Emission Reduction Measures Research Articles

Can urban low-carbon transitions promote enterprise digital transformation?

To address the escalating environmental issues and foster the progress of green development, the Chinese government has initiated the low-carbon city pilot (LCCP) policy since 2010. Assessing the effects of this extensive and multifaceted environmental policy holds substantial theoretical and practical significance, particularly in terms of scrutinizing its impact on enterprise digital transformation (EDT). This study uses the LCCP as a quasi-natural experiment, utilizing the data obtained from Chinese listed companies spanning from 2007 to 2019, and empirically discovers that the LCCP can indeed stimulate enterprises to implement digital transformation strategies. After undergoing a series of robustness checks, including placebo tests and the instrumental variable method, the result obtained was still strongly supported. Heterogeneity tests reveal that the aforementioned conclusion is more typical for samples of non-state-owned and high-tech enterprises. A further mechanistic analysis indicates that the LCCP promotes EDT through two channels: encouraging enterprises to increase R&D expenditure and focusing on improving the resource allocation efficiency. This research, based on the empirical data obtained from China, enriches our understanding of the economic consequences of employing environmental regulations, and also offers valuable insights for other nations into formulating urban emission reduction measures to reap more policy benefits.

Impacts of energy-saving and emission-reduction on sustainability of cement production

How energy saving and emission reduction measures influence the interrelationship between carbon emissions and environmental sustainability of cement industry is not very clear, and this could lead to trade-off. This study investigates effects of three representative energy saving and emission reduction measures (steam Rankine cycle power generation (SRC), organic Rankine cycle power generation (ORC) and carbon capture and utilization (CCU)) on environmental performance of cement production using an improved emergy analysis and carbon emissions amounting. The proposed approach can investigate resource efficiency and emissions' impact as well as carbon footprint of a system in study simultaneously. A cement enterprise, in Jilin Province in China, as a case, is explored using the proposed combined approach based on scenario analysis. The results illustrate that combination of SRC + ORC can promote environmental sustainability of cement production and carbon emission reduction by 4.61 % and 4.28 % respectively, mainly derived from SRC application (by 4.06 % and 3.81 %), (2) CCU can promote carbon emission reduction by 3.97 % but weaken the environmental sustainability by 2.10 % due to enhancing share of purchased nonrenewable electricity, and (3) combination of SRC + ORC + CCU can promote the environmental sustainability (by 2.43 %) and carbon emission reduction by 8.25 %. In addition, due to strong dependence on nonrenewable resources, combination of SRC + ORC + CCU is still limited in improvement of environmental performance of the industry. In the future, China's cement industry should further enhance environmental performance of CCU and efficiency of ORC, as well as improve the resource structure of this industry.

Human capital and cooking fuel choices in rural China: Perspective from cognitive and noncognitive skills

Choosing renewable fuels for household cooking is of great significance for environmental protection. Using data from the 2016 China Family Panel Studies, this study empirically explores the impact of the human capital of rural household heads, measured by cognitive and noncognitive skills, on household cooking fuel choices and their mechanisms. The findings of this study are as follows: (1) improving the cognitive and noncognitive skills of rural household heads can significantly promote the use of renewable cooking fuels; (2) these two skills influence household renewable fuel choices through income effects and information-seeking effects; (3) raising human capital significantly increases the consumption of renewable fuels for household heads aged less than or equal to 65; and (4) compared with unmarried household heads, improving the human capital of married ones can significantly increase the use of renewable cooking fuels. Therefore, universal compulsory education, village/community adult training and education, and environmental protection publicity are important measures for carbon emission reduction, environmental protection, and sustainable economic development, particularly for developing countries.

Exploring the nexus between structural capital, carbon neutrality and sustainable competitiveness: Evidence from natural resource exploitation in the post-COVID-19 era

In the aftermath of global commitments post the Paris Climate Conference, the onus on developing nations to achieve sustainable economic growth and carbon neutrality has intensified. The mining sector, particularly in major economies like the People’s Republic of China and India, stands at the forefront of this challenge due to its significant carbon footprint. Despite the sector’s pivotal role in carbon emissions, scant research has delved into the influence of structural capital in achieving carbon neutrality and sustainable competitiveness within this domain. To address this lacuna, the present study scrutinizes the interplay between structural capital, carbon neutrality, and sustainable competitiveness, employing panel data from 2015 to 2019. Advanced panel estimators are utilized, taking into account nuances such as cross-sectional dependence, stationarity, and potential structural breaks. The findings illuminate that adept management of structural capital not only bolsters carbon neutrality but also enhances sustainable competitiveness. Furthermore, carbon neutrality emerges as a significant mediator in the relationship between structural capital and sustainable competitiveness. These insights are buttressed by robustness checks. In light of these revelations, the study advocates for strategic integration of environmental considerations in the mining sector’s operations, emphasizing the adoption of green practices and carbon emission reduction measures. The paper concludes by offering both theoretical insights and pragmatic recommendations for the mining sector’s trajectory towards a carbon-neutral future.

How will CBAM affect the decarbonisation of steel industry in China? A system dynamics approach

EU's Carbon Border Adjustment Mechanism (CBAM) has been promulgated. The steel industry is included in CBAM, which will undoubtedly affect the export trade of China's steel manufacturers to the EU. It is necessary to analyse the impact of CBAM on China’s steel industry quantitatively. By establishing a system dynamics model that can simulate the operation mechanism of the steel supply chain in China, we have clarified the impacts of CBAM on the China's steel export and carbon reduction. Moreover, we have reasonably set up scenarios that represent the possible decarbonisation paths of China's steel industry and evaluated the implementation effect of each policy in these scenarios. The analysis shows that CBAM will lead to a decrease of 32% in export volume and a loss of 58% in export profits for China's steel exporters. The environmental benefits of CBAM are limited. Due to the low trade exposure, CBAM is unable to promote a large-scale carbon reduction in the China’s steel industry. Although CBAM will bring great pressure to steel exporters, a comprehensive policy of adopting more emission reduction measures can greatly improve the carbon reduction efficiency, effectively reduce the negative impact of CBAM, and maintain the stability of steel exports.

Analysis of Spatio-temporal Characteristics of Air Quality, Meteorological Impact, and Emission Reduction Effect During the Winter Olympics in Beijing-Tianjin-Hebei and Its Surrounding Areas

From January 31st to February 20th, 2015 to 2022, the spatio-temporal characteristics of the PM2.5 pollution evolution of 44 cities in the Beijing-Tianjin-Hebei Region and its surrounding areas were analyzed. The contribution of simultaneous meteorology, coordinated emission reduction, and cross-regional transmission to PM2.5 concentration and chemical component changes were quantified, respectively, with the aim to provide scientific reference for regional air quality joint prevention and control under adverse meteorological conditions. The results showed that the mean value of PM2.5 concentration of 44 cities in 2022 was the lowest(46 μg·m-3) without heavy pollution in the same period of the lunar calendar since 2015, whereas the proportion of days with good air quality was the highest(83.3%). PM2.5 pollution was more serious in the southern region than that in the northern region, and the high values were mainly concentrated along the Taihang Mountains and Yanshan transmission channel cities. In 2016, under the unregulated emission of fireworks in the Spring Festival, the proportion of unpolluted days was 93.5%, which means that the strong atmospheric diffusion ability was crucial to improve air quality. In 2022, the static weather index(SWI) increased by 2.1 compared with that in 2021, indicating unfavorable atmospheric diffusion capacity. The average and peak values of PM2.5 decreased by 14 μg·m-3 and 76 μg·m-3, respectively. The reduction in PM2.5 concentration in Beijing owing to emission reduction measures increased by 96% compared with that before one month. Under the adverse atmospheric background in the Shanxi-Shandong-Henan Region, which contributed to the increase in PM2.5 concentration, the peak value of PM2.5 concentration decreased by 87 μg·m-3, indicating that the peak clipping effect of regional collaborative pollution emission reduction was significant. From 2019 to 2022, the concentration of PM2.5 chemical components decreased yearly with narrowed spatial differences, and the high value was concentrated in the central and southern regions. The descending order of PM2.5 secondary component concentration decrease was as follows:organic matter(21.9%)>sulfate(20%)>ammonium salt(16.7%)>nitrate(9.7%). The proportion of nitrate in PM2.5 increased yearly to 30.1%, indicating that the contribution of traffic pollution was relatively prominent. In 2022, the SNA concentration in the Shanxi-Shandong-Henan Region increased. Pollution from external sources accounted for the main contribution in the Beijing-Tianjin-Hebei Region(>50%). Liaoning Province contributed significantly to the PM2.5 concentration in cities along the Bohai(>10%), and nitrate and sulfate were easy to transport over long distances. It is necessary to strengthen the coordinated control of precursors of secondary chemical components SO2, NOx, and NH3.

Climate targets by major steel companies: An assessment of collective ambition and planned emission reduction measures

This article systematically assesses the status, robustness, and potential impact of greenhouse gas emission reduction targets set by the largest steel producer companies as of mid-2022. The assessment covers the 60 largest steel companies by volume, accounting for more than 60 % of global steel production. Data on company-level greenhouse gas emission reduction targets and emission reduction measures were collected from publicly available documents.We found that only 30 companies have their own greenhouse gas emission reduction targets of varying timeframes between 2025 and 2060. Even when excluding the 15 Chinese state-owned companies that are under the national 2060 net zero target, 15 companies had no emission reduction targets. Twenty-one companies had long-term targets (2040 or after), of which 18 were net zero emission targets; all but one also had interim targets. If all climate targets identified among the 60 companies are achieved, annual CO2 emissions for the 60 companies could be reduced by up to 12 % by 2030 and up to 39 % by 2050 in comparison to a baseline scenario. Assuming a gradual increase in global crude steel demand from 1.9 Gt in 2019 to 2.5 Gt in 2050 and assuming similar trends for the rest of the global iron and steel sector as observed for the 60 companies, we estimate that the current ambition of the global iron and steel sector on emission reductions would lead to a reduction of 38 % to 53 % by 2050 from 2019 levels (3.4 GtCO2 to 1.6–2.1 GtCO2), or compared to a 32 % to 43 % reduction in a baseline scenario in 2050.Steel companies are also lagging in setting clear emission reduction plans to achieve their targets. We found that 14 out of the 30 steel producers with targets did not provide an emission reduction plan. The most popular measures amongst the 16 companies that identified at least one measure to achieve their target in their emission reduction plans were hydrogen-based DRI (n = 14), enhanced use of renewable electricity (n = 13) and Carbon Capture Utilisation and Storage (CCU/S) for blast furnaces (n = 9). While it is encouraging that the steel companies have started acting toward long-term deep decarbonisation, our findings suggest that there is a long way ahead and the action needs to be accelerated considerably.

Research on Carbon Emission Characteristics and Differentiated Carbon Reduction Pathways in the Yangtze River Delta Region Based on the STIRPAT Model

With the changes in the spatial structure of China’s economic development, urban clusters have become the primary carriers of China’s regional economy and green growth. We used annual data from 2010 to 2021 to study the carbon emission characteristics and carbon reduction pathways of 36 cities in the Yangtze River Delta region. Firstly, based on the decoupling elasticity coefficient and carbon intensity index, the researchers divided the cities in the Yangtze River Delta into six types of carbon emissions. Then, the STIRPAT model was used to regress the panel data of different carbon emission types for 11 years, analyze the driving factors of carbon emissions, and develop differentiated carbon emission reduction paths for cities with six carbon emission types. According to the results, the cities of Type I need to accelerate low-carbon technology innovation; the cities of Type II need to improve energy efficiency and strengthen low-carbon technology research and development; the cities of Type V need to suppress foreign investment in high-energy consumption and high-emission projects in the local area; the cities of Type VI need to accelerate the process of new urbanization and optimize industrial structure. However, the researchers found that the cities of Types III and IV have not yet received effective emission reduction pathways, and their emission reduction policies and measures need to be further studied.

Medium- and Long-Term Prediction of Airport Carbon Emissions under Uncertain Conditions Based on the LEAP Model

As important nodes in the air transport system, it is of great significance for airports to achieve the carbon-peaking goal before 2030 under the target of peaking carbon emissions in China’s civil aviation industry. However, it remains unknown whether airports will be able to realize this ambitious goal due to a variety of uncertain factors, such as the social economy, epidemic impact, and emission reduction measures. According to the possibilities of uncertain factors, 12 uncertain scenarios were constructed. Using the case of Guangzhou Baiyun International Airport (CAN), this study predicted medium- and long-term carbon emission trends under 12 uncertain scenarios based on the Long-range Energy Alternatives Planning System (LEAP) model. Furthermore, the effects of carbon abatement measures and emission reduction responsibilities were analyzed. The results show that CAN cannot guarantee that it will realize the goal under the established abatement policy. If socioeconomic development is rapid, carbon emissions will peak at about 90 kt tons in 2030, and if socioeconomic development is slow, it will plateau at about 1 million tons between 2030 and 2035. What is more, airlines bear the greatest responsibility for reducing emissions, and technological progress measures have the highest abatement potential. This study provides decision support for airport stakeholders in abatement work so as to ensure that airports can achieve the carbon-peaking goal.

Carbon Emission Projection and Carbon Quota Allocation in the Beijing–Tianjin–Hebei Region of China under Carbon Neutrality Vision

Supported by the coordinated development strategy, the Beijing–Tianjin–Hebei (BTH) region has achieved rapid development but also faces severe energy consumption and environmental pollution problems. As the main responsibility of emission reduction, the coordinated and orderly implementation of carbon emission reduction in Beijing, Tianjin, and Hebei is of great significance to the realization of the carbon neutrality target. Based on this, this study comprehensively uses the expanded STIRPAT model, optimized extreme learning machine (ELM) network, entropy method, and zero-sum gains DEA (ZSG-DEA) model to explore the carbon emission drivers, long-term emission reduction pathway, and carbon quota allocation in the BTH region. The results of the driving factor analysis indicate that the proportion of non-fossil energy consumption is a significant driving factor for Beijing’s carbon emissions, and the improvement of the electrification level can inhibit the carbon emissions. The total energy consumption has the greatest impact on the carbon emissions of Tianjin and Hebei. The simulation results reveal that under the constraint of the carbon neutrality target, Beijing, Tianjin, and Hebei should formulate more stringent emission reduction measures to ensure that the overall carbon emission will reach its peak in 2030. The cumulative emission reduction rate should exceed 60% in 2060, and negative carbon technology should be used to offset carbon emissions of not less than 360 million tons (Mt) per year by 2060. Furthermore, the allocation results show that Beijing will receive a greater carbon quota than Hebei. The final allocation scheme will greatly promote and encourage carbon emission reduction in Hebei Province, which is conducive to achieving the goal of carbon neutrality.

Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model.

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.

Sources and composition of elemental carbon during haze events in North China by a high time-resolved study

Despite the implementation of stringent emission reduction measures in the past year, severe winter haze events still occurred frequently in North China, with only marginal decreases observed in elemental carbon (EC) concentrations. EC not only constitutes a fraction of particle mass but also interacts with boundary layer and influences haze formation. Given the complex composition of EC, characterizing its sources and composition during haze processes is challenging yet crucial for understanding haze formation and evolution. Here, hourly-resolution PM2.5 samples were collected during three different haze event (P1-P3) in the North China Plain to investigate dynamic changes in EC source across different haze processes. The average EC concentrations and char/soot ratios were 9.94 ± 4.80 μgC·m−3, 14.5 ± 6.93 μgC·m−3, 15.9 ± 5.54 μgC·m−3, and 2.42 ± 0.98, 2.70 ± 0.88, 2.61 ± 0.95 for P1, P2 and P3, respectively. Backward trajectory analysis showed distinct variations in EC concentration and composition under the influence of different air masses during the three haze events, with local air masses influenced days exhibiting higher EC concentrations and char/soot ratios. The char/soot ratio based diagnostic method suggested that EC was predominantly influenced by coal combustion and vehicle emissions. Further positive matrix factorization analysis suggested that biomass burning and residential coal combustion were the main contributors of EC (58 %) and played a dominant role in driving variations in EC concentrations during haze episodes. Potential source contribution function analysis results highlight that local biomass burning and residential coal combustion could be major reason for the EC elevation in different haze events. Our results provide valuable insights into the sources and composition of EC during haze events, facilitating the implementation of effective measures for mitigating both EC and PM pollution.

Sustainability enablers with price-based preservation technology and carbon reduction investment in an inventory system to regulate emissions

PurposeWith the pressing need for environmental conservation, regulatory authorities are actively looking for measures to prevent global warming. In the proposed inventory model for deteriorating items, demand is dependent on the selling price and green technology investment (or carbon reduction investment) for the green product (GP), as well as an investment in price-based preservation technology to slow down the pace of deterioration. Furthermore, emission reduction measures are put in place to reduce carbon emissions (CEs).Design/methodology/approachThe current study executed a thorough literature review to determine how to improve supply chain management performance. Furthermore, assumptions are made to fill research gaps, and a mathematical model is created to address the problem mentioned above. To collect the data, the available inventory literature was reviewed. Additionally, numerical illustrations and sensitivity analyses are presented to emphasize the model's robustness.FindingsThe research indicates that it is more prudent to invest in preservation technology based on its selling price in order to control the rate of deterioration. In addition, the proposed model facilitates the management of deteriorated waste through salvage trading and emission reduction investment. The findings validate sustainable practices with a 20.86% increase in profit and a 21.4% decrease in CEs, thereby signifying environmental and economic benefits.Originality/valueThe proposed model enhances understanding of the impact of investments in price-based preservation technology and carbon reduction efforts on consumer perceptions of their intention to purchase GPs. Moreover, the study provides valuable insights by identifying important recommendations for policymakers regarding areas that require further investigation. This guideline can help identify both current and unexplored gaps, enabling researchers to direct future research efforts toward producing new products.

Climate Change Mitigation in Thailand’s Domestic Aviation: Mitigation Options Analysis towards 2050

Thailand’s civil aviation industry has expanded rapidly in the past ten years resulting in increasing aviation greenhouse gas (GHG) emissions and energy consumption. The rapid growth in air transport is anticipated to continue further. Presently, domestic aviation and the economy of many countries are recovering rapidly in the post-COVID-19 period, resulting in fuel consumption and GHG emissions gradually increasing again. However, despite implementing the ICAO’s CORSIA (International Civil Aviation Organization’s Carbon Offsetting and Reduction Scheme for International Aviation) rule for international aviation, GHG emissions in the domestic aviation sector are largely unregulated. Moreover, the literature lacks a GHG emissions analysis that considers this sector’s potential growth and mitigation policies for future GHG emissions. To close the gap, this study conducted a GHG emissions analysis from this sector under various scenarios through 2050 using historical data during 2008–2020 to forecast future trends. It evaluates the impact of the mitigation policies, such as fuel switching and aircraft technology, on improving fuel efficiency due to technological advancements in aircraft and carbon pricing. The results show that the fuel switching option would result in a significant long-term reduction in GHG emissions, whereas the carbon pricing option and aircraft technology option are desirable in reducing GHG emissions in the short term. Therefore, to meet GHG emissions reduction targets more successfully, all measures must be simultaneously executed to address short- and long-term mitigation strategies. These findings have significant implications for both present and future GHG emissions reduction measures, supporting Thailand’s 2050 climate targets and energy efficiency policies as the domestic aviation industry adjusts.

Study on the coupling coordination development of China's multidimensional digital economy and industrial carbon emission efficiency.

Exploring the coupling coordination between China's digital economy (DE) and industrial carbon emission efficiency (ICEE) is of great significance for achieving sustainable development goals. In the study, a multidimensional indicator system was established to evaluate DE, and spatiotemporal analysis and network analysis methods were used to reveal the dynamic evolution characteristics of DE and ICEE. The coupling coordination model and convergence model were adopted to explore the development trend of coupling coordination between DE and ICEE. The results show that the ICEE and DE in various provinces of China exhibit obvious spatial heterogeneity and spillover effects. Currently, the coupling coordination degree between the development of China's DE and ICEE has reached the level of primary coordination or above. The coupling coordination degree between DE and ICEE in the eastern, central, and northeastern regions has reached an intermediate level or above, with the highest degree in the eastern region. The fluctuation of China's ICEE has consistent σ-convergence and β-convergence, and the convergence effect is higher with the introduction of the DE than without it. The condition β-convergence result indicates that underdeveloped regions can narrow the gap between their ICEE and that of developed regions by utilizing their resource endowments, industrial structure, human capital, and other conditions, improving emission reduction measures and policies. This study provides a certain reference for the green and low-carbon development of industry in China and other developing countries in the digital economy era.

Willingness to pay for climate change mitigation measures in households: Bundling up renewable energy, energy efficiency, and renovation

AbstractThrough their consumption behavior, households are responsible for more than 70% of total global greenhouse gas emissions. Therefore, the GHG emission reduction potential due to the household behavior is very high. Energy consumption is the main source of the GHG emission in households. There are two main ways to reduce GHG emissions in households: use of renewable energy, energy efficiency improvement, and energy conservation due to changes in the energy use patterns. The highest energy saving potential in households is linked with building renovation, followed by the use of energy efficient appliances (including lighting). Renewable energy microgeneration technologies in households also provide opportunities for GHG emission reduction. Although there have been many policies developed to reduce GHG emissions from energy consumption in households, they still need to be more effective. This paper aims to assess willingness of Lithuanian households to reduce GHG emissions from energy consumption in households by embarking on energy renovation of buildings, use of energy efficient appliances and use of renewable energy technologies. The willingness to pay for these GHG emission reduction measures allows to compare household preferences with respect to available support measures and assess the adequacy of such measures. The paper also discusses household attitudes toward the main policies and measures for GHG emission reduction. The results show the highest willingness to pay for energy efficient appliances, followed by renewable energy technologies. The willingness to pay for energy renovation is the lowest one and such s measure requires significant state support.

Correlation between carbon emissions and water consumption in different industries in China: Spatial and temporal distribution characteristics and driving factors

In order to quantitatively analyze the correlation between carbon emissions and water consumption and identify the main driving factors. Energy consumption and water consumption data of different industries in different provinces of China were collected to calculate the water consumption per unit of carbon dioxide emissions (WCCD) and analyze the characteristics of its spatial and temporal distribution, as well as spatial correlation. The driving factors of WCCD change were quantitatively analyzed by correlation analysis and principal component analysis methods. The results showed that: (1) From 2000 to 2020, WCCD values in each province gradually decreased and approached a plateau. The overall spatial distribution showed more concentration in the south and less in the north, with the North Coastal Economic Zone showing lower values. (2) The spatial correlation of WCCD values is positively correlated, that is, WCCD values have agglomeration characteristics. The WCCD values are mainly ‘high-high aggregation’ and ‘low-low aggregation, which suggests a significant convergence trend and serious polarization. (3) Per capita GDP, per capita energy consumption, and urbanization rate showed an extremely strong correlation with WCCD, while the proportion of the tertiary industry in GDP showed a strong correlation with WCCD. Per capita water resources showed a weak correlation with WCCD. The principal component analysis revealed an increasing impact of economic development on WCCD indicators. This study provides a new perspective for carbon-water relationship research and serves as a fundamental support for water-saving and emission reduction measures.

Management and Reduction Techniques Strategies of Ammonia Emission in Agricultural Sectors in China

Agricultural ammonia (NH3) emissions (including farmland, livestock and poultry) are China’s main sources of NH3 emissions. China’s government has proposed a national strategic goal to reduce NH3 emissions. Excessive protein feeds, unreasonable manure treatments and agricultural fertilizer applications result in large emissions of NH3. Agricultural activities such as the breeding of livestock and fertilization in farmlands are the main sources of atmospheric NH3 emissions. This article discussed the progress and characteristics of typical NH3 emission inventory, calculated the nationwide NH3 emissions and analyzed the NH3 emission control strategy in the past 40 years in China. There was also an integration analysis of national documents on emission reduction technologies (including government reports) in China. The results showed that there existed single calculation methods and insufficient localization of emission factors in the estimation of domestic NH3 emissions. NH3 emission inventories varied greatly influenced by meteorology, planting structure and breeding pattern. The control strategy of NH3 emission in China has transformed from emission standards to technical guidelines to national strategic control, and it involves the coordination and cooperation of the Ministry of Agriculture and Rural Affairs departments. Current domestic NH3 emission management strategy needs scientific emission verification specification, multi-department and inter-provincial regional coordination mechanisms, and suggestions for further improvement have been put forward. It is urgent to evaluate precise NH3 emission inventories at different regional scales, followed by intensive NH3 emission controls in the key regions (such as North China). Government, agriculture, and breeding industries should vigorously promote low-protein feeds, large-scale livestock farming (including poultry), and pilot projects on closed negative pressure farming. Also, agriculture-related NH3 emission reduction measures should be fully implemented by providing technical support for NH3 emission control in domestic agricultural farms.

Insights into quantitative evaluation technology of PM2.5 transport at multi–perspective and multi–spatial and temporal scales in the north China plain

Cross-border transport is a crucial factor affecting air quality, while how to quantify the transport contribution through different technologies at multi–perspective and multi–scale have not been fully understood. This study established three quantification techniques, and conducted a systematic assessment of PM2.5 transport over the North China Plain (NCP) based on numerical simulations and vertical observations. Results suggested that the annual local emissions, inter–urban and outer–regional transport contributed 44.5%–64.6%, 15.2%–27.9% and 18.0%–28.2% of total surface PM2.5 concentrations, respectively, with transport intensity stronger in July and April, yet weaker in January and October. The southwest–northeast, northeast–southwest, and southeast–northwest were three prevailing transport directions near the surface. By comparison, the annual PM2.5 transport contribution below the atmospheric boundary layer height increased by 16.8%–24.5% in Beijing, Tianjin and Shijiazhuang, with inter–urban and outer–regional contribution of 29.8%–32.1% and 18.5%–23.1%. Furthermore, observed fluxes from fixed-point and vehicle-based mobile lidar were in good agreement with the simulated flux. PM2.5 net flux intensity varied with height, with generally larger at the middle- and high-altitude layer than that of low-altitude layer. In the early, during and late period of haze peak formation (Stage Ⅰ, Ⅱ, Ⅲ, respectively), the largest absolute flux intensity on average was Stage Ⅱ (566.7 t/d), followed by Stage Ⅲ (307.0 t/d) and Ⅰ (191.4 t/d). Besides, external transport may dominate the second concentration peak, while local emissions may play a more vital role in the first and third peaks. It has been noted that joint prevention and control measures should be proposed 1–2 days before reaching PM2.5 extremes. These findings could improve our understanding of transport influence mechanism of PM2.5 and propose effective emission reduction measures in the NCP region.

A novel fractional-order grey prediction model: a case study of Chinese carbon emissions.

Objective and accurate prediction of carbon emissions can provide a basis for the country to achieve carbon emission reduction targets and can also comprehensively and scientifically predict the peak of carbon emissions effectively, providing valuable reference information for the implementation of specific emission reduction policies and measures at each stage. In this paper, a novel fractional-order grey multivariate forecasting model is established to analyze and forecast China's carbon emissions, reflecting the principle of new information priority. The model adds fractional-order cumulative sequences to the traditional integer-order cumulative sequences, uses the Gamma function to represent the fractional-order sequences and the time-response equation, and uses the particle swarm algorithm to find the optimal order of the cumulative sequence. Finally, the modeling steps of the model are given. Then, the new model is analyzed for its effectiveness from three different perspectives using 21years of Chinese carbon emission data. The results of the first and second cases show that the newly established particle swarm optimization fractional-order model is superior to the grey multivariate comparison model. The results of the third case show that the new model is superior to the three classical grey prediction comparison models. It has stable characteristics for both simulation and prediction and also shows high accuracy, and all three cases fully illustrate the effectiveness of the new model. Finally, this new model is applied to forecast China's carbon emissions from 2022-2026, analyze the forecast results, and make relevant recommendations.