06/11/2023]
Abstract
In the context of tremendously promoting bioenergy utilization, regional suitability for industrial development of biomass power generation is a critical factor when deploying region-specific strategies. An integrated framework is developed incorporating resource potential, development demands and development conditions to evaluate the suitability for regional industrial development of power generation utilizing agricultural bioresources. Twelve indicators reflecting local resource, environmental and socioeconomic features are used to measure the suitability of 31 provincial regions in China. An improved matter-element extension model combined with the entropy weight method is adopted to attain holistic and hierarchical suitability ranks. The results reveal that the distribution of holistic suitability ranks among regions is imbalanced with the eastern regions presenting more advantages compared with the western regions. Three regions belonging to Rank I (optimum) are Henan, Shandong and Xinjiang. Hainan, Tibet, Qinghai are classified into Rank V (unsuited). Moreover, there are great differences in the limiting factors of the suitability among regions. Resource potential is a limiting factor for Beijing, Shanghai, Fujian, Hainan and Guizhou; Development demands refrain Fujian, Guangxi and Yunnan; Tianjin and Ningxia are limited by development conditions. Tibet and Qinghai have the worst performance on each criterion. The results and region-targeted policy recommendations can provide insights for bioenergy utilization development in accordance with local conditions closely.
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Affiliation(s)
- Junnian Song
- Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, 130021 Changchun, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, 130021 Changchun, China; College of New Energy and Environment, Jilin University, 130012 Changchun, China.
| | - Kexin Li
- College of New Energy and Environment, Jilin University, 130012 Changchun, China
| | - Jingzheng Ren
- Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Wei Yang
- Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, 130021 Changchun, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, 130021 Changchun, China; College of New Energy and Environment, Jilin University, 130012 Changchun, China
| | - Xiaoyu Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, 100875 Beijing, China
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16
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Liu Z, Liu Z, Song T, Gao W, Wang Y, Wang L, Hu B, Xin J, Wang Y. Long-term variation in CO 2 emissions with implications for the interannual trend in PM 2.5 over the last decade in Beijing, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 266:115014. [PMID: 32650300 DOI: 10.1016/j.envpol.2020.115014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 06/09/2020] [Accepted: 06/09/2020] [Indexed: 06/11/2023]
Abstract
Long-term CO2 and PM2.5 measurements in urban areas have important impacts on understanding the roles of urbanization in climate change and air pollution. From 2009 to 2017, CO2 fluxes were measured by the eddy covariance (EC) system at a height of 140 m on the Beijing Meteorological Tower. The CO2 fluxes followed a typical two-peak diurnal pattern all year round. The PM2.5 concentrations followed a similar diurnal pattern as the CO2 fluxes in summer but a different diurnal pattern in winter (low in the day and high at night). On a seasonal time scale, both the CO2 fluxes and the PM2.5 concentrations showed a pronounced seasonal variation (high in winter and low in summer). The spatial variations in CO2 fluxes were dominated by the prevailing land use types within the flux footprint, particularly dense residential areas and heavy traffic roads. On both diurnal and annual time scales, the urban underlying surface was a net source of CO2. The 9-year average annual total CO2 flux was 36.4 kg CO2·m-2 yr-1. Depending on the yearly prevailing wind direction, the effect of the heterogeneity correction on the annual total CO2 fluxes based on the gap-filled dataset could reach up to 3.5%. Over the 9-year period, both the CO2 fluxes and the PM2.5 concentrations exhibited a declining interannual trend, and CO2 fluxes could account for 64% of the interannual variability in PM2.5 concentrations. In summer, emissions were more likely to control the interannual variability in PM2.5 concentrations, whereas in winter, meteorological conditions had a greater impact on the interannual variability in PM2.5 concentrations.
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Affiliation(s)
- Zan Liu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zirui Liu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Tao Song
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; National Earth System Science Data Center, Beijing, 100101, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, Fujian, China.
| | - Wenkang Gao
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Yinghong Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Lili Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Bo Hu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Jinyuan Xin
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Yuesi Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, Fujian, China
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17
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Kang Y, Yang Q, Bartocci P, Wei H, Liu SS, Wu Z, Zhou H, Yang H, Fantozzi F, Chen H. Bioenergy in China: Evaluation of domestic biomass resources and the associated greenhouse gas mitigation potentials. RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2020; 127:109842. [PMID: 34234613 PMCID: PMC7144861 DOI: 10.1016/j.rser.2020.109842] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 03/18/2020] [Accepted: 03/30/2020] [Indexed: 05/16/2023]
Abstract
As bioenergy produces neutral or even negative carbon emissions, the assessment of biomass resources and associated emissions mitigation is a key step toward a low carbon future. However, relevant comprehensive estimates lack in China. Here, we measure the energy potential of China's domestic biomass resources (including crop residues, forest residues, animal manure, municipal solid waste and sewage sludge) from 2000 to 2016 and draw the spatial-temporal variation trajectories at provincial resolution. Scenario analysis and life cycle assessment are also applied to discuss the greenhouse gas mitigation potentials. Results show that the collectable potential of domestic biomass resources increased from 18.31 EJ in 2000 to 22.67 EJ in 2016 with overall uncertainties fluctuating between (-26.6%, 39.7%) and (-27.6%, 39.5%). Taking energy crops into account, the total potential in 2016 (32.69 EJ) was equivalent to 27.6% of China's energy consumption. If this potential can be realized in a planned way to displace fossil fuels during the period 2020-2050, cumulative greenhouse gas emissions mitigation would be in the range of 1652.73-5859.56 Mt CO2-equivalent, in which the negative greenhouse gas emissions due to the introduction of bioenergy with carbon capture and storage would account for 923.78-1344.13 Mt CO2-equivalent. Contrary to increasing bioenergy potentials in most provinces, there are declining trends in Tibet, Beijing, Shanghai and Zhejiang. In addition, Yunnan, Sichuan and Inner Mongolia would have the highest associated greenhouse gas mitigation potentials. This study can provide valuable guidance on the exploitation of China's untapped biomass resources for the mitigation of global climate change.
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Affiliation(s)
- Yating Kang
- China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, 430074, PR China
| | - Qing Yang
- China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, 430074, PR China
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei, 430074, PR China
- Department of New Energy Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei, 430074, PR China
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
- Corresponding author. State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei, 430074, PR China.
| | - Pietro Bartocci
- Department of Engineering, University of Perugia, Via G. Duranti 67, 06125, Perugia, Italy
| | - Hongjian Wei
- Department of New Energy Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei, 430074, PR China
| | - Sylvia Shuhan Liu
- Department of Engineering Science, University of Oxford, OX1 3DR, United Kingdom
| | - Zhujuan Wu
- China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, 430074, PR China
| | - Hewen Zhou
- China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, 430074, PR China
| | - Haiping Yang
- China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, 430074, PR China
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei, 430074, PR China
- Department of New Energy Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei, 430074, PR China
| | - Francesco Fantozzi
- Department of Engineering, University of Perugia, Via G. Duranti 67, 06125, Perugia, Italy
| | - Hanping Chen
- China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, 430074, PR China
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei, 430074, PR China
- Department of New Energy Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei, 430074, PR China
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18
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Coal-Biomass Co-Firing Power Generation Technology: Current Status, Challenges and Policy Implications. SUSTAINABILITY 2020. [DOI: 10.3390/su12093692] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The severity of climate change and the urgency of ecological environment protection make the transformation of coal power imperative. In this paper, the relevant policies of coal-biomass co-firing power generation are combed, and the technical and economic evaluation of coal-biomass co-firing power generation technology is carried out using Levelized Cost of Electricity (LCOE) model. The result is that the LCOE of coal-biomass indirect co-firing power generation project is significantly higher than that of the pure coal-fired unit, with the LCOE rising by nearly 8%. Through sensitivity analysis, the LCOE will increase by 10.7% when it combusts 15% biomass, and increase by 19.1% when it combusts 20% biomass. The LCOE corresponding to wood chips increased by 5.71% and the LCOE to rice husks decreased by 6.06%. Finally, this paper puts forward some relevant policy suggestions, hoping to provide some reference for the promotion of coal-biomass co-firing power generation in China.
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