Emission Model Research Articles

Dispersion model of NOx emissions from a liquefied natural gas facility

Natural gas used widely in terms of energy production. Energy production is among the most prominent sectors of humankind. Combustion processes inevitably produces air pollutants. The major pollutant during a combustion process is nitrogen oxide emissions. The term of nitrogen oxides primarily include nitrogen monoxide and nitrogen dioxide. These pollutants are generated regardless of the fuel content since air composition itself is the major source for these pollutants. It is possible to calculate emissions through the activity data and emission factors. Calculation of emissions is not enough for an environmental assessment. The impact of pollutants on human health relies on their concentration in the atmosphere. In order to determine their concentrations several modelling practices are developed. In this study, AERMOD used for modelling purpose of NOx emissions from a liquefied natural gas facility. It was observed that the pollutants were dispersed mostly towards south-southwest of the facility, where Marmaraereğlisi district is located. Although the pollutants transported directly to the settlement, the concentrations remained limited. During operation conditions, the highest daily NOx concentration was 1.7 μg/m3 and the highest annual concentration was 0.1 μg/m3. At maximum operating conditions, the highest daily NOx concentration was 16.2 μg/m3 and the highest annual concentration was 2.5 μg/m3. At minimum operating conditions, the highest daily NOx concentration was 1.1 μg/m3 and the highest annual concentration was 0.2 μg/m3.

Modelling Current-State N- and P-Fluxes into Surface Waters in Germany

For the first time, the AGRUM model consortium—consisting of the agro-economic model RAUMIS, the water balance model mGROWA, the hydrological nutrient transport models DENUZ, WeKu and MEPhos, and the urban emission model MONERIS—was jointly set up throughout Germany (357,000 km2). This provided a nationwide consistent nutrient model to capture the current status of N and P inputs to surface waters from diffuse sources and urban areas. Diffuse nutrient emissions were quantified in high spatial resolution for the input pathways’ groundwater, drainage runoff, and natural interflow (100 m × 100 m), as well as for water erosion and wash-off (25 m × 25 m). The sum of diffuse nutrient inputs to surface waters is about 385,000 metric tons N/yr and ca. 11,530 metric tons P/yr. Urban emissions were quantified either as point source inputs (wastewater treatment plants, industrial direct dischargers) or at municipality scale for different collection and treatment systems, e.g., rainwater sewers or decentralized treatment plants, and sum up to ca. 95,000 t N/yr and 7500 t P/yr. As modelled, total N and P inputs into surface waters correspond well with observed N and P loads in rivers. The model results represent valuable information for water managers, being responsible for the preparation of management plans for the third management cycle of the EC Water Framework Directive spanning from 2021 to 2027.

Modeling air pollution around major dumpsites in Lagos State using geospatial methods with solutions

Air pollution is gradually becoming a major health concern globally and Nigeria is especially susceptible to air pollution Available data indicates that vehicular traffics, industries, wildfire and biomass burning are significant sources of air pollutants such as Particulate Matter PM, Carbon Monoxide CO, Nitrogen Dioxide NO2, Sulphur Dioxide SO2 and Volatile Organic Compounds VOCs in the air in Lagos State. The aim of this study is to perform geospatial modelling of air pollution around Olusosun and Solous dumpsites in Lagos State. The study analyses, assesses, and predicts air pollution emission in these dumpsites and examines their possible health impact on residents. Using both ground and satellite-based data, different GIS techniques were used to model this pollution concentration. These methods include Kriging, Regression, and Landfill Gas Emissions Model (LANDGEM) which helps to analyse, model and predict the air pollutant levels around these dumpsite. The average pollution level of Particulate Matter with a diameter that is 2.5 micrometers (PM2.5) in Olusosun was 94.29 μg/m3 while those in Solous was 94.12 μg/m3 over a 24 h period. This is way above the Air Quality Guideline recommended by the WHO which is 15 μg/m3 over a 24 h period. This is similar to the high pollution level of NO2 which was 237.64 μg/m3 in Olusosun and 255.84 μg/m3 in Solous as against the WHO Air Quality Guideline of 25 μg/m3 over a 24 h period. SO2 level is also varyingly high in Olusosun and Solous. The reading of 109.27 μg/m3 in Olusosun is considerably high as compared to the reading of 43.82 μg/m3 in Solous. However, both readings are relatively higher than the Air Quality Guideline recommended by the WHO which was 40 μg/m3. The CO air quality level of 0.55 mg/m3 in Olusosun and 0.52 mg/m3 in Solous is within the accepted level of the Air Quality Guideline recommended by the WHO. This shows that CO is not a pollution threat in the major dumpsites in Lagos. This is similar to the readings of PM5 and PM10 which are within the WHO Air Quality Guidelines over a 24 h period. The Moderate Resolution Imaging Spectroradiometer (MODIS) data shows a trend of increasing concentration of PM2.5 over the 5 study months due to an increase in the quantity of waste disposal, which shows that high waste disposal is directly proportional to high level of PM2.5. It is recommended that living farther (more than 15 km) away from the dumpsites can reduce the high risk of air pollution and its related diseases for residents. Also, there is need for proper incineration and maintenance of waste to reduce the level of air and other pollutants in and around the dumpsites.

Extensive validation of a combustion and pollutant emission model of a pre-chamber engine including different pre-chamber geometries

In this work, a 0D/1D model of a single-cylinder pre-chamber spark ignition (PCSI) engine is extensively validated with experimental data in terms of performance, combustion, and emissions by varying the pre-chamber geometry and operating conditions.In the first stage, an experimental study is carried out on the PCSI engine at 1600 rpm and wide-open throttle, exploring different pre-chamber (PC) designs and various relative air/fuel (A/F) ratios, λ, in the main chamber.In the second stage, a phenomenological combustion model for PCSI engines is coupled with additional user-defined sub-models of in-cylinder turbulence, heat transfer, and pollutant emissions. These are integrated into a 1D engine model and used to reproduce a set of 16 measured data for a reference PC geometry. The model adequately describes the performance, pollutant production, and burn rate in both the pre- and main-chamber considering the effects of jet-induced turbulence and distributed multiple flame kernels in the main chamber.The predictive capability of the 1D model is further tested on an extended dataset composed of 163 points, including variations in the PC geometry, proving to satisfactorily reproduce experiments in terms of performance, combustion, and emissions. This last aspect represents the novelty of the present work, demonstrating the reliability of the physical background included within the in-cylinder sub-models.

The calculation and distribution of CAV carbon emissions on urban transportation systems: A comparative analysis of renewable and non-renewable energy sources

When powered by electricity, Connected and Autonomous Vehicles (CAVs), an emerging mode of transportation, possess the capacity to reduce exhaust emissions greatly. However, accurately measuring carbon emissions in urban transportation remains a challenge, especially considering emissions from electricity generation and gasoline consumption. This paper proposes an innovative method for calculating CAVs' carbon emissions distribution, utilizing both renewable and non-renewable energy. The study employs SUMO, an agent-based simulation platform, to develop an intelligent driver model and cooperative adaptive cruise control modules, tracking vehicle movement behavior across various vehicle types, including Gasoline Vehicles (GVs), Electric Vehicles (EVs), Human-Driven Vehicles (HDVs), and CAVs. Subsequently, a lifecycle electric carbon emission model is constructed, integrating the energy consumption model of EVs with carbon emission factors of renewable and non-renewable energy. Visualization models are then developed to clarify the carbon emission distribution within the traffic network. A case study conducted in Suzhou, China validates the model, analyzing the spatiotemporal distribution of carbon emissions. Results show EVs can reduce carbon emissions by 70%–90 % compared to GVs on urban roads during rush hour, while CAVs can further reduce emissions by 35%–50 % compared to HDVs. Additionally, carbon emissions from non-renewable energy sources were found to exceed those from renewable sources.

CO2 emission characteristics modeling and low-carbon scheduling of thermal power units under peak shaving conditions

To accommodate the high penetration of renewable power, the operational flexibility of thermal power units is highly required by the power system, so thermal power units frequently operate under peak shaving conditions. The CO2 emission characteristics of a thermal power unit are significantly influenced by the load factor, and also by the installed capacity and design parameters of the thermal power unit. Therefore, detailed CO2 emission characteristics of thermal power units should be modeled and considered in the low-carbon scheduling of the power system. In this study, thermal power units are classified by live steam parameters, installed capacities, reheat stages, cooling modes, and operation modes, and the models of detailed CO2 emission characteristics are developed and validated. The turbine isentropic efficiency, boiler thermal efficiency, and auxiliary power consumption are comprehensively considered in the CO2 emission model. A new indicator is defined to evaluate the reduction of carbon emissions in a power system when a TPU accommodates per unit of renewable power generation, and an optimization method for low-carbon scheduling based on it is proposed. Finally, a reference case study is conducted. Results show that the total carbon emissions for a day of the proposed low-carbon scheduling method are 0.06 %−0.20 % less than the efficiency priority method, and 1.53 %−2.36 % less than the capacity weighting method.

Fuzzy logic based thermal image processing for temperature monitoring of rotating cylindrical surfaces

When a non-contact and remote temperature measurement is of importance, several environmental factors must be considered. Of these, two are of crucial importance — the surface emissivity of the observed object and the directional emissivity in the object–sensor relationship. This paper proposes FDEM — a Fuzzy Directional Emissivity Model built with the assumption that the observed surface is cylindrically shaped. The model consists of two submodels — for vertical and horizontal directions separately. The main contribution of this paper is the formalization of a linguistic description of measurement conditions typical for an industrial environment. Additionally, dual linguistic fuzzy modifiers were used to increase the accuracy of the modeled emissivity. Finally, the proposed model achieved up to 40% reduction of RMSE error when used for correcting the temperature measurements of a rotating cylinder. Overall, our model provides a novel and reliable way for temperature readout correction on the entire surface of a drying roller, such as ones used in the papermaking industry. Furthermore, the proposed approach is not only inherently capable of compensating for the position of the camera, but also does it in an interpretable, and hence expert-verifiable, manner.

Effectiveness of spatial measurement model based on SDM-STIRPAT in measuring carbon emissions from transportation facilities

To gain a deeper understanding of the carbon emission mechanism from transportation facilities, all system elements affecting carbon emissions from regional transportation facilities are identified and analyzed according to panel data from 30 regions in China. A spatial econometric model for carbon emissions from transportation facilities is constructed using the Spatial Dolbin model from 2004 to 2022 as the research period. From the results, the carbon dioxide emissions from transportation facilities added from 318 million tons in 2004 to 752 million tons in 2022, with an average annual growth rate of 4.9%. The global spatial auto-correlation coefficient was significant at the 5%, with an obvious spatial correlation between carbon dioxide emissions within a geographical range. In addition, through stability testing, the model showed high stability in both spatial lag testing and spatial error testing, demonstrating strong ability to interpret data. The research shows that the carbon emission is affected by independent variables, including population, economy, technology, and transportation, and exhibit significant spatial distribution characteristics in different regions and years, providing a basis for policy formulation and carbon emission management.

Sensitivity of the model of methane emission from the Arctic shelf seas to the parameterization of the gas exchange process

Sensitivity of the model of methane emission from the Arctic shelf seas to the parameterization of the gas exchange process

Carbon peaking prediction scenarios based on different neural network models: A case study of Guizhou Province.

Global warming, caused by greenhouse gas emissions, is a major challenge for all human societies. To ensure that ambitious carbon neutrality and sustainable economic development goals are met, regional human activities and their impacts on carbon emissions must be studied. Guizhou Province is a typical karst area in China that predominantly uses fossil fuels. In this study, a backpropagation (BP) neural network and extreme learning machine (ELM) model, which is advantageous due to its nonlinear processing, were used to predict carbon emissions from 2020 to 2040 in Guizhou Province. The carbon emissions were calculated using conversion and inventory compilation methods with energy consumption data and the results showed an "S" growth trend. Twelve influencing factors were selected, however, five with larger correlations were screened out using a grey correlation analysis method. A prediction model for carbon emissions from Guizhou Province was established. The prediction performance of a whale optimization algorithm (WOA)-ELM model was found to be higher than the BP neural network and ELM models. Baseline, high-speed, and low-carbon scenarios were analyzed and the size and time of peak carbon emissions in Liaoning Province from 2020 to 2040 were predicted using the WOA-ELM model.

Revealing asymmetry on mid-plane of protoplanetary disc through modelling of axisymmetric emission: methodology

ABSTRACT This study proposes an analytical framework for deriving the surface brightness profile and geometry of a geometrically thin axisymmetric disc from interferometric observation of continuum emission. Such precise modelling facilitates the exploration of faint non-axisymmetric structures, such as spirals and circumplanetary discs. As a demonstration, we simulate interferometric observations of geometrically thin axisymmetric discs. The proposed method can reasonably recover the injected axisymmetric structures, whereas Gaussian fitting of the same data yielded larger errors in disc orientation estimation. To further test the applicability of the method, it was applied to the mock data for $m=1,2$ spirals and a point source, which are embedded in a bright axisymmetric structure. The injected non-axisymmetric structures were reasonably recovered except for the innermost parts, and the disc geometric parameter estimations were better than Gasussian fitting. The method was then applied to the real data of Elias 20 and AS 209, and it adequately subtracted the axisymmetric component, notably in Elias 20, where substantial residuals remained without our method. We also applied our method to continuum data of PDS 70 to demonstrate the effectiveness of the method. We successfully recovered emission from PDS 70 c consistently with previous studies, and also tentatively discovered new substructures. The current formulation can be applied to any data for disc continuum emission, and aids in the search of spirals and circumplanetary discs, whose detection is still limited.

Exploring the paths of energy conservation and emission reduction in aluminum industry in Henan province, China

As the world's largest producer and consumer of aluminum, China's aluminum industry is facing enormous pressure for energy conservation and emission reduction due to its traditional high energy consumption, high emissions, and high pollution. To explore the green and low-carbon development path of the aluminum industry, an integrated modelling framework was developed, including aluminum production forecasting and scrap recycling model, energy consumption-carbon emission-air pollutant emission model, and conservation supply curve method. Five different scenarios were set up to evaluate the energy saving and emission reduction potential of Henan Province from 2023 to 2050, by adopting four measures of accelerating the recycled aluminum, promoting energy efficiency technologies, expanding the scale of electrolytic cells, and increasing the share of renewable energy. The results showed that the production of alumina and primary aluminum decreased to 9.6 and 1.8 Mt respectively in 2050, while the future production of aluminum processing products and recycled aluminum showed a continuous upward trend. By 2050, production of recycled aluminum will increase to 14.0 Mt, expanding by about 5.7 times compared to 2022. The energy-saving and emission reduction potential of the integrated scenario is the most significant, with energy consumption, CO2, SO2, NOX, PM10, PM2.5 and fluoride decreased by 54.3, 73.0, 96.5, 72.7, 82.6, 82.6, and 98.9% compared to the business-as-usual scenario, respectively. The results also indicated that accelerating the recycled aluminum and optimizing energy structure are more effective for mitigation. Finally, policy suggestions for mitigating energy consumption and pollutant emissions of the aluminum industry in Henan Province were proposed. The systematic framework of this study can be widely applicable to other provinces and the whole country, which can provide some references for the aluminum industry to achieve the carbon peak and carbon neutrality targets.

Acoustic Emission during Non-Uniform Progression of Processes in Composite Failure According to the Von Mises Criterion

In the study, based on the model of acoustic emission during the destruction of a composite material by shear force according to the Von Mises criterion, the effect of non-uniformity of the destruction process on the generated acoustic emission signal is simulated. The study under the accepted modeling conditions allows us to determine the patterns of changes in the amplitude envelope of acoustic emission signals at various stages of developing processes. In theoretical and experimental studies of acoustic emission signals when searching for patterns in their parameter changes and developing methods for monitoring or diagnosing the state of composite materials, the problem lies in the interpretation of recorded information. This issue arises from the complexity and diversity of processes occurring in the material structure at micro and macro levels, and the high sensitivity of the acoustic emission method to these processes, wherein structural changes lead to observable alterations in the characteristics of acoustic emissions. Solving this problem requires both theoretical and experimental studies to understand the influence of various factors on the characteristics of the generated acoustic emission. The results of the presented study can be used to assess the condition of composite materials and structures, such as bridges, e.g., in terms of defectiveness, property dispersion, damage during operation, and other characteristics.

Shockingly Bright Warm Carbon Monoxide Molecular Features in the Supernova Remnant Cassiopeia A Revealed by JWST

We present JWST NIRCam (F356W and F444W filters) and MIRI (F770W) images and NIRSpec Integral Field Unit (IFU) spectroscopy of the young Galactic supernova remnant Cassiopeia A (Cas A) to probe the physical conditions for molecular CO formation and destruction in supernova ejecta. We obtained the data as part of a JWST survey of Cas A. The NIRCam and MIRI images map the spatial distributions of synchrotron radiation, Ar-rich ejecta, and CO on both large and small scales, revealing remarkably complex structures. The CO emission is stronger at the outer layers than the Ar ejecta, which indicates the re-formation of CO molecules behind the reverse shock. NIRSpec-IFU spectra (3–5.5 μm) were obtained toward two representative knots in the NE and S fields that show very different nucleosynthesis characteristics. Both regions are dominated by the bright fundamental rovibrational band of CO in the two R and P branches, with strong [Ar vi] and relatively weaker, variable strength ejecta lines of [Si ix], [Ca iv], [Ca v], and [Mg iv]. The NIRSpec-IFU data resolve individual ejecta knots and filaments spatially and in velocity space. The fundamental CO band in the JWST spectra reveals unique shapes of CO, showing a few tens of sinusoidal patterns of rovibrational lines with pseudocontinuum underneath, which is attributed to the high-velocity widths of CO lines. Our results with LTE modeling of CO emission indicate a temperature of ∼1080 K and provide unique insight into the correlations between dust, molecules, and highly ionized ejecta in supernovae and have strong ramifications for modeling dust formation that is led by CO cooling in the early Universe.

Speed limits in traffic emission models using multi-objective optimization

AbstractClimate change compels a reduction of greenhouse gas emissions, yet vehicular traffic still contributes significantly to the emission of air pollutants. Hence, in this paper we focus on the optimization of traffic flow while simultaneously minimizing air pollution using speed limits as controllable parameters. We introduce a framework of traffic emission models to simulate the traffic dynamic as well as the production and spread of air pollutants. We formulate a multi-objective optimization problem for the optimization of multiple aspects of vehicular traffic. The results show that multi-objective optimization can be a valuable tool in traffic emission modeling as it allows to find optimal compromises between ecological and economic objectives.

Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine.

With emissions regulations becoming increasingly restrictive and the advent of real driving emissions limits, control of engine-out NOx emissions remains an important research topic for diesel engines. Progress in experimental engine development and computational modelling has led to the generation of a large amount of high-fidelity emissions and in-cylinder data, making it attractive to use data-driven emissions prediction and control models. While pure data-driven methods have shown robustness in NOx prediction during steady-state engine operation, deficiencies are found under transient operation and at engine conditions far outside the training range. Therefore, physics-based, mean value models that capture cyclic-level changes in in-cylinder thermo-chemical properties appear as an attractive option for transient NOx emissions modelling. Previous experimental studies have highlighted the existence of a very strong correlation between peak cylinder pressure and cyclic NOx emissions. In this study, a cyclic peak pressure-based semi-empirical NOx prediction model is developed. The model is calibrated using high-speed NO and NO2 emissions measurements during transient engine operation and then tested under different transient operating conditions. The transient performance of the physical model is compared to that of a previously developed data-driven (artificial neural network) model, and is found to be superior, with a better dynamic response and low (<10%) errors. The results shown in this study are encouraging for the use of such models as virtual sensors for real-time emissions monitoring and as complimentary models for future physics-guided neural network development.

Dynamic traffic noise simulation at signal-controlled intersections based on cellular automata model

Predicting and assessing road traffic noise is a crucial task in urban environmental management. To accurately predict traffic noise at signal-controlled intersections, this study introduces a dynamic traffic noise simulation model based on cellular automata. Firstly, a cellular automata traffic flow model for a multi-lane signal-controlled intersection is established. The vehicle noise emission and noise propagation models are incorporated into the dynamic simulation of traffic flow to accurately calculate traffic noise. Subsequently, the simulated data is compared with the measured data. The results demonstrate that the proposed method can effectively predict the statistical distribution of traffic noise at the intersection, with prediction errors of less than 2 dB(A) for Leq, L50, L90, and less than 3 dB(A) for L10. Finally, the proposed method is employed to simulate the operational status and noise emissions of vehicles at intersections under various traffic conditions. Based on the simulation results, the intrinsic connection between traffic operation and noise emission is explored, and the characteristics of traffic noise emission and distribution at intersections are analyzed. The method and its findings are anticipated to serve as a valuable reference for the management of road traffic noise.

Uncovering the spatiotemporal patterns of traffic-related CO2 emission and carbon neutrality based on car-hailing trajectory data

Traffic-related carbon dioxide (CO2) emissions constitute a significant portion of greenhouse gas emissions in urban areas, necessitating a comprehensive understanding of their spatiotemporal patterns and their implications for carbon neutrality. While previous studies have focused on exploring CO2 emission patterns, they often overlook the current carbon neutrality associated with these emissions. This study addresses this gap by investigating spatiotemporal patterns and carbon neutrality using car-hailing trajectory data from Xi'an, China. Initially, a link-based CO2 emission model is formulated by considering various driving conditions. Subsequently, the spatiotemporal patterns of traffic-related CO2 emissions are examined by kernel density estimation (KDE) and Getis-Ord models. Finally, carbon neutrality is assessed using the coupling coordination degree (CCD) model, which explores the relationship between traffic-related CO2 emissions and urban vegetation coverage. Empirical experiments found that hotspots of traffic-related CO2 emissions are concentrated at main roads or intersections, while coldspots are dispersed on residential roads. The findings highlight the significant influence of land use type, public services, and road levels on CO2 emission patterns, as well as the critical role of vegetation coverage in improving carbon neutrality levels. Based on these findings, several strategies are recommended to foster low-carbon city development, including expanding public transport services, integrating subway stations along main roads, optimizing traffic lights, increasing urban green spaces, and diversifying commercial facilities in traffic-related areas. These strategies emphasize the importance of incorporating carbon neutrality into urban planning and policy-making to achieve sustainable urban development.

Correlation analysis of energy consumption, carbon emissions and economic growth

In today's highly advanced industrialised and modernised world, China's economy is still growing, and its demand for energy is increasing daily. It is crucial to examine the connection between energy consumption, carbon emissions, and economic growth in order to promote economic growth based on energy conservation and emission reduction. Using Dezhou City in Shandong Province as an example, the study builds a VAR model of carbon emission, energy consumption, and economic growth in Dezhou City based on simplified macroeconomic sub-models, energy sub-models, and environmental sub-models. It then determines the correlation and influence mechanism between the three using tests like ADF unit root and Granger causality. The pertinent elements affecting Dezhou's carbon emissions were then investigated using grey correlation analysis. Finally, based on the study's findings, policy suggestions are made regarding energy use, carbon emissions, and economic expansion. It is necessary not only to restrain high-energy consumption industries and fundamentally optimize the energy consumption structure, but also to find new economic growth points and improve economic growth channels, so as to optimize the industrial structure. In this process, increasing the proportion of the tertiary industry is a key measure. In addition, the government needs to advocate the citizens to adopt a low-carbon lifestyle, and the concept of low-carbon environmental protection will be deeply rooted in the hearts of the people. This study will provide suggestions and theoretical guidance for China's energy consumption and carbon emissions, and help achieve high-quality growth of China and even the world economy.

Research on Carbon Emission Peaks in Large Energy Production Region in China —Based on the Open STIRPAT Model

&lt;p&gt;The interdependence between different regions within a country has an important impact on the prediction of regional carbon emission peaks. In this paper, taking Shanxi Province, a large energy producing region in China, as an example, we select the driving factors to construct an open STIRPAT model from both Shanxi and national levels, and use scenario analysis to predict the carbon emissions of Shanxi Province from 2021 to 2040. The study shows that： (1) In the Open STIRPAT model, the year and amount of peak carbon emissions in Shanxi province are mainly determined by the national scenarios. If the baseline scenario is chosen for the whole country, then no matter which scenario is chosen for Shanxi, it will not be able to realize the carbon peak in 2030. Under the scenario combination of low carbon-low carbon, Shanxi will realize the carbon peak in 2027. Under the scenario combination of low carbon-energy saving and energy saving-low carbon, Shanxi will realize the carbon peak in 2028. (2) The peak carbon emissions in Shanxi are mainly affected by the national scenario setting and Shanxi's emission reduction strategy. Controlling per capita GDP and energy consumption of 10,000 yuan GDP is an effective emission reduction strategy. (3) Compared with the open STIRPAT model, the closed STIRPAT model delays the year of peak carbon in Shanxi and overestimates carbon emissions by 20%-44%. Therefore, Shanxi should pay attention to the development of energy saving at the national level in formulating its carbon emission strategy, take it as an important constraint for Shanxi's peak carbon policy, and formulate a flexible carbon emission peaking program.&lt;/p&gt;