Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019 DOI Creative Commons
Mengqi Zhang, Guijian Liu

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 858, P. 159588 - 159588

Published: Nov. 2, 2022

Language: Английский

The impact of digital financial inclusion on carbon dioxide emissions: Empirical evidence from Chinese provinces data DOI Creative Commons
Hanghang Zheng, Xia Li

Energy Reports, Journal Year: 2022, Volume and Issue: 8, P. 9431 - 9440

Published: Aug. 1, 2022

Green development is becoming increasingly important in current society. Many countries have set the goal for reducing energy consuming green development. However, needs improvement of financial resources and services. Using panel annual data 30 Chinese provinces over period 2013–2020, we find that digital inclusion has a negative impact on carbon dioxide emission. Our conclusion still robust by using system General Method Moments (GMM) method Instrumental Variable regression (IV). Heterogeneity analysis shows finance usage depth digitization level contribute to reduction emissions but coverage breadth might not same effect. Moreover, more pronounced payment investment. Regarding effect different regions, greater central region China which are moderately developed areas. Besides, Quantile Regression (QR), no obvious inhibitory high emission Finally, mechanism test lowers per capita consumption improving capital GDP. research suggests government should support inclusive pay attention level, will achieving neutrality transition low-carbon economy.

Language: Английский

Citations

62

Large loss and rapid recovery of vegetation cover and aboveground biomass over forest areas in Australia during 2019–2020 DOI Creative Commons
Yuanwei Qin, Xiangming Xiao, Jean‐Pierre Wigneron

et al.

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 278, P. 113087 - 113087

Published: May 25, 2022

Language: Английский

Citations

57

Estimated regional CO2 flux and uncertainty based on an ensemble of atmospheric CO2 inversions DOI Creative Commons
Naveen Chandra, Prabir K. Patra,

Yousuke Niwa

et al.

Atmospheric chemistry and physics, Journal Year: 2022, Volume and Issue: 22(14), P. 9215 - 9243

Published: July 18, 2022

Abstract. Global and regional sources sinks of carbon across the earth's surface have been studied extensively using atmospheric dioxide (CO2) observations chemistry-transport model (ACTM) simulations (top-down/inversion method). However, uncertainties in flux distributions remain unconstrained due to lack high-quality measurements, simulations, representation data errors inversion systems. Here, we assess a suite 16 cases derived from single transport (MIROC4-ACTM) but different sets priori (bottom-up) terrestrial biosphere oceanic fluxes, as well prior observational (50 sites) estimate CO2 fluxes for 84 regions over period 2000–2020. The ensembles provide mean field that is consistent with global growth rate, land ocean sink partitioning −2.9 ± 0.3 (± 1σ uncertainty on ensemble mean) −1.6 0.2 PgC yr−1, respectively, 2011–2020 (without riverine export correction), offsetting about 22 %–33 % %–18 fossil fuel emissions. rivers carry 0.6 yr−1 into deep ocean, thus effective −2.3 −2.2 0.3, respectively. Aggregated 15 compare reasonably best estimations 2000s (∼ 2000–2009), given by REgional Carbon Cycle Assessment Processes (RECCAP), all appeared 2011–2020. Interannual variability seasonal cycle are more consistently two distinct when greater degree freedom (increased uncertainty) system. We further evaluated meridional independent (not used inversions) aircraft suggesting (model–observation standard deviation = −0.3 3 ppm) suited budgets than an individual −0.35 3.3 ppm). Using 11 at 5-year intervals, show promise capability track changes toward supporting ongoing future emission mitigation policies.

Language: Английский

Citations

52

Climate change threatens our health and survival within decades DOI
Anthony Costello,

Marina Romanello,

Stella M. Hartinger

et al.

The Lancet, Journal Year: 2022, Volume and Issue: 401(10371), P. 85 - 87

Published: Nov. 16, 2022

Language: Английский

Citations

49

Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019 DOI Creative Commons
Mengqi Zhang, Guijian Liu

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 858, P. 159588 - 159588

Published: Nov. 2, 2022

Language: Английский

Citations

48