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In China, there has been a significant increase in carbon emissions in the new era. Therefore, evaluating the influence of industrial structure upgrades and energy structure optimization on reducing carbon emissions is the objective of this research. Based on the provincial panel data of 30 provinces and cities across China from 1997 to 2019, this paper builds up a fixed-effect panel quantile STIRPAT model to investigate the differences in the impact of industrial structure on carbon emission intensity at different quantile levels from the provincial perspective, and as a way of causality test, the mediation effect model is adopted to empirically test the transmission path of "industrial structure upgrading-energy structure optimization-carbon emission reduction". The research results show that: (1) Both industrial structure upgrades and energy structure optimization have significant inhibitory effects on carbon emissions, and there are regional heterogeneities. (2) The upgrading of industrial structure has a significant positive effect on optimizing energy structure. (3) The upgrading of industrial structure can not only directly restrain carbon emissions but also indirectly have a significant inhibitory effect on carbon emissions by promoting the optimization of energy structure. Based on the above conclusions, corresponding policy recommendations are proposed to provide suggestions for China to achieve the goal of carbon neutrality.
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To achieve carbon emission reduction target (CERT) by 2030 and carbon-neutrality in 2050, it is important to actively reduce the emission gap in the private building sector. However, the ongoing COVID-19 pandemic and the Russian-Ukraine war are threatening the green remodeling policy (GRP) worldwide. Therefore, this study analyzed energy consumption savings, GHG emission reduction, and net present value when applying green remodeling to a private building to predict whether or not the current GRP could achieve 2030 CERT and 2050 carbon-neutrality. The main findings are as follows. First, yearly electricity and gas consumption of 84.97 m2 type households can be reduced by 6.19% and 15.58% through green remodeling. Second, based on the energy saving, yearly GHG emission can be reduced about 0.34tCO2eq. Third, the economic feasibility of green remodeling cannot be achieved via the current policy, and NPV17 decreases up to USD-51,485 depending on the credit loan interest rate and the green remodeling interest subsidy program. In other words, it is difficult to reach 2030 CERT and 2050 carbon-neutrality via the current policy. Therefore, the South Korean government is required to reorganize financial policies, establish active systems, increase public awareness of the policy, and improve energy efficiency technology. © 2023 Elsevier B.V.
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PurposeCarbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.Design/methodology/approachThis paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.FindingsResults suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.Originality/valueThis paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.
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To mitigate aviation's carbon emissions of the aviation industry, the following steps are vital: accurately quantifying the carbon emission path by considering uncertainty factors, including transportation demand in the post-COVID-19 pandemic period; identifying gaps between this path and emission reduction targets; and providing mitigation measures. Some mitigation measures that can be employed by China's civil aviation industry include the gradual realization of large-scale production of sustainable aviation fuels and transition to 100% sustainable and low-carbon sources of energy. This study identified the key driving factors of carbon emissions by using the Delphi Method and set scenarios that consider uncertainty, such as aviation development and emission reduction policies. A backpropagation neural network and Monte Carlo simulation were used to quantify the carbon emission path. The study results show that China's civil aviation industry can effectively help the country achieve its carbon peak and carbon neutrality goals. However, to achieve the net-zero carbon emissions goal of global aviation, China needs to reduce its emissions by approximately 82%-91% based on the optimal emission scenario. Thus, under the international net-zero target, China's civil aviation industry will face significant pressure to reduce its emissions. The use of sustainable aviation fuels is the best way to reduce aviation emissions by 2050. Moreover, in addition to the application of sustainable aviation fuel, it will be necessary to develop a new generation of aircraft introducing new materials and upgrading technology, implement additional carbon absorption measures, and make use of carbon trading markets to facilitate China's civil aviation industry's contribution to reduce climate change.
Subject(s)
Aviation , COVID-19 , Humans , Carbon Dioxide/analysis , Uncertainty , Pandemics , COVID-19/prevention & control , Economic Development , China , Carbon/analysisABSTRACT
In March 2022, the resurgence of COVID-19 cases in Shenzhen, a megacity in the Pearl River Delta (PRD) region of China, led to unusual restrictions on anthropogenic activities within a single city, in contrast to the restrictions COVID-19 caused on a national scale at the beginning of 2020. In this unique event, we found that only under unfavorable meteorological conditions did substantial urban local emission reductions have an impact on air pollutant changes (−42.4%–6.6%), whereas the deweathered changes were very small (−8.3%–3.4%) under favorable meteorological conditions. Primary anthropogenic pollutants, such as NO2, toluene, BC, and primary organic aerosol (POA), responded most considerably to emission reductions from early morning to noon during unfavorable meteorological days;for secondary organic aerosol (SOA), regulating the daytime total oxidant (Ox = O3 + NO2) was found to be more effective than controlling its precursors within the city scale, whereas secondary nitrate displayed the opposite trend. Since Ox changed little during the urban lockdown despite the remarkable decrease in precursors, it is emphasized that regionally coordinated control of VOCs and NOx is necessary to effectively reduce Ox levels. In addition, Shenzhen's NOx emission reduction efforts should be sustained in order to control PM2.5 and O3 pollution synergistically for long-term attainment.
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This paper illustrates the study carried out by ARPA Lombardia to quantify the variation in daily emissions of the main pollutants and their impacts on air quality in Lombardy during the anti-COVID-19 lockdown between the end of February and the end of May 2020. A methodology for emission estimates was developed over Lombardy for this purpose and later was extended to larger areas: the Po-basin, (LIFE PREPAIR 2020) and the entire Italy (PULVIRUS 2021). In this study, the daily emissions estimates were derived by combining data from air emission inventory of Lombardy and a set of indicators that allowed to update the estimates and describe the temporal and spatial variations of the emission sources. The calculation of emission variation was conducted for all the main pollutants (PM10, NH3, NOx, SO2, NMVOC) and the greenhouse gases; then, the impact on air quality concentrations was simulated by the chemical and transport model FARM, that also allows to track secondary particulate and its variability in time and space on the basis of nonlinear processes and weather conditions. The estimated emission reduction, compared to the expected average value in the absence of anti-COVID-19 measures, daily varies depending on pollutants and is mainly affected by reductions in road traffic emissions and an estimated increase in domestic heating emissions. Simulations confirm strong reductions of NO2 atmospheric average concentrations, slightly variations of PM10 averages and a potential growth of tropospheric ozone.
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The social processes of industrialisation, modernisation and globalisation create drastic and threatening interventions in human living conditions, particularly in terms of the development of productive forces, market integration, and the relationships that exist between property and power. These social processes continue to prod societies and nation states into the cycle of ‘what ifs' and ‘maybe'. But again, due to unintended maybes, such pursuit of quantum growth over the years by scientists or science and technology, policymakers, sovereigns, and so on has resulted in errors and deceptions under the guise of acceptable maximum regulation of composition uncovered through proof of causality, coupled with practical experiences of side effects by people in societies. To achieve sustainable development, there is a need to pioneer and/or contribute to debates, queries, and enquiries confronting [in]actions, policies, initiatives, and interventions instituted – or not – to maintain the environment at a life-sustaining level with the attendant economic development. Therefore, the begging question: Are clamours for sustainability merely rhetoric, echoed and re-echoed only when convenient, or are they conscientiously adhered to while pursuing economic prosperity?. © Creative Commons With Attribution (CC-BY).
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The rise of dockless bike sharing and the development of bike-only roads have facilitated a low-carbon transition in transportation. In this direction, Beijing opened the first bike-only road in 2019. Shortly after opening this bike road, the city encountered the COVID-19 pandemic. In this study, we evaluate the impacts of the first bike-only road and COVID-19 on the travel modal shift of the public and quantify the corresponding changes in carbon emissions. Using proposed integrated choice and latent variable models, we find that respondents who were previously low-carbon travelers were more likely to choose low-carbon traffic options and more sensitive to perceived instrumental usefulness of the bike sharing system than were high-carbon respondents. The bike lanes significantly increased the probability for all respondents to switch their travel mode to bike sharing. The COVID-19 pandemic significantly inhibited the high-carbon group from switching to bike sharing for both long- and short-distance trips, while the pandemic significantly promoted the low-carbon group to choose bike sharing for short-distance trips. However, there was no significant effect on this group by the pandemic for long-distance trips. Furthermore, we find that bike sharing can reduce carbon emissions by 41.55% and 33.43% for short- and long-distance trips, respectively. The overall emission reduction effect of bike sharing increased from 28.29% to 47.21% when the bike lane was introduced;whereas the COVID-19 pandemic decreased bike sharing emission reduction from 43.74% before to 29.87% during the pandemic. Our research reveals that the bike sharing system plays both an alternative role to other traffic modes for short-distance traffic and a complementary role to public transport for long-distance traffic. © 2022 Elsevier Ltd
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By using WRF-Chem coupled with a heterogeneous reaction mechanism for sulfate formation, this study investigated the impact of meteorological condition and emission changes on chemical species, atmospheric oxidizing capacity (AOC), and secondary aerosol formation during the COVID-19 lockdown period from 23 January to April 8, 2020, focusing on a severe haze event on 7-14 February. The model with the new sulfate formation scheme reasonably reproduces the spatial-temporal distribution of meteorological variables and chemical species, and significantly improves predictions for both sulfate and SO2 concentrations, as well as for PM2.5, ammonium, and nitrate to some extent. It is found that the adverse meteorological conditions were the main cause for the haze event formation, whereas emission reduction due to the lockdown somewhat decreased PM2.5 concentration on average in the Beijing-Tianjin-Hebei (BTH) region. Compared with the same period in 2019, increased surface air temperature and relative humidity (RH) and decreased planetary boundary layer height (PBLH) facilitated accumulation of pollutants and formation of secondary aerosols during the haze episode in 2020, whereas the emission reduction due to the lockdown led to decreases in SO2, NO2, primary PM2.5 (PPM2.5), black carbon (BC), primary organic aerosols (POA), nitrate and ammonium concentrations, but increases in O3, sulfate and secondary organic aerosol (SOA) concentrations, due to the combined effect of changes in emissions and AOC. Gas and aqueous phase oxidation of SO2 accounted for approximately 24% of sulfate formation, while the heterogeneous reaction of Mn-catalytic oxidation of SO2 on aerosol surfaces dominated sulfate formation (76%) during the haze episode in the BTH region. Both adverse meteorological conditions and emission reductions increased heterogeneous sulfate formation rate mainly through altering aerosol surface area (ASA), pH, and Mn2+ concentration. Chemical species varied diversely during the three subperiods before (Period-1, 15-22 January) and during the lockdown (Period-2, 23 January to 5 March and Period-3, 6 March to 8 April) over the BTH. NO2 concentration firstly decreased and then rebounded, whereas O3 concentration increased gradually from the Period-1 to Period-3. All aerosols except SOA decreased throughout the lockdown period, whereas SOA peaked in the Period-2 due to its strong sensitivity to increasing AOC. Sulfate concentration decreased from the Period-1 to Period-2, mainly due to more adverse meteorological conditions in the Period-1, although sulfate increased slightly due to increasing AOC in the Period-2. The large difference in the direction and magnitude of species variations during the COVID-19 lockdown indicates the complex interplay among meteorology, emission, and chemistry. Copyright © 2022 Elsevier Ltd
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To prevent the rapid spreading of the COVID-19 pandemic, the Egyptian government had imposed partial lockdown restrictions which led emissions reduction. This served as ideal conditions for a natural experiment, for study the effect of partial lockdown on the atmospheric aerosol chemistry and the enhanced secondary inorganic aerosol production in a semi-desert climate area like Egypt. To achieve this objective, SO2, NO2, and PM2.5 and their chemical compositions were measured during the pre-COVID, COVID partial lockdown, and post-COVID periods in 2020 in a suburb of Greater Cairo, Egypt. Our results show that the SO2, NO2, PM2.5 and anthropogenic elements concentrations follow the pattern pre-COVID > post-COVID > COVID partial lockdown. SO2 and NO2 reductions were high compared with their secondary products during the COVID partial lockdown compared with pre-COVID. Although, PM2.5, anthropogenic elements, NO2, SO2, SO4 2-, NO3 -, and NH4 + decreased by 39%, 38-55%, 38%, 32.9%. 9%, 14%, and 4.3%, respectively, during the COVID partial lockdown compared with pre-COVID, with the secondary inorganic ions (SO4 2-, NO3 -, and NH4 +) being the dominant components in PM2.5 during the COVID partial lockdown. Moreover, the enhancement of NO3 - and SO4 2- formation during the COVID partial lockdown was high compared with pre-COVID. SO4 2- and NO3 - formation enhancements were significantly positive correlated with PM2.5 concentration. Chemical forms of SO4 2- and NO3 - were identified in PM2.5 based on their NH4 +/SO4 2- molar ratio and correlation between NH4 + and both NO3 - and SO4 2-. The particles during the COVID partial lockdown were more acidic than those in pre-COVID.
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The high level of aerosol pollution in South Asia has a measurable impact on clouds, radiation, and precipitation. Here, exploring multiple observational data sets and simulations of the state-of-the-art ECHAM6-HAMMOZ chemistry-climate model, we report that the reduction in anthropogenic emissions during the COVID-19 lockdown period has enhanced precipitation by 5-25% over India. This precipitation enhancement is the result of the combined effect of an enhancement in cloud cover, a reduction in aerosol induced cloud invigoration and dynamical changes. We observed that the increase in cloud cover was associated with a reduction in cloud base height and an increase in the effective radius of cloud particles which led to an increase in cloud water content. In response to sudden emission reduction, an anomalous northward moisture transport was observed adding convection and precipitation over the Indian region. Importantly, we show that there is an advantage of anthropogenic pollution reduction for water availability in addition to benefits of air quality, human health, and crop yield.
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With the rapid development of information technology, the electricity consumption of Internet Data Centers (IDCs) increases drastically, resulting in considerable carbon emissions that need to be reduced urgently. In addition to the introduction of Renewable Energy Sources (RES), the joint use of the spatial migration capacity of IDC workload and the temporal flexibility of the demand of Electric Vehicle Charging Stations (EVCSs) provides an important means to change the carbon footprint of the IDC. In this paper, a sustainability improvement strategy for the IDC carbon emission reduction was developed by coordinating the spatial-temporal dispatch flexibilities of the IDC workload and the EVCS demand. Based on the Minkowski sum algorithm, a generalized flexible load model of the EVCSs, considering traffic flow and Road Impedance (RI) was formulated. The case studies show that the proposed method can effectively increase the renewable energy consumption, reduce the overall carbon emissions of multi-IDCs, reduce the energy cost of the DCO, and utilize the EV dispatching potential. Discussions are also provided on the relationship between workload processing time delay and the renewable energy consumption rate.
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The Chinese government triggered the immediate implementation of a lockdown policy in China following the outbreak of the COVID-19 pandemic, leading to drastic decreases in air pollutant emissions. However, concentrations of PM2.5 and other pollutants increased during the COVID-19 lockdown over the Jing-Jin-Ji region compared with those averaged over 2015-2019, and two PM2.5 pollution events occurred during the lockdown. Using the ERA5 reanalysis data, we found that the Jing-Jin-Ji region during the COVID-19 lockdown was characterized by higher relative humidity, lower planetary boundary layer height, and anomalous updraft. These conditions were favorable for condensation and the secondary formation of aerosols and prevented turbulent diffusion of pollutants. Furthermore, we conducted sensitivity tests using the WRF-Chem model and found that ρ(PM2.5) increased by 20-55 µg·m-3(60%-170%) over the middle region of Jing-Jin-Ji during the COVID-19 lockdown due to changes in meteorological conditions. Furthermore, the enhanced aerosol chemistry and unfavorable diffusion conditions were identified as the key factors driving increases in PM2.5 concentrations during the lockdown. Planetary boundary layer height and relative humidity may become the important factors in forecasting PM2.5 pollution events over the Jing-Jin-Ji region under the background of emission reduction.
Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Aerosols/analysis , Air Pollutants/analysis , Air Pollution/analysis , COVID-19/epidemiology , COVID-19/prevention & control , China/epidemiology , Communicable Disease Control , Environmental Monitoring , Humans , Pandemics/prevention & control , Particulate Matter/analysisABSTRACT
Meteorological normalization refers to the removal of meteorological effects on air pollutant concentrations for evaluating emission changes. There currently exist various meteorological normalization methods, yielding inconsistent results. This study aims to identify the state-of-the-art method of meteorological normalization for characterizing the spatiotemporal variation of NOx emissions caused by the COVID-19 pandemic in China. We obtained the hourly data of NO2 concentrations and meteorological conditions for 337 cities in China from January 1, 2019, to December 31, 2020. Three random-forest based meteorological normalization methods were compared, including (1) the method that only resamples meteorological variables, (2) the method that resamples meteorological and temporal variables, and (3) the method that does not need resampling, denoted as Resample-M, Resample-M&T, and Resample-None, respectively. The comparison results show that Resample-M&T considerably underestimated the emission reduction of NOx during the lockdowns, Resample-None generates widely fluctuating estimates that blur the emission recovery trend during work resumption, and Resample-M clearly delineates the emission changes over the entire period. Based on the Resample-M results, the maximum emission reduction occurred during January to February 2020, for most cities, with an average decrease of 19.1 ± 9.4% compared to 2019. During April of 2020 when work resumption initiated to the end of 2020, the emissions rapidly bounced back for most cities, with an average increase of 12.6 ± 15.8% relative to those during the strict lockdowns. Consequently, we recommend using Resample-M for meteorological normalization, and the normalized NO2 concentration dynamics for each city provide important implications for future emission reduction.
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We tested the capabilities of urban greenhouse gas (GHG) measurement networks to detect abrupt changes in emissions, such as those caused by the roughly 6-week COVID-19 lockdown in March 2020 using hourly in situ GHG mole fraction measurements from six North American cities. We compared observed changes in CO2, CO, and CH4 for different mole fraction metrics (diurnal amplitude, vertical gradients, enhancements, within-hour variances, and multi-gas enhancement ratios) during 2020 relative to previous years for three periods: pre-lockdown, lockdown, and ongoing recovery. The networks showed decreases in CO2 and CO metrics during the lockdown period in all cities for all metrics, while changes in the CH4 metrics were variable across cities and not statistically significant. Traffic decreases in 2020 were correlated with the changes in GHG metrics, whereas changes in meteorology and biology were not, implying that decreases in the CO2 and CO metrics were related to reduced emissions from traffic and demonstrating the sensitivity of these tower networks to rapid changes in urban emissions. The enhancements showed signatures of the lockdowns more consistently than the three micrometeorological methods, possibly because the urban measurements are collected at relatively high altitudes to be sensitive to whole-city emissions. This suggests that urban observatories might benefit from a mixture of measurement altitudes to improve observational network sensitivity to both city-scale and more local fluxes.
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Air pollution during the COVID-19 epidemic in Beijing and its surrounding regions has received substantial attention. We collected observational data, including air pollutant concentrations and meteorological parameters, during January and February from 2018 to 2021. A statistical and a numerical model were applied to identify the formation of air pollution and the impact of emission reduction on air quality. Relative humidity, wind speed, SO2, NO2, and O3 had nonlinear effects on the PM2.5 concentration in Beijing, among which the effects of relative humidity, NO2, and O3 were prominent. During the 2020 epidemic period, high pollution concentrations were closely related to adverse meteorological conditions, with different parameters having different effects on the three pollution processes. In general, the unexpected reduction of anthropogenic emissions reduced the PM2.5 concentration, but led to an increase in the O3 concentration. Multi-scenario simulation results showed that anthropogenic emission reduction could reduce the average PM2.5 concentration after the Chinese Spring Festival, but improvement during days with heavy pollution was limited. Considering that O3 enhances the PM2.5 levels, to achieve the collaborative improvement of PM2.5 and O3 concentrations, further research should explore the collaborative emission reduction scheme with VOCs and NOx to achieve the collaborative improvement of PM2.5 and O3 concentrations. The conclusions of this study provide a basis for designing a plan that guarantees improved air quality for the 2022 Winter Olympics and other international major events in Beijing.
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The effects of the COVID-19 lockdown on deposition and surface water chemistry were investigated in an area south of the Alps. Long-term data provided by the monitoring networks revealed that the deposition of sulfur and nitrogen compounds in this area has stabilized since around 2010; in 2020, however, both concentrations and deposition were significantly below the average values of the previous decade for SO4 and NO3. Less evident changes were observed for NH4 and base cation. The estimated decrease of deposition in 2020 with respect to the previous decade was on average - 54% and - 46% for SO4 and NO3, respectively. The lower deposition of SO4 and NO3 recorded in 2020 was caused by the sharp decrease of SO2 and particularly of NOx air concentrations mainly due to the mobility restrictions consequent to the COVID-19 lockdown. The limited effects on NH4 deposition can be explained by the fact that NH3 emission was not affected by the lockdown, being mainly related to agricultural activities. A widespread response to the decreased deposition of S and N compounds was observed in a group of pristine freshwater sites, with NO3 concentrations in 2020 clearly below the long-term average. The rapid chemical recovery observed at freshwater sites in response to the sharp decrease of deposition put in evidence the high resilience potential of freshwater ecosystems in pristine regions and demonstrated the great potential of emission reduction policy in producing further substantial ameliorations of the water quality at sensitive sites.
Subject(s)
Air Pollutants , COVID-19 , Air Pollutants/analysis , Communicable Disease Control , Ecosystem , Environmental Monitoring/methods , HumansABSTRACT
The outbreak of the 2019 novel coronavirus (COVID-19) had a large impact on human health and socio-economics worldwide. The lockdown implemented in China beginning from January 23, 2020 led to sharp reductions in human activities and associated emissions. The declines in primary pollution provided a unique opportunity to examine the relationship between anthropogenic emissions and air quality. This study reports on air pollutant and meteorological measurements at different heights from a tall tower in the Pearl River Delta. These measurements were used to investigate the vertical scale response of pollutants to understand reductions in human activities. Compared to that in the pre-lockdown period (from December 16, 2019), the concentrations of surface layer nitric oxide (NOx), fine particulate matter (PM2.5), and daily maximum 8 h average ozone (MDA8O3) declined significantly during the lockdown by 76.8%, 49.4%, and 18.6%, respectively. Although the vertical profiles of NOx and O3 changed during the lockdown period, those of PM2.5 remained the same. During the lockdown period, there were statistically significant correlations between PM2.5 and O3 but not between PM2.5 and NOx at four heights, indicating that the main composition of PM2.5 have dramatically changed, during which the impact of NOx on PM2.5 became insignificant. Additionally, O3 concentrations were also insensitive to NOx concentrations during the lockdown, implying that O3 levels were more of a representative of regional background level. In this case, local photochemical formation is no longer a significant ozone source. This evidence suggests that it is possible to mitigation of PM2.5 and O3 levels simultaneously by significant reductions in anthropogenic emissions.
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The COVID-19 has slowed down the global economic growth. Meanwhile, it also significantly cuts the global carbon emission, which provides a golden opportunity for the whole world to combat the climate change together. While the former policies (e.g., the CAFE standards, renewable portfolio standards, etc.) have reduced certain level of fossil fuel consumption, the most effective measures (such as carbon tax, cap-and-trade programs) are still far from ready for global implementation. This paper investigates an alternative way to achieve a more carbon efficient power grid using the uplift payment scheme. Specifically, we propose an effective algorithm to guarantee the carbon efficiency with the minimal uplift payments. We also submit that this scheme provides more flexibility to realize carbon reduction than carbon tax, which is exemplified by thorough numerical studies. Furthermore, we show that the stability of the power grid can be ensured under our uplift payment scheme, both from theoretical analysis and numerical studies. The results strengthen our belief that our uplift payment scheme is practicable for electricity market. IEEE
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The COVID-19 pandemic has caused great shocks on economic activities and carbon emissions. This paper aims to monitor the CO2 emission trajectory in China before and after the pandemic outbreak, and analyze the emission reduction effects by ETS and its market performances, which are important determinants underlying the trajectory and key drivers for emission reductions. We firstly find out a rather consistent trajectory of CO2 emissions in pre- and post-pandemic China over a 2-year time horizon, using the near-real-time datasets of daily CO2 emissions by Carbon Monitor and applying the Cox-Stuart trend test and mean equality test. We then examine the emission reduction effects by China's carbon ETS and its pilot market performances, using the methodologies of DID and PSM-DID as well as pre-pandemic region-level emission datasets by CEADs. Furthermore, it's found that the ETS pilot markets, which are immature with defects, have been performing more vulnerably in terms of liquidity and transaction continuity under pandemic shocks, thus undermining the emission reduction effects by ETS. These findings are providing insights into further mechanism design of the carbon ETS to the end of steady emission reductions even under shocks for post-pandemic China. It's of particular importance now that the nationwide market has been launched and needs to be enhanced based on lessons learned.