Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDecreasing emissions and increasing sink capacity to support China in achieving carbon neutrality before 2060
73
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
In September 2020, President Xi Jinping announced that China strives to achieve carbon neutrality before 2060. This ambitious and bold commitment was well received by the global community. However, the technology and pathway are not so clear. Here, we conducted an extensive review covering more than 200 published papers and summarized the key technologies to achieve carbon neutrality. We projected sectoral CO2 emissions for 2020-2050 based on our previous studies and published scenarios. We applied a medium sink scenario for terrestrial sinks due to the potential resource competition and included an ocean sink, which has generally not been included in previous estimates. We analyzed and revisited Chinas historical terrestrial carbon sink capacity from 1980-2020 based on multiple models and a literature review. To achieve neutrality, it is necessary to increase sink capacity and decrease emissions from many sources. On the one hand, critical measures to reduce emissions include decreasing the use of fossil fuels; substantially increasing the proportion of the renewable energy and nuclear energy. On the other hand, the capacity of future carbon sinks is projected to decrease due to the natural evolution of terrestrial ecosystems, and anthropogenic management practices are needed to increase sink capacity, including increasing the forest sinks through national ecological restoration projects and large-scale land greening campaigns; increasing wood harvesting and storage; and developing CCUS. This paper provides basic source and sink data,and established and promising new technologies for decreasing emissions and increasing sinks for use by the scientific community and policy makers.
rate research
Read More
Chinas pledge to reach carbon neutrality before 2060 is an ambitious goal and could provide the world with much-needed leadership on how to limit warming to +1.5C warming above pre-industrial levels by the end of the century. But the pathways that would achieve net zero by 2060 are still unclear, including the role of negative emissions technologies. We use the Global Change Analysis Model to simulate how negative emissions technologies, in general, and direct air capture (DAC) in particular, could contribute to Chinas meeting this target. Our results show that negative emissions could play a large role, offsetting on the order of 3 GtCO2 per year from difficult-to-mitigate sectors such as freight transportation and heavy industry. This includes up to a 1.6 GtCO2 per year contribution from DAC, constituting up to 60% of total projected negative emissions in China. But DAC, like bioenergy with carbon capture and storage and afforestation, has not yet been demonstrated at anywhere approaching the scales required to meaningfully contribute to climate mitigation. Deploying NETs at these scales will have widespread impacts on financial systems and natural resources such as water, land, and energy in China.
Global Climate Models (GCMs) provide forecasts of future climate warming using a wide variety of highly sophisticated anthropogenic CO2 emissions models as input, each based on the evolution of four emissions drivers: population p, standard of living g, energy productivity (or efficiency) f and energy carbonization c. The range of scenarios considered is extremely broad, however, and this is a primary source of forecast uncertainty. Here, it is shown both theoretically and observationally how the evolution of the human system can be considered from a surprisingly simple thermodynamic perspective in which it is unnecessary to explicitly model two of the emissions drivers: population and standard of living. Specifically, the human system grows through a self-perpetuating feedback loop in which the consumption rate of primary energy resources stays tied to the historical accumulation of global economic production - or p times g - through a time-independent factor of 9.7 +/- 0.3 milliwatts per inflation-adjusted 1990 US dollar. This important constraint, and the fact that f and c have historically varied rather slowly, points towards substantially narrowed visions of future emissions scenarios for implementation in GCMs.
Ozone (O$_{3}$) is a key oxidant and pollutant in the lower atmosphere. Significant increases in surface O$_{3}$ have been reported in many cities during the COVID-19 lockdown. Here we conduct comprehensive observation and modeling analyses of surface O$_{3}$ across China for periods before and during the lockdown. We find that daytime O$_{3}$ decreased in the subtropical south, in contrast to increases in most other regions. Meteorological changes and emission reductions both contributed to the O$_{3}$ changes, with a larger impact from the former especially in central China. The plunge in nitrogen oxide (NO$_{x}$) emission contributed to O$_{3}$ increases in populated regions, whereas the reduction in volatile organic compounds (VOC) contributed to O$_{3}$ decreases across the country. Due to a decreasing level of NO$_{x}$ saturation from north to south, the emission reduction in NO$_{x}$ (46%) and VOC (32%) contributed to net O$_{3}$ increases in north China; the opposite effects of NO$_{x}$ decrease (49%) and VOC decrease (24%) balanced out in central China, whereas the comparable decreases (45-55%) in these two precursors contributed to net O$_{3}$ declines in south China. Our study highlights the complex dependence of O$_{3}$ on its precursors and the importance of meteorology in the short-term O$_{3}$ variability.
For astronomers to make a significant contribution to the reduction of climate change-inducing greenhouse gas emissions, we first must quantify our sources of emissions and review the most effective approaches for reducing them. Here we estimate that Australian astronomers total greenhouse gas emissions from their regular work activities are $gtrsim$25 ktCO$_2$-e/yr (equivalent kilotonnes of carbon dioxide per year). This can be broken into $sim$15 ktCO$_2$-e/yr from supercomputer usage, $sim$4.2 ktCO$_2$-e/yr from flights (where individuals flight emissions correlate with seniority), $>$3.3 ktCO$_2$-e/yr from the operation of observatories, and 2.6$pm$0.4 ktCO$_2$-e/yr from powering office buildings. Split across faculty scientists, postdoctoral researchers, and PhD students, this averages to $gtrsim$37 tCO$_2$-e/yr per astronomer, over 40% more than what the average Australian non-dependant emits in total, equivalent to $sim$5$times$ the global average. To combat these environmentally unsustainable practices, we suggest astronomers should strongly preference use of supercomputers, observatories, and office spaces that are predominantly powered by renewable energy sources. Where facilities that we currently use do not meet this requirement, their funders should be lobbied to invest in renewables, such as solar or wind farms. Air travel should also be reduced wherever possible, replaced primarily by video conferencing, which should also promote inclusivity.
This work considers a Poisson noise channel with an amplitude constraint. It is well-known that the capacity-achieving input distribution for this channel is discrete with finitely many points. We sharpen this result by introducing upper and lower bounds on the number of mass points. Concretely, an upper bound of order $mathsf{A} log^2(mathsf{A})$ and a lower bound of order $sqrt{mathsf{A}}$ are established where $mathsf{A}$ is the constraint on the input amplitude. In addition, along the way, we show several other properties of the capacity and capacity-achieving distribution. For example, it is shown that the capacity is equal to $ - log P_{Y^star}(0)$ where $P_{Y^star}$ is the optimal output distribution. Moreover, an upper bound on the values of the probability masses of the capacity-achieving distribution and a lower bound on the probability of the largest mass point are established. Furthermore, on the per-symbol basis, a nonvanishing lower bound on the probability of error for detecting the capacity-achieving distribution is established under the maximum a posteriori rule.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
