Search Results
Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China
2021, Xing, Xiaofan, Wang, Rong, Bauer, Nico, Ciais, Philippe, Cao, Junji, Chen, Jianmin, Tang, Xu, Wang, Lin, Yang, Xin, Boucher, Olivier, Goll, Daniel, Peñuelas, Josep, Janssens, Ivan A., Balkanski, Yves, Clark, James, Ma, Jianmin, Pan, Bo, Zhang, Shicheng, Ye, Xingnan, Wang, Yutao, Li, Qing, Luo, Gang, Shen, Guofeng, Li, Wei, Yang, Yechen, Xu, Siqing
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.
Global irrigation contribution to wheat and maize yield
2021, Wang, Xuhui, Müller, Christoph, Elliot, Joshua, Mueller, Nathaniel D., Ciais, Philippe, Jägermeyr, Jonas, Gerber, James, Dumas, Patrice, Wang, Chenzhi, Yang, Hui, Li, Laurent, Deryng, Delphine, Folberth, Christian, Liu, Wenfeng, Makowski, David, Olin, Stefan, Pugh, Thomas A. M., Reddy, Ashwan, Schmid, Erwin, Jeong, Sujong, Zhou, Feng, Piao, Shilong
Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.
Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic
2020, Liu, Zhu, Ciais, Philippe, Deng, Zhu, Lei, Ruixue, Davis, Steven J., Feng, Sha, Zheng, Bo, Cui, Duo, Dou, Xinyu, Zhu, Biqing, Guo, Rui, Ke, Piyu, Sun, Taochun, Lu, Chenxi, He, Pan, Wang, Yuan, Yue, Xu, Wang, Yilong, Lei, Yadong, Zhou, Hao, Cai, Zhaonan, Wu, Yuhui, Guo, Runtao, Han, Tingxuan, Xue, Jinjun, Boucher, Olivier, Boucher, Eulalie, Chevallier, Frédéric, Tanaka, Katsumasa, Wei, Yiming, Zhong, Haiwang, Kang, Chongqing, Zhang, Ning, Chen, Bin, Xi, Fengming, Liu, Miaomiao, Bréon, François-Marie, Lu, Yonglong, Zhang, Qiang, Guan, Dabo, Gong, Peng, Kammen, Daniel M., He, Kebin, Schellnhuber, Hans Joachim
The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (−1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.
State-of-the-art global models underestimate impacts from climate extremes
2019, Schewe, Jacob, Gosling, Simon N., Reyer, Christopher, Zhao, Fang, Ciais, Philippe, Elliott, Joshua, Francois, Louis, Huber, Veronika, Lotze, Heike K., Seneviratne, Sonia I., van Vliet, Michelle T. H., Vautard, Robert, Wada, Yoshihide, Breuer, Lutz, Büchner, Matthias, Carozza, David A., Chang, Jinfeng, Coll, Marta, Deryng, Delphine, de Wit, Allard, Eddy, Tyler D., Folberth, Christian, Frieler, Katja, Friend, Andrew D., Gerten, Dieter, Gudmundsson, Lukas, Hanasaki, Naota, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Lawrence, Peter, Morfopoulos, Catherine, Müller, Christoph, Müller Schmied, Hannes, Orth, René, Ostberg, Sebastian, Pokhrel, Yadu, Pugh, Thomas A. M., Sakurai, Gen, Satoh, Yusuke, Schmid, Erwin, Stacke, Tobias, Steenbeek, Jeroen, Steinkamp, Jörg, Tang, Qiuhong, Tian, Hanqin, Tittensor, Derek P., Volkholz, Jan, Wang, Xuhui, Warszawski, Lila
Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.