CO₂ emission and socioeconomic inventories of Guangdong-Hong Kong-Macao Greater Bay Area and surrounding cities 2000-2022

Abstract

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is a leading economic region and a pilot demonstration region of carbon peaking in China. The city-level time-series CO₂ emission inventories of the GBA region are crucial for the formulation of policies on climate change mitigation pathways. However, the region lacked a consistent and comparable time-series city-level CO₂ emissions inventory dataset. In this study, we provided CO₂ emission and socioeconomic inventories of the GBA cities and their surrounding twelve Guangdong cities from 2000 to 2022. The CO₂ emission inventories were compiled by 47 economic sectors, 17 types of fossil fuels, and four industrial processes. The dataset provides temporal emissions estimates that support the design of regions’ mitigation strategies, and help China achieve its goal of peaking carbon emissions before 2030.

Background & Summary

Cities are responsible for over 70% of global carbon dioxide (CO₂) emissions from energy consumption¹, highlighting their critical importance in addressing climate change and emissions reduction. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in southern China, featuring rapid urbanization and world-class city clusters, is at the forefront of promoting comprehensive green transition in economic and social development^2,3. The GBA consists of nine Guangdong Province cities, Hong Kong, and Macao (Fig. 1). This region contributed 11% of the national Gross Domestic Product (GDP) in 2024 with only 0.58% of the territory and 6% of the population⁴. The other twelve Guangdong cities surrounding the GBA had close ties with the GBA cities in terms of industry and infrastructure⁵. In 2022, the GBA’s GDP growth rate of 9.3% ranked first among the four globally prominent bay areas, followed by the New York Metropolitan Area (7.2%), the Tokyo Bay Area (3.5%), and the San Francisco Bay Area (3.3%)^6,7,8. But the energy consumption per unit of GDP in GBA was higher than the three bay areas’ average due to high economic growth⁹. With the continuous growth of population and economy, the energy consumption and resource pressure in GBA were expected to increase¹⁰. Adapting to global climate challenges, the Chinese government set ambitious goals to peak carbon emissions by 2030 and reach carbon neutrality by 2060, and the GBA was identified as one of the pilot demonstration regions for peaking carbon emissions and achieving carbon neutrality³. The GBA has taken measures to reduce CO₂ emissions, including replacing fossil fuels with clean energy and optimizing the industrial structure¹¹.

Fig. 1

Geolocation of the Guangdong-Hong Kong-Macao Greater Bay Area and surrounding cities.

Consistent, comparable, transparent, and time-series emission inventories are crucial for city-level decision-makers to assess the effectiveness of emission mitigation efforts and develop targeted climate action plans through identifying key emission sources. Existing studies have estimated CO₂ emissions of GBA but mainly focused on specific socioeconomic sectors, such as residential sector¹² and power generation¹³. Some studies focused on individual core cities^14,15 or specific years^16,17,18, limiting the understanding of time-series variations in carbon emissions. The comparability of some cities’ emission inventories is limited due to inconsistencies in accounting system boundaries and emission factor selections^{16,17,19,20,21}. Some studies estimated city-level CO₂ emissions with proxy data (e.g., GDP, night-time light imagery, building morphology)^12,15,19,21, which may overlook sectoral information that helps identify key emission contributors (Table 1).

Table 1 Previous studies on carbon dioxide emission accounting in the Greater Bay Area.

Guangdong Province is the largest greenhouse gas (GHGs) emitter in southern China^22,23, and CO₂ was identified as the key contributor (92%) of total GHGs^24,25. The CO₂ emissions monitoring and urban climate change mitigation efforts were further elevated by the continuing urbanization and population growth in the region. This dataset provided comparable, transparent, and verifiable CO₂ emissions inventories for nine GBA cities and twelve surrounding cities. The inventories covered 17 types of fossil fuel and 47 socioeconomic sectors, which were consistent with China’s national and provincial inventories.

The dataset supports the refinement of low-carbon strategies and the design of sustainable development policies at city-level. Consistent city-level emission estimates would facilitate multi-scale and inter-city carbon mitigation evaluation and comparative studies. Detailed sectoral and energy-specific emissions could be used for city-level studies focusing on mitigation pathways and related policy making.

Methods

Emission scope

This study followed the Intergovernmental Panel on Climate Change (IPCC) guidance²⁶ to estimate in-boundary CO₂ emissions from fossil fuel combustion and industry processes of prefectural-level cities in Guangdong Province, 2000–2022. Seventeen types of fossil fuel consumption (Table 2), 47 socioeconomic sectors (Table 3), and four types of industrial processes were considered from the production side. The emissions from electricity and heat production are calculated through primary energy inputs, without considering imports outside the administrative territorial boundary. Energy losses from transport and transformation processes, or used as chemical raw material, were removed from energy consumption to avoid double-counting.

Table 2 Emission factors of fossil fuels.

Table 3 Socioeconomics sectors and category.

Emission calculation and inventory construction

This study constructed the CO₂ emission inventories based on a uniform carbon emission accounting framework (Fig. 2) proposed by our previous work^27,28. This study considered 17 types of energy, which can generally be categorized as coal, oil, and natural gas (Table 2). The inventories also incorporated emissions from four key industrial processes, including the production of cement, coke as a reducing agent, ammonia, and lime, which together contribute more than 95% of China’s process-related emissions²⁹.

Fig. 2

Diagram of CO₂ emission inventories construction for GBA and surrounding cities.

Energy-related CO₂ emissions (CE_e) were calculated based on the mass balance of fossil fuel consumption converted to CO₂ emissions (Eq. 1).

$$C{E}_{e}=\sum {AD}\times {EF}=\mathop{\sum }\limits_{i=1}^{17}\mathop{\sum }\limits_{j=1}^{47}{{AD}}_{{ij}}\times {{NCV}}_{j}\times {{CC}}_{j}\times {O}_{{ij}}$$

(1)

where, i and j denoted the energy types and socioeconomic sector, respectively; AD referred to the activity data (i.e., fossil fuel consumption); ${NC}{V}_{j},C{C}_{j}$ and ${O}_{{ij}}$ represented three emission factors (EF), namely, net caloric value in the ${j}^{{th}}$ sector, carbon content in the ${j}^{{th}}$ sector, and oxygenation efficiency of ${i}^{{th}}$ energy type in ${j}^{{th}}$ sector. These emission factors were collected from our previous work³⁰ and listed in Table 2.

Process-related emissions (${{CE}}_{p}$) were produced during chemical reactions in industrial processes. They were estimated using Eq. 2.

$${{CE}}_{p}=\mathop{\sum }\limits_{t=1}^{4}{{AD}}_{t}\times {{CE}}_{t}$$

(2)

where, ${{AD}}_{t}$ and ${{CE}}_{t}$ denoted the activity data (i.e., production of the industrial products) and the corresponding emission factor of industrial process of product t, respectively. The emission factor for cement and lime production were sourced from Liu et al.³¹ and Shan et al.³², respectively, and the rest of the emission factors were sourced from IPCC²⁶.

Activity data of fossil fuels were collected from the Energy Balance Tables (EBTs), which provided the transformation and final consumption of each fuel^27,28,33. The EBTs for Guangzhou (2000–2013), Qingyuan (2005–2014), and Yangjiang (2006–2022) were collected from the city’s statistical yearbooks^34,35,36. For other cities and individual years without EBTs, Guangdong provincial EBTs sourced from national energy statistical yearbooks³⁷ were scaled down to the city-level by the city’s share of the sector’s GDP and population. Energy consumption data were missing in Dongguan (2000–2013), Jiangmen (2004), Shenzhen (2004, 2006, and 2007), Zhongshan (2004), and Zhuhai (2000), and their energy consumption data were derived from the industry’s value-added from adjacent years. Due to data limitations, statistics from Hong Kong and Macao could not be included in this accounting framework. Emissions data for the two cities from 2000 to 2022 were sourced from the Emissions Database for Global Atmospheric Research (EDGAR) dataset version 8.0^38,39,40, and appended as supplementary references to ensure the completeness of the inventory. The EDGAR dataset and our inventories adhered to the IPCC guidelines for emission estimations.

Socioeconomic data

Data on population and GDP of 23 cities were collected from each city’s statistical yearbook. Detailed sources could be found in our dataset at Figshare⁴¹. The carbon emissions per unit of GDP and per capita in the inventory are derived using the population and GDP data.

Data Records

The datasets consisted of CO₂ emission inventories and socioeconomic data for the GBA and surrounding Guangdong cities, spanning from 2000 to 2022. The dataset has been made available at Figshare⁴¹. All inventories were organized in Microsoft Excel spreadsheets using a uniform structure. The carbon emission inventories were arranged as follows:

• 1. Summed CO₂ emissions year-by-year at the city level [“Emission inventory.xlsx”, in sheet “Overview”];

• 2. Detailed CO₂ emissions by 47 industry sectors [“Emission inventory.xlsx”, in sheet “CityEmission_byEnergy”] and by 17 energy types for each city [“Emission inventory.xlsx”, in sheet “CityEmission_bySector”]. Detailed emission data for Dongguan (2000–2013), Shenzhen (2004, 2006, and 2007), Jiangmen (2004), Zhuhai (2000), and Zhongshan (2004) were unavailable due to limited data accessibility.

Apart from the emission inventories, the socioeconomic data were compiled as a reference for the users. To make the records comparable across the year, the constant price of 2022 was applied to estimate GDP in chained volume. They were arranged in a single Excel file and recorded as follows:

• 1. Year-end population at city-level, in 10 thousand person [“Socioeconomic data.xlsx”, in sheet “Population”];

• 2. GDP in chained (2022) volume at the city-level, in 100 million Renminbi (RMB) [“Socioeconomic data”, in sheet “Gross Domestic Product”];

• 3. Price deflators of GDP (year 2022 = 100) at the city level [“Socioeconomic data.xlsx”, in sheet “Gross Domestic Product”].

Technical Validation

Statistical analysis

Figure 3a illustrated the temporal evolution of the emissions in the GBA and surrounding cities from 2000 to 2022. Over the 23-year period, the CO₂ emissions have increased at an average of 5.23% per year, reaching a maximum of 819 million tons in 2021. A rapid increase occurred during 2000–2007 with an annual growth rate of 9.65%, and surrounding cities grew 3.91% faster than GBA cities. Growth rates fluctuated after 2008 and slowed down to 2.97% after 2011. The slowdown in growth is attributed to fossil fuel reduction policies and technological innovations (e.g., clean energy promotion in energy production, industrial manufacture, transportation, and residence⁴²), which lowered carbon intensity and emissions²⁰. From 2020 to 2021, there was a 10.97% surge as energy demand rebounded following the COVID-19 pandemic, aligning with the national trend of economic recovery^43,44.

Fig. 3

CO₂ emissions of GBA and surrounding cities. (a) CO₂ emission trend 2000–2022; (b) comparison of socioeconomic emissions across global bay areas; (c) CO₂ emission intensity; (d) CO₂ emissions per capita. Note that Macao is enlarged in size to make it visible on maps in (c) and (d).

During the period 2000–2022, the average emission intensity (ratio of total CO₂ emissions to GDP) of GBA cities dropped from 0.24 to 0.10 t/10⁴ CNY (Fig. 3c). In terms of per capita CO₂ emissions (Fig. 3d), eight cities (i.e., Qingyuan, Foshan, Guangzhou, Hong Kong, Shenzhen, Meizhou, Yunfu, and Zhongshan) showed decreasing trends from 2010 to 2022, but the average of GBA and surrounding cities increased from 4.91 to 5.13 t/capita. Despite the overall increasing trend, 12 cities that accounted for 62% region’s population had per capita CO₂ emissions in 2022 lower than the European Union average (6.1 t/capita⁴⁵). In comparison with other global bay areas (Fig. 3b), the GBA had the highest total emissions but the lowest per capita emissions. The 2022 emission intensity of GBA (396 t/10⁶USD) was comparable to that of the San Francisco Bay Area (331 t/10⁶USD^4,46), as high-tech industries and service sectors dominated both bay areas^4,47. These patterns were consistent with previous findings that GBA and the surrounding cities had made carbon decoupling progress through improving energy efficiency and industrial structure⁴⁸, thus demonstrating the robustness of these inventories.

Uncertainties

The uncertainties of the inventories were mainly introduced from the activity data and emission factors^49,50. Industrial process-related carbon emissions were not considered due to their relatively small share of total emissions (<9%) and usually have low uncertainty^28,31. Uncertainties in energy-related carbon emissions were calculated using the Monte Carlo method recommended by the IPCC²⁶. Due to data limitations, we assumed that both fossil fuel consumption and emission factors followed normal distributions, and coefficient of variation (CV, defined as the standard deviation divided by the mean) was set to 0.03 for coal, 0.01 for oil, and 0.02 for natural gas, and fossil fuel consumption have CV ranged from 5% to 30% depending on the sector³¹. Assuming both the fossil fuel consumption data and emission factors followed normal distributions³¹, their uncertainties were evaluated through 20,000 simulations, and a 97.5% confidence interval was estimated. The annual uncertainties of the CO₂ emission estimations laid within the interval of [−13.07%, 13.07%] (Fig. 4). The largest uncertainty was observed from Shantou in 2018 ([−10.53%, 10.53%]), while the smallest uncertainty was from Chaozhou in 2000 ([−0.64%, 0.64%]).

Fig. 4

Energy consumption and total CO₂ emissions in GBA and surrounding cities, 2000–2022.

Comparison with existing work

Publicly available datasets on city-level carbon dioxide emissions for the GBA and surrounding Guangdong cities are currently rare. We collected comparable emission estimates for this area from existing literature to facilitate data comparison (Table 4). Shan et al.¹⁸, Luo et al.²⁰, and Lin et al.¹⁷ employed the sectoral approach to estimate carbon dioxide emissions in Guangdong at the city level. The total emissions from Luo et al. are close to our estimations, with a gap ranging between 19.2% (2017) and 0.6% (2009). CO₂ emissions for Guangzhou, Shenzhen, Zhuhai, and Shantou from Shan et al. were very close to our estimations, with a range from 1.86% to 4.12%. Variance existed in cities reliant on energy and the manufacturing sectors (e.g., Shaoguan, Maoming, Yangjiang, and Huizhou). The quality of activity data for these cities causes these variances. This study has updated the activity data based on the latest available statistical releases. Complete energy balance tables and detailed statistical data beyond major cities are essential for accurate emissions estimation. Lin et al. provided 2017 emissions for 21 Guangdong cities, categorized by energy consumption, industrial processes, and household energy use. While differences in sectoral categorization hindered direct comparisons at the sectoral level, total emissions of most cities are consistent with our 2017 inventory results, except for Guangzhou, Dongguan, Shenzhen, Yangjiang, and Maoming (>25% difference). Our estimations updated emission factors to cover 17 energy sources, while Lin et al. only considered coal, oil, and natural gas. This disparity may contribute to the differences.

Table 4 Comparisons of emission accounting results with existing works.

Limitations and future work

Our inventories have some limitations that may lead to uncertainty. (1) Hong Kong and Macao could not be directly incorporated into the accounting framework. Future work will leverage bottom-up statistical data and calibrated general observations (e.g., satellite imagery) to provide more accurate CO₂ emission estimates for these cities. (2) Renewable energies (e.g., solar power, wind power, and hydropower) were assumed as zero-carbon energy sources in this study, and the emissions from manufacturing are excluded. Indirect emissions along the supply chain will be incorporated. (3) This dataset only covers CO₂ emissions. Agricultural production is the prominent contributor to non-CO₂ greenhouse gases (e.g., CH₄ and N₂O). More efforts are needed to incorporate non-CO₂ greenhouse gases into the accounting framework by leveraging process-based models and the satellite-based inversion method.