Novel Cs-Mg-Al mixed oxide with improved mobility of oxygen species for passive NO_(x)adsorption

The development of passive NO_(x)adsorbers with cost-benefit and high NO_(x)storage capacity remains an on-going challenge to after-treatment technologies at lower temperatures associated with cold-start NO_(x)emissions.Herein,Cs_(1)Mg_(3)Al catalyst prepared by sol-gel method was cyclic tested in NO_(x)storage under 5 vol%water.At 100°C,the NO_(x)storage capacity(1219 μmol g^(-1))was much higher than that of Pt/BaO/Al_(2)O_(3)(610 μmol g^(-1)).This provided new insights for non-noble metal catalysts in low-temperature passive NO_(x)adsorption.The addition of Cs improved the mobility of oxygen species and thus improved the NO_(x)storage capacity.The XRD,XPS,IR spectra and in situ DRIFTs with NH3 probe showed an interaction between CsO_(x)and AlO_(x)sites via oxygen species formed on Cs_(1)Mg_(3)Al catalyst.The improved mobility of oxygen species inferred from O2-TPD was consistent with high NO_(x)storage capacity related to enhanced formation of nitrate and additional nitrite species by NO_(x)oxidation.Moreover,the addition of Mg might improve the stability of Cs_(1)Mg_(3)Al by stabilizing surface active oxygen species in cyclic experiments.

Decoding the inconsistency of six cropland maps in China

Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades,considerable discrepancies exist among these products both in total area and in spatial distribution of croplands,impeding further applications of these datasets.The factors influencing their inconsistency are also unknown.In this study,we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020,including three state-of-the-art 10-m products(i.e.,Google Dynamic World,ESRI Land Cover,and ESA WorldCover)and three 30-m ones(i.e.,GLC_FCS30,GlobeLand 30,and CLCD).We also investigated the effects of landscape fragmentation,climate,and agricultural management.Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy(92.3%).These maps collectively overestimated Chinese cropland area by up to 56%.Up to 37%of the land showed spatial inconsistency among the maps,concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps,cropland fragmentation and management practices such as irrigation.Our work shed light on the promotion of future cropland mapping efforts,especially in highly inconsistent regions.

Optimizing air quality and health Co-benefits of mitigation technologies in China: An integrated assessment

Carbon mitigation technologies lead to air quality improvement and health co-benefits,while the practical effects of the technologies are dependent on the energy composition,technological advancements,and economic development.In China,mitigation technologies such as end-of-pipe treatment,renewable energy adoption,carbon capture and storage(CCS),and sector electrification demonstrate significant promise in meeting carbon reduction targets.However,the optimization of these technologies for maximum co-benefits remains unclear.Here,we employ an integrated assessment model(AIM/enduse,CAM-chem,IMED|HEL)to analyze air quality shifts and their corresponding health and economic impacts at the provincial level in China within the two-degree target.Our findings reveal that a combination of end-of-pipe technology,renewable energy utilization,and electrification yields the most promising results in air quality improvement,with a reduction of fine particulate matter(PM2.5)by−34.6μg m^(−3) and ozone by−18.3 ppb in 2050 compared to the reference scenario.In contrast,CCS technology demonstrates comparatively modest improvements in air quality(−9.4μg m^(−3) for PM2.5 and−2.4 ppb for ozone)and cumulative premature deaths reduction(−3.4 million from 2010 to 2050)compared to the end-of-pipe scenario.Notably,densely populated regions such as Henan,Hebei,Shandong,and Sichuan experience the most health and economic benefits.This study aims to project effective future mitigation technologies and climate policies on air quality improvement and carbon mitigation.Furthermore,it seeks to delineate detailed provincial-level air pollution control strategies,offering valuable guidance for policymakers and stakeholders in pursuing sustainable and health-conscious environmental management.

Choice of conodont Idiognathodus simulator （sensu stricto） as the event marker for the base of the global Gzhelian Stage （Upper Pennsylvanian Series, Carboniferous System）

We propose that the level at which the conodont species Idiognathodus simulator （Ellison 1941） （sensu stricto） first appears be selected to mark the base of the Gzhelian Stage, because we believe that this is the optimal level by which this boundary can be correlated. This taxon has a short range and a wide distribution, as shown by correlation of glacial-eustatic cyclothems across the Kasimovian-Gzhelian boundary interval among Midcontinent North America and the Moscow and Donets basins of eastern Europe, based on scale of the cyclothems along with several aspects of biostrati- graphy. Outside of these areas, I. simulator （sensu stricto） is known also from other parts of the U.S., and is reported from the southern Urals and south-central China in its expected position between other widespread taxa. Its first appearance is consistent with the current ammonoid placement of the boundary （first appearance of Shumardites cuyleri）, and it is also compatible with certain aspects of the distribution of Eurasian fusulinid faunas （e.g., lectotype ofRauserites rossicus）.

The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model

An ensemble optimal interpolation(EnOI)data assimilation method is applied in the BCCCSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework.Pseudoobservations of sea surface temperature(SST),sea surface height(SSH),sea surface salinity(SSS),temperature and salinity(T/S)profiles were first generated in a free model run.Then,a series of sensitivity tests initialized with predefined bias were conducted for a one-year period;this involved a free run(CTR)and seven assimilation runs.These tests allowed us to check the analysis field accuracy against the"truth".As expected,data assimilation improved all investigated quantities;the joint assimilation of all variables gave more improved results than assimilating them separately.One-year predictions initialized from the seven runs and CTR were then conducted and compared.The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles,but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies.The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles,while surface data assimilation became more important at higher latitudes,particularly near the western boundary currents.The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables.Finally,a central Pacific El Ni?o was well predicted from the joint assimilation of surface data,indicating the importance of joint assimilation of SST,SSH,and SSS for ENSO predictions.

A Deeper Understanding of the CO_(2)Emission Pathway Under China’s Carbon Emission Peak and Carbon Neutrality Goals

Achieving carbon neutrality is crucial in dealing with climate change and containing the increase in global temperature at below 1.5℃compared with preindustrial levels.During the general debate at the 75th session of the United Nations General Assembly in September 2020,President Xi Jinping announced that China would adopt more vigorous policies and measures against climate change.

Impact of Revised Trigger and Closure of the Double-Plume Convective Parameterization on Precipitation Simulations over East Asia

A double-plume convective parameterization scheme is revised to improve the precipitation simulation of a global model(Global-to-Regional Integrated Forecast System;GRIST).The improvement is achieved by considering the effects of large-scale dynamic processes on the trigger of deep convection.The closure,based on dynamic CAPE,is improved accordingly to allow other processes to consume CAPE under the more restricted convective trigger condition.The revised convective parameterization is evaluated with a variable-resolution model setup(110–35 km,refined over East Asia).The Atmospheric Model Intercomparison Project(AMIP)simulations demonstrate that the revised convective parameterization substantially delays the daytime precipitation peaks over most land areas,leading to an improved simulated diurnal cycle,evidenced by delayed and less frequent afternoon precipitation.Meanwhile,changes to the threshold of the trigger function yield a small impact on the diurnal amplitude of precipitation because of the consistent setting of dCAPE-based trigger and closure.The simulated mean precipitation remains reasonable,with some improvements evident along the southern slopes of the Tibetan Plateau.The revised scheme increases convective precipitation at the lower levels of the windward slope and reduces the large-scale precipitation over the upper slope,ultimately shifting the rainfall peak southward,which is in better agreement with the observations.

Estimating the value of a statistical life in China: A contingent valuation study in six representative cities

The value of a statistical life(VSL)is a crucial tool for monetizing health impacts.To explore the VSL in China,this study examines people’s willingness to pay(WTP)to reduce death risk from air pollution in six representative cities in China based on face-to-face contingent valuation interviews(n=3936)from March 7,2019 to September 30,2019.The results reveal that the WTP varied from CNY 455 to 763 in 2019(USD 66-111),corresponding to a VSL range of CNY 3.79-6.36 million(USD 549395-921940).The VSL in China in 2019 is estimated to be CNY 4.76 million(USD 689659).The statistics indicate that monthly expenditure levels,environmental concerns,risk attitudes,and assumed market acceptance,which have seldom been dis‐cussed in previous studies,significantly impact WTP and VSL.These findings will serve as a reference for ana‐lyzing mortality risk reduction benefits in future research and for policymaking.

A systematic review of the inequality of health burdens related to climate change

Climate change significantly impacts human health,exacerbating existing health inequalities and creating new ones.This study addresses the lack of systematic review in this area by analyzing 2440 publications,focusing on four key terms:health,disparities,environmental factors,and climate change.Strict inclusion criteria limited the selection to English-language,peer-reviewed articles related to climate health hazards,ensuring the relevance and rigor of the synthesized studies.This process synthesized 65 relevant studies.Our investigation revealed that recent research,predominantly from developed countries,has broadened its scope beyond temperature-related impacts to encompass diverse climate hazards,including droughts,extreme weather,floods,mental health issues,and the intersecting effects of Coronavirus Disease 2019.Research has highlighted exposure as the most studied element in the causal chain of climate change-related health inequalities,followed by adaptive capability and inherent sensitivity.The most significant vulnerabilities were observed among populations with low socioeconomic status,ethnic minorities,and women.The study further reveals research biases and methodological limitations,such as the paucity of attention to underdeveloped regions,a narrow focus on non-temperature-related hazards,challenges in attributing climate change effects,and a deficit of large-scale empirical studies.The findings call for more innovative research approaches and a holistic integration of physical,socio-political,and economic dimensions to enrich climate-health discourse and inform equitable policy-making.

An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

Watermarks can provide reliable and secure copyright protection for optical coherence tomography(OCT)fundus images.The effective image segmentation is helpful for promoting OCT image watermarking.However,OCT images have a large amount of low-quality data,which seriously affects the performance of segmentationmethods.Therefore,this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network(RCNN).First,the rough-set-based feature discretization module is designed to preprocess the input data.Second,a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable the model to adaptively select important information for fusion.Finally,the refinement module for enhancing the extraction power of multi-scale information is added to improve the edge accuracy in segmentation.RCNN is compared with CE-Net and MultiResUNet on 83 gold standard 3D retinal OCT data samples.The average dice similarly coefficient(DSC)obtained by RCNN is 6%higher than that of CE-Net.The average 95 percent Hausdorff distance(95HD)and average symmetric surface distance(ASD)obtained by RCNN are 32.4%and 33.3%lower than those of MultiResUNet,respectively.We also evaluate the effect of feature discretization,as well as analyze the initial learning rate of RCNN and conduct ablation experiments with the four different models.The experimental results indicate that our method can improve the segmentation accuracy of OCT fundus images,providing strong support for its application in medical image watermarking.

Synergetic strategies for carbon neutrality and clean air

Human activities,including the burning of fossil fuels,industrial production,transportation,residential,etc.,are the main sources of both air pollution and greenhouse gas(GHG)emissions.Thus,the same efforts may at once improve air quality and help to avoid climate change,and it is a research priority to investigate which interventions are most cost-effective and at what scale to meet both environmental goals[[1],[2],[3]].

A Neural-network-based Alternative Scheme to Include Nonhydrostatic Processes in an Atmospheric Dynamical Core

Here,a nonhydrostatic alternative scheme(NAS)is proposed for the grey zone where the nonhydrostatic impact on the atmosphere is evident but not large enough to justify the necessity to include an implicit nonhydrostatic solver in an atmospheric dynamical core.The NAS is designed to replace this solver,which can be incorporated into any hydrostatic models so that existing well-developed hydrostatic models can effectively serve for a longer time.Recent advances in machine learning(ML)provide a potential tool for capturing the main complicated nonlinear-nonhydrostatic relationship.In this study,an ML approach called a neural network(NN)was adopted to select leading input features and develop the NAS.The NNs were trained and evaluated with 12-day simulation results of dry baroclinic-wave tests by the Weather Research and Forecasting(WRF)model.The forward time difference of the nonhydrostatic tendency was used as the target variable,and the five selected features were the nonhydrostatic tendency at the last time step,and four hydrostatic variables at the current step including geopotential height,pressure in two different forms,and potential temperature,respectively.Finally,a practical NAS was developed with these features and trained layer by layer at a 20-km horizontal resolution,which can accurately reproduce the temporal variation and vertical distribution of the nonhydrostatic tendency.Corrected by the NN-based NAS,the improved hydrostatic solver at different horizontal resolutions can run stably for at least one month and effectively reduce most of the nonhydrostatic errors in terms of system bias,anomaly root-mean-square error,and the error of the wave spatial pattern,which proves the feasibility and superiority of this scheme.

Assessment of the cropland classifications in four global land cover datasets： A case study of Shaanxi Province, China

Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring. As several global land cover datasets have been independently released, an inter-comparison of these data products on the classification of cropland is highly needed. This paper presents an assessment of cropland classifications in four global land cover datasets, i.e., moderate resolution imaging spectrometer （MODIS） land cover product, global land cover map of 2009 （GlobCover2009）, finer resolution observation and monitoring of global cropland （FROM-GC） and 30-m global land cover dataset （GlobeLand30）. The temporal coverage of these four datasets are circa 2010. One of the typical agricultur- al regions of China, Shaanxi Province, was selected as the study area. The assessment proceeded from three aspects： accuracy, spatial agreement and absolute area. In accuracy assessment, 506 validation samples, which consist of 168 cropland samples and 338 non-cropland ones, were automatically and systematically selected, and manually interpreted by referencing high-resolution images dated from 2009 to 2011 on Google Earth. The results show that the overall accuracy （OA） of four datasets ranges from 61.26 to 80.63%. GlobeLand30 dataset, with the highest accuracy, is the most accurate dataset for cropland classification. The cropland spatial agreement （mainly located in the plain ecotope of Shaanxi） and the non-cropland spatial agreement （sparsely distributed in the south and middle of Shaanxi） of the four datasets only makes up 33.96% of the whole province. FIROM-GC and GlobeLand30, obtaining the highest spatial agreement index of 62.40%, have the highest degree of spatial consistency. In terms of the absolute area, MODIS underestimates the cropland area, while GlobCover2009 significantly overestimates it. These findings are of value in revealing to which extent and on which aspect that these global land cover datasets may agree with each other at small scale on each ecotope region. The approaches taken in this study could be used to derive a fused cropland classification dataset.

Contrasting the Skills and Biases of Deterministic Predictions for the Two Types of El Nio

The tropical Pacific has begun to experience a new type of El Nio, which has occurred particularly frequently during the last decade, referred to as the central Pacific(CP) El Nio. Various coupled models with different degrees of complexity have been used to make real-time El Nio predictions, but high uncertainty still exists in their forecasts. It remains unknown as to how much of this uncertainty is specifically related to the new CP-type El Nio and how much is common to both this type and the conventional Eastern Pacific(EP)-type El Nio. In this study, the deterministic performance of an El Nio–Southern Oscillation(ENSO) ensemble prediction system is examined for the two types of El Nio. Ensemble hindcasts are run for the nine EP El Nio events and twelve CP El Nio events that have occurred since 1950. The results show that(1) the skill scores for the EP events are significantly better than those for the CP events, at all lead times;(2) the systematic forecast biases come mostly from the prediction of the CP events; and(3) the systematic error is characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for the CP El Nio. Further improvements to coupled atmosphere–ocean models in terms of CP El Nio prediction should be recognized as a key and high-priority task for the climate prediction community.

Effects of topography on the sub-tidal circulation in the southwestern Huanghai Sea(Yellow Sea)in summer

A nested circulation model system based on the Princeton ocean model （POM） is set up to simulate the currentmeter data from a bottom-mounted Acoustic Doppler Profiler （ADP） deployed at the 30 m depth in the Lunan（South Shandong Province, China） Trough south of the Shandong Peninsula in the summer of 2008, and to study the dynamics of the circulation in the southwestern Huanghai Sea （Yellow Sea）. The model has reproduced well the observed subtidal current at the mooring site. The results of the model simulation suggest that the bottom topography has strong steering effects on the regional circulation in summer. The model simulation shows that the Subei （North Jiangsu Province, China）coastal current flows north- ward in summer, in contrast to the southeastward current in the center of the Lunan Trough measured by the moored currentmeter. The analyses of the model results suggest that the southeastward current at the mooring site in the Lunan Trough is forced by the westward wind-driven current along the Lunan coast, which meets the northward Subei coastal current at the head of the Haizhou Bay to flow along an offshore path in the southeastward direction in the Lunan Trough. Analysis suggests that the Subei coastal current, the Lunan coastal current, and the circulation in the Lunan Trough are independent current systems con- trolled by different dynamics. Therefore, the current measurements in the Lunan Trough cannot be used to represent the Subei coastal current in general.

Impacts of Irrigation and Vegetation Growth on Summer Rainfall in the Taklimakan Desert

In recent decades,a greening tendency due to increased vegetation has been noted around the Taklimakan Desert(TD),but the impact of such a change on the local hydrological cycle remains uncertain.Here,we investigate the response of the local hydrological cycle and atmospheric circulation to a green TD in summer using a pair of global climate model(Community Earth System Model version 1.2.1)simulations.With enough irrigation to support vegetation growth in the TD,the modeling suggests first,that significant increases in local precipitation are attributed to enhanced local recycling of water,and second,that there is a corresponding decrease of local surface temperatures.On the other hand,irrigation and vegetation growth in this low-lying desert have negligible impacts on the large-scale circulation and thus the moisture convergence for enhanced precipitation.It is also found that the green TD can only be sustained by a large amount of irrigation water supply since only about one-third of the deployed water can be“recycled”locally.Considering this,devising a way to encapsulate the irrigated water within the desert to ensure more efficient water recycling is key for maintaining a sustainable,greening TD.

RF-CLASS： A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

Predicting Levels of Crude Protein, Digestibility, Lignin and Cellulose in Temperate Pastures Using Hyperspectral Image Data

Hyperspectral sensors provide the potential for direct estimation of pasture feed quality attributes. However, remote sensing retrieval of digestibility and fibre (lignin and cellulose) content of vegetation has proven to be challenging since tissue optical properties may not be propagated to the canopy level in mixed cover types. In this study, partial least squares regression on spectra from HyMap and Hyperion imagery were used to construct predictive models for estimation of crude protein, digestibility, lignin and cellulose concentration in temperate pastures. HyMap and Hyperion imagery and field spectra were collected over four pasture sites in southern Victoria, Australia. Co-incident field samples were analyzed with wet chemistry methods for crude protein, lignin and cellulose concentration, and digestibility was calculated from fiber determinations. Spectral data were subset based on sites and time of year of collection. Reflectance spectra were extracted from the hyperspectral imagery and collated for analysis. Six different transformations including derivatives and continuum removal were applied to the spectra to enhance absorption features sensitive to the quality attributes. The transformed reflectance spectra were then subjected to partial least squares regression, with full cross-validation “leave-one-out” technique, against the quality attributes to assess effects of the spectral transformations and post-atmospheric smoothing techniques to construct predictive models. Model performance between spectrometers, subsets and attributes were assessed using a coefficient of variation (CV), —the interquantile (IQ) range of the attribute values divided by the root mean square error of prediction (RMSEP) from the models. The predictive models with the highest CVs were obtained for digestibility for all spectra types, with HyMap the highest. However, models with slightly lower CVs were obtained for crude protein, lignin and cellulose. The spectral regions for diagnostic wavelengths fell within the chlorophyll well, red edge, and 2000-2300 nm ligno-cellulose-protein regions, with some wavelengths selected between the 1600 and 1800 nm region sensitive to nitrogen, protein, lignin and cellulose. The digestibility models with the highest CV’s had confidence intervals corresponding to ±5% digestibility, which constitutes approximately 30% of the measured range. The cellulose and lignin models with the highest CV’s also had similar confidence intervals but the slopes of the prediction lines were substantially less than 1:1 indicating reduced sensitivity. The predictive relationships established here could be applied to categorizing pasture quality into range classes and to determine whether pastures are above or below for example threshold values for livestock productivity benchmarks.

Synergies of carbon neutrality, air pollution control, and health improvement - a case study of China Energy Interconnection scenario

Climate change and air pollution are primarily caused by the combustion and utilization of fossil fuels.Both climate change and air pollution cause health problems.Based on the development of China,it is extremely important to explore the synergies of the energy transition,CO_(2) reduction,air pollution control,and health improvement under the target of carbon peaking before 2030 and carbon neutrality before 2060.This study introduces the policy evolution and research progress related to energy,climate change,and the environment in China and proposes a complete energy-climate-air-health mechanism framework.Based on the MESSAGE-GLOBIOM integrated assessment model,emission inventory and chemical transport model,and exposure-response function,a comprehensive assessment method of energy-climate-air-health synergies was established and applied to quantify the impacts of Chinese Energy Interconnection Carbon Neutrality(CEICN)scenario.The results demonstrate that,by 2060,the SO_(2),NO_(x) and PM_(2.5) emissions are estimated to be reduced by 91%,85%,and 90%respectively compared to the business-as-usual(BAU)scenario.The direct health impacts brought by achieving the goal of carbon neutrality will drive the proactive implementation of more emission reduction measures and bring greater benefits to human health.

Five tips for China to realize its co-targets of climate mitigation and Sustainable Development Goals(SDGs)

In 2018,a total of US$166 billion global economic losses and a new high of 55.3 Gt of CO_(2)equivalent emission were generated by 831 climate-related extreme events.As the world’s largest CO_(2)emitter,we reported China’s recent progresses and pitfalls in climate actions to achieve climate mitigation targets(i.e.,limit warming to 1.5-2°C above the pre-industrial level).We first summarized China’s integrated actions(2015 onwards)that benefit both climate change mitigation and Sustainable Development Goals(SDGs).These projects include re-structuring organizations,establishing working goals and actions,amending laws and regulations at national level,as well as increasing social awareness at community level.We then pointed out the shortcomings in different regions and sectors.Based on these analyses,we proposed five recommendations to help China improving its climate policy strategies,which include:1)restructuring the economy to balance short-term and long-term conflicts;2)developing circular economy with recycling mechanism and infrastructure;3)building up unified national standards and more accurate indicators;4)completing market mechanism for green economy and encouraging green consumption;and 5)enhancing technology innovations and local incentives via bottom-up actions.