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This study evaluates the efficiency of China's seven emission trading schemes (ETS) piloted in 2013. We evaluate seven pilots' overall technical and scale efficiencies and temporal dynamics during 2014–2023. We use a bootstrap correction data envelopment analysis (bootstrap-DEA), which guarantees a more accurate efficiency estimation than the traditional DEA model. The results show that the average overall (pure technical) efficiency of the seven pilot markets increased from 0.612 (0.844) in 2014 to 0.898 (0.990) in 2023. Furthermore, we document that seven ETS pilots differ remarkably in efficiency and transaction price, whilst all have shortages. Specifically, the small-scale market transaction is the main constraint effect on the average scale efficiency of the ETS. This study provides concrete recommendations for policy makers to consummate institutional designs to improve ETS efficiency. [ABSTRACT FROM AUTHOR]

This study aims to analyse the spatial and temporal characteristics of the transfer of pollutants, including carbon monoxide (CO), ammonia (NH3), non‐methane volatile organic compounds (NMVOCs), sulphur dioxide (SO2), nitrogen oxide (NOX), and particulate matter with particle size below 10 microns (PM10), which are embodied in the bilateral trade between China and India, and the transfer paths between various industries. A multiregional input–output model (MRIO) is constructed based on the EORA Database to track the economic connections and the effects of specific products across regional supply chains. The empirical results indicate the following significant findings. China is a net exporter of embodied pollutants in bilateral trade, and India is a net importer. CO accounted for the most significant amount of pollutants emitted by the bilateral trade between China and India. The energy intensity has mainly adverse effects on emissions of six pollutants, while the consumption scale has overall positive effects. Metal products and mining and quarrying are the top two industries producing the highest emissions at the export ends. The emissions of certain pollutants, such as SO2, are also particularly pronounced in individual sectors. Construction and electrical and machinery are the highest CO‐importing sectors of China and India. [ABSTRACT FROM AUTHOR]

The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals may fill some of this gap. Here, we estimate China's carbon sink using atmospheric inversions of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals within different platforms, including the Global Carbon Assimilation System (GCAS) v2, the Copernicus Atmosphere Monitoring Service, and the OCO‐2 Model Inter‐comparison Project (MIP). We find that they consistently place the largest net biome production (NBP) in the south on an annual basis compared to the northeast and other main agricultural areas during peak growing season, coinciding well with the distribution of forests and crops, respectively. Moreover, the mean seasonal cycle amplitude of NBP in OCO‐2 inversions is obviously larger than that of biosphere model simulations and slightly greater than surface CO2 inversions. More importantly, the mean seasonal cycle of the OCO‐2 inversions is well constrained in the temperate, tropical, and subtropical monsoon climate zones, with better inter‐model consistency at a sub‐regional scale compared to in situ inversions and biosphere model simulations. In addition, the OCO‐2 inversions estimate the mean annual NBP in China for 2015–2019 to be between 0.34 (GCASv2) and 0.47 ± 0.16 PgC/yr (median ± std; OCO‐2 v10 MIP), and indicate the impacts of climate extremes (e.g., the 2019 drought) on the interannual variations of NBP. Our results suggest that assimilating OCO‐2 XCO2 retrievals is crucial for improving our understanding of China's terrestrial carbon sink regime. Plain Language Summary: The magnitude and distribution of China's terrestrial carbon sink remain underconstrained; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals from NASA's Carbon Observatory 2 (OCO‐2) could help reduce this uncertainty. This study revisited China's terrestrial carbon sink estimates based on state‐of‐the‐art OCO‐2 XCO2 inversions, including the Global Carbon Assimilation System OCO‐2 inversion, the Copernicus Atmosphere Monitoring Service OCO‐2 inversion, and those in the OCO‐2 Model Inter‐comparison Project. We found that the assimilation of OCO‐2 XCO2 retrievals offers effective constraints on the spatiotemporal patterns of the terrestrial carbon sink of China. This result suggests that the OCO‐2 XCO2 inversions allow an improved understanding of China's land carbon sink over in situ CO2 inversions and bottom‐up biosphere model simulations, including better representations in spatial distributions and seasonal cycles and more plausible interannual variations. These improvements suggest that the assimilation of OCO‐2 XCO2 retrievals offers effective constraints on the spatiotemporal patterns of the terrestrial carbon sink of China. Key Points: Orbiting Carbon Observatory 2 (OCO‐2) inversions reveal the largest carbon sink in China is in the south on an annual basis, while in the northeast during peak growing seasonThe seasonal cycle appears to be well constrained in the monsoon climate zonesOCO‐2 inversions are able to capture the impacts of climate extremes on China's carbon sink interannual variability [ABSTRACT FROM AUTHOR]

Land carbon sink is a vital component for the achievement of China's ambitious carbon neutrality goal, but its magnitude is poorly known. Atmospheric observations and inverse models are valuable tools to constrain the China's land carbon sink. Space‐based CO2 measurements from satellites form an emerging data stream for application of such atmospheric inversions. Here, we reviewed the satellite missions that is dedicated to the monitoring of CO2, and the recent progresses on the inversion of China's land carbon sink using satellite CO2 measurements. We summarized the limitations and challenges in current space platforms, retrieval algorithms, and the inverse modeling. It is shown that there are large uncertainties of contemporary satellite‐based estimates of China's land carbon sink. We discussed future opportunities of continuous improvements in three aspects to better constrain China's land carbon sink with space‐based CO2 measurements. [ABSTRACT FROM AUTHOR]

After stagnating in the early 2000s, the atmospheric methane growth rate has been positive since 2007 with a significant acceleration starting in 2014. While the causes for previous growth rate variations are still not well determined, this recent increase can be studied with dense surface and satellite observations. Here, we use an ensemble of six multi-species atmospheric inversions that have the capacity to assimilate observations of the main species in the methane oxidation chain – namely, methane, formaldehyde, and carbon monoxide – to simultaneously optimize both the methane sources and sinks at each model grid. We show that the surge of the atmospheric growth rate between 2010–2013 and 2014–2017 is most likely explained by an increase of global CH 4 emissions by 17.5±1.5 Tg yr -1 (mean ± 1σ), while variations in the hydroxyl radicals (OH) remained small. The inferred emission increase is consistently supported by both surface and satellite observations, with leading contributions from the tropical wetlands (∼ 35 %) and anthropogenic emissions in China (∼ 20 %). Such a high consecutive atmospheric growth rate has not been observed since the 1980s and corresponds to unprecedented global total CH 4 emissions. [ABSTRACT FROM AUTHOR]

In order to track progress towards the global climate targets, the parties that signed the Paris Climate Agreement will regularly report their anthropogenic carbon dioxide (CO2) emissions based on energy statistics and CO2 emission factors. Independent evaluation of this self-reporting system is a fast-growing research topic. Here, we study the value of satellite observations of the column CO2 concentrations to estimate CO2 anthropogenic emissions with 5 years of the Orbiting Carbon Observatory-2 (OCO-2) retrievals over and around China. With the detailed information of emission source locations and the local wind, we successfully observe CO2 plumes from 46 cities and industrial regions over China and quantify their CO2 emissions from the OCO-2 observations, which add up to a total of 1.3 Gt CO2 yr -1 that accounts for approximately 13 % of mainland China's annual emissions. The number of cities whose emissions are constrained by OCO-2 here is 3 to 10 times larger than in previous studies that only focused on large cities and power plants in different locations around the world. Our satellite-based emission estimates are broadly consistent with the independent values from China's detailed emission inventory MEIC but are more different from those of two widely used global gridded emission datasets (i.e., EDGAR and ODIAC), especially for the emission estimates for the individual cities. These results demonstrate some skill in the satellite-based emission quantification for isolated source clusters with the OCO-2, despite the sparse sampling of this instrument not designed for this purpose. This skill can be improved by future satellite missions that will have a denser spatial sampling of surface emitting areas, which will come soon in the early 2020s. [ABSTRACT FROM AUTHOR]

The concentrations of particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) and 10 µm (PM10) is a widespread concern and has been demonstrated for 103 countries. During the past few years, the exposure-response function (ERf) has been widely used to estimate the health effects of air pollution. However, past studies are either based on the cost-of-illness or the willingness-to-pay approach, and therefore, either do not cover intangible costs or costs due to the absence of work. To address this limitation, a hybrid health effect and economic loss model is developed in this study. This novel approach is applied to a sample of environmental and cost data in China. First, the ERf is used to link PM2.5 concentrations to health endpoints of chronic mortality, acute mortality, respiratory hospital admission, cardiovascular hospital admission, outpatient visits-internal medicine, outpatient visits-pediatrics, asthma attack, acute bronchitis, and chronic bronchitis. Second, the health effect of PM2.5 is monetized into the economic loss. The mean economic loss due to PM2.5 was much heavier in the North than the South of China. Furthermore, the empirical results from 76 cities in China show that the health effects and economic losses were over 4.98 million cases and 382.30 billion-yuan in 2014 and decreased dramatically compared with those in 2013. [ABSTRACT FROM AUTHOR]

Atmospheric carbon monoxide (CO) concentrations have been decreasing since 2000 as observed by both satellite- and ground-based instruments, but global bottom-up emission inventories surprisingly estimate increasing anthropogenic CO emissions concurrently. In this study, we use a multi-species atmospheric Bayesian inversion approach to attribute satellite-observed atmospheric CO variations to its sources and sinks in order to achieve a full closure of the global CO budget during 2000–2017. Our observation constraints include satellite retrievals of the total column mole fraction of CO, formaldehyde (HCHO), and methane (CH4) that are all major components of the atmospheric CO cycle. Three inversions (i.e., 2000–2017, 2005–2017, and 2010–2017) are performed to use the observation data to the maximum extent possible as they become available and assess the consistency of inversion results to the assimilation of more trace gas species. We identify a declining trend in the global CO budget since 2000 (three inversions are broadly consistent during overlapping periods), driven by reduced anthropogenic emissions in the U.S. and Europe (both likely from the transport sector), and in China (likely from industry and residential sectors), as well as by reduced biomass burning emissions globally, especially in Equatorial Africa (associated with reduced burned areas). We show that the trends and drivers of the inversion-based CO budget are not affected by the inter-annual variation assumed for prior CO fluxes. All three inversions estimate that surface CO emissions contradict the global bottom-up inventories in the world's top two emitters for the sign of anthropogenic emission trends in China (e.g., here −0.8 ± 0.5 % yr−1 since 2000 while the prior gives 1.3 ± 0.4 % yr−1) and for the rate of anthropogenic emission increase in South Asia (e.g., here 1.0 ± 0.6 % yr−1 since 2000 smaller than 3.5 ± 0.4 % yr−1 in the prior inventory). The posterior model CO concentrations and trends agree well with independent ground-based observations and correct the prior model bias. The comparison of the three inversions with different observation constraints further suggests that the most complete constrained inversion that assimilates CO, HCHO, and CH4 has a good representation of the global CO budget, therefore matches best with independent observations, while the inversion only assimilating CO tends to underestimate both the decrease in anthropogenic CO emissions and the increase in the CO chemical production. The global CO budget data from all three inversions in this study can be accessed from https://doi.org/10.6084/m9.figshare.c.4454453.v1 (Zheng et al., 2019). [ABSTRACT FROM AUTHOR]

In this study, a structural decomposition method was applied to research the factors affecting the changes in air pollution emissions in China. Based on 1995-2009 data from the World IO Database, we combine China’s (Import) Noncompetitive IO Table and the Environmental Account Table. The results indicate that emission intensities represent the most important factor for reducing air pollution emissions in China. In contrast, economies of scale and the intermediate input product structure constitute the major causes for acceleration in the growth of air pollution emissions in China. From the perspective of final demand, the economic scale effect caused by investment demand is the main reason for this accelerated growth in China’s air pollution emissions in recent years. Consumption-driven economic growth is cleaner, while investment-driven economic growth is dirtier. This study constructed a structure decomposition model based on the input-output tables, which is suitable for studying the driving forces of various economic indicators, such as energy, carbon dioxide, and economic growth. At the same time, this method is helpful for analyzing the factors that influence changes in economic indicators that result from different economic pull modes, such as the final demand mode. However, the model does have limitations; for example, it does not consider the difference between general trade and processing trade in exports. [ABSTRACT FROM AUTHOR]

• Untargeted VOC metabolomics profiling was performed to fingerprint the rice samples. • Potential marker compounds for discriminating rice samples were identified. • Models, K-NN and DD-SIMCA were used for geographical classification of rice samples. Rice is one of the most important cereals for human nutrition and is a basic staple food for half of the global population. The assessment of rice geographical origins in terms of its authenticity is of great interest to protect consumers from misleading information and fraud. In the present study, a head space gas chromatography mass spectrometry (HS-GC–MS) strategy for characterising volatile organic compounds (VOCs) profiles to distinguish rice samples from China, India and Vietnam is described. Partial Least Square Discriminant Analysis (PLS-DA) model exhibited a good discrimination (R2 = 0.98182, Q2 = 0.9722, and Accuracy = 1.0) for rice samples from China, India and Vietnam. Moreover, Data-Driven Soft Independent Modelling of Class Analogy (DD-SIMCA) and K-nearest neighbors shown good specificity 100% and accuracy 100% in identifying the origin of samples. The present study established VOC fingerprinting as a highly efficient approach to identify the geographical origin of rice. [ABSTRACT FROM AUTHOR]

Highlights • Ghosh input-output model. • Structural decomposition analysis of air pollutant emissions. • Covering China, India, USA, and Japan. • Key role of adjusting allocation structures. Abstract China’s air quality has become a major issue affecting people’s livelihood and continues to deteriorate in recent times. It is an important issue of common concern for economists and policymakers to explore the drivers of the growth of air pollution emissions and the deteriorating environmental quality in China. From the perspective of supply-side structures, this paper adopts Ghosh input-output model to decompose the factors affecting the changes of air pollutant emissions into economic activities, economic structures, allocation structures and emission intensity. Using this model, we conduct a structural decomposition analysis of air pollutant emissions in China, India, USA, and Japan for 1995–2009. The results reveal that China’s economic structure initially promoted air pollutant emissions, but later played a role in reducing them. Further, whereas in Japan and particularly, China, allocation structures were found to be a key factor in increasing air pollutant emissions, in America and India, it played a critical role in reducing emissions. Our findings suggest that adjusting the distribution structure of intermediate products is crucial to reduce air pollution. [ABSTRACT FROM AUTHOR]

In 2009, the Chinese government announced its carbon intensity target for 2020. Can China achieve this carbon intensity while sustaining economic growth? To address this essential issue, the contributions of this study lie in three aspects. First, we quantitatively capture the relationship between China's economic growth and energy consumption using the cointegration theory, and predict China's energy consumption by 2020 according to different economic growth targets. Second, we forecast China's energy structure in 2020 using compositional data and ARIMA models under different scenarios. Third, we deduce China's CO 2 emission and carbon intensity in 2020. Furthermore, we investigate whether (or not) China can realize the carbon intensity target in premise of ensuring economic growth, and evaluate the contribution of the energy structure's adjustment to meeting this target. Finally, we put forward some relevant policy implications for achieving China's carbon intensity target. [ABSTRACT FROM AUTHOR]

• Empirical framework for estimating income growth effect and income distribution. • Mincer model, OLS and GMM methods. • CHNS surveys from rural residents during 1991–2015. • Energy poverty threatens individual health and household efficiency. • Energy poverty harms resident income growth. This paper proposes a novel empirical framework for inclusive growth estimation by extending the traditional Mincer model. Based on the 42,126 observations from 1991 to 2015 obtained in the China Health and Nutrition Survey, this paper applies the ordinary least squares regression and generalized system method of moments estimation to explore the growth effect and inclusiveness of energy out-of-poverty on rural resident income. The results confirm the significant income inclusive growth effect prompted by energy out-of-poverty. Those with water infrastructure accessibility and medical insurance gain more than those without energy out-of-poverty in rural China. The better-educated benefit more than their counterparts from energy out-of-poverty in the eastern and middle regions. This paper provides valuable insights to understand the positive and vital role of energy out-of-poverty on income-inclusive growth. [ABSTRACT FROM AUTHOR]

Sustainable development emphasizes the sustainability of natural resources and the environment. Under the background of resource scarcity and environmental constraints, it is of great significance to explore the impacts of the ecological footprint on the resource-rich but unbalanced China's Yangtze River Economic Belt from two perspectives of flow capital occupation and stock capital occupation. This study uses a fixed-effect regression model to investigate the impacts of ecological footprint breadth and depth on sustainable development by the panel data of 108 prefecture-level cities in China's Yangtze River Economic Belt from 2006 to 2018. Further combining the panel threshold regression model results, we identify ecological footprint breadth and depth as having inverted N-shaped and inverted U-shaped relationships with sustainable development. Firstly, there is a double threshold effect between ecological footprint breadth and sustainable development. Then, there is a single threshold effect between ecological footprint depth and sustainable development. When ecological footprint breadth is within the threshold of (0.2802, 0.7997), it is conducive to sustainable development. When ecological footprint depth is less than 2.5404, it is conducive to sustainable development. Secondly, ecological footprint breadth improves sustainable development of the Yangtze River Economic Belt by rationalizing and advancing industrial structure. Ecological footprint depth achieves sustainable development through rationalizing industrial structure. Thirdly, the mechanism of the ecological footprint on sustainable development has regional heterogeneity. Finally, this paper proposes several policy implications for achieving China's sustainable development. [ABSTRACT FROM AUTHOR]

Measuring the effectiveness of GHG emission reduction policies against agreed-upon international targets require accurate and precise estimates of emissions and their trends. These estimates need to be established and regularly updated using transparent methods, traceable to international standards. The EU-funded VERIFY project proposes to quantify more accurately carbon stocks and the fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) across the EU based on independent observations in support of inventories that rely only on statistical data. The same approach will also be tested for US, China and Indonesia, in collaboration with foreign partners. Accurate characterization of the space-time variations of GHG fluxes, separating their anthropogenic and natural components and their drivers, will be based on advanced modeling approaches using atmospheric GHG measurements, tracer transport inversions and various arrays of land observations, in-situ and from space. The improved knowledge of GHG budgets from VERIFY will be used to improve national inventories, in collaboration with national inventory agencies, and to deliver policy-relevant information to track progress of the EU mitigation efforts to meet the targets of the Paris Agreement on Climate.This presentation will review the objectives of the project that started in February 2018. We will focus on the methodology that is deployed and also present few preliminary results. [ABSTRACT FROM AUTHOR]