Emission Patterns Research Articles

Characterization of on-road nitrogen oxides and black carbon emissions from high emitters of heavy-duty diesel vehicles in China

Heavy-duty diesel vehicles (HDDVs) significantly contribute to atmospheric nitrogen oxides (NOX) and black carbon (BC), with high emitters within the HDDV fleet impacting the total emissions. However, emission patterns and contributions of high emitters are rarely explored from a fleet-perspective. We investigated NOX and BC emission factors (EFs) from 1925 HDDVs in Shenzhen by the plume-chasing method, and found that the fleet-average EFs decreased with stricter emission standards. Unexpectedly, the average NOX EF for the China IV fleet was comparable with that for the China III fleet due to possible ineffective aftertreatment in high-emitter sectors of China IV HDDVs. Decreasing trend in average NOX EF since 2017 reflected the effective emission controls by the implementation of China V standard. Besides, semi-trailer tractors exhibited a higher incidence of NOX over-emissions, whereas BC high emitters were more pronounced in box trucks. Total NOX and BC emissions from HDDVs in Shenzhen were revisited, reaching 54.0 and 1.1 Gg·yr−1, with updated NOX EF correcting a 26.2 % underestimation in national guidelines. Notably, eliminating high emitters yields greater emission reduction benefits than merely retiring old HDDVs, with BC reduction outpacing NOX. This study provides new insights into the implementation of targeted emission reduction measures for HDDVs.

Exploring the dynamics and trends of carbon emission spatiotemporal patterns in the Chengdu–Chongqing Economic Zone, China, from 2000 to 2020

Analysis of the spatial–temporal pattern and trend of carbon emissions provides an important scientific basis for the development of a low-carbon economy. Based on the corrected NPP-VIIRS and DMSP/OLS nighttime light data, a carbon emission model for the Chengdu–Chongqing Economic Zone (CCEZ) in China is constructed. Furthermore, the article establishes an integrated qualitative and quantitative research system. The qualitative results show that at the city and county scales, the high carbon emission areas and counties are mainly distributed in Chengdu and Chongqing, while the low carbon emission areas are concentrated in the marginal cities of the CCEZ and the counties with low levels of industrialization around the Sichuan Basin. The high-carbon emission zone tended to expand to the north, and the low-carbon emission zone tended to expand to the south. At the grid scale, the carbon emissions of the CCEZ fluctuated and increased from 2000 to 2020, forming a trend connected with those of the central city, with high carbon emissions at the core and radiating outward expansion. Quantitative analysis revealed that carbon emissions at the county and grid scales exhibited a significant positive global spatial correlation, and the overall correlation degree exhibited an increasing trend.

Purely Optical, Reversible, Read‐Write‐Erase Cycling Using Photoswitchable Beads in Micropatterned Arrays

AbstractUsing surface‐templated electrophoretic deposition, arrays of polymer beads (photonic units) incorporating photo‐switchable DAE molecules are created, which can be reversibly and individually switched between high and low emission states by direct photo‐excitation, without any energy or electron transfer processes within the molecular system. The micropatterned array of these photonic units is spectroscopically characterized in detail and optimized with respect to both signal contrast and cross‐talk. The optimum optical parameters including laser intensity, wavelength and duration of irradiation are elucidated and ideal conditions for creating reversible on/off cycles in a micropatterned array are determined. 500 such cycles are demonstrated with no obvious on/off contrast attenuation. The ability to process binary information is demonstrated by selectively writing information to the given photonic unit, reading the resultant emissive signal pattern and finally erasing the information again, which in turn demonstrates the possibility of continuous recording. This basic study paves the way for building complex circuits using spatially well‐arranged photonic units.

Ultraviolet-B Resonant-Cavity Light-Emitting Diodes with Tunnel Junctions and Dielectric Mirrors.

We demonstrate the first electrically injected AlGaN-based ultraviolet-B resonant-cavity light-emitting diode (RCLED). The devices feature dielectric SiO2/HfO2 distributed Bragg reflectors enabled by tunnel junctions (TJs) for lateral current spreading. A highly doped n++-AlGaN/n++-GaN/p++-AlGaN TJ and a top n-AlGaN current spreading layer are used as transparent contacts, resulting in a good current spreading up to an active region mesa diameter of 120 μm. To access the N-face side of the device, the substrate is removed by electrochemically etching a sacrificial n-AlGaN layer, leading to a smooth underetched surface without evident parasitic etching in the n- and n++-doped layers of the device. The RCLEDs show a narrow emission spectrum with a full width at half-maximum (FWHM) of 4.3 nm compared to 9.4 nm for an ordinary LED and a more directional emission pattern with an angular FWHM of 52° for the resonance at 310 nm in comparison to ∼126° for an LED. Additionally, the RCLEDs show a much more stable emission spectrum with temperature with a red-shift of the electroluminescence peak of about ∼18 pm/K and a negligible change of the FWHM compared to LEDs, which shift ∼30 pm/K and show spectrum broadening with temperature. The demonstration of those devices, where a highly reflective mirror is spatially separated from an ohmic metal contact, opens up a new design space to potentially increase the poor light extraction efficiency in UV LEDs and is an important step toward electrically injected UV vertical-cavity surface-emitting lasers.

Characterizing formaldehyde emissions from forklifts in China based on real-world online measurements

Formaldehyde (HCHO) is an important precursor for secondary pollution in the atmosphere and is carcinogenic. Previous studies have underscored the substantial contribution of non-road construction equipment (NRCE) to HCHO emissions, yet the understanding of HCHO emissions from NRCE remains limited. In this study, we conducted real-world measurements on six China III forklifts, which are an important type of NRCE in China, to characterize their second-by-second HCHO emission patterns. We quantified the fuel-based emission factors (EFs) of HCHO under various conditions including operating conditions, engine power, and the presence of an after-treatment device. Results showed that the cumulative weighted HCHO EF for the low-power forklifts was 1.37 ± 0.74 g/kg fuel, which was 3 times greater than that for high-power forklifts. Higher HCHO EFs were found under idling conditions than under moving and working conditions. Notably, the installation of a diesel particulate filter (DPF) potentially resulted in an increase in HCHO emissions. Our research indicates that the HCHO emissions from forklifts were considerably high and may have been previously underestimated by offline measurements, which suggests the need for further investigation of HCHO emissions from forklifts utilizing advanced online techniques.

Modeling quantum optical phenomena using transition currents

Spontaneous light emission is central to a vast range of physical systems and is a founding pillar for the theory of light–matter interactions. In the presence of complex photonic media, the description of spontaneous light emission usually requires advanced theoretical quantum optics tools such as macroscopic quantum electrodynamics, involving quantized electromagnetic fields. Although rigorous and comprehensive, the complexity of such models can obscure the intuitive understanding of many quantum-optical phenomena. Here, we review a method for calculating spontaneous emission and other quantum-optical processes without making explicit use of quantized electromagnetic fields. Instead, we introduce the concept of transition currents, comprising charges in matter that undergo transitions between initial and final quantum states. We show how predictions that usually demand advanced methods in quantum electrodynamics or quantum optics can be reproduced by feeding these transition currents as sources to the classical Maxwell equations. One then obtains the relevant quantum observables from the resulting classical field amplitudes, without washing out quantum optical effects. We show that this procedure allows for a straightforward description of quantum phenomena, even when going beyond the dipole approximation and single emitters. As illustrative examples, we calculate emission patterns and Purcell-enhanced emission rates in both bound-electron and free-electron systems. For the latter, we derive cathodoluminescence emission and energy-loss probabilities of free electrons interacting with nanostructured samples. In addition, we calculate quantum-beat phenomena in bound-electron systems and wave function-dependent optical coherence in free-electron systems. Remarkably, the transition-current formalism captures more complex phenomena, such as many-body interference effects and super-radiance of both bound- and free-electron systems, second-order processes such as two-photon emission, and quantum recoil corrections to free-electron radiation. We review a variety of light–matter interactions in fields ranging from electron microscopy to nanophotonics and quantum optics, for which the transition-current theoretical formalism facilitates practical simulations and a deeper understanding of novel applications.

Application of the STIRPAT Model in unravelling Carbon Dioxide (CO2) emission patterns in the India and Global scales

This study explores the significance of Kaya’s Identity in understanding and addressing CO2-emissions (CO2-emi) in both India and globally, utilising FAOSTAT data from 1991 to 2021. Kaya's Identity breaks down CO2-emi into population, GDP per capita (GDP-PC), emissions intensity (EI), and CO2-Emissions Intensity (CO2-EI). The STIRPAT model was used to analyse these factors, with Ridge regression applied to address multicollinearity. The findings highlight that population growth is a major driver of emissions, with increases of 4.14% in India and 21.36% globally. India's GDP-PC growth of 7.69%, compared to 3.67% globally, also significantly contributes to emissions. Despite improvements in energy efficiency and transitions to renewable energy, CO2-emi rose by 6.46% in India and 2.29% globally. The study identifies positive associations between population growth and GDP-PC with CO2-emi, while EI and CO2-EI show negative associations. Forecasts suggest that in India, sustained GDP-PC growth initially curbed CO2-emi, but post-2080, rising population and energy demands accelerated emissions. Globally, consistent GDP-PC growth initially slowed emissions, but after 2000, population growth and increased energy consumption led to a significant surge, driven by slower economic expansion and higher fossil fuel use. The results also indicate a long-term cointegration relationship between CO2-emi and the selected variables at both the all-India and global levels. The significantly negative coefficient for CO2-emi lagged by one period (CO2emi(-1)) suggests a strong long-run adjustment mechanism both at the all-India and global levels. This study underscores the need for integrated strategies addressing population growth, GDP-PC, energy efficiency, and clean energy adoption to combat climate change sustainably. Policymakers should focus on emerging technologies such as carbon capture, understanding consumer behaviour's impact on emissions, analysing regional disparities, and developing long-term emissions scenarios. JEL classification: Q54, Q56, Q58.

Retraction Note: How do environmental regulations affect carbon emission and energy efficiency patterns? A provincial-level analysis of Chinese energy-intensive industries.

Retraction Note: How do environmental regulations affect carbon emission and energy efficiency patterns? A provincial-level analysis of Chinese energy-intensive industries.

Assessing carbon budgets and reduction pathways in different income levels with neural network forecasting

Understanding carbon emissions across income levels is vital to limiting global warming to below 1.5 °C and 2 °C, as the Paris Agreement outlines. Unequal emission patterns can affect global remaining carbon budgets, necessitating differentiated strategies. This paper introduces a unique perspective to forecasting carbon emissions and determining the remaining carbon budget relative to income levels and associated socioeconomic factors. Leveraging Pearson correlation analysis and Bayesian-optimised Artificial Neural Network methods, the study examines emissions and carbon budgets of thirty countries, representing 82 % of global carbon emissions in 2022, under a business-as-usual scenario across various income levels. Findings reveal distinct patterns, with high-income countries experiencing declining emissions while middle- and low-income countries anticipate increases. High-income countries are projected to deplete their 2.0 °C budgets later than middle- and low-income countries, with an estimated depletion around 2052 and 2046–2048, respectively. Proposed mitigation and adaptation strategies tailored to income levels include investment in advanced technologies for high-income countries, promoting energy efficiency and renewable energy transition for middle-income countries, and expanding clean energy access for low-income countries. This study advocates the importance of integrating income levels and socioeconomic factors into climate policymaking, offering insights for policymakers, environmentalists, and businesses to tailor interventions and promote equitable climate action.

Prediction of changes in war-induced population and CO2 emissions in Ukraine using social media

Monitoring the changes in population and anthropogenic CO2 emissions caused by geopolitical conflicts is significant for humanitarian assistance and also for revealing the CO2 emission patterns of human activities. However, the changes in population and anthropogenic CO2 emissions are highly dynamic, and representative survey data are generally unavailable. We monitor the near-real-time and fine-grained changes in war-induced population and anthropogenic CO2 emissions in Ukraine through social media data. One year after the invasion, over 11 million Ukrainians are displaced from the baseline 0.1∘*0.1∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${0.1}^{\\circ}\\,*\\,{0.1}^{\\circ}$$\\end{document} gridded regions. There is a significant correlation between the estimated changes and the reference changes for each month in all CO2 emission sectors, with R2 respectively ranging from 0.57 to 0.93, 0.41 to 0.9, 0.74 to 0.99 for residential consumption, ground transport, and industry sectors. Overall, the proposed method provides a new perspective to monitor Ukrainian refugee crisis and measure the spatio-temporal response of anthropogenic CO2 emissions.

Temporal, Developmental, and Comparative Characterization of the Floral Volatile Emissions of the Famously Scented Violet Species, Viola odorata

Violets (Viola) are potential candidates for aroma-focused breeding research. Though most Viola species and modern hybrids lack fragrance, the genus contains a famously scented species, Viola odorata L. This species and its cultivars are genetic resources of aroma traits that could be used to investigate the selection for and transmission of fragrance during the breeding process. Despite its famous scent, however, the floral volatile emissions of V. odorata have not been characterized using modern headspace techniques. Using static and dynamic headspace volatile collection methods and gas chromatography–mass spectrometry, the floral volatile emissions of V. odorata were temporally and developmentally characterized. Floral volatiles were also sampled from 10 V. odorata cultivars, three Parma violet cultivars, five violet species, and one hybrid, and variation in scent among these violets was investigated. Total volatile emissions in V. odorata were highest from 0600 HR to 1900 HR, suggesting a diurnal pattern of emission. Volatile emissions also varied over the developmental lifespan of the flower, with the highest emission of individual and total volatiles occurring, in general, from stages 0 or +1 to stages +3 or +4. Floral scent qualitatively and quantitatively differed among assorted violets. The floral volatile emissions of V. odorata exhibit temporal and developmental variation. Compared with the other violet species in this study, sweet violets are intensely fragrant. The quantity and quality of floral scent differs among V. odorata cultivars, providing genetic variation from which selections could be made in a fragrance-focused breeding program.

Quantifying the impact of earthquakes on urban energy consumption and carbon emissions in Tokyo from a nexus perspective

Unexpected events can have profound impacts on urban resource supply and consumption. The Great East Japan Earthquake (3.11 hereafter) triggered not only the planned blackout in the Tokyo Metropolitan Region soon after the disaster but also the energy shift to fossil fuels to recover from the disfunction of Fukushima nuclear power plants. Previous research has mainly focused on the direct energy consumption and carbon emissions of different sectors while the intensity and extensity of the impact on industries and the environment have never been empirically addressed. This study explores energy-use efficiency and carbon emissions in Tokyo from 2011 to 2015 through a lens of nexus using environmentally extended input-output analysis and community-wide carbon analytic approaches. Results show that the energy consumption is the largest exporter and importer of carbon emissions, whereas energy losses and carbon emissions caused by energy conversion and transmission are almost twice as much as those caused by the direct parts. Strong nexus effects among building and material, transportation, and energy consumption were observed. The 3.11 greatly impacted the energy structure and carbon emission patterns because of the increased consumption of coal for electricity. The share of energy consumption and carbon emissions by raw materials for construction also increased because of the increased demand for the reparation and reconstruction of buildings and transport systems. This structural change provided new scientific evidence for governments to implement decarbonization policies while preparing for unprecedented events.

Binary and heterostructured microplates of iridium and ruthenium complexes: Preparation, characterization, and thermo-responsive emission

Thermo-responsive microcrystals exhibiting obvious emission intensity or color changes have great potentials in sensing, information encryption, and microelectronics. We report herein the binary assembly of a blue-emissive iridium complex and a red-emissive ruthenium complex into homogeneously-doped or optically-heterostructured microcrystals with thermo-responsive properties. Depending on the assembly conditions, lateral or longitudinal triblock heterostructures with a microplate shape are obtained, which display distinct emission pattern changes upon heating as a result of the decreased efficiency of energy transfer. In addition, branched heterostructures are prepared by a stepwise assembly. The luminescence polarization of the homogeneously-doped binary crystals and the waveguiding property of the longitudinal triblock heterostructure are further examined. This work evidences the versatility of transition metal complexes in the assembly into various luminescent nano/micro structures with potential applications in thermo-sensing and nanophotonics.

Estimation of methane greenhouse gas emissions from livestock in Egypt during 1989 to 2021

This study investigates methane emissions from the livestock sector, representing by enteric fermentation and manure management, in Egypt from 1989 to 2021, focusing on spatial and temporal variations at the governorate level. Utilizing IPCC guidelines and emission factors, methane emissions were estimated for dairy and non-dairy cattle, buffalo, sheep and goat, poultry, and other livestock categories. Results reveal fluctuating emission patterns over the study period, with notable declines in certain governorates such as Kafr El-Sheikh and Red Sea, attributed to reductions in livestock populations. However, increasing trends were observed overall, driven by population growth in other regions. Hotspots of methane emissions were identified in delta governorates like Behera and Sharkia, as well as agriculturally rich regions including Menia and Suhag. While livestock populations varied between regions, factors such as water availability, climatic conditions, and farming practices influenced distribution. Notably, cluster analysis did not reveal regional clustering among governorates, suggesting emissions changes were not dependent on specific geographic or climatic boundaries. Manure management accounted for only 5–6% of total emissions, with emissions at their lowest in the last three years due to population declines. Despite the highest livestock populations being sheep and goats, emissions from enteric fermentation and manure management were highest from buffalo and cattle. This study underscores the importance of accurate data collection and adherence to IPCC recommendations for estimating GHG emissions, enabling the development of targeted mitigation strategies to address climate change challenges in the livestock sector.

ANALYZING AND FORECASTING CO2 EMISSIONS IN THE ALUMINUM SECTOR USING ARIMA MODEL

The urgent and pressing challenge posed by global climate change underscores the critical need for immediate and decisive action, particularly in the mitigation of greenhouse gas (GHG) emissions to facilitate a trajectory towards sustainability. The significance of the aluminium industry, characterized by its notable carbon footprint, accentuates the importance of conducting comprehensive and environmentally conscious analyses within this sector. Heightened environmental apprehensions surrounding aluminium production necessitate the development of effective approaches for emission forecast. The primary aim of the article is to elucidate the present situation and future predictions of carbon emissions within the global aluminium industry, particularly in the context of escalating concerns regarding global climate security. The research concluded that there will be a decrease in CO2 emissions within the aluminium industry in the future as a result. Through meticulous assessments and exhaustive forecasts of CO2 emissions across the global aluminium industrial chain system, this study employed the Autoregressive Integrated Moving Average (ARIMA) model to scrutinize data spanning from 2005 to 2030. By furnishing valuable insights into prospective emission patterns and providing guidance for the formulation of sustainable policy measures, this research assumes a pivotal role in shaping data-driven strategies aimed at mitigating the environmental impact of aluminum production. Consequently, it contributes significantly to the collective endeavor to combat climate change and foster a more resilient and sustainable future for humanity.

Surface water monitoring of chemicals associated with animal husbandry in an agricultural region in the Netherlands using passive sampling

Compounds originating from animal husbandry can pollute surface water through the application of manure to soil. Typically, grab sampling is employed to detect these residues, which only provides information on the concentration at the time of sampling. To better understand the emission patterns of these compounds, we utilized passive samplers in surface water to collect data at eight locations in a Dutch agricultural region, during different time intervals. As a passive sampler, we chose the integrative-based Speedisk® hydrophilic DVB. In total, we targeted 46 compounds, among which 25 antibiotics, three hormones, nine antiparasitics, and nine disinfectants. From these 46 compounds, 22 compounds accumulated in passive samplers in amounts above the limit of quantification in at least one sampling location. Over the 12-week deployment period, a time integrative uptake pattern was identified in 53% of the examined cases, with the remaining 47% not displaying this behavior. The occurrences without this behavior were primarily associated with specific location, particularly the most upstream location, or specific compounds. Our findings suggest that the proposed use of passive samplers, when compared in this limited context to traditional grab sampling, may provide enhanced efficiency and potentially enable the detection of a wider array of compounds. In fact, a number of compounds originating from animal husbandry activities were quantified for the first time in Dutch surface waters, such as flubendazole, florfenicol, and tilmicosine. The set-up of the sampling campaign also allowed to distinguish between different pollution levels during sampling intervals on the same location. This aspect gains particular significance when considering the utilization of different compounds on various occasions, hence, it has the potential to strengthen ongoing monitoring and mitigation efforts.

Advancing Regional–Scale Spatio–Temporal Dynamics of FFCO2 Emissions in Great Bay Area

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a “core–subcore–periphery” emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers.

Correction: Peng et al. A Comparison of Greenhouse Gas Emission Patterns in Different Water Levels in Peatlands. Water 2024, 16, 985

There were errors in the original publication [...]

Nitrous oxide (N2O) emissions at the air-water-sediment interfaces of cascade reservoirs in the Yunnan-Guizhou Plateau: Spatial patterns and environmental controls

The construction of cascade reservoirs can interfere with the natural hydrologic cycles of basins, causing negative environmental effects such as altering the emission patterns of the Nitrous oxide (N2O), a potent greenhouse gas. To elucidate the impact of cascade reservoirs construction on river N2O emissions, we utilized the thin boundary model and the incubation experiments to estimate the N2O fluxes at the air-water interface and at the water-sediment interface of cascade reservoirs on the Yunnan-Guizhou Plateau, respectively. Additionally, we explored the influence of various factors, with particular emphasis on damming, on N2O emissions and production. Moreover, we identified the main pathways of N2O production and proposed management strategies to mitigate N2O emissions from cascade reservoirs. The findings revealed that N2O fluxes at the air-water interface and the water-sediment interface were 4.73 ± 1.32 μmol m−2 · d−1 and 15.56 ± 1.98 μmol m−2 · d−1, respectively. Influenced by temperature, dissolved oxygen (DO), resource substances (active nitrogen substrates and dissolved organic carbon (DOC)) and reservoir properties (scale, hydraulic retention time (HRT), reservoir age, etc.), the N2O concentration and flux exhibited notable spatial heterogeneity, gradually increasing downstream. Temperature has a significant direct impact on N2O flux, as well as indirect effects through DO and resource chemicals. Furthermore, the correlation between dissolved oxygen utilization rate (AOU) and net N2O flux (ΔN2O) indicated that N2O emissions at the water-air interface were primarily attributable to nitrification, whereas those at the water-sediment interface were predominantly driven by denitrification. These findings not only enhance our comprehension of N2O emissions at various interfaces of cascade reservoirs but also offer theoretical backing for the formulation of management strategies aimed at efficiently mitigating N2O emissions from continuously dammed rivers.

Spatiotemporal dynamics and influencing factors of CO2 emissions under regional collaboration: Evidence from the Beijing-Tianjin-Hebei region in China

Against the backdrop of global climate change and the “dual carbon” target, cities have a significant responsibility to achieve carbon reduction targets. As a crucial urban agglomeration in northern China, effectively balancing economic growth with CO2 emission reduction to achieve high-quality economic development remains a significant challenge that the Beijing-Tianjin-Hebei region should address both presently and in the future. The objective of this study is to utilize nighttime lighting data and energy consumption information to quantify the CO2 emissions of diverse cities within the Beijing-Tianjin-Hebei region spanning from 2006 to 2020. The research aims to analyze the spatial progression patterns of CO2 emissions across these urban centers, identify key determinants and their interrelations, and delve into the underlying mechanisms pivotal for advancing carbon mitigation strategies within urban agglomerations. The results indicate that: with an exception in Beijing where CO2 emissions slightly decreased compared to 2006, CO2 emissions increased across cities in the Beijing-Tianjin-Hebei region by 2020. High-value CO2 emission areas are primarily concentrated in central of the study area, exhibiting negative spatial correlation characteristics. Based on its urban development positioning, it is imperative for the Beijing-Tianjin-Hebei urban agglomeration to formulate and implement carbon reduction strategies on innovative development, industrial upgrading, and ecological protection among other aspects towards coordinated low-carbon development.