Abstract. To tackle the problem of severe air pollution, China has implemented active
clean air policies in recent years. As a consequence, the emissions of major
air pollutants have decreased and the air quality has substantially improved.
Here, we quantified China's anthropogenic emission trends from 2010 to 2017
and identified the major driving forces of these trends by using a
combination of bottom-up emission inventory and index decomposition analysis
(IDA) approaches. The relative change rates of China's anthropogenic
emissions during 2010–2017 are estimated as follows: −62 % for
SO2, −17 % for NOx, +11 % for nonmethane
volatile organic compounds (NMVOCs), +1 % for NH3, −27 %
for CO, −38 % for PM10, −35 % for PM2.5, −27 %
for BC, −35 % for OC, and +16 % for CO2. The IDA results
suggest that emission control measures are the main drivers of this
reduction, in which the pollution controls on power plants and industries are
the most effective mitigation measures. The emission reduction rates markedly
accelerated after the year 2013, confirming the effectiveness of China's
Clean Air Action that was implemented since 2013. We estimated that during
2013–2017, China's anthropogenic emissions decreased by 59 % for
SO2, 21 % for NOx, 23 % for CO, 36 % for
PM10, 33 % for PM2.5, 28 % for BC, and 32 % for OC.
NMVOC emissions increased and NH3 emissions remained stable during
2010–2017, representing the absence of effective mitigation measures for
NMVOCs and NH3 in current policies. The relative contributions of
different sectors to emissions have significantly changed after several
years' implementation of clean air policies, indicating that it is paramount
to introduce new policies to enable further emission reductions in the
future.
The carbon intensity of economic activity, or CO emissions per unit GDP, is a key indicator of the climate impacts of a given activity, business, or region. Although it is well-known that the carbon intensity of countries varies widely according to their level of economic development and dominant industries, few studies have assessed disparities in carbon intensity at the level of cities due to limited availability of data. Here, we present a detailed new inventory of emissions for 337 Chinese cities (every city in mainland China including 333 prefecture-level divisions and 4 province-level cities, Beijing, Tianjin, Shanghai, and Chongqing) in 2013, which we use to evaluate differences of carbon intensity between cities and the causes of those differences. We find that cities' average carbon intensity is 0.84 kg of CO per dollar of gross domestic product (kgCO per $GDP), but individual cities span a large range: from 0.09 to 7.86 kgCO per $GDP (coefficient of variation of 25%). Further analysis of economic and technological drivers of variations in cities' carbon intensity reveals that the differences are largely due to disparities in cities' economic structure that can in turn be traced to past investment-led growth. These patterns suggest that "carbon lock-in" via socio-economic and infrastructural inertia may slow China's efforts to reduce emissions from activities in urban areas. Policy instruments targeted to accelerate the transition of urban economies from investment-led to consumption-led growth may thus be crucial to China meeting both its economic and climate targets.
We propose and demonstrate a method to generate ultrawideband (UWB) signals in the optical domain based on the chaotic dynamics of an optically injected semiconductor laser with optical feedback. The chaotic-UWB pulses with a fractional bandwidth of 116% and central frequency of 6.88 GHz are experimentally generated by controlling the injection strength and frequency detuning of the chaotic laser. The spectrum of the UWB signals is in full compliance with the Federal Communications Commission spectral mask, and the experimental results are qualitatively consistent with the simulated results.
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