Geospatial Big Data-Driven Fine-Scale Carbon Emission Modeling

Highlights

What are the main findings?

• A bibliometric analysis reveals that the research focus in spatial carbon emission modeling has shifted from early coarse, large-scale accounting to the current emphasis on fine-grained analysis that is sector-specific, high-resolution, and multidimensional. The global commitment to "carbon neutrality" targets has significantly accelerated the application of data-driven methods, particularly machine learning, in this domain.
• A comprehensive literature review indicates that the most prevalent modeling strategy is the hybrid model, which synergistically integrates "top-down" and "bottom-up" approaches. By coupling mechanistic models with machine learning techniques, this method effectively reconciles the global consistency of macro-scale data with the local heterogeneity of micro-scale emission sources, thereby significantly enhancing model accuracy and applicability.

What is the implication/innovation of the main finding?

• A key innovation of this review is the explicit identification of geospatial big data—such as nighttime light (NTL) remote sensing, mobile phone signaling, and Points of Interest (POI)—as the primary driver of this modeling transformation. With their unprecedented high resolution and multidimensionality, these datasets provide the essential foundation for transitioning from macro-scale statistical analysis to micro-scale simulation. This leap forward dramatically improves the ability to characterize the sources, intensities, and spatiotemporal heterogeneity of carbon emissions within complex urban systems, including functional zones, morphology, and transportation networks.
• This study underscores the critical application value of fine-scale modeling in achieving "carbon neutrality" targets. It not only deepens the quantitative understanding of the driving mechanisms behind carbon emissions but also enables robust scenario simulations for carbon neutrality pathways. Consequently, it provides a solid theoretical and technical foundation for formulating effective low-carbon spatial planning and precision urban management policies.

Abstract

As nations worldwide commit to carbon neutrality targets in response to accelerating climate change, the spatial modeling of carbon emissions has emerged as an indispensable tool for policy implementation and assessment. This paper presents a systematic review of the field from bibliometric and methodological perspectives. We synthesize key developments in spatial allocation techniques, data-driven models, and emission characterization methods. A central focus is the transformative role of geospatial big data in improving model accuracy and applicability, particularly how fine-grained, high-resolution modeling enhances the efficacy of emission reduction strategies. Our analysis reveals several key conclusions. First, the literature on carbon emission spatial modeling is expanding rapidly, with a discernible shift in focus from coarse, large-scale assessments toward more granular analyses that are sector-specific, high-resolution, and multidimensional. Second, hybrid models that integrate top-down and bottom-up approaches are now the predominant strategy for enhancing both accuracy and applicability; coupling mechanistic models with machine learning techniques effectively reconcile macro-scale data consistency with micro-scale heterogeneity. Third, the integration of geospatial big data is revolutionizing the field by providing the high-resolution, multidimensional, and dynamic inputs necessary to transition from macro- to micro-scale analysis. This is particularly evident in fine-grained assessments of urban systems—including spatial functions, morphology, and transportation networks—where such data dramatically improve the characterization of emission sources, intensities, and their spatiotemporal heterogeneity. This study ultimately elucidates the critical role of fine-grained modeling in advancing the quantitative understanding of carbon emission drivers, enabling robust scenario simulations for carbon neutrality, and informing effective low-carbon spatial planning. The synthesis presented here aims to provide a firm theoretical and technical foundation to support the ambitious carbon reduction targets set by nations worldwide.

1. Introduction

Global climate change has become one of the most severe environmental challenges facing the world today. Carbon emissions are the primary source of greenhouse gases and the main factor contributing to global warming [1]. With increasing international attention on climate change, nations have successively established carbon neutrality targets, aiming to mitigate the rise of global temperatures by reducing carbon emissions [2]. As core centers of human activity and industrial development, the critical role of cities in achieving global “carbon peak” and “carbon neutrality” goals is widely recognized [3,4,5]. Although cities occupy less than 2% of the world’s land area, their carbon emissions exceed 70% of the global total; furthermore, urban expansion and the process of rapid urbanization have exacerbated the growth of these emissions [6,7].

Achieving carbon neutrality hinges on the accurate estimation and effective management of emissions. The Intergovernmental Panel on Climate Change (IPCC) underscores the necessity of accounting for emission contributions across diverse spatial scales [8]. Consequently, spatial models that quantify the geographic distribution of carbon emissions have garnered significant academic and governmental interest [9,10]. These models leverage Geographic Information Systems (GIS) to integrate traditional energy consumption statistics with various geospatial datasets. This synthesis enables a critical shift in analysis, moving beyond coarse administrative boundaries to high-resolution, pixel-level representations of emission patterns [11,12,13]. By precisely identifying emission hotspots and analyzing their underlying drivers, spatial modeling provides the scientific foundation necessary to formulate effective, geographically targeted mitigation policies and comprehensive response strategies.

As critical implementation units for achieving global climate objectives, the compilation and accounting of greenhouse gas (GHG) inventories for cities are of paramount importance. To ensure standardization and comparability in accounting, institutions such as the World Resources Institute (WRI) and the C40 Cities Climate Leadership Group have jointly developed the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) [14], which categorizes city-related emissions into three scopes. Within the context of this review, we focus on Scope 1 emissions, which encompass the direct CO₂-equivalent emissions generated within a city’s geographical boundary from activities such as fossil energy consumption, industrial processes, transportation, and residential life. Accounting methods are broadly categorized into two pathways: “top-down” and “bottom-up”. Top-down models rely on spatial proxy data, such as nighttime lights and population density, to downscale national or regional totals onto a grid, primarily serving large-scale research. In contrast, bottom-up models adhere to GPC standards by aggregating activity data from specific emission sources—such as factories, buildings, and vehicles—and often employ hybrid methods. However, a significant “scale mismatch” often emerges when these two approaches are applied at the urban scale. Research has revealed substantial discrepancies; for instance, the median difference between the ODIAC and Hestia inventories at a 1 km² grid resolution can be as high as 47–84% [15], while analysis of 78 C40 cities shows significant divergence between estimates from EDGAR and ODIAC and the cities’ self-reported inventories [16]. The root of this issue lies in the fact that proxies used by traditional top-down models often fail to capture the high degree of spatial heterogeneity within cities. Conversely, bottom-up models have long been constrained by the scarcity and high acquisition cost of micro-scale activity data, thereby limiting their application in city-level policy evaluation [15,17].

Confronting this bottleneck, the rise of geospatial big data has brought new opportunities for urban carbon emission modeling [18,19]. For top-down models, novel data sources such as high-resolution remote sensing imagery [20] and Points of Interest (POI) [21] distributions can serve as more refined and physically meaningful proxy factors, significantly enhancing the accuracy of spatial downscaling. For bottom-up models, dynamic big data—including vehicle GPS trajectories [22], mobile phone signaling data [23], and 3D building vectors [24]—directly provide high-resolution spatiotemporal information on micro-scale activities, greatly reducing the reliance on expensive field surveys and statistical reports. Consequently, geospatial big data is driving a paradigm shift in urban carbon accounting, transitioning from static, macroscopic estimations of total emissions toward the dynamic, micro-scale modeling of spatiotemporal processes [25].

Despite its growing importance amid the urgency of achieving carbon neutrality, the field of spatial emission modeling faces several critical challenges. A comprehensive synthesis of research trends is needed to identify robust methodological directions. Furthermore, no consensus exists on a universally applicable modeling framework. Additionally, the specific mechanisms by which fine-grained modeling informs and enhances emission reduction outcomes require further elucidation. To address these gaps, this review investigates two primary scientific questions: (1) What is the current state of research in spatial carbon emission modeling, including the respective advantages and limitations of established methods? (2) How can geospatial big data be leveraged to improve the accuracy and applicability of these models? Employing bibliometric and methodological analyses, we systematically review the evolution of spatial emission modeling and assess the research frontier incorporating geospatial big data. This review clarifies how these advanced data and models can improve emission characterization and mitigation effectiveness, thereby aiming to advance the theoretical foundations of the field while providing robust scientific support for evidence-based carbon management and policymaking.

2. Materials and Methods

2.1. Literature Screening Methods and Data Processing

We conducted a systematic review to synthesize and analyze the literature on urban carbon emission spatial modeling, following established protocols for rigor and impartiality [26]. A comprehensive literature search was performed using the Web of Science Core Collection, selected for its broad multidisciplinary coverage. The search was limited to peer-reviewed articles and reviews published in English since 1 August 2024. To identify all potentially relevant studies, the following query was applied to the topic field: ((“carbon emission” OR “CO₂ emission” OR “carbon dioxide” OR “CO₂”) AND (“spatial modeling” OR “mapping”) AND (“fine” OR “resolution”)).

The screening and selection process adhered to the PRISMA guidelines (Figure 1). The initial search yielded 2000 records. After removing duplicates, we screened titles and abstracts to exclude irrelevant studies. The full texts of the remaining articles were then assessed for eligibility against the specific scope of our review. This multi-stage process resulted in a final corpus of 613 articles. We performed in-depth content analysis and bibliometric evaluation on this curated collection using visualization software, including CiteSpace 6.3, VOSviewer 1.6.18, and the R package ‘Bibliometrix 3.0’. Specific exclusion criteria included: studies that focused solely on atmospheric transport modeling without providing emission estimations; research that exclusively addressed non-CO₂ greenhouse gases; and descriptive studies that lacked a clear spatial modeling methodology. This review is based on an in-depth analysis of the literature selected through this screening process.

2.2. Analysis of Publication Trends and Keyword Temporal Trends

Figure 2 illustrates the publication trend in the field of carbon emission spatial modeling, Figure 3 illustrates the temporal evolution of keywords in this field.Research in this domain has progressed from a slow start and gradual development to a phase of significant growth in recent years. Notably, the past five years (2020–2024) have witnessed an explosive increase in publication volume, accounting for 64.03% of the total historical output—a momentum built upon decades of accumulated research. The field’s origins can be traced to 1985, when Marland et al. [27] achieved a breakthrough by estimating and mapping the first geographical distribution of fossil fuel carbon dioxide (FF CO₂) emissions based on global energy statistics from 1980. This pioneering work spurred the development of global carbon emission products based on national energy inventories and reliant on energy consumption and trade statistics, such as the Carbon Dioxide Information Analysis Center (CDIAC) dataset [28,29,30]. In the 1990s, as atmospheric CO₂ observation and modeling systems grew in complexity, research demand shifted from global totals to more refined spatial distributions. The discovery by Elvidge et al. [31] that nighttime light data are highly correlated with human activity provided a critical proxy variable for emission spatialization. This greatly advanced the development of regional and national-scale gridded emission data products, including Emissions Database for Global Atmospheric Research (EDGAR) and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) [32,33,34]. Since the 21st century, international climate governance frameworks, exemplified by the 2010 Copenhagen Climate Change Conference and the 2015 Paris Agreement, have imposed stricter requirements for emissions accounting at smaller administrative units such as cities. This has catalyzed the emergence of fine-grained inventory compilation at the urban scale as a new research frontier. Consequently, high-precision, “bottom-up” inventory projects like Vulcan and Hestia were developed [35,36]. Capable of resolving emission details within cities down to the street level, these projects signify the continuous evolution of carbon emission spatial modeling toward higher spatiotemporal resolution and stronger policy-support capabilities [37,38].

The most dramatic growth occurred from 2020 to 2024, a period that saw an explosion in research activity, accounting for 64.03% of all publications in the field to date. This surge directly correlates with the global mobilization around carbon neutrality targets, catalyzed by the 75th UN General Assembly in 2020. This policy imperative precipitated a rapid pivot in research priorities. Keywords such as “carbon neutrality,” “carbon peaking,” and “urban low-carbon transition” became prevalent, alongside methodological terms like “machine learning” and thematic focuses such as “urban morphology,” indicating a clear turn toward high-precision modeling and sector-specific mitigation strategies. This trend is exemplified by the rise of “machine learning” as a research topic, which grew from 5% of publications in 2016 to 21% in 2023. This demonstrates how major policy directives not only shape research agendas but also accelerate methodological innovation, driving a paradigm shift from traditional statistical models toward more sophisticated, data-driven approaches for high-resolution emission modeling and prediction.

2.3. Keyword Co-Occurrence Networks and Research Directions

A keyword co-occurrence analysis identified four distinct research clusters that illustrate the thematic evolution of the carbon emission spatial modeling field (Figure 4).

Cluster #1 (Basic Modeling and Prediction): This foundational cluster represents the field’s origins, characterized by top-down, large-scale modeling. Early studies primarily utilized remote sensing data, atmospheric flux measurements, and traffic data to generate initial emission estimates. These pioneering methods, while critical for establishing theoretical and methodological frameworks, were often limited by coarse spatial resolution and necessitated rigorous uncertainty analysis to ensure model reliability.

Cluster #2 (Urban and Land Use Analysis): Reflecting accelerating global urbanization, this cluster signifies a shift toward examining the impacts of urban land use on carbon emissions. Research within this theme explored the differential emission profiles of various land use categories. A significant portion of this work concentrated on rapidly developing economic hubs, with “Shanghai” and the “Yangtze River Delta” emerging as key case study regions.

Cluster #3 (Spatiotemporal Analysis and Methodological Innovation): This cluster highlights a move toward more sophisticated analytical techniques. Researchers in this domain integrate diverse geospatial datasets—such as population distribution grids, nighttime light (NTL) imagery, and point-source emission inventories—with advanced spatial statistical models like Geographically Weighted Regression (GWR). The primary objective is to conduct in-depth investigations of the spatiotemporal heterogeneity and underlying drivers of carbon emissions.

Cluster #4 (Domain-Specific Application and Fine-Grained Analysis): Representing the current research frontier, this cluster focuses on the intricate linkages between urban form, sectoral activities, and emissions. Key themes such as “cities,” “sector,” and “urban form” indicate a push toward fine-grained, multi-dimensional analysis. This research disaggregates emissions by industrial, commercial, and transportation sectors and examines the influence of physical morphology, such as building configurations. This trend signifies a critical shift toward leveraging geospatial big data, including high-resolution satellite imagery and 3D urban models, to achieve a more granular understanding of emission patterns.

This bibliometric analysis reveals a field in rapid transformation, characterized by exponential growth in scholarly output, a clear pivot from macro- to micro-scale analysis, and the emergence of new data-driven paradigms.

(1) Literature Growth Trajectory: The volume of literature has grown exponentially since the field’s inception around 1997. This growth has accelerated dramatically in recent years, with the period between 2020 and 2024 alone contributing 64.03% of the total scholarly output to date.

(2) Evolution of Research Themes: Research topics have undergone a distinct macro-to-micro evolution. The initial focus on large-scale modeling and national emission inventories has given way to more granular investigations, with fine-grained modeling and machine learning applications now at the forefront. This thematic shift encompasses a progression from foundational modeling to sophisticated analyses of urban land use, the identification of spatiotemporal drivers, and detailed, sector-specific assessments of urban systems.

(3) Scientific Collaboration Landscape: The scientific collaboration network is polycentric, with the United States and China forming the principal axis of research activity. Our analysis reveals complementary specializations, with US-based research often centered on global satellite monitoring and atmospheric modeling, while China-based research demonstrates significant strengths in developing high-resolution emission inventories and integrating point-source data.

(4) Emerging Methodological Trends: The field is undergoing a paradigm shift driven by geospatial big data. The integration of multi-source, heterogeneous datasets—including NTL, POIs, and traffic trajectories—is supplanting the reliance on conventional statistical data. This data revolution is fueling the development of hybrid models that effectively merge top-down constraints with bottom-up, source-level detail, enabling a more robust and granular characterization of carbon emissions.

3. Results

A systematic understanding of carbon emission spatial modeling requires a critical review of its foundational components. This section deconstructs the field by examining its core methodological pillars: spatial allocation techniques, data integration frameworks, and emission characterization approaches.

3.1. Spatial Allocation Methods in Carbon Emission Modeling

3.1.1. Top-Down Spatial Allocation Methods

Top-down methods spatially disaggregate macro-scale emission inventories—typically compiled at national or regional levels—into finer grid cells using a set of proxy data as allocation factors [15]. A variety of spatial proxies are employed for this purpose, with the most common being NTL [34], population density grids [39], and LULUCF [40]. These datasets are widely adopted because they correlate strongly with socioeconomic activity, are often publicly accessible, and provide consistent global or regional coverage [41]. The primary strengths of the top-down approach are its computational efficiency and reliance on readily available data. These attributes make it particularly well-suited for generating large-scale, long-term, and consistent gridded emission datasets with considerable savings in time and resources [12]. Prominent examples of this methodology include two of the most widely utilized global emission inventories: the Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) [33] and the Emissions Database for Global Atmospheric Research (EDGAR) [42]. ODIAC primarily uses NTL data and point-source information from power plants for spatial allocation, whereas EDGAR relies on proxies related to global land use and various indicators of human activity.

3.1.2. Bottom-Up and Hybrid Inventory Models

The hybrid modeling approach has been widely applied globally, resulting in numerous representative case studies, as detailed in Table 1. For example, the Fossil Fuel Data Assimilation System (FFDAS) [43] integrates data such as satellite-observed nighttime lights, power plant locations, population distribution, and land use, employing data assimilation techniques to downscale national totals to a 10 km grid using a “top-down” method. The fine-grained estimation of North American FFCO₂ spatiotemporal distribution by Gregg et al. [44] is a representative work of early high-resolution inventories. In the United States, Vulcan 3.0 provides an hourly FFCO₂ emission data product at a 1 km² resolution covering the continental U.S [45]. Similarly, the Anthropogenic Carbon Emissions System (ACES) inventory, developed by Gately et al. [46,47], offers a high-resolution inventory for the northeastern U.S., with a particular focus on on-road traffic emissions. In Asia, the Regional Emission inventory in ASia (REAS), developed by Kurokawa et al. [48], provides long-term time-series emission data for various pollutants and greenhouse gases. In Europe, Bun et al. [49] constructed a high-resolution spatial inventory for Poland by systematically integrating point, line, and area source data. At the urban scale, the Mahuika-Auckland project [22] built a CO₂ inventory for Auckland, New Zealand, with a 500-m spatial and 1-h temporal resolution, detailing six major sectors: road transport, industrial, commercial, residential, aviation, and maritime. Likewise, Cape Town, South Africa, developed a spatiotemporally resolved emission inventory by integrating local data such as vehicle counts, census data, industrial fuel consumption, and port and airport activities [50]. In China, the Multi-resolution Emission Inventory for China (MEIC) [51] integrates facility-level data from nearly 100,000 point sources. It uses a “bottom-up” method to account for industrial sector emissions while employing a “top-down” spatial downscaling approach for other area and line sources, providing gridded data at multiple spatial resolutions. The China High-Resolution Emission Database (CHRED) adopted a similar hybrid strategy, utilizing data from 1.58 million industrial enterprises from the first national pollution source census to estimate industrial emissions with a “bottom-up” approach. This was combined with statistical data to calculate other sectors “top-down,” ultimately generating a 1 km resolution emission product [52].

Exploration into high-resolution urban inventories began more than a decade ago. For instance, VandeWeghe and Kennedy [53] pioneered the allocation of residential greenhouse gas emissions in Toronto to the census tract scale. The Hestia project was the first to quantify FFCO₂ emissions at the building and road level in Indianapolis, USA, and has since been successfully applied to several other U.S. cities, including Los Angeles and Salt Lake City [54,55,56]. Inspired by these pioneering efforts, numerous high-resolution urban inventory practices have emerged globally. For example, Moran et al. [57] utilized OpenStreetMap data to estimate CO₂ emission inventories for over 100,000 cities and local government units across Europe. More recent research has leveraged geospatial big data to push the resolution even further, down to the neighborhood and building scales. For instance, Mouzourides et al. [10] used the ‘Local Climate Zones’ (LCZ) framework to conduct a spatial correlation analysis of carbon emissions at the neighborhood level in London; Wang et al. [12] constructed a 10-m resolution carbon balance map for Eindhoven, Netherlands, by integrating population, traffic, and building data; and ultra-fine emission databases have been developed for cities in China, such as Jinjiang (30-m resolution) [58] and Hong Kong (100-m resolution) [59]. These cases amply demonstrate that the hybrid method is a feasible pathway for achieving urban carbon emission modeling that is both macroscopically constrained and microscopically resolved.

Table 1. Comparison of Spatial Allocation Methods in Carbon Emission Modeling.

Feature	Top-Down Spatial Allocation Methods	Bottom-Up and Hybrid Methods
Core Concept	Relies on allocation factors to downscale aggregate data from macro- to micro-spatial units.	Balances macroscopic consistency with microscopic precision by using bottom-up source data as a core, constrained by top-down aggregate totals.
Data Foundation	Aggregate statistical inventories; spatial proxy variables such as nighttime lights, population density grids, and land use data.	Integration of micro-scale source data and macro-data: point source data, traffic networks, population density, building data, and provincial/municipal energy statistics.
Representative Datasets	ODIAC, EDGAR	Vulcan 3.0, CHRED
Key Advantages	Computationally efficient, suitable for large-scale and long time-series analysis; data are relatively easy to acquire [41].	Accurately characterizes local spatial heterogeneity; strongly linked to the physical mechanisms of emission processes and highly policy-relevant [60].
Key Limitations	Overlooks local variations; exhibits lower accuracy and significant uncertainty at finer scales [61].	Data acquisition is difficult and costly, and the computational process is complex and time-consuming, making global-scale application challenging [21].
Applicable Scenarios	Macroscopic studies requiring rapid assessment of large-scale, long-term spatial patterns of carbon emissions.	Fine-grained research that requires high-precision, high-resolution emission data to support atmospheric inversions, urban policy formulation, and the assessment of mitigation effectiveness.

Table 1. Comparison of Spatial Allocation Methods in Carbon Emission Modeling.

Feature	Top-Down Spatial Allocation Methods	Bottom-Up and Hybrid Methods
Core Concept	Relies on allocation factors to downscale aggregate data from macro- to micro-spatial units.	Balances macroscopic consistency with microscopic precision by using bottom-up source data as a core, constrained by top-down aggregate totals.
Data Foundation	Aggregate statistical inventories; spatial proxy variables such as nighttime lights, population density grids, and land use data.	Integration of micro-scale source data and macro-data: point source data, traffic networks, population density, building data, and provincial/municipal energy statistics.
Representative Datasets	ODIAC, EDGAR	Vulcan 3.0, CHRED
Key Advantages	Computationally efficient, suitable for large-scale and long time-series analysis; data are relatively easy to acquire [41].	Accurately characterizes local spatial heterogeneity; strongly linked to the physical mechanisms of emission processes and highly policy-relevant [60].
Key Limitations	Overlooks local variations; exhibits lower accuracy and significant uncertainty at finer scales [61].	Data acquisition is difficult and costly, and the computational process is complex and time-consuming, making global-scale application challenging [21].
Applicable Scenarios	Macroscopic studies requiring rapid assessment of large-scale, long-term spatial patterns of carbon emissions.	Fine-grained research that requires high-precision, high-resolution emission data to support atmospheric inversions, urban policy formulation, and the assessment of mitigation effectiveness.

3.2. Data-Driven Approaches in Spatial Carbon Emission Modeling

3.2.1. Single-Data-Driven Models

Single-data models represent the most straightforward approach to top-down spatial allocation, utilizing a single type of geospatial proxy to disaggregate macro-scale emission inventories. These inventories, typically derived from socioeconomic statistics on sectoral and regional energy consumption [62], are downscaled using proxies like NTL The primary advantage of these models lies in their methodological simplicity and lower computational overhead, making them suitable for applications where data availability is constrained.

NTL is arguably the most widely used single proxy, as regional light intensity serves as a strong indicator of energy consumption and economic activity. The foundational work of Elvidge [31] and Doll [63] first established this correlation and demonstrated its utility for carbon emission mapping. For decades, large-scale studies have predominantly relied on data from DMSP-OLS (1 km) and NPP-VIIRS (500 m) [43,64]. These sensors have enabled the construction of consistent, long-term emission datasets spanning from 1992 to the present [33,34,65]. Advanced algorithms, such as PSO-BP used by Chen [66], have been developed to harmonize these different sensor records for robust time-series analysis. More recently, the advent of very-high-resolution sensors such as Luojia-1-01 (130 m) [67,68] and SDGSAT-1 (10 m) [69,70] offers the potential for more detailed mapping. The improved spatial fidelity is significant; for instance, carbon mapping using Luojia-1-01 has demonstrated a median locational error of less than one pixel [71]. However, the limited temporal coverage and data availability of these newer sensors currently restrict their application primarily to recent, localized studies.

Population density is another fundamental proxy, predicated on the well-established positive linear correlation between population size and carbon emissions, with reported elasticity coefficients ranging from 1.0 to 1.65 [72,73]. Widely used global population datasets, such as LandScan (1 km) and WorldPop (100 m), have served as the basis for prominent gridded emission products. For instance, the Carbon Dioxide Information Analysis Center (CDIAC) inventory used population density for disaggregation [33], while other studies have used it to reconstruct historical emission trends [74]. More complex models combine population with economic data to delineate urban footprints, such as the 250-m GGMCF, which has been used to estimate the carbon footprints of 13,000 cities worldwide [57,75].

LULUCF data offer a framework for assigning emission coefficients to different surface types, a methodology aligned with IPCC guidelines for greenhouse gas inventories [76,77]. The utility of LULC data has grown substantially as the spatial resolution of remote sensing products has improved, evolving from early 1-km datasets to the current 30-m standard, with emerging products now reaching 1–2 m resolution [78]. This has enabled diverse applications, from constructing global historical emission datasets [42] to simulating the spatial distribution of emissions based on contemporary land use patterns [79]. Advanced applications now leverage machine learning and multi-source data to achieve more detailed classifications of developed land, thereby improving multi-scale emission characterization and prediction [80].

Despite their utility, single-data models possess significant, inherent limitations [15,21]. A single proxy—whether NTL, population, or land use—oversimplifies the complex, multifaceted socioeconomic processes that drive emissions, leading to substantial allocation errors [81]. These inaccuracies are not trivial; studies have documented downscaling error rates as high as 50% for NTL data [82] and have found that top-down global datasets diverge from high-resolution regional inventories by 50–250% at the city scale [61]. This issue is systemic, as all single proxies tend to neglect the internal heterogeneity of urban areas. Moreover, these models are acutely vulnerable to the quality and intrinsic flaws of the proxy data itself, such as the well-documented issues of sensor saturation and background noise in NTL imagery. These profound shortcomings underscore the critical need for more sophisticated modeling approaches that can integrate multiple data sources.

3.2.2. Multi-Data-Driven and Data Fusion Models

To overcome the limitations of single-proxy methods, multi-data models integrate diverse datasets to improve the spatial resolution and accuracy of emission inventories (see Table 2). This approach allows for a more nuanced disaggregation by leveraging data specific to different emission sectors. For example, researchers have combined NTL data with traffic flow data to better resolve transportation emissions [83], fused population data with building energy consumption metrics to refine emissions from the building sector [24], and used POI data to differentiate commercial and industrial emissions within mixed-use areas [84,85]. By capturing the complex correlations among these multidimensional inputs, such models can produce inventories at resolutions of 1 km or finer with significantly enhanced reliability [86]. However, this approach introduces significant challenges. A primary obstacle is the need for data harmonization, as datasets often have disparate spatial resolutions, temporal frequencies, and formats, complicating preprocessing and integration [87]. The computational burden also increases substantially with the volume and variety of data, demanding greater resources and more efficient algorithms [88]. Finally, a critical methodological issue is how to optimally weight the various data inputs during the fusion process to accurately reflect their respective quality and contribution to the final estimate [89,90].

Data fusion models represent an advanced evolution of multi-data approaches, employing sophisticated algorithms like machine learning and deep learning to move beyond simple data layering. Instead of mere superposition, these models aim for synergistic integration, capturing the complex, non-linear relationships between various data sources to more accurately model the spatial features of carbon emissions. While a common baseline approach involves concatenating features from multiple sources into a single vector, this naive method often fails to account for the intricate interdependencies and inherent heterogeneity across different data types. More advanced techniques have emerged to address this limitation. Some studies utilize Bayesian networks, which incorporate prior knowledge from sources like land cover data to guide the fusion process [91]. Others employ sophisticated deep learning architectures to extract modality-specific features—for instance, using CNNs for imagery—while leveraging attention mechanisms and BiLSTM networks to capture salient relationships and temporal dependencies [88,92]. A third frontier involves tensor-based frameworks, which represent multi-source data as a multi-dimensional tensor. This structure allows for the capture of higher-order, latent relationships among datasets that are missed by traditional vector-based methods [93]. The primary advantage of these advanced fusion models is their ability to leverage data complementarity to uncover latent relationships and translate them into highly accurate spatial emission patterns [18,80,94]. This makes them exceptionally well-suited for high-resolution modeling, as they can effectively characterize micro-scale heterogeneity and have demonstrated robustness even with sparse or incomplete data [95].

Table 2. Comparison of Data-Driven Approaches in Spatial Carbon Modeling.

Feature	Single-Data-Driven Model	Multi-Data-Driven Model	Data Fusion Model
Core Concept	Relies on a single type of proxy data for spatial allocation.	Integrates multiple data sources, assigning different weights for different sectors.	Utilizes complex algorithms for the deep fusion of multi-source data to uncover non-linear relationships.
Advantages	Simple methodology, low computational cost, suitable for data-limited regions or rapid macroscopic assessments.	Offers higher accuracy than single-data models and can reflect more diverse features [84,96].	Achieves high predictive accuracy and strong robustness, supporting detailed, high-resolution modeling [18,94].
Challenges	Low accuracy, fails to capture complexity, and is overly simplistic [81].	Presents difficulties in data integration, with weight allocation being a core challenge [89].	Weak model interpretability, algorithmic complexity, and extremely high computational costs [88,93].
Applicable Scope	Data-limited regions or large-scale macroscopic estimations.	Regional and city-level research requiring higher precision.	Cutting-edge scientific research or modeling tasks demanding extremely high fidelity in capturing micro-scale heterogeneity.

Table 2. Comparison of Data-Driven Approaches in Spatial Carbon Modeling.

Feature	Single-Data-Driven Model	Multi-Data-Driven Model	Data Fusion Model
Core Concept	Relies on a single type of proxy data for spatial allocation.	Integrates multiple data sources, assigning different weights for different sectors.	Utilizes complex algorithms for the deep fusion of multi-source data to uncover non-linear relationships.
Advantages	Simple methodology, low computational cost, suitable for data-limited regions or rapid macroscopic assessments.	Offers higher accuracy than single-data models and can reflect more diverse features [84,96].	Achieves high predictive accuracy and strong robustness, supporting detailed, high-resolution modeling [18,94].
Challenges	Low accuracy, fails to capture complexity, and is overly simplistic [81].	Presents difficulties in data integration, with weight allocation being a core challenge [89].	Weak model interpretability, algorithmic complexity, and extremely high computational costs [88,93].
Applicable Scope	Data-limited regions or large-scale macroscopic estimations.	Regional and city-level research requiring higher precision.	Cutting-edge scientific research or modeling tasks demanding extremely high fidelity in capturing micro-scale heterogeneity.

3.3. Characterization Methods of Spatial Carbon Emission Models

(1) Conventional Statistical Approaches.

Early carbon emission modeling was dominated by conventional statistical approaches, primarily linear and multiple regression models. These methods operate on the assumption of a linear or near-linear relationship between carbon emissions and various socioeconomic or physical proxies, such as population density, GDP, or NTL intensity [51,96,97]. The core objective is to fit a regression model that establishes a quantitative relationship between these independent variables and known emission totals, which can then be used to spatially allocate emissions across a landscape [62,98]. However, these models are constrained by several limitations. They demand rigorous statistical validation, are sensitive to data quality and quantity, and are often hampered by issues of multicollinearity among predictor variables, which can compromise model stability and interpretability [82].

(2) Spatial Mechanistic Methods Based on Spatial and Multiscale Regression.

To address the critical issues of spatial dependence and heterogeneity inherent in carbon emission data—which violate the assumptions of conventional regression—researchers have adopted local regression techniques. The most prominent of these is Geographically Weighted Regression (GWR), which accounts for spatial non-stationarity by allowing regression coefficients to vary locally. However, studies have found it can still yield unsatisfactory goodness-of-fit, largely due to GWR’s constraining assumption that all relationships being modeled operate at the same spatial scale, as defined by a single bandwidth [99,100]. In reality, the processes influencing carbon emissions are often multiscale; for instance, the impact of population may be highly localized, while policy factors might operate over a broader region. This recognition has led to the adoption of more advanced methods, such as Geographically and Temporally Weighted Regression (GTWR) for spatiotemporal analysis [100] and, notably, Multiscale Geographically Weighted Regression (MGWR) [79,101]. The key innovation of MGWR is that it allows the bandwidth to be optimized independently for each predictor variable, thereby enabling the model to simultaneously capture relationships operating at different spatial scales [99,102]. Despite these advances, a fundamental limitation persists: GWR and its derivatives are still predicated on linear relationships. As such, they often struggle to capture the complex, non-linear dynamics that characterize the drivers of carbon emissions.

(3) Machine Learning-Based Methods.

To overcome the linearity constraints of conventional statistical models, researchers have increasingly turned to machine learning (ML) methods. A diverse array of algorithms—including Artificial Neural Networks (ANN) [103], Multi-Layer Perceptron (MLP) [104,105], Random Forests (RF) [106,107,108], and Long Short-Term Memory (LSTM) networks [88,109]—has been applied to the spatial modeling of carbon emissions. The primary objective of using these methods is to capture the complex, non-linear relationships between emissions and a wide range of influencing factors, thereby creating more accurate and robust spatial characterization models. These applications demonstrate the versatility of ML. For instance, some studies employ deep neural networks to model the non-linear relationships between sectoral emissions and geospatial inputs, reporting characterization accuracies exceeding an R² of 0.99 [110,111]. Others have developed hybrid approaches for predictive modeling; Fattah [104] combined an MLP with a Markov chain model to forecast future emissions based on land use change, achieving a predictive accuracy of 94.12%. Another innovative direction is the fusion of spatial statistics with ML, such as integrating GWR with an ANN to construct a high-performance regression model that also achieved an R² of 0.99 [80,112].

(4) Integrated Statistical and Machine Learning Frameworks.

A frontier in carbon emission modeling involves developing hybrid frameworks that couple conventional statistical models with machine learning algorithms. This approach aims to synergistically combine the strengths of both paradigms: the interpretability of spatial regression with the predictive power and flexibility of machine learning [111,113]. The design of these hybrid models is often adaptive, with no single architecture being universally optimal. Instead, researchers select and combine methods to achieve a desired balance between predictive accuracy, computational complexity, and the interpretability of the results, tailored to the specific problem and data environment.

A range of innovative applications demonstrates this trend (see

Table 3. Comparison of Characterization Methods for Spatial Carbon Emission Models.

Method	Statistical Mechanistic	Spatial Regression	Machine Learning	Coupled Modeling
Core Concept	Establishes linear quantitative relationships.	Considers spatial dependence and heterogeneity.	Fits complex non-linear relationships.	Integrates the advantages of mechanistic, spatial, and machine learning methods.
Advantages	Simple, with a partial mechanistic basis.	Captures and explains spatial patterns [100].	Achieves very high predictive accuracy; suitable for complex non-linear problems [110].	Balances accuracy with interpretability [115].
Limitations	Cannot handle non-linear problems; sensitive to multicollinearity [82].	Relies on linear assumptions; has limited goodness-of-fit in complex scenarios [116].	“Black box” problem; model processes are difficult to interpret [106].	Complex model construction and calibration process [111].
Applicable Scope	Fundamental, macroscopic, and exploratory analyses.	Studies requiring the analysis of spatial patterns and differentiation.	Tasks primarily aimed at high-precision prediction and fitting.	Comprehensive research requiring high precision, interpretability, and scenario simulation.

).For instance, some studies integrate machine learning algorithms with spatial regression models to leverage their complementary strengths. Luo et al. [111] developed an optimized neural network (SWO-PSO-BP NN) that incorporates spatial heterogeneity, reporting a goodness-of-fit (R²) of 0.999 for carbon emission prediction in the Yangtze River Delta. Similarly, Zhang et al. [112] proposed a hybrid GWR-RF model, which significantly outperformed standalone models in predicting urban carbon emissions, while preserving spatial interpretability. Others integrate advanced ML models like CatBoost with spatial frameworks; for example, Yang et al. [99] coupled CatBoost with spatial lag models to enhance predictive power for scenario analysis while retaining spatial interpretability. Extending beyond simple regression, researchers have also coupled spatial statistical models with system dynamics for scenario prediction; Ding et al. [114] integrated system dynamics with land use simulation models to project future carbon emissions under different policy scenarios in the Pearl River Delta.

3.4. Uncertainty Analysis in Carbon Emission Spatial Modeling

Uncertainty analysis is a critical component for evaluating and improving carbon emission spatial models, yet it remains a significant gap in the literature; our review indicates that approximately 80% of studies do not conduct a formal uncertainty assessment. This omission is particularly concerning given that national-level CO₂ emission estimates for countries like China already carry an uncertainty of 8–24% [117]. In spatial modeling, this uncertainty is compounded by several factors specific to the downscaling process:

(1) Input Data Uncertainty: The foundational activity data used to calculate emissions are subject to inherent uncertainties arising from inconsistent statistical methodologies, reporting lags, commercial confidentiality, and susceptibility to short-term disruptions.

(2) Emission Factor Heterogeneity: Emission factors exhibit high spatiotemporal variability, influenced by micro-level factors and macro-level drivers. This heterogeneity is a primary source of uncertainty, which can range from 50% to 300% in fine-grained, multi-sector models [118].

(3) Structural Model Uncertainty: Significant uncertainty arises from the model structure itself, particularly the simplified, often linear, relationships assumed between spatial proxies and actual emission sources. These relationships are, in reality, highly non-linear, indirect, and spatiotemporally variable, and ignoring this complexity introduces substantial structural error.

(4) Scale Effects and Aggregation: The choice of spatial resolution directly impacts uncertainty. As demonstrated by Stagakis [119], aggregating fine-scale estimates to coarser resolutions can substantially reduce error—for instance, by up to 80% when moving from 1-km to 100-km grids—highlighting the influence of the modifiable areal unit problem on model outputs.

Currently, researchers have attempted to reduce uncertainty through methods such as optimizing multi-source data fusion, improving spatial proxies, and selecting appropriate resolutions [120,121]. The Vulcan project, developed by Gurney [122], quantifies the uncertainty of each process at the level of individual emission sources and then aggregates it upwards to determine the uncertainty range for total emissions. In carbon footprint research, Moran [57] used Monte Carlo simulation, employing random sampling and repeated calculations to generate an emissions dataset containing multiple possible outcomes. By analyzing the distribution of this resulting set, they quantified the confidence interval for the final emission estimates. However, developing more targeted quantification methods for the aforementioned domain-specific sources of uncertainty, and constructing modeling frameworks that can adapt to policy and technological changes, remain core directions for future research.

4. Discussion

4.1. The Role of Geospatial Big Data in High-Resolution Carbon Modeling

Geospatial big data—encompassing multi-source, heterogeneous data streams with explicit geographic attributes—is revolutionizing carbon emission modeling [21,87,112,123]. The high spatiotemporal resolution, dynamic nature, and multidimensionality of these datasets provide unprecedented opportunities for the fine-grained characterization of emission-producing activities [124]. This section examines how key categories of geospatial big data are enabling more precise and detailed emission models.

High-resolution remote sensing data presents a dual nature for carbon emission spatial modeling [123]. On the one hand, the detailed granularity of the data allows for the identification of features within the built environment, such as buildings and roads, which can enhance spatial representation and predictive accuracy. On the other hand, reliance on high-resolution data also introduces several challenges. First, data acquisition is costly, as the expense of purchasing and processing commercial satellite imagery can limit its application in developing countries and less-developed regions. Second, the uneven coverage of such data often leads to issues with missing information or outdated updates [125]. Furthermore, processing high-resolution data demands substantial computational resources and specialized algorithmic expertise, posing a significant technical barrier for many research institutions and government departments.

(1) Earth Observation Data: NTL data remain a critical tool for spatializing energy statistics and are widely used to estimate CO₂ emissions linked to electricity consumption and industrial and commercial activity [83,113]. Fusing NTL with other data, such as mobile phone signaling, can further enhance estimation accuracy [12,83,126]. Meanwhile, high-resolution optical imagery from platforms like Landsat, Sentinel, and Planet enables the detailed mapping of urban morphology. By identifying features such as buildings, roads, and green spaces, and extracting physical parameters like 3D building geometry and rooftop materials, these data provide essential inputs for sophisticated urban energy consumption and emission models. This imagery is also crucial for identifying and mapping point and line emission sources, such as industrial facilities and transportation networks [127,128].

(2) Human Mobility and Activity Data: Data streams capturing human behavior and mobility provide direct measurements of activity levels, a significant advance over static proxies. Mobile phone signaling records and GPS trajectory data [129] reveal the dynamics of population distribution, commuting patterns, and traffic volumes, enabling high-resolution, bottom-up estimation of emissions from the transportation and residential sectors. For example, researchers have used mobile data to differentiate between residential and commuting populations to create detailed maps of residential carbon emissions [21]. In parallel, social media check-ins and POI data offer novel proxies for micro-scale human activity hotspots and consumption patterns. This information can be used to refine emission estimates from commercial and service sectors or to improve models by parameterizing emissions based on explicitly identified urban functional zones [113,130,131].

(3) Foundational Geospatial Datasets: Foundational urban geospatial datasets, including detailed building footprints, comprehensive road networks, and official land use or zoning data, provide the essential spatial framework for high-resolution modeling [19]. These vector layers act as a geospatial scaffold, allowing researchers to link energy consumption and emission data to specific geographic entities. This enables precise spatial localization and robust sectoral disaggregation in bottom-up and hybrid models, facilitating in-depth analysis of intra-urban emission heterogeneity [132].

4.2. Geospatial Big Data Driving the Transition to Fine-Scale Carbon Emission Modeling

4.2.1. A Paradigm Shift: From Macro-Scale Statistics to Micro-Scale Modeling

The evolution of carbon emission spatial modeling toward finer resolutions represents a fundamental paradigm shift. This transition is driven by a move away from a reliance on conventional, aggregate statistical data toward the integration of geospatial big data. The profound differences between these two data paradigms—in their core characteristics, associated modeling techniques, and ultimate application potential—dictate the accuracy, spatiotemporal detail, and policy relevance of the resulting emission inventories.

The integration of geospatial big data has catalyzed a profound transformation in the spatial granularity of carbon emission inventories. This evolution is evident in a clear historical progression. Around 2010, emission products were typically coarse, with resolutions of 10 km, 25 km, or even 1° [75,84], as they relied on macro-scale statistical data and broad population proxies for spatial allocation. By the period of 2015–2020, the improved availability of global datasets for population, land use, and nighttime lights had established 1 km as the mainstream resolution [52]. In the last few years, the explosive growth of multi-source geospatial big data has enabled a further leap, with numerous studies now producing ultra-high-resolution emission maps at scales of 300 m, 100 m, and even 30 m [10,133].

While higher spatial resolution generally improves inventory accuracy and supports more targeted mitigation policies, selecting an “optimal” scale is a complex methodological challenge. It requires balancing the need for detailed feature capture against computational cost and the risk of obscuring broader regional patterns. Research demonstrates that the optimal resolution is not universal but is contingent on the specific geographic context, data inputs, and analytical objectives. Different studies have employed various techniques to identify appropriate scales, such as analyzing landscape metrics to determine that a 200-m resolution is ideal for county-level research [89], or identifying characteristic scale domains of 90 m and 1260 m for a province [101,102]. Others have argued for a 30-m standard for urban studies to avoid distorting key features [125] or have used spatial autocorrelation statistics like Moran’s I to identify 350 m as the optimal scale for spatiotemporal analysis in a specific city [21].

Given these trade-offs, a single, fixed resolution often struggles to efficiently balance accuracy in dense urban cores with computational feasibility in sparse suburban and rural landscapes. To address this, a promising direction is the development of “scale-adaptive” or multi-scale modeling frameworks [99]. Such a framework would dynamically adjust the analytical resolution based on regional emission characteristics. For example, a high resolution could be employed in urban centers, leveraging detailed POI and building data for fine-grained estimation. Concurrently, a coarser resolution could be used in surrounding areas where emission sources are less dense, relying on nighttime light and land use data for rapid and efficient allocation.

4.2.2. Sector-Specific and Multidimensional Modeling

The application of geospatial big data enables a shift from generic spatial allocation to more nuanced, sector-specific modeling. Recent literature indicates that over 80% of urban carbon emissions can be attributed to three key dimensions of the urban system: its spatial function, physical morphology, and transportation networks [134]. Consequently, developing models that can characterize and disaggregate emissions along these dimensions has become a central focus of current research.

First, Modeling the urban spatial function is a primary application. Urban functional zones—such as residential, commercial, and industrial areas—act as spatial units with distinct sectoral attributes and emission profiles. A key innovation has been the use of geospatial big data, particularly POI, to automatically identify these zones [19]. For instance, Luo [40] used POI kernel density analysis to classify functional zones in Xi’an, China, with a reported accuracy of 84.77%. Identifying these zones is critical because carbon intensity varies substantially among them, allowing for the development of more sophisticated, zone-specific allocation models that move beyond one-size-fits-all proxies. This tailored approach enables more precise emission estimation. For example, emissions in industrial zones can be directly allocated using industrial point-source data or identified via thermal remote sensing [86,135]; emissions from residential and public service zones can be modeled using high-resolution population data and building footprints [95,136]; and emissions in commercial zones can be correlated with proxies for economic activity, such as nighttime light intensity or social media data [137].

Second, the urban spatial morphology—the physical shape and pattern of the built environment—is a critical determinant of emissions, explaining up to 45.9% of their variation in urban areas [138]. Early research in this area focused on 2D metrics, establishing that more compact and contiguous urban forms are generally associated with lower carbon emissions [24]. The recent availability of high-resolution building vector and height data has enabled a significant evolution in this research, from coarse 2D geometric analysis to fine-grained, 3D characterizations at the building or plot level [139]. These modern approaches can model the relationship between emissions and specific morphological attributes. For example, studies have shown that metrics such as floor area ratio, building height, and commercial activity intensity are effective predictors of land parcel energy consumption [140]. By coupling these morphological data with other proxies like nighttime lights and POI density, researchers can achieve a more comprehensive characterization of urban emissions. Building materials and age are critical indicators of energy efficiency, as they not only reflect the energy conservation standards adhered to at the time of construction but also indirectly correlate with the type and efficiency of heating, ventilation, and air conditioning systems, as well as the insulation performance of walls and roofs, potentially leading to significant increases in operational carbon emissions [136,141,142].

Third, the transportation system is a major source of urban emissions, and accurately modeling its spatial distribution has long been a key research challenge Traditional approaches relied on static proxies like road network length and density, which failed to capture dynamic traffic conditions and thus had limited accuracy [143]. This has been revolutionized by the advent of new data streams. Data from Intelligent Transportation Systems (ITS) provide real-time information on vehicle speeds, traffic volumes, and fleet composition, dramatically improving the resolution and fidelity of on-road emission models [83]. In parallel, human mobility data from sources like mobile phone records, public transit card swipes, origin-destination matrices, and vehicle GPS trajectories provide powerful, bottom-up inputs for quantifying emissions from all modes of urban transport [144,145].

4.3. Challenges in Geospatial Data Integration for Carbon Modeling

Advancing the accuracy and predictive power of spatial carbon emission models hinges on overcoming fundamental challenges in data integration and model development [126].

First, integrating geospatial big data remains a primary obstacle. These datasets, while information-rich, are inherently heterogeneous, originating from multiple sources and spanning diverse spatial and temporal scales. This heterogeneity creates significant problems with spatiotemporal misalignment and data inconsistency, which are compounded by a lack of robust frameworks for their synergistic fusion [85,146]. Future research must prioritize the development of standardized geospatial databases that adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) principles to create a coherent analytical foundation [147].

Second, undeveloped data exchange and sharing mechanisms create critical barriers to progress. The prevalence of data silos among government agencies, research institutions, and industries severely limits interdisciplinary collaboration [123]. These barriers are both institutional and technical, manifesting as incompatibilities in data formats, coordinate systems, and measurement units, all of which necessitate complex and labor-intensive preprocessing. Establishing unified data governance and sharing frameworks is therefore imperative. Such frameworks must include standardized protocols and application programming interfaces to ensure the seamless exchange, consistency, and availability of carbon-related data across sectors.

Third, the limited generalizability of current models across different geographical and temporal scopes curtails their practical utility. Existing spatial models are often overfitted to the unique characteristics of a specific region or dataset, causing their predictive performance to degrade substantially when applied to new geographical contexts, socioeconomic conditions, or time periods. This lack of transferability highlights a critical need to develop more generalizable modeling methodologies [21,138]. Future efforts should focus on creating algorithms that can discern common underlying drivers from regional-specific patterns, thereby enhancing the transfer learning capabilities of models for robust cross-regional application, particularly in data-scarce environments [94,112].

Fourth, Data privacy and ethical considerations present a significant and growing impediment to research that utilizes human activity data. Although novel sources such as mobile phone signaling records, vehicle GPS trajectories, and social media check-ins offer unprecedented spatiotemporal resolution, this information is inherently sensitive [148,149]. Its fine-grained detail can be used to infer personal routines, residential and workplace locations, and even reveal individual identities [123]. These profound ethical and legal risks create substantial barriers to data sharing and dissemination, prompting corporations and governments to exercise extreme caution when releasing such information. A critical research frontier, therefore, is the development and implementation of robust privacy-preserving frameworks. This includes advancing anonymization techniques, designing secure data aggregation protocols, and establishing clear ethical guidelines for data acquisition and stewardship.

Fifth, despite significant progress in fine-grained carbon emission spatial modeling, its development exhibits a pronounced geographical imbalance. In many of the world’s less-developed regions, precise carbon emission spatial modeling using hybrid methods is often infeasible due to challenges such as missing or lagging official statistical data, a lack of localized emission factors, and inadequate infrastructure [50,150,151,152]. Therefore, future research for these regions must prioritize the development of transferable and simplified modeling frameworks. Such frameworks could involve several key strategies. A foundational approach is to utilize globally available, low-resolution datasets, such as remote sensing data from NTL and LULC, as well as open-source geographic data like OSM. This can be complemented by developing robust downscaling algorithms to effectively integrate sparse local data points. Moreover, methods like model transfer and knowledge distillation offer a promising path by pre-training a model in a data-rich region and then fine-tuning it with minimal local data from a data-scarce region, thereby reducing the dependency on large-scale local datasets. Finally, implementing data collection through crowdsourcing and citizen science, by engaging the public via community mapping and mobile applications, can not only provide a valuable supplement to official statistics but also embodies the governance principle of public participation.

4.4. Assessment of Mitigation Efficacy Using Fine-Scale Spatial Modeling

Conventional macro-scale analyses have been instrumental in identifying broad drivers of carbon emissions, consistently showing, for example, that emissions are elevated in densely populated and industrialized regions. However, these coarse frameworks are incapable of quantitatively assessing the mitigation efficacy of targeted interventions. They cannot, for example, evaluate the effectiveness of densification policies by determining the precise population density at which emissions peak, nor can they assess the impact of industrial restructuring by identifying the relative contributions of specific industrial activities, or characterize the potentially non-linear associations between urban form and carbon output [12,102,153]. Critically, carbon emissions exhibit significant spatial heterogeneity, with sources and drivers varying dramatically between cities and even across neighborhoods. Traditional large-scale studies obscure these local nuances, thereby limiting the practical application of findings for evaluating precision carbon management and effective policymaking [154].

The emergence of geospatial big data and advanced modeling techniques provides the high-resolution inputs necessary to move beyond these limitations. By enabling micro-scale analysis, these tools allow for a direct and comprehensive assessment of mitigation policies and a deeper investigation into the complex interplay between carbon emissions and their drivers at the urban and sub-urban levels [71,155].

Recent studies leveraging these fine-grained approaches have yielded critical and sometimes contradictory insights, highlighting the importance of local context when assessing the potential efficacy of different mitigation strategies [72].

(1) Urban Functional Zones: While industrial zones have traditionally been considered the primary source of urban carbon emissions, fine-scale analyses present a more complex picture, enabling a more precise assessment of mitigation potential within different zones. Although industrial land use has traditionally been considered the dominant contributor to urban carbon emissions, fine-scale analyses are revealing conflicting evidence. For instance, one study using statistical data and urban morphology identified industry as the dominant emitting sector (37.9%), followed by transportation (31.3%) and residential areas (18.6%) [85]. In contrast, another analysis of Shanghai, employing OpenStreetMap (OSM) road network and POI data to delineate functional zones, found that commercial zones contributed most significantly (34%), followed by residential zones (26%) and, distantly, industrial zones (12%) [11]. Such research also suggests that the spatial granularity of functional zoning itself influences emission levels, with total emissions exhibiting an inverted U-shaped trend as the resolution of the zones increases [81,109].

(2) Urban Spatial Morphology: There is a growing consensus that well-designed urban morphology—including appropriate floor area ratios, building and street density, functional mixing, and public transit accessibility—can positively influence energy consumption [156,157]. High-resolution modeling quantifies these relationships with increasing precision. For example, Ding [158] determined that targeting a medium level of urban morphological compactness can maximize carbon reduction effects. Examining building patch configuration, Shen [159] found that carbon emissions could be reduced by maximizing the area of building plots while minimizing their spatial dispersion. Other work has established specific quantitative thresholds, finding that residential land emissions peak at a population density of 6634 persons/km² and that residential buildings with 10 stories are associated with maximum carbon output [72,160].

(3) Land Use Patterns: Fine-scale analysis of land use has also revealed opportunities for mitigation. At the patch scale, optimizing for low-carbon land use has been shown to potentially increase annual carbon sinks by over 129 tons [161]. At the city scale, research indicates that a 1% increase in land use intensity can correspond to a 1.3% improvement in urban energy efficiency, demonstrating a direct link between compact development and reduced energy demand [162].

5. Conclusions

Traditional spatial models for carbon emissions are no longer sufficient to meet the complex research demands of modern cities. Hybrid methods that integrate top-down and bottom-up approaches leverage the advantages of both, achieving multi-scale, fine-grained modeling through macroscopic constraints and microscopic allocation, making them adaptable to diverse research scenarios. A single data source is often inadequate for accurately reflecting complex carbon emission patterns; consequently, multi-source data fusion significantly enhances model accuracy and applicability. Mechanistic-based approaches, by combining the strengths of statistical and machine learning models, demonstrate excellent adaptability across various scales and complex environments. Geospatial big data has overcome the limitations of traditional spatial data in terms of spatiotemporal resolution and depth of information, revealing micro-scale relationships between human activities and carbon emissions. This enables a paradigm shift in carbon emission modeling, transitioning from traditional national and provincial statistical analysis to micro-scale research at the city, neighborhood, and even building levels. However, the lack of cross-regional generalizability in models and the absence of robust data-sharing mechanisms remain key bottlenecks hindering the field’s development.

Looking ahead, fine-grained spatial modeling of carbon emissions, supported by geospatial big data, will be foundational for achieving carbon neutrality goals. This will be based on data integration and high-resolution research, utilizing multi-scenario simulations and interdisciplinary collaboration as key methods. The primary future research directions are as follows:

Constructing a multimodal spatiotemporal data fusion platform based on current AI technology: Research should focus on developing a multimodal platform that integrates data from satellite remote sensing, ground sensors, social media, the Internet of Things (IoT), and unmanned aerial vehicles (UAVs). This will enable the real-time collection, dynamic analysis, and high-precision modeling of carbon emission data. By combining multi-source spatiotemporal big data, it will be possible to conduct high-frequency monitoring of urban carbon emission status. This allows for real-time tracking and adjustment of the implementation of ‘carbon peak and carbon neutrality’ action plans, leading to the formation of dynamically updatable technical methods for carbon accounting and scenario prediction.

Developing scenarios for future simulation and policy analysis: This involves promoting multi-scenario simulations that integrate high-resolution spatial models with urban-scale studies based on the Shared Socioeconomic Pathways (SSPs), enabling the quantitative simulation of future carbon emission spatial patterns under various urban development models. This framework can then be extended to quantitatively assess the mitigation effectiveness of specific policies. Ultimately, by combining scenario analysis with multi-objective optimization—and comprehensively considering dynamic factors such as technological progress, policy interventions, and behavioral changes—it will be possible to design and evaluate various ‘carbon peak and carbon neutrality’ roadmaps. Such an approach allows for a comprehensive measurement of the socioeconomic costs and environmental benefits associated with different pathways.

Building a global, open, real-time carbon emission database: Future efforts should establish a global, open, real-time carbon emission database covering various scales and temporal resolutions. This database should integrate regional and sectoral emission factor data to support high-precision modeling and the formulation and coordination of global carbon management policies. It will require the comprehensive use of new technologies such as remote sensing, artificial intelligence, machine learning, and high-throughput computing to process production data and cross-validate it with ground-based measurements, ultimately forming a near-real-time carbon emission dataset with high spatiotemporal resolution.