Regional Scale Research Articles

Ecohydrological processes can predict biocrust cover at regional scale but not global scale

Ecohydrological processes can predict biocrust cover at regional scale but not global scale

Development of a tailored questionnaire instrument to evaluate thermal sensation of elderly people in India

Purpose India’s growing elderly population necessitates ensuring indoor thermal comfort because of their vulnerability to temperature-related illnesses and reduced capacity to regulate body temperature. Currently, thermal sensation (TS) assessment scales, designed for those between 20 and 60 years of age, may not accurately capture the preferences of elderly adults. To address the gap, this study aims to identify appropriate scaling methods to help the elderly understand the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE-55) seven-point TS scale clearly. Design/methodology/approach Four scaling methods – color, emoji, landscape images and regional images scales – identified from literature were analyzed using quantitative approaches. The differences between two age groups (<60 years and = 60 years) were examined using frequency distribution differences and independent sample t-test methods. Findings Results indicated that both younger/middle-aged (<60 years) and elderly individuals (= 60 years) were adept at identifying color and regional images scale, while emoji and landscape images scale posed challenges for the elderly. Furthermore, a tailored questionnaire instrument was developed to enhance the comprehension of TS questions for the elderly. One sample t-test results indicated that the proposed questionnaire instrument is a better fit to support ASHRAE-55 seven-point TS scale, making it particularly effective for the elderly population. Originality/value This research presents a novel, tailored questionnaire instrument that significantly enhances the elderly population’s comprehension of TS questions, thereby improving the accuracy of thermal comfort assessments and contributing to the creation of better indoor thermal environments for the elderly people.

Intercomparison of machine learning methods for statistical downscaling of daily temperature under CMIP6 scenarios: a case study from Iran

ABSTRACT General circulation models (GCM) represent a contemporary and advanced tool designed to simulate the response of climate system to alterations in greenhouse gas levels. Increasing spatial resolutions of the outputs of GCMs on a regional scale requires downscaling process. In this study, four machine learning (ML) models, namely, decision tree regression (DTR), support vector regression, artificial neural network, and K-nearest neighbors (KNN) were employed to downscale the outputs of four Coupled Model Intercomparison Project 6 climate models. The daily observations of temperature data at Nazmakan station in Kohgiluyeh and Boyer-Ahmad Province, Iran, were trained and tested for the periods 1995–2009 and 2009–2015, respectively. According to results of four metrics, DTR and KNN obtained the most valid evaluation in the training and test data, respectively. In addition, downscaling methods were used to predict future daily temperature for 2015–2045 under two Shared Socioeconomic Pathway (SSP2-4.5 and SSP5-8.5) scenarios. Mann–Kendall test results revealed an increasing trend of temperature in most of the coming years. The projected trend expects cooler summers and warmer winters in future. Furthermore, the predicted data under two climate scenarios were assessed in comparison with observed data for 2015–2022. Finally, this study demonstrates the strengths and weaknesses of nonlinear ML techniques in statistical downscaling.

Substantial Increase in Sub–Daily Precipitation Extremes of Flooding Season Over China

AbstractUnderstanding sub‐daily precipitation extremes (SPEs) can provide scientific insights for taking effective measures to mitigate climate risks. Leveraging gauge observations at hourly precipitation in 2,312 meteorological stations and extreme sub‐daily precipitation indices (ESPIs), we investigate the changes of SPEs in flooding season of 1971–2022 in China. On country scale, the occurrences and intensity of SPEs have significantly increased and even accelerated since the 21st century, suggesting increases in 2010s by 15%–38% compared with that in 1970s. The SPE risks for 20‐year and 50‐year return‐period increased by 2–4 and 8–20 times in 2001–2022 compared with that in 1971–2000, respectively. Over 80% stations are found to have positive trends in all ESPIs. On regional scale, seven sub‐regions experienced significant increases in ESPIs with larger magnitudes in the East China. The enlarged 500‐hPa geopotential height, 700‐hPa pseudoequivalent potential temperature, 700‐hPa specific humidity, saturated vapor pressure and urbanization ratio may be bonded to more SPEs.

Assessment of long-term exposure to traffic-related air pollution: An exposure framework.

Exposure to ambient air pollution is associated with morbidity and mortality, making it an important public health concern. Emissions from motorized traffic are a common source of air pollution but evaluating the contribution of traffic-related air pollution (TRAP) emissions to health risks is challenging because it is difficult to disentangle the contribution of individual air pollution sources to exposure contrasts in an epidemiological study. This paper describes a new framework to identify whether air pollution differences reflect contrasts in TRAP exposures. Because no commonly measured pollutant is entirely specific to on-road motor vehicles, this exposure framework combined information on pollutants, spatial scale (i.e., geographic extent), and exposure assessment methods and their spatial scale to determine whether the estimated effect of air pollution in a given study was related to differences in TRAP. The exposure framework extended beyond the near-road environment to include differences in exposure to TRAP at neighborhood resolution ( ≤ 5 km) across urban, regional, and national scales. It also embedded a stricter set of criteria to identify studies that provided the strongest evidence that exposure contrasts were related to differences in traffic emissions. Application of the framework to the transparent selection of epidemiological studies for a systematic review produced insights on assessing and improving comparability of TRAP exposure measures, particularly for indirect measures such as distances from roads. It also highlighted study design challenges related to the duration of measurements and the structure of epidemiological models. This manuscript describes a new exposure framework to identify studies of traffic-related air pollution, a case study of its application in an HEI systematic review, and its implications for exposure science and air pollution epidemiology experts. It identifies challenges and provides recommendations for the field going forward. It is important to bring this information to the attention of researchers in air pollution exposure science and epidemiology because applying the broader lessons learned will improve the conduct and reporting of studies going forward.

Revealing How Human Activity and Native Plants Outshine Climate in Shaping New Invasive Alien Plant Elevational Patterns in Nature Reserves

ABSTRACTAimWe aim to investigate whether invasive alien plants introduced at different times exhibit variations in elevational distribution patterns and to explore the correlations of these patterns with climate, native plants and human activity.LocationGuangxi, China.MethodsWe recorded plant richness and cover across elevational gradients at 25–34 sites within each reserve, utilising 84–134 roadside plots per site. Using generalised linear mixed model (GLMM), we assessed the impacts of climate, native plants and human activity on the distributions of all, old and new invasive alien plants across the region and within the nature reserves.ResultsAt regional scales, the cover of all, new and old invasive alien plants decreased with increasing elevation, while only the richness of old invasive alien plants exhibited a similar pattern. Contrasting patterns were observed in Dayao Mountain, where the cover of old invasive alien plants decreased while the cover of new invasive alien plants increased. The richness patterns of all and new invasive alien plants were opposite between Dayao Mountain and Yachang Orchid. Moreover, native plant richness was negatively correlated with the richness of invasive alien plants, while interference intensity positively affected new invasive alien plants at both regional and reserve scales. At the regional scale, native plant richness accounted for 78.27%, 91.94% and 60.9% of all, old and new invasive alien plants respectively. Interference intensity accounted for 33.85% of the variation in new invasive alien plants. Annual mean temperature positively influenced the cover of all and old invasive alien plants at both regional and reserve scales, explaining 36.91% and 74.28% of their regional variation. Additionally, interference intensity and distance to human settlements positively impacted the cover of new invasive alien plants, contributing to 50.95% and 31.92% of their variation at regional scales.Main ConclusionThe distinct distribution patterns of old and new invasive alien plants highlight the significance of residence time in understanding their dynamics. Climate factors constrain the cover distribution of all and old invasive alien plants, whereas interference intensity and distance to settlements determine the distribution of new invasive alien plants. Notably, native plants play a vital role in preventing the establishment and spread of invasive alien plants within nature reserves, and their effectiveness increases with longer residence times. Our findings highlight the critical importance of minimising human interference and conserving native species for the effective management of invasive alien plants in nature reserves.

The key role of forest disturbance in reconciling estimates of the northern carbon sink

AbstractNorthern forests are an important carbon sink, but our understanding of the driving factors is limited due to discrepancies between dynamic global vegetation models (DGVMs) and atmospheric inversions. We show that DGVMs simulate a 50% lower sink (1.1 ± 0.5 PgC yr−1 over 2001–2021) across North America, Europe, Russia, and China compared to atmospheric inversions (2.2 ± 0.6 PgC yr−1). We explain why DGVMs underestimate the carbon sink by considering how they represent disturbance processes, specifically the overestimation of fire emissions, and the lack of robust forest demography resulting in lower forest regrowth rates than observed. We reconcile net sink estimates by using alternative disturbance-related fluxes. We estimate carbon uptake through forest regrowth by combining satellite-derived forest age and biomass maps. We calculate a regrowth flux of 1.1 ± 0.1 PgC yr−1, and combine this with satellite-derived estimates of fire emissions (0.4 ± 0.1 PgC yr−1), land-use change emissions from bookkeeping models (0.9 ± 0.2 PgC yr−1), and the DGVM-estimated sink from CO2 fertilisation, nitrogen deposition, and climate change (2.2 ± 0.9 PgC yr−1). The resulting ‘bottom-up’ net flux of 2.1 ± 0.9 PgC yr−1 agrees with atmospheric inversions. The reconciliation holds at regional scales, increasing confidence in our results.

Aerial Photo-Identification of Sperm Whales (Physeter macrocephalus)

Photo-identification is a staple tool used in cetacean conservation studies since the 1970s to monitor individuals on a regional and ocean basin-wide scale to infer critical information about habitat use, suitability, and shifts. This technique has been extensively used on sperm whales globally since it was developed in 1982, initially using the tail fluke from deep diving whales and the dorsal fin when appropriate. From the mid 2010s onwards, the emergence of domestically available unoccupied aerial systems (drones) has reshaped how whale research can be conducted. Herein, we describe the suitability of aerial images to determine the identity of individual sperm whales (Physeter macrocephalus) using all available identifiable markings along their dorsal side to complement the use of fluke notches and dorsal fin scars photographed from the surface of the sea from boat-based platforms for photo-identification and to maximize opportunities to identify and monitor sperm whales. Drone data were gathered while flying over sperm whales in Andenes, Norway; Shetland, Scotland; Dursey Island, Ireland; and Faial and São Miguel Islands, Azores, Portugal, between 2017 and 2024, which enabled the entire dorsal surface of sperm whales to be captured and assessed. Aerial photographs and videos were used to differentiate between 336 individual sperm whales using physical characteristics. We identified the main features of sperm whales through aerial drone images, as well as their prevalence in Atlantic high latitude foraging grounds and lower latitude nursery grounds. We discuss the advantages of using aerial drone photographs to identify sperm whales in addition to traditional boat-based photo-identification.

Reconstruction of 30 m Land Cover in the Qilian Mountains from 1980 to 1990 Based on Super-Resolution Generative Adversarial Networks

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved.

Green Infrastructure Mapping in Almeria Province (Spain) Using Geographical Information Systems and Multi-Criteria Evaluation

Green infrastructure (GI) is increasingly prioritised in landscape policy and planning due to its potential to benefit ecosystems and enhance wildlife conservation. However, due to the uneven distribution of protected areas (PAs) and the fragmentation of habitats more generally, multi-level policy strategies are needed to create an integrated GI network bridging national, regional and local scales. In the province of Almeria, southeastern Spain, protected areas are mainly threatened by two land use/land cover changes. On the one hand, there is the advance of intensive greenhouse agriculture, which, between 1984 and 2007, increased in surface area by more than 58%. On the other hand, there is the growth of artificial surfaces, including urban areas (+64%), construction sites (+194%) and road infrastructures (+135%). To address this challenge, we present a proposal for green infrastructure deployment in the province of Almeria. We combine Geographic Information Systems (GISs) and multi-criteria evaluation (MCE) techniques to identify and evaluate suitability for key elements to be included in GI in two key ways. First, we identify the most suitable areas to form part of the GI in order to address vulnerability to degradation and fragmentation. Second, we propose 15 ecological corridors connecting the 35 protected areas of the province that act as core areas. The proposed GI network would extend along the western coast of the province and occupy the valleys of the main rivers. The river Almanzora plays a leading role. Due to its remoteness from the coast and its climatic conditions, it has not attracted intensive greenhouse agriculture and urban development, the main drivers of the transformation and fragmentation of traditional land uses. Around 50% of the area occupied by the proposed corridors would be located in places of medium and high suitability for the movement of species between core areas.

Extremes of Temperature and Precipitation Under CMIP6 Scenarios Projections Over Central Hokkaido, Japan

ABSTRACTClimate extreme events are intensifying globally, posing increasing risks across various sectors. Understanding climate extremes' spatiotemporal patterns and responses to climate change is crucial for effective management, especially on a regional scale. This study examines temperature and precipitation extremes, as well as compound dry‐hot events (CDHEs), in the Ishikari River basin (IRB) of Northeastern Japan, an area of significant socioeconomic importance. We focus on spatiotemporal analysis under multiple scenarios of temperature/precipitation extremes and CDHEs based on statistical downscaled datasets from the Coupled Model Intercomparison Project Phase 6. Results indicate that IRB underwent increased trends of extreme hot periods, extreme droughts, and heavy rainfalls during 1985–2014, which are significantly affected by the North Pacific Oscillation and Southern Oscillation Index. Future projections show that warming temperatures and less rainfall shift asymmetrical impacts on temperature and precipitation extremes, expecting increased warm spells and CDHEs but increased wet durations and less heavy rainfalls. Emission scenarios analysis suggests low‐emission scenarios (SSP1‐2.6) could mitigate their exacerbations, especially for CDHEs (decreased by 139%). Moreover, spatial‐pattern analysis reveals regional heterogeneity in temperature and precipitation extremes, with northern mountainous regions more susceptible to thermal extremes and southern plain regions (e.g., Sapporo city) experiencing prolonged drought and CDHEs. This study provides valuable insights into climate risk management and adaptation strategies.

Determinants of District/City Income Disparity in Bali Province

In regional scale of Bali Province infected symptom disparity marked income​ with Still concentrated development of the South Bali region compared to other parts of Bali. The goal study This For identify disparity income districts / cities in Bali Province 2018-2023 and analyze influence growth economy, power work, level education, allocation funds, and capital expenditures on disparity income district / city Bali Province is good in a way simultaneous and also partial. Research This use method quantitative and using secondary data obtained​ from the Central Bureau of Statistics. Data analysis techniques using Williamson index and Klassen typology as analysis disparity and continued with multiple linear regression For analyze influence growth economy, power work, level education, allocation funds, and capital expenditures on disparity income. Research results show growth economy, power work, level education, allocation funds, and capital expenditures simultaneous influential and significant to disparity income. Furthermore, growth economy, level education, allocation funds, and capital expenditures partial influential negative and significant to disparity income. Meanwhile, energy Work in a way partial influential positive and significant to disparity income. Research suggestions among other things to do equalization development for poor and depressed areas decrease, increase transportation common connecting​ all areas, development infrastructure and creation field work, availability help education and also scholarship to college tall and also postgraduate, government center still provide allocation funds especially to poor and depressed areas , and maximize potential area so that investors can invest.

Physically based probabilistic prediction of regional rainfall-induced shallow landslides: from initiation to runout analysis

ABSTRACT In this study, a physically based method is proposed to estimate the exceedance probability of the intensity measure of the regional rainfall-induced shallow landslides. This method consists of a digital elevation model, an initiation model, a runout model and a probabilistic analysis model. In the suggested method, a study area is discretised into a grid of cells, and the stability of each cell can be evaluated through the initiation model. Then, the runout behaviour of the unstable cells can be simulated by the runout model. Finally, the exceedance probability of the intensity measures of each cell can be estimated through Monte Carlo simulation considering the uncertainties in the initiation model and runout model. The proposed method is applied to analyse the rainfall-induced landslides in a study area in the southwest of China, which can provide a reasonably accurate prediction of the impact zones of the landslides. The suggested method provides a useful tool to probabilistically predict the impacted area of the rainfall-induced landslides at a regional scale.

Textbooks, hunger, and humanitarian narratives: representations of U.S. food policies to Germany on a regional scale (1949–89)

AbstractThis article investigates the significance of United States (U.S.) post-WWII food aid to Germany in West German textbooks from a regional angle during the Cold War period. Previous studies have predominantly focused on interpreting the significance of U.S. aid in West Germany from a national standpoint, overlooking regional variations in humanitarian narratives. This study challenges the national scale as the exclusive basis of collective meaning. By exploring educational materials from three different German regions—Berlin, Bavaria and North Rhine-Westphalia, it examines the different afterlives of U.S. aid in West German humanitarian narratives. Further attention for nuances in reconstructing humanitarian narratives not only does justice to the distinct regional histories and memory cultures, but also gives scope to reassessments of dominant narratives.

Tectonostratigraphic models of an extensional back-arc basin: inferences for the evolution of the Pannonian Basin System

The subsidence history and sedimentary architecture of extensional basins are controlled by the interplay between tectonics, mantle dynamics and climatic variations. Observations from outcrop to regional seismic scales coupled with numerical modelling techniques, including basin-scale stratigraphic, crustal-scale tectonic and mantle-scale subduction models, contribute to the quantification of basin evolution. Here, we review and discuss both tectonic and stratigraphic applications of such models and compare them with observations from the Pannonian Basin system of Central Europe. We show that diachronous localization of back-arc extension, crustal thinning, asthenospheric upwelling and subsequent thermal relaxation are superimposed on an overall slow plate convergence. These processes result in contrasting subsidence and uplift patterns and a variable heat flow evolution in different parts of the basin system. The crustal stress-field of the sub-basins does not always reflect their contrasting vertical motions. Extensional deformation generally migrates from the basin margins towards the basin centre. Structural inversion is localized along hot and weak inherited zones from the Middle Miocene to Present. Tectonic subsidence focuses sedimentation along deep depocentres, sourced by exhumed footwalls and from the neighbouring orogens. The overall post-rift sediment progradation is influenced by inherited bathymetries from the preceding syn-rift stage and by enhanced differential vertical motions.

Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines

Abstract. Energy scenarios require realistic estimates of technical wind energy potentials – estimates for how much electricity can be generated by wind turbines given a certain level of deployed capacity within a region. These are typically obtained using observed wind speeds, neglecting the depletion of the wind energy resource with increasing deployment at the regional scale. Here, we use the kinetic energy budget of the atmosphere (KEBA) approach to evaluate the importance of this resource depletion effect for the technical potential of Kansas, USA. To do so, we first apply the KEBA approach to a previously conducted set of numerical simulations with the Weather Research Forecasting (WRF) model. This set simulated the resource depletion effect for a range of different levels of wind turbine deployments within Kansas, which we use to test the KEBA approach. We specifically test the approach for nighttime and daytime conditions to capture the different mixing regimes of the atmospheric boundary layer. We find that KEBA can adequately capture the effect for both settings. We then extend our analysis by using ERA-5 forcing to the climatological scale. We find that this resource depletion effect increases almost linearly with the level of wind turbine deployment. Compared to previously published estimates for the technical potential for Kansas, the resource depletion effect lowers capacity factors by a third to a half. Since this resource depletion effect increases with installed capacity within a region, it is policy relevant even at relatively low deployment levels.

Combining LiDAR, SAR, and DEM Data for Estimating Understory Terrain Using Machine Learning-Based Methods

Currently, precise estimation of understory terrain faces numerous technical obstacles and challenges that are difficult to overcome. To address this problem, this paper combines LiDAR, SAR, and DEM data to estimate understory terrain. The high multivariable-precision spaceborne LiDAR ICESat-2 data, validated by the NEON, are divided into training and validation sets. The training dataset is used as a dependent variable, the SRTM DEM and Sentinel-1 SAR data are regarded as independent variables, a total of 13 feature parameters with high contributions are extracted to construct a Multiple Linear Regression model (MLR), BAGGING model, Random Forest model (RF), and Long Short-Term Memory model (LSTM). The results indicate that the RF model exhibits the highest accuracy among the four models, with R2 = 0.999, RMSE = 0.701 m, and MAE = 0.249 m. Then, based on the RF model, the understory terrain at the regional scale is generated, and an accuracy assessment is performed using the validation dataset, yielding R2 = 0.999, RMSE = 0.847 m, and MAE = 0.517 m. Furthermore, this paper quantitatively analyzes the effects of slope, vegetation coverage, and canopy height on the estimation accuracy of understory terrain. The results show that as slope, and canopy height increase, the estimation accuracy of the RF model for understory terrain gradually decreases. The accuracy of the understory terrain estimated by the RF model is relatively stable and not easily affected by slope, vegetation coverage, and canopy height. The research on the estimation of understory terrain holds significant practical implications for forest resource management, ecological conservation, and biodiversity protection, as well as natural disaster prevention.

Formation of single-dominant-species patches of Dicranopteris dichotoma primarily influenced by understory light intensity

IntroductionThe Dicranopteris dichotoma fern community plays vital roles in nutrient sequestration, succession regulation, and ecological threshold control. However, the mechanisms underlying the formation of the D. dichotoma–dominant community remain unclear.MethodsThis study established four different community types to investigate the effects of environmental factors on the formation of a D. dichotoma–dominant community.ResultsWe found that climate was the primary factor affecting the formation of patches dominated by D. dichotoma at the regional scale. Specifically, higher annual mean temperature and annual mean precipitation were associated with larger single-dominant-species patches of D. dichotoma. Understory light intensity was the major factor affecting the formation of the D. dichotoma community at the community scale. Light intensity ranging from 200 to 500 µmol·m⁻²·s⁻¹ was most conducive to the development of a large D. dichotoma community. Additionally, understory light intensity enhanced the importance value of D. dichotoma in the herb community by decreasing its biomass proportion of support modules and increasing its biomass proportion of photosynthetic and reproductive modules. Soil properties and D. dichotoma characteristics showed interactions with each other. Acidic red-yellow soil was most suitable for the formation of single-dominant-species patches of D. dichotoma, and the growth of D. dichotoma further decreased the soil pH. Soil total phosphorus content was identified as a limiting factor for formation of the D. dichotoma community.DiscussionIn summary, the formation of single-dominant-species patches of D. dichotoma is mainly influenced by a combination of climate, community, and soil.

Aerodynamic performance evaluation of subsonic compressor cascade blade with leading-edge damage

Abstract Material loss at the leading edge of in-service gas-turbine engine blades is detrimental to the aerodynamic performances of compressors. In this study, computational simulations and experiments using pneumatic probes were conducted to measure the total pressure loss of compressor cascade blades with material loss at their leading edges. The correlations between blade leading-edge damage geometries and cascade blade total pressure losses were analyzed. The geometrical features of blade leading-edge damage that most affect the cascade blade total pressure loss were distinguished, and the influences of leading-edge damage region cross-sectional shapes on the cascade blade total pressure loss were examined. Two methods for evaluating the total pressure loss of damaged cascade blades were developed using experimental and numerical data. In the first method, an attempt is made to correlate the cascade blade total pressure loss with a characteristic parameter that is defined using scale and angle parameters of the leading-edge damage region. The second method is based on the probability distribution function as well as the critical loss boundary of the geometrical parameters of the leading-edge damage region. The results of this study provide a better understanding of the influence of damaged leading edges on compressor cascade blade aerodynamic performance and can help establish low-cost methods for the estimation of compressor performance degradation.

Advanced method for compiling a high-resolution gridded anthropogenic CO2 emission inventory at a regional scale

ABSTRACT As global climate change accelerates, the need for precise and high-resolution carbon emission inventories becomes increasingly urgent, particularly in rapidly developing regions. This study introduces an advanced methodology for constructing regional-scale gridded anthropogenic CO2 emission inventories, integrating the latest energy consumption data and remote sensing technologies. By systematically calculating emissions across various sectors and energy types, and utilizing Geographic Information System (GIS) for spatial distribution, we have generated a high-resolution emission map with a 1 km × 1 km grid. The accuracy of the map was validated through regression analysis with the Multi-resolution Emission Inventory for China (MEIC) dataset, ensuring its reliability. This work provides critical insights for targeted emission reduction strategies, contributing to the formulation of sustainable development policies that align with global climate objectives.