Heterogeneity in carbon footprint trends and trade-induced emissions in China’s urban agglomerations

Structural change in carbon footprint of the urban agglomerations from 2012 to 2017

Carbon footprint of the 16 urban agglomerations was 5981.6 Mt, 5823.1 Mt and 6038.9 Mt in 2012, 2015 and 2017, respectively, accounting for 80.1%, 79.4% and 78.7% of the total emissions in China. The top three carbon footprint urban agglomerations in 2012 were Yangtze River Delta, Middle reaches of the Yangtze River, and Beijing-Tianjin-Hebei, which accounted for 40.2% of the total carbon footprint of all 16 urban agglomerations (Fig. 1). These three urban agglomerations were also the top three in terms of GDP in 2012 (Supplementary Fig. 1). However, in 2017, the Central Plains replaced Beijing-Tianjin-Hebei as the third-highest carbon footprint urban agglomeration due to the increasing proportion of heavy industry, although its GDP only ranked fifth. Within an urban agglomeration, the carbon footprint is not evenly distributed and is often concentrated in the core cities, whose carbon footprint exceeded one standard deviation from the mean of the urban agglomeration (as marked in Fig. 1). In urban agglomerations with relatively weak economies, the carbon footprint is more concentrated in the core cities. For example, five urban agglomerations have only one core city, and the economy of the Shandong Peninsula is more developed than the other four regions. Qingdao accounted for only 27.2% of the carbon footprint in the Shandong Peninsula, which is lower than the proportion of the carbon footprint of core cities in the other four urban agglomerations, especially Xi’an, Yinchuan, and Urumqi, which accounted for more than 50% of the carbon footprint in their respective urban agglomerations. The weaker the economy, the more dependent it is on a single pole in the urban agglomeration. In an urban agglomeration, the larger the proportion of the carbon footprint accounted for by the core cities, the more it indicates a high concentration of industrial and economic activities in those cities. In contrast, when the carbon footprint is more evenly distributed among multiple cities, it reflects a more diversified economic structure with less reliance on a few key cities. For example, the Yangtze River Delta has four carbon core cities, and Shanghai, with the highest carbon footprint, only accounted for 13.6% of the carbon footprint because cities in the Yangtze River Delta are developed evenly (Fig. 1S).

Of the 16 urban agglomerations, five demonstrated a reduction in their carbon footprint over the period from 2012 to 2017, six exhibited an increase, and five reached a plateau (Fig. 1). The five urban agglomerations with carbon footprint decreased are Central Shanxi, Harbin-Changchun, Liaozhongnan, Beijing-Tianjin-Hebei, and Shandong Peninsula. These urban agglomerations are situated in North China and Northeast China, where industrialisation commenced at an early stage. The transformation and upgrading of traditional industries leaded their carbon mitigation. For example, the heavy manufacturing and construction sectors contributing 74.1% and 38.6%, respectively, to their carbon reduction from 2012 to 2017. Notably, the carbon footprint of heavy manufacturing in Shandong Peninsula decreased the most by 136.9 Mt, contributing 80.3% to the total carbon reduction, due to the removal of outdated capacity and reduction of excess capacity in the region. The share of heavy manufacturing in the region’s total carbon footprint declined from 44.1% in 2012 to 23.6% in 2017. In Central Shanxi, which is rich in coal resources, the carbon footprint of the mining sector decreased by 3.3% more than in other agglomerations. However, the power and service sectors increased in their carbon footprint by 8.5% and 14.6%, respectively.

The carbon footprint of six urban agglomerations increased from 2012 to 2017. These are the Tianshan North Slope, Guanzhong Plain, Qianzhong, Yangtze River Delta, the West Side of the Straits and the Pearl River Delta. It should be noted that the Yangtze River Delta, the West Side of the Straits and the Pearl River Delta are situated on the southeastern coast of China. The economies of these urban agglomerations continue to expand at a rapid rate, with growth of 45.9%, 55.5% and 72.0% from 2012 to 2017. In such urban agglomerations, the service sector is the primary factor responsible for the observed growth in carbon footprint. The carbon footprint of the service sector in the Yangtze River Delta increased by 54.5%, in the Pearl River Delta by 63.8%, and in the West Side of Straits by 85.1%. In contrast, the Tianshan North Slope, Guanzhong Plain and Qianzhong are situated in the western region of China. To provide support for the development of underdeveloped areas in the western regions, these urban agglomerations have given priority to the implementation of development strategies. The economic growth mode of the Tianshan North Slope and Qianzhong is dependent on infrastructure, which consequently results in the construction sector becoming a contributor to the growth of the carbon footprint. The carbon footprint of the construction in the Tianshan North Slope and Qianzhong increased by 39.2% and 57.7%, respectively. However, in the Guanzhong Plain, heavy manufactory has become the dominant contributor to carbon footprint growth, with an increase of 72.1%.

Carbon footprint of the remaining five urban agglomerations showed a plateau from 2012 to 2017. Among them, carbon footprint of Ningxia along the Yellow River, Central Plains, Hubaoeyu and the Middle reaches of the Yangtze River declined from 2012 to 2015 but rebounded from 2015 to 2017. These urban agglomerations are usually stimulated by the economy without considering environment protect after 2015, leading to a sudden increase in their emissions. Ningxia along the Yellow River is the urban agglomeration with the largest rebound, with a decrease of 4.2% from 2012 to 2015 but an increase of 70.2% from 2015 to 2017. Because its value is relatively small, and it is more vulnerable to the impact of policy and the economic environment. Power, construction, and service were mainly promoted economic recovery in these regions, especially contributing 86.7% of the rebound in Ningxia along the Yellow River. In addition, Chengyu was the only one urban agglomeration showing a carbon peak during 2012 to 2017, whose carbon footprint increased by 12.4% from 2012 to 2015, but decreased by 7.2% from 2015 to 2017. The decrease in carbon footprint of the construction sector by 43.5 Mt from 2015 to 2017 is the main factor contributing to Chengyu’s carbon peak. Meanwhile, the share of heavy manufacturing in Chengyu’s carbon footprint dropped from 24.2% in 2012 to 18.0% in 2017, causing it to fall from the second-largest to the third-largest emitting sector in the region.

The mechanisms of carbon footprint changes in urban agglomeration

The patterns of carbon footprint changes for these three models are driven by different socio-economic factors. We selected five factors, including two production factors (carbon intensity and production structure) and three consumption factors (consumption structure, per capita consumption and population). The carbon footprint reduction achieved by five urban agglomerations before 2015 was mainly due to production side factors, and there were differences after 2015 (Fig. 2), which indicates that the marginal emission reduction benefits on the production side will decrease. This trend is closely related to the Chinese economy entering a “new normal” during the research period. As economic growth slowed, the lock-in effects of traditional high-carbon infrastructure made it difficult to decrease carbon emissions in line with the slowdown, resulting in a rebound in carbon intensity per unit of output³⁹. Of the five urban agglomerations whose carbon footprint decreased, four were mainly driven by consumption factors from 2015 to 2017. Although the production structure played a dominant role in reducing emissions, the increase in carbon intensity offset part of this effect, leading to a net emission increase from the production side in Beijing-Tianjin-Hebei, Harbin-Changchun, Liaozhongnan, and Central Shanxi. Among the four urban agglomerations analyzed, Harbin-Changchun, Liaozhongnan, and Central Shanxi followed an economic decline model. Declines in per capita consumption and population contributed to carbon footprint reductions of 51.2 Mt in Harbin-Changchun, 22.5 Mt in Liaozhongnan, and 18.8 Mt in Central Shanxi, respectively. These reductions, driven primarily by constrained economic activity and weakened consumption demand, reflected the economic hardships faced by these regions. In contrast, the Beijing-Tianjin-Hebei achieved a 47.7 Mt carbon footprint reduction driven by shifts in consumption structure, despite increases of 36.6 Mt from per capita consumption and 3.3 Mt from population growth, reflecting a low-carbon consumption model. The Shandong Peninsula was the only urban agglomeration to achieve carbon footprint reductions through a technological advancement model. Between 2015 and 2017, production structure promoted 148.7 Mt reduction in carbon footprint. This production-driven reduction was largely facilitated by technological advancements and enhanced supply chain management.

Urban agglomerations are grouped into three types based on their carbon footprint trends: decline, plateau, and increase. Positive bars indicate emission-increasing effects, while negative bars indicate emission-reducing impacts for each driver.

For six urban agglomerations with carbon footprint increase from 2012 to 2017, they have been in a period of rapid economic development, and the growth of consumption led to the increase of these carbon footprints. Among consumption factors, per capita consumption contribution most for these urban agglomerations. For example, per capita consumption promoted of contributed carbon footprint increased by 473.4 Mt in Yangtze River Delta from 2012 to 2017. We found that the consumption structure contributed to the reduction of the carbon footprint in West side of the Straits and Yangtze River Delta from 2015 to 2017, indicating that these two have the potential to form a low-carbon consumption model. However, the consumption structure of the Pearl River Delta and Tianshan North Slope reduced the carbon footprint from 2012 to 2015, but increased the carbon footprint in 2015-2017, indicating that the consumption structure has greater uncertainty, easily to change according to the current policy. Consumption factors were the dominated drivers of carbon footprint increases, despite production factors slightly promoting a decline in footprint for all urban agglomerations, which shows that most urban agglomerations have made progress in production technology and supply government. However, both production and consumption factors contributed to the carbon footprint of the Pearl River Delta, because high rapid urbanization and economic development⁴¹.

The plateau phase observed in five urban agglomerations can be attributed to the competing dynamics between production-side technological advancements, which drive emission reductions, and consumption-driven demand growth, which exerts upward pressure on emissions. The trajectory of carbon footprint changes in urban agglomerations during this stage remains uncertain and is expected to be shaped by external factors, like policy interventions. The sharp rise in carbon footprints in the Central Plains, Ningxia along the Yellow River, and the middle reaches of the Yangtze River by 133.8 Mt, 50.7 Mt, and 172.9 Mt, respectively, was predominantly driven by increased per capita consumption from 2015 to 2017. The increase in investment contributed to 62.7%, 45.6%, and 38.9% of the rise in per capita consumption in these urban agglomerations, respectively (Supplementary Fig. 2). These urban agglomerations should address the risk of investment-driven emissions by prioritizing investments in the photovoltaic new energy industry and adopting low-carbon consumption models to mitigate emissions. In Hubaoeyu, carbon intensity was the primary driver of the carbon footprint increase from 2015 to 2017. As a key energy supply base in China, the region experienced a surge in coal supply in 2017, and the high carbon intensity of coal contributed to the sharp rise in emissions during this period⁴². Chengyu is the only urban agglomeration whose carbon footprint turned down after 2015, because production structure improvement reduced emissions by 125.5 Mt. This trajectory exemplifies how structural transformation, coupled with the deployment of cleaner technologies, can contribute to decarbonization—for instance, by replacing high-emission sectors (e.g., coal-fired power) with low-emission alternatives (e.g., wind or solar energy).

The trade-induced emissions between urban agglomerations

The carbon footprint dynamics of urban agglomerations reflect the interplay between local actions and intercity trade between urban agglomerations, which together meet demand through supply chains. Figure 3 shows the changes in the sources of carbon footprint composition in urban agglomerations. From 2012 to 2017, local carbon emissions decreased by 351.9 Mt, but emissions from other urban agglomerations surged by 801.7 Mt, driven by intensified trade relationships, as trade prioritizes cheaper goods over low-carbon alternatives. For urban agglomerations with declined carbon footprint, a reduction of 293.8 Mt in local emissions is the largest contributor to emission reductions, followed by a reduction of 287.4 Mt in emissions from other cities. Urban agglomerations in the economic decline model, such as Harbin-Changchun, reduced emissions primarily through reduced local activity, accounting for 89.3% of the carbon footprint reduction, with trade patterns playing a secondary role. In contrast, urban agglomerations in the low-carbon consumption model (i.e., Beijing-Tianjin-Hebei), the contribution of local carbon emissions reduction accounted for only 54.0% of the carbon footprint reduction, relying more on trade to reduce emissions by switching to low-carbon imports, especially from 2015 to 2017, carbon inflow decreased by 24%. For agglomerations following the technological advancement model, local production improvements drove emissions reductions, but trade dynamics often moderated these gains. For instance, the Chengyu achieved local reductions (−53.0 Mt from 2012 to 2017) but faced a net increase (+17.2 Mt) due to trade. These dynamics underscore the necessity of integrating trade considerations into carbon management strategies, as supply chain linkages influence emissions outcomes.

Carbon footprint changes are broken down into three sources: local emissions, emissions outsourced from other urban agglomerations, and emissions outsourced from external regions. Negative values indicate that the urban agglomeration contributed to a decrease in emissions from the corresponding source, while positive values indicate that it led to an increase in emissions from that source.

To evaluate the impact of trade on carbon emissions, we constructed a no-trade counterfactual scenario in which local demand is fully met by local production. Based on Eq. (18), we calculated trade-induced emission changes shown in Fig. 4. From 2012 to 2017, total trade-induced carbon emissions reduction across the 16 urban agglomerations declined from 1562.7 Mt to 1374.5 Mt, indicating a weakening mitigation effect. In 2017, 11 out of 16 urban agglomerations still achieved net emission reductions through trade. Liaozhongnan, Hubaoeyu, and Harbin-Changchun achieved the largest trade-induced emission reductions, with decreases of 479.8 Mt, 309.0 Mt, and 101.2 Mt in 2017, respectively. In contrast, Beijing-Tianjin-Hebei exhibited the largest trade-induced emission increase, reaching 98.5 Mt in 2017. Several urban agglomerations, such as Shandong Peninsula, Central Shanxi, Ningxia along the Yellow River, and Tianshan North Slope, shifted from net mitigation to net increases, reflecting diverging regional trajectories in trade-related carbon flows. These shifts highlight how evolving regional trade networks and industrial specialization patterns are reshaping the spatial distribution of carbon emissions. While urban agglomerations remained key actors in trade-enabled decarbonization, non-agglomerated regions increasingly contributed, with trade-induced emission reductions rising from 472.1 Mt in 2012 to 1470.3 Mt in 2017. This shift indicates that non-agglomerated cities have become more active contributors to trade-based emission mitigation, likely due to structural transformation and changing patterns of interregional trade.

Negative values indicate that trade contributed to emission reductions, while positive values indicate that trade led to increased emissions.

To further identify the drivers of trade-induced emission changes in urban agglomerations, we distinguish between intra-agglomeration and inter-agglomeration trade effects. Even when intra-agglomeration trade is balanced, uneven emission intensities and production effectiveness cause net changes in total emissions. Intra-agglomeration trade contributed to mitigation overall, with associated reductions declining from 548.7 Mt in 2012 to 233.3 Mt in 2017. In 2017, 10 out of 16 urban agglomerations achieved emission reductions through intra-agglomeration trade, reflecting improved internal efficiency. The Middle Reaches of the Yangtze River recorded the highest intra-agglomeration trade-induced reduction at 142.1 Mt, likely due to well-coordinated industrial structures and relatively low emission intensities in key production cities, enabling more efficient internal trade. However, unbalanced or inefficient industrial division within agglomerations may result in the opposite effect. In 6 urban agglomerations, intra-agglomeration trade led to increased emissions, suggesting the potential for carbon leakage within agglomerations. For example, Liaozhongnan experienced an intra-agglomeration trade-induced emission increase of 21.4 Mt in 2017, due to an uneven internal division of labour and the concentration of high-emission industries, which may have resulted in intra-agglomeration carbon leakage.

The internal emission spatial structure of urban agglomerations can influence industrial specialization and production efficiency, thereby shaping regional emission outcomes⁴³. Building on this perspective, our study further incorporates trade-embedded carbon flows to quantify how these structural differences translate into inter-city emission redistribution. Network analysis provides further insight into how the internal structure of urban agglomerations affects trade-induced emissions. Modularity, which reflects the extent to which a network is divided into relatively independent subgroups, indicates weaker integration when values are higher. In 2017, the average modularity across all urban agglomerations was 0.62. In contrast, strength measures the overall trade intensity within an agglomeration, reflecting the degree of economic connectivity among cities, with an average value of 0.48 in 2017. As shown in Supplementary Fig. 3, urban agglomerations with lower modularity (e.g., Middle Reaches of the Yangtze River: 0.56) and higher strength (0.54) tend to exhibit more cohesive trade networks that facilitate efficient resource sharing and reduce redundant high-emission production. Conversely, agglomerations such as Liaozhongnan exhibited higher modularity (0.65) and lower strength (0.39), indicating fragmented internal structures that may hinder low-carbon coordination and lead to carbon leakage within the agglomeration. These findings suggest that intra-agglomeration trade contributes more effectively to decarbonization when supported by well-integrated and strongly connected internal trade networks.

Trade between urban agglomerations serves as a critical mechanism for interregional product exchange and resource coordination. From 2012 to 2017, trade-induced emission reductions resulting from inter-agglomeration trade increased from 1,014.0 Mt to 1,141.2 Mt, underscoring its growing role in optimizing resource allocation and supporting low-carbon development across regions. Several urban agglomerations, including Liaozhongnan (501.2 Mt), achieved substantial emission reductions through trade with other regions (Fig. 5). These areas typically feature high-carbon industrial structures, and inter-agglomeration trade enables them to meet local demand by importing relatively low-carbon products. Notably, 24% of Liaozhongnan’s external inflows came from more developed agglomerations, such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta, reducing the need for high-emission local production and thereby lowering their consumption-based carbon footprints. In contrast, some urban agglomerations experienced increased emissions as a result of inter-agglomeration trade. For example, Beijing-Tianjin-Hebei recorded a trade-induced emission increase of 117.6 Mt in 2017. This region imports a volume of carbon-intensive products to meet growing demand for infrastructure and consumption, resulting in the displacement of carbon emissions to external production regions. At the same time, it also acts as a foundational supply base, exporting large quantities of industrial products to other regions. A defining feature of these dual-role agglomerations is that exports exceed imports, making trade a key driver of local production and associated emissions. For instance, Tangshan, a major steel-producing city within the agglomeration, contributed 108 Mt of emissions through steel exports alone. While Beijing-Tianjin-Hebei has made efforts to reduce emissions through low-carbon consumption models, the continued outsourcing of demand to its industrial base has offset much of these gains.

The colour of the source of carbon flow is consistent with the colour of the urban agglomeration. The side length of the urban agglomeration represents the size of the carbon flow flowing through it, and the depth of the colour represents the net inflow and net inflow.