High Emission Regions Research Articles

Assessing the carbon footprint of soybean transportation in Brazil: a network equilibrium model approach.

Brazil has historically invested few resources in its transport infrastructure, leaving gaps and reducing its efficiency. The country presents a high dependence on road transport, which results in increased operational costs and higher greenhouse gas (GHG) emissions. Consequently, the performance of cargo transportation in Brazil has been deteriorating, accompanied by a rise in the consumption of fossil fuels and noteworthy levels of GHG emissions. This article assesses the carbon intensity of soybean transport operations within Brazil. Utilizing a network equilibrium model, this study estimated the soybean transportation flows that minimize the total cost of transporting this product across the origins and destinations within the grain handling system. The modeling also calculated the GHG emissions in transportation. The results show that the transportation of soybeans produced 2.74 million tonnes of CO2 equivalent annually, with road transport accounting for 81.7% of these emissions. The state of Mato Grosso, responsible for 44.08kg CO2 equivalent per tonne of soybeans transported, contributed almost 49% of the total emissions due to the extensive distances involved. In contrast, states like Paraná and Rio Grande do Sul, located closer to southern ports, exhibited the lowest emissions, with rates of 11.55kg CO2 eq/t and 12.52kg CO2 eq/t, respectively. The analysis highlights the significant potential for reducing GHG emissions by increasing the use of rail and barge transport, particularly in high-emission regions such as Mato Grosso.

Carbon and Greenhouse Gas Budgets of Europe: Trends, Interannual and Spatial Variability, and Their Drivers

AbstractIn the framework of the RECCAP2 initiative, we present the greenhouse gas (GHG) and carbon (C) budget of Europe. For the decade of the 2010s, we present a bottom‐up (BU) estimate of GHG net‐emissions of 3.9 Pg CO2‐eq. yr−1 (using a global warming potential on a 100 years horizon), which are largely dominated by fossil fuel emissions. In this decade, terrestrial ecosystems acted as a net GHG sink of 0.9 Pg CO2‐eq. yr−1, dominated by a CO2 sink that was partially counterbalanced by net emissions of CH4 and N2O. For CH4 and N2O, we find good agreement between BU and top‐down (TD) estimates from atmospheric inversions. However, our BU land CO2 sink is significantly higher than the TD estimates. We further show that decadal averages of GHG net‐emissions have declined by 1.2 Pg CO2‐eq. yr−1 since the 1990s, mainly due to a reduction in fossil fuel emissions. In addition, based on both data driven BU and TD estimates, we also find that the land CO2 sink has weakened over the past two decades. A large part of the European CO2 and C sinks is located in Northern Europe. At the same time, we find a decreasing trend in sink strength in Scandinavia, which can be attributed to an increase in forest management intensity. These are partly offset by increasing CO2 sinks in parts of Eastern Europe and Northern Spain, attributed in part to land use change. Extensive regions of high CH4 and N2O emissions are mainly attributed to agricultural activities and are found in Belgium, the Netherlands and the southern UK. We further analyzed interannual variability in the GHG budgets. The drought year of 2003 shows the highest net‐emissions of CO2 and of all GHGs combined.

Analysis of regional differences and spatial and temporal evolution of tourism carbon emissions in China: considering carbon sink effect

ABSTRACT The purpose of this study is to analyze the regional differences and spatial-temporal evolutionary characteristics of carbon dioxide emissions of tourism in China during 2001–2022 from the perspective of carbon sink effect by using an econometric modeling approach. The findings show that CO2 emissions from China's tourism industry exhibit non-equilibrium spatial characteristics or non-linear relationships. Secondly, the results of Theil's index show that the intra-regional differences in tourism CO2 emissions are greater than the inter-regional differences and exhibit a gradient of ‘West > Central > East’. Finally, under the condition of considering the existence of spatial regional heterogeneity, it is found that tourism CO2 emissions have significant spatial agglomeration, with high CO2 emission regions primarily clustered within the eastern region and the economically developed regions, and low CO2 emission regions distributed centrally within the central region and less developed areas of the tourism economy. It is worth noting that the intra-regional differences mainly come from the eastern belt, and that there are large regional differences in tourism CO2 emissions within the eastern region, while the western region is the next largest and the central region the smallest.

Household size and transport carbon emissions in China: Direct, heterogeneity and mediating effects

Shrinking household sizes presents a significant sustainable challenge by reducing the sharing of means of transportation and increasing individual resource consumption and carbon emissions. Research from the historical literature reveals that larger households generally exhibit lower per capita energy consumption and carbon emissions. However, it remains uncertain how widely these trends extend and their implications for carbon emissions within the expanding transportation industry. This paper employs inter-provincial data from China spanning 2003–2021 to investigate the effects, regional heterogeneity, and mechanisms by which household size influences carbon emissions from the transport sector. The findings show that the expansion of household size in China significantly reduces carbon emissions from transport by 0.2805 %. Households with 2 to 4 members are more effective in achieving transport carbon emission reductions, with an average reduction level of 0.1853 %. Moreover, in terms of geographic factors, reducing transport carbon emissions is more effective in low-density areas than in high-density areas. At the income and carbon emissions level, household size significantly reduces transport carbon emissions in high-income and low-emission regions, and to a lesser extent in low-income and high-emission regions. Additionally, the study revealed that transport consumption expenditure and energy consumption indirectly strengthen the effect of household size on reducing transport carbon emissions. Future sustainable development strategies should focus on regulating household size and promoting moderate household size to decrease personal resource consumption and transportation carbon emissions, and to achieve the objective of sustainable development.

Increase in precipitation scavenging contributes to long-term reductions of light-absorbing aerosol in the Arctic

Abstract. We investigated long-term changes using a harmonised 22-year data set of aerosol light absorption measurements, in conjunction with air mass history and aerosol source analysis. The measurements were performed at Zeppelin Observatory, Svalbard, from 2002 to 2023. We report a statistically significant decreasing long-term trend for the light absorption coefficient. However, the last 8 years of 2016–2023 showed a slight increase in the magnitude of the light absorption coefficient for the Arctic haze season. In addition, we observed an increasing trend in the single-scattering albedo from 2002 to 2023. Five distinct source regions, representing different transport pathways, were identified. The trends involving air masses from the five regions showed decreasing absorption coefficients, except for the air masses from Eurasia. We show that the changes in the occurrences of each transport pathway cannot explain the reductions in the absorption coefficient observed at the Zeppelin station. An increase in contributions of air masses from more marine regions, with lower absorption coefficients, is compensated for by an influence from high-emission regions. The proportion of air masses en route to Zeppelin, which have been influenced by active fires, has undergone a noticeable increase starting in 2015. However, this increase has not impacted the long-term trends in the concentration of light-absorbing aerosol. Along with aerosol optical properties, we also show an increasing trend in accumulated surface precipitation experienced by air masses en route to the Zeppelin Observatory. We argue that the increase in precipitation, as experienced by air masses arriving at the station, can explain a quarter of the long-term reduction in the light absorption coefficient. We emphasise that meteorological conditions en route to the Zeppelin Observatory are critical for understanding the observed trends.

Establishment and verification of anthropogenic speciated VOCs emission inventory of Central China

Improving the accuracy of anthropogenic volatile organic compounds (VOCs) emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution. In this study, an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km × 3 km spatial resolution based on the emission factor method. The 2019 VOCs emission in Henan Province was 1003.5 Gg, while industrial process source (33.7%) was the highest emission source, Zhengzhou (17.9%) was the city with highest emission and April and August were the months with the more emissions. High VOCs emission regions were concentrated in downtown areas and industrial parks. Alkanes and aromatic hydrocarbons were the main VOCs contribution groups. The species composition, source contribution and spatial distribution were verified and evaluated through tracer ratio method (TR), Positive Matrix Factorization Model (PMF) and remote sensing inversion (RSI). Results show that both the emission results by emission inventory (EI) (15.7 Gg) and by TR method (13.6 Gg) and source contribution by EI and PMF are familiar. The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R-value of 0.73. The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable.

Assessing climate change vulnerability in Canadian marine conservation networks: implications for conservation planning and resilience

Marine protected areas (MPAs) are critical in safeguarding biodiversity and ecosystem functions under climate change. The long-term effectiveness of these static conservation measures will depend on how well they represent current and future ocean changes. Here, we use the Climate Risk Index for Biodiversity to assess the vulnerability representation of marine ecosystems within the Canadian marine conservation network (CMCN) under two divergent emissions scenarios. We found that MPAs best represent climate vulnerability in Atlantic Canada (85% representativity overall, and 93% in the Gulf of Saint Lawrence under low emissions), followed by the Pacific (78%) and Arctic (63%; lowest in the Eastern Arctic (41% under high emissions) regions). Notably, MPAs with lower climate vulnerability are proportionally overrepresented in the CMCN. Broad-scale geographic targets employed in the Scotian Shelf-Bay of Fundy network planning process achieve over 90% representativity of climate vulnerabilities, underscoring the importance of ensuring habitat representativity and geographic distribution in conservation planning to enhance climate resilience, even if not explicitly prioritized. Moving towards Canada’s target to protect 30% of its waters by 2030, prioritizing representativity and designation of MPAs in currently underrepresented climate-vulnerable regions may be crucial to enhancing the resilience of the CMCN amidst an ever-changing climate.

Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at high-elevation sites in North America and western Europe

Abstract. With a few exceptions, most studies on tropospheric ozone (O3) variability during and following the COrona VIrus Disease (COVID-19) economic downturn focused on high-emission regions or urban environments. In this work, we investigated the impact of the societal restriction measures during the COVID-19 pandemic on surface O3 at several high-elevation sites across North America and western Europe. Monthly O3 anomalies were calculated for 2020 and 2021, with respect to the baseline period 2000–2019, to explore the impact of the economic downturn initiated in 2020 and its recovery in 2021. In total, 41 high-elevation sites were analyzed: 5 rural or mountaintop stations in western Europe, 19 rural sites in the western US, 4 sites in the western US downwind of highly polluted source regions, and 4 rural sites in the eastern US, plus 9 mountaintop or high-elevation sites outside Europe and the United States to provide a “global” reference. In 2020, the European high-elevation sites showed persistent negative surface O3 anomalies during spring (March–May, i.e., MAM) and summer (June–August, i.e., JJA), except for April. The pattern was similar in 2021, except for June. The rural sites in the western US showed similar behavior, with negative anomalies in MAM and JJA 2020 (except for August) and MAM 2021. The JJA 2021 seasonal mean was influenced by strong positive anomalies in July due to large and widespread wildfires across the western US. The polluted sites in the western US showed negative O3 anomalies during MAM 2020 and a slight recovery in 2021, resulting in a positive mean anomaly for MAM 2021 and a pronounced month-to-month variability in JJA 2021 anomalies. The eastern US sites were also characterized by below-mean O3 for both MAM and JJA 2020, while in 2021 the negative values exhibited an opposite structure compared to the western US sites, which were influenced by wildfires. Concerning the rest of the world, a global picture could not be drawn, as the sites, spanning a range of different environments, did not show consistent anomalies, with a few sites not experiencing any notable variation. Moreover, we also compared our surface anomalies to the variability of mid-tropospheric O3 detected by the IASI (Infrared Atmospheric Sounding Interferometer) satellite instrument. Negative anomalies were observed by IASI, consistent with published satellite and modeling studies, suggesting that the anomalies can be largely attributed to the reduction of O3 precursor emissions in 2020.

Benefits of net-zero policies for future ozone pollution in China

Abstract. Net-zero emission policies principally target climate change but may have a profound influence on surface ozone pollution. To investigate this, we use a chemistry–climate model to simulate surface ozone changes in China under a net-zero pathway and examine the different drivers that govern these changes. We find large monthly mean surface ozone decreases of up to 16 ppb in summer and small ozone decreases of 1 ppb in winter. Local emissions are shown to have the largest influence on future ozone changes, outweighing the effects of changes in emissions outside China, changes in global methane concentrations, and a warmer climate. Impacts of local and external emissions show strong seasonality, with the largest contributions to surface ozone in summer, while changes in global methane concentrations have a more uniform effect throughout the year. We find that while a warmer climate has a minor impact on ozone change compared to the net-zero scenario, it will alter the spatial patterns of ozone in China, leading to ozone increases in the south and ozone decreases in the north. We also apply a deep learning model to correct biases in our ozone simulations and to provide a more robust assessment of ozone changes. We find that emission controls may lead to a surface ozone decrease of 5 ppb in summer. The number of days with high-ozone episodes with daily mean ozone greater than 50 ppb will be reduced by 65 % on average. This is smaller than that simulated with the chemistry–climate model, reflecting overestimated ozone formation under present-day conditions. Nevertheless, this assessment clearly shows that the strict emission policies needed to reach net zero will have a major benefit in reducing surface ozone pollution and the occurrence of high-ozone episodes, particularly in high-emission regions in China.

Carbon footprint of residents' online consumption in China

Although China boasts the largest online consumption market, there has been no prior case study addressing the carbon footprint of online consumption. The rapid expansion of online consumption has posed a challenge to China's carbon neutrality pledge. This paper aims to fill this crucial gap by conducting a pioneering study based on survey data collected from 6000 households. To achieve this goal, this study has combined provincial Multi-Regional Input-Output (MRIO) tables with residents' online consumption calculated by the Non-Linear Optimization model, and applied the Environmental Input-Output Life Cycle Assessment (EIO-LCA) model to calculate the carbon footprint of residents' online consumption. Result indicated that the average carbon footprint of residents' online consumption was 0.46 tCO2e in 2016, accounting for 12.30% of the total consumption carbon footprint. Notably, high-emission regions include Beijing-Tianjin, Jiangsu-Zhejiang-Shanghai, and Hubei. Moreover, the dominant sources of the online consumption carbon footprint were electricity, food, clothing, and beauty products. Furthermore, we found that the carbon footprint of online consumption tends to increase with income, with differential income elasticities observed for various products and income groups. We also quantified the average expenditure-based life-cycle carbon coefficient for parcel delivery (0.22 kgCO2e/yuan) and conducted two scenario analyses to assess its impact. From a policy perspective, our research on measuring the carbon footprint of residents' online consumption can provide valuable insights for designing future policies and decisions related to e-commerce and key sectors along the supply chains. Additionally, it will further encourage Chinese residents to adopt low-carbon lifestyles.

Updating emission inventories for vehicular organic gases: Indications from cold-start and temperature effects on advanced technology cars

How would the organic gas emission inventories of future urban vehicles change with new features of advanced technology cars? Here, volatile organic compounds (VOCs) and intermediate volatile organic compounds (IVOCs) from a fleet of Chinese light-duty gasoline vehicles (LDGVs) were characterized by chassis dynamometer experiments to grasp the key factors affecting future inventory accuracy. Subsequently, the VOC and IVOC emissions of LDGVs in Beijing, China, from 2020 to 2035, were calculated and the spatial-temporal variations were recognized under a scenario of fleet renewal. With the tightening of emission standards (ESs), cold start contributed a larger fraction of the total unified cycle VOC emissions due to the imbalanced emission reductions between operating conditions. It took 757.47 ± 337.75 km of hot running to equal one cold-start VOC emission for the latest certified vehicles. Therefore, the future tailpipe VOC emissions would be highly dependent on discrete cold start events rather than traffic flows. By contrast, the equivalent distance was shorter and more stable for IVOCs, with an average of 8.69 ± 4.59 km across the ESs, suggesting insufficient controls. Furthermore, there were log-linear relationships between temperatures and cold-start emissions, and the gasoline direct-injection vehicles performed better adaptability under low temperatures. In the updated emission inventories, the VOC emissions were more effectively reduced than the IVOC emissions. The start emissions of VOCs were estimated to be increasingly dominant, especially in wintertime. By winter 2035, the contribution of VOC start emissions could reach 98.98 % in Beijing, while the fraction of IVOC start emissions would decrease to 59.23 %. Spatially allocation showed that the high emission regions of tailpipe organic gases from LDGVs have transferred from road networks to regions of intense human activities. Our results provide new insights into tailpipe organic gas emissions of gasoline vehicles, and can support future emission inventories and refined assessment of air quality and human health risk.

Evolution of Pollution Levels from COVID-19 Lockdown to Post-Lockdown over India.

The spread of the COVID-19 pandemic forced the administration to lock down in many countries globally to stop the spread. As the lockdown phase had only the emergency use of transportation and most of the industries were shut down, there was an apparent reduction in pollution. With the end of the lockdown period, pollution is returning to its regular emission in most places. Though the background was abnormally low in emissions (during the lockdown phase) and the reduced pollution changed the radiation balance in the northern hemispheric summer period, a modified pollution pattern is possible during the unlock phases of 2020. The present study analysed the unlock 1 and 2 stages (June-July) of the COVID-19 lockdown over India. The rainfall, surface temperature and cloud cover anomalies of 2020 for understanding the differences in pollutants variation were also analysed. The unlock phases show remarkable differences in trends and mean variations of pollutants over the Indian region compared to climatological variations. The results indicated changing high-emission regions over India to climatological variations and identified an AOD dipole with future emissions over India.

Correcting ozone biases in a global chemistry–climate model: implications for future ozone

Abstract. Weaknesses in process representation in chemistry–climate models lead to biases in simulating surface ozone and to uncertainty in projections of future ozone change. We here develop a deep learning model to demonstrate the feasibility of ozone bias correction in a global chemistry–climate model. We apply this approach to identify the key factors causing ozone biases and to correct projections of future surface ozone. Temperature and the related geographic variables latitude and month show the strongest relationship with ozone biases. This indicates that ozone biases are sensitive to temperature and suggests weaknesses in representation of temperature-sensitive physical or chemical processes. Photolysis rates are also an important factor, highlighting the sensitivity of biases to simulated cloud cover and insolation. Atmospheric chemical species such as the hydroxyl radical, nitric acid and peroxyacyl nitrate show strong positive relationships with ozone biases on a regional scale. These relationships reveal the conditions under which ozone biases occur, although they reflect association rather than direct causation. We correct model projections of future ozone under different climate and emission scenarios following the shared socio-economic pathways. We find that changes in seasonal ozone mixing ratios from the present day to the future are generally smaller than those simulated without bias correction, especially in high-emission regions. This suggests that the ozone sensitivity to changing emissions and climate may be overestimated with chemistry–climate models. Given the uncertainty in simulating future ozone, we show that deep learning approaches can provide improved assessment of the impacts of climate and emission changes on future air quality, along with valuable information to guide future model development.

The temporal and spatial pattern evolution of land-use carbon emissions in China coastal regions and its response to green economic development

Carbon emissions based on land use change have attracted extensive attention from scholars, but the current land use carbon emission accounting model is still relatively rough. Despite the continuous promotion of China’s ecological civilization strategy, whether green economic development promotes carbon emission reduction remains to be studied. This study uses the Exploratory Spatial-temporal Data Analysis (ESTDA) framework system to revise the land-use carbon emission accounting model; it integrates the NDVI adjustment index and systematically analyzes the spatial and temporal patterns and evolutionary path characteristics of carbon emissions from 2000 to 2020 for 130 prefecture-level cities in the eastern coastal region of China, a high carbon emission region. The spatial econometric model is further used to explore the impact of green economy development on carbon emissions. The results show that the spatial distribution of carbon sources and sinks in the eastern coastal cities demonstrates a year-on-year increase during the study period. The spatial distribution of carbon sources is higher in the north than in the south, and the economically developed regions are more elevated than less developed economic areas. Net carbon emissions show prominent spatial clustering characteristics. The south has a more stable internal spatial structure than the north, and the inland has a more stable internal spatial structure than the coast. Green economic development can significantly reduce carbon emission intensity and has a significant spatial spillover effect. The findings imply that policy-makers need to consider the spatial and temporal distribution and spatial correlation of carbon emissions among cities; they can achieve carbon emission reduction by formulating a more reasonable green economy development approach and implementing regional linkages.

Exploring the impact of green credit policies on corporate financing costs based on the data of Chinese A-share listed companies from 2008 to 2019

Green credit policy (GCP) is an important measure to realize green transformation of economy and society. Taking the panel data of 1359 Chinese A-share listed companies from 2008 to 2019 as samples, this paper analyzes the effect and mechanism of green credit policies on corporate financing costs by using difference in difference (DID) method. After the implementation of GCP, the financing cost of enterprises with high energy consumption and high pollution increases compared with those with energy conservation and environmental protection. In addition, GCP not only affects the financing cost of corporate debt by changing the financing scale and commercial credit, but also affects the financing cost of corporate equity by changing financial status and information disclosure degree. However, compared with enterprises in medium and low carbon emission regions, GCP has a more significant impact on the financing cost of enterprises in high carbon emission regions.

Significant contribution of secondary particulate matter to recurrent air pollution: Evidence from in situ observation in the most polluted city of Fen-Wei Plain of China

Particulate matter (PM) pollution in high emission regions will affect air quality, human health and climate change on both local and regional scales, and thus attract worldwide attention. In this study, a comprehensive study on PM2.5 and its chemical composition were performed in Yuncheng (the most polluted city of Fen-Wei Plain of China) from November 28, 2020 to January 24, 2021. The average concentration of PM2.5 was 87.8 ± 52.0 μg/m3, which were apparently lower than those observed during the same periods of past five years, attributable to the clean air action plan implemented in this region. NO3− and organic carbon (OC) were the dominant particulate components, which on average contributed 22.6% and 16.5% to PM2.5, respectively. The fractions of NO3−, NH4+, OC and trace metals increased while those of crustal materials and elemental carbon decreased with the degradation of PM2.5 pollution. Six types of PM2.5 sources were identified by the PMF model, including secondary inorganic aerosol (35.3%), coal combustion (28.7%), vehicular emission (20.7%), electroplating industry (8.6%), smelt industry (3.9%) and dust (2.8%). Locations of each identified source were pinpointed based on conditional probability function, potential source contribution function and concentration weighted trajectory, which showed that the geographical distribution of the sources of PM2.5 roughly agreed with the areas of high emission. Overall, this study provides valuable information on atmospheric pollution and deems beneficial for policymakers to take informed action to sustainably improve air quality in highly polluted region.

Deep Learning to Evaluate US NOx Emissions Using Surface Ozone Predictions

AbstractEmissions of nitrogen oxides (NOx = NO + NO2) in the United States have declined significantly during the past three decades. However, satellite observations since 2009 indicate total column NO2 is no longer declining even as bottom‐up inventories suggest continued decline in emissions. Multiple explanations have been proposed for this discrepancy including (a) the increasing relative importance of nonurban NOx to total column NO2, (b) differences between background and urban NOx lifetimes, and (c) that the actual NOx emissions are declining more slowly after 2009. Here, we use a deep learning model trained by NOx emissions and surface observations of ozone to assess consistency between the reported NOx trends between 2005 and 2014 and observations of surface ozone. We find that the satellite‐derived trends best reproduce ozone in low NOx emission (background) regions. The 2010–2014 trend from older satellite‐derived emission estimates produced at low spatial resolution results in the largest bias in surface ozone in regions with high NOx emissions, reflecting the blending of urban and background NOx in these low‐resolution top‐down analyses. In contrast, the trend from higher resolution satellite‐based estimates, which are more capable of capturing the urban emission signature, is in better agreement with ozone in high NOx emission regions, and is consistent with the trend based on surface observations of NO2. Our results confirm that the satellite‐derived trends reflect anthropogenic and background influences.

Tropospheric ozone changes and ozone sensitivity from the present day to the future under shared socio-economic pathways

Abstract. Tropospheric ozone is important to future air quality and climate. We investigate ozone changes and ozone sensitivity to changing emissions in the context of climate change from the present day (2004–2014) to the future (2045–2055) under a range of shared socio-economic pathways (SSPs). We apply the United Kingdom Earth System Model, UKESM1, with an extended chemistry scheme including more reactive volatile organic compounds (VOCs) to quantify ozone burdens as well as ozone sensitivities globally and regionally based on nitrogen oxide (NOx) and VOC mixing ratios. We show that the tropospheric ozone burden increases by 4 % under a development pathway with higher NOx and VOC emissions (SSP3-7.0) but decreases by 7 % under the same pathway if NOx and VOC emissions are reduced (SSP3-7.0-lowNTCF) and by 5 % if atmospheric methane (CH4) mixing ratios are reduced (SSP3-7.0-lowCH4). Global mean surface ozone mixing ratios are reduced by 3–5 ppb under SSP3-7.0-lowNTCF and by 2–3 ppb under SSP3-7.0-lowCH4. However, surface ozone changes vary substantially by season in high-emission regions under future pathways, with decreased ozone mixing ratios in summer and increased ozone mixing ratios in winter when NOx emissions are reduced. VOC-limited areas are more extensive in winter (7 %) than in summer (3 %) across the globe. North America, Europe, and East Asia are the dominant VOC-limited regions in the present day, but North America and Europe become more NOx-limited in the future mainly due to reductions in NOx emissions. The impacts of VOC emissions on ozone sensitivity are limited in North America and Europe because reduced anthropogenic VOC emissions are partly offset by higher biogenic VOC emissions. Ozone sensitivity is not greatly influenced by changing CH4 mixing ratios. South Asia becomes the dominant VOC-limited region under future pathways. We highlight that reductions in NOx emissions are required to transform ozone production from VOC to NOx limitation, but that these lead to increased ozone mixing ratios in high-emission regions, and hence emission controls on VOC and CH4 are also necessary.

Towards carbon neutrality: The role of different paths of technological progress in mitigating China's CO2 emissions

China's carbon neutrality in 2060 is a hot issue discussed by policymakers and scholars. Previous literature shows that technological progress is the key to CO2 emission mitigation, but ignores the role of different paths of technological progress. This paper uses the panel data covering China's 30 provinces from 2008 to 2017 to study the effect of different paths of technological progress on CO2 emissions including domestic innovation, foreign technology import and regional technology transfer. Furthermore, the assimilation effect of technologies from domestic and foreign is investigated, and its impact on CO2 emissions is explored. The empirical evidence indicates that: (1) there is an inverted U-shaped relationship between domestic innovation and China's CO2 emissions. (2) Foreign technology imports cannot decrease CO2 emissions and carbon intensity in high-emission regions. Meanwhile, domestic technology transfer increase CO2 emissions and carbon intensity in high-emission regions. However, foreign technology import of low-emission areas can achieve CO2 emission mitigation. (3) The assimilation of technologies from abroad and domestic can increase CO2 emissions and carbon intensity in high-emission regions. But the absorption effect of technologies from domestic can decrease CO2 emissions and carbon intensity in low-carbon regions. According to the above results, this paper proposes some targeted policy suggestions for the choice of technological progress paths for CO2 emission mitigation in different regions of China.

The nature of non-Gaussianity and statistical isotropy of the 408 MHz Haslam synchrotron map

Accurate component separation of full-sky maps in the radio and microwave frequencies, such as the cosmic microwave background (CMB), relies on a thorough understanding of the statistical properties of the Galactic foreground emissions. Using scalar Minkowski functionals and their tensorial generalization known as Minkowski tensors, we analyze the statistical properties of one of the major foreground components, namely the Galactic synchrotron given by the full sky 408 MHz Haslam map. We focus on understanding the nature of non-Gaussianity and statistical isotropy of the cooler regions of the map as a function of angular scale. We find that the overall level of the non-Gaussian deviations does decrease as more high emission regions are masked and as we go down to smaller scales, in agreement with the results obtained in earlier works. However, they remain significantly high, of order 3.3σ, at the smallest angular scales relevant for the Haslam map. We carry out a detailed examination of the non-Gaussian nature using the generalized skewness and kurtosis cumulants that arise in the perturbative expansion of Minkowski functionals for weakly non-Gaussian fields. We find that the leading sources of non-Gaussianity are the kurtosis terms which are considerably larger than the skewness terms at all angular scales. Further, for the cooler regions of the Haslam map, we find that the non-Gaussian deviations of the Minkowski functionals can be well explained by the perturbative expansion up to second-order (up to kurtosis terms), with first-order terms being sub-dominant. Lastly, we test the statistical isotropy of the Haslam map and find that it becomes increasingly more isotropic at smaller scales.