China's embodied environmental impact on the Global Commons through provincial and spillover perspectives

2.1.1. Two accounting methods to capture subnational-level environmental impacts

The CS-GCS index system uses PBA and CBA (Kanemoto et al 2012). After 2010, market forces and government policies combined to shift China's economy toward domestic consumption-led growth (Mi et al 2021). Considering that the environmental impacts embodied in interprovincial trade account for almost half of the total domestic environmental impacts (Zhao et al 2022), to provide more detailed information on subnational-level sustainable governance, this study further splits the two aforementioned accounting methods into 'international', 'interprovincial', and 'provincial' components, respectively, as shown in figure 1(a). There is usually a high correlation between the environmental impacts accounted for by PBA and CBA (SDSN 2021). Figure 1 also shows how the components of PBA and CBA overlap. To avoid double-counting the portion of impacts labeled 'provincial production for provincial final demand and use phase', we construct four intermediary pillars (figure 1(b)).

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Specifically, the four intermediary pillars shown in figure 1(b) are from the perspective of a single province, let us call it Province A. First, (a) 'spillover: interprovincial supply' reflects impacts that occur in other Chinese provinces for Province A's final demand. Second, (b) 'spillover: international supply' accounts for impacts that occur in other countries for Province A's final demand. The two intermediary spillover pillars make the importing provinces responsible for the negative environmental impacts generated by extra-provincial and interprovincial activities, respectively. Next, (c) 'provincial: domestic demand' includes the impacts that occur within Province A to satisfy domestic final demand across China, which is equivalent to the interprovincial scale PBA. This can be split into two components: (3.1) 'provincial: internal demand' representing only Province A's final demand, and (3.2) 'provincial: interprovincial demand', representing only final demand from other Chinese provinces. Lastly, (d) 'provincial: international demand' includes impacts that occur within a province to satisfy final demand from other countries.

2.1.2. Conceptual framework

It is crucial to stress that the CS-GCS index does not assess the state of the GC per se, but, rather, the impact that regions have over our shared resources (SDSN 2021). We separately assess indicators of impacts that happen within provincial boundaries (provincial pillar) from those that occur beyond borders (spillover pillar), with a hierarchy of indicators, subpillars, intermediary pillars, and pillars. The two pillars are split into four intermediary pillars, and the impacts of each intermediary pillar are further categorized into four subpillars, respectively: aerosol emissions, GHG emissions, nutrient-cycles disruptions, and water-cycle disruptions (figure 2); i.e. the four broad environmental domains of the GC.

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Indicators are drawn from a mix of official data sources and nongovernmental organizations' data sources, to track the environmental impact of China's provinces on the GC as closely as possible (see supplementary section 1), with the priorities of using metrics that are statistically valid and reliable, up-to-date, and available for a large range of provinces. Most of the data follow a specific accounting method, for example, the multi-regional input-output (MRIO) model, the basis for this research, especially on regional spillover impacts (see supplementary sections 3 and 4).

Two main types of data are required in this study: the MRIO databases (Liu et al 2012, Lenzen et al 2013, Liu et al 2014, Stadler et al 2021, Zheng et al 2021) and environmental extension data relevant to the GC, such as SO₂ emissions, NO_x emissions, and black carbon (BC) (Li et al 2017, Zheng et al 2018, McDuffie et al 2020), GHGs such as CO₂ (Gütschow et al 2016, 2021), CH₄, and N₂O emissions (Jalkanen et al 2012, Johansson et al 2017, Crippa et al 2020), phosphate fertilizer (FAO 2021, IFA 2021, NBSC 2022a), nitrogen surplus (Duan et al 2021), scarce water use (Zhao et al 2021, NBSC 2022b), water stress (Boulay et al 2018, Stadler et al 2021, Zhao et al 2022), etc. We choose annual time series data in 2007, 2010, 2012, and 2015 (the latest year with available data) and calculated the CS-GCS index score (see details in tables S1–S3). Ultimately, the CS-GCS index system included a total of 35 indicators at the provincial level over time.

We present the indicators in two forms: proportional and absolute (Lenzen et al 2018). Proportional indicators are standardized by population to allow cross-region comparison, regardless of region size (see table S1). Absolute indicators present unstandardized metrics.

We then followed a five-step decision tree (Sachs et al 2021) to score the provincial data arrays for each indicator. These methods of identifying an upper and lower bound minimize the potential effects of skewed data distributions (see table S2). To ensure comparability across subpillars, the indicator values use a standard scale, where 1 is the worst-performing score and 100 is the best-performing score (see table S3).

We aggregate scores into subpillar first, then intermediary pillar. By using geometric means in the aggregation, low scores in any of the indicators result in a steeper penalty. Within the subpillars, we weigh each indicator equally, while for each intermediary pillar, we give one-third of the total weight to GHG emissions due to the important impact of GHGs, with the balance of the weight distributed equally across the remaining subpillars. This study does not attempt to discuss the aggregated overall scores, as the index focuses on each subpillar in its own right, which relates to the multilateral management of the GC. However, the overall scores can be found in table S4 as a reference for assessing the performance of provinces (see supplementary section 2 and table S4).

Here, figure 3 presents the four CS-GCS index intermediary pillar scores for provinces; the results can further be disaggregated by subpillar and indicators. We also present scores and dashboards for each indicator (see table S4), with additional analysis of overall performance.

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Generally, the four intermediary pillars capture different spatial patterns of China's environmental impacts (table 1). Similar to some previous studies, such as the estimation of China's environmental quality by Hao et al (2018) and the assessment of China's SDG performance by Xu et al (2020), the southeast had a higher score on the 'provincial: domestic demand' pillar than northwest (NW) regions in 2015. Benefiting from their geographical location, coastal and borderline provinces trade more frequently with neighboring entities, resulting in an increased footprint embodied in foreign exports, which is reflected in the lower 'provincial: international demand' scores for these provinces compared to inland provinces. However, the spatial pattern of spillover pillars scores differs significantly from the provincial pillars. Developed provinces, e.g. Beijing, Tianjin, and Shanghai, have implemented single-minded regional environmental policies, resulting in higher 'provincial: domestic demand' scores, but the unintended spillovers resulting from their imports have kept their scores on the 'spillover: interprovincial supply' well behind other regions. For the 'spillover: international supply' pillar, eastern regional scores are lower than western regions, especially in the case of the central coast (CC), south coast (SC), and Beijing-Tianjin (BT) (see supplementary section 9).

Provinces (such as Beijing and Shanghai) with more developed economies and more skilled workers typically have greater spillover effects as they have higher demand for imported environmental resource-intensive goods. Yet developing provinces (such as Shandong and Liaoning) that have always relied on heavy industry such as mining and energy have greater provincial effects. The results are validated by figure 4, which shows the correlation between the intermediary pillar scores and the industrial-specific value added per capita for provinces from 2007 to 2015. A higher intermediary pillar score means fewer negative impacts. It can be seen that the value added in resources-intensive industries (mining and energy) and the intermediary provincial pillar scores show a negative correlation, a result that indicates that the more resource-intensive industries dominate a region, the worse the embodied provincial impact. In contrast, the value added of advanced technology-dependent industries (service) shows a significant negative correlation with intermediary spillover pillar scores (see supplementary section 8). China's economic transition strategy has driven interregional industrial transfer; the phase-out of resource-intensive industries in developed regions (e.g. BT, the south, and CC, which account for 44% of the national value added in service) has been accompanied by a transfer in their consumption toward greater reliance on resource imports from less developed regions (e.g. the NW, which accounts for 22% of the national value added in mining and energy-related industries). If the imbalance of economic growth and wealth gaps continues to rise, the risk of breaching the threshold of local 'safe' planetary boundary in some regions of China will also increase.

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In the post-crisis era, China's economic structure showed a shift from a high reliance on heavy industry to an inclusive development model after 2010 (Mi et al 2021). Here, we focus on tracking China's environmental performance trajectory during the critical period in which the effect of economic transformation was highlighted under the dual regulation of markets and policies, namely 2010–2015.

Our results show an improvement in China's environmental impact in proportional terms, with the average overall CS-GCS index score increasing from 61 in 2010 to 65 in 2015, an increase of approximately 7% (see table S5). For the regional groups, the CC regions had the most significant increase in overall score (+24%). The largest decrease in overall score was observed in the southwest (SW) region (−7%) (see more in supplementary section 7).

The change of scores across the four intermediary pillars illustrate that China's developing provinces perform better, particularly on the two intermediary spillover pillars (two times and 1.5 times higher for 'spillover: international supply' and 'spillover: interprovincial supply', respectively). Specifically, it is worth noting that the average 'spillover: interprovincial supply' scores of developed provinces have improved, indicating that China's developed provinces are showing a gradual shift away from dependence on interprovincial environmental resources (figure 5(a)). In addition, signs of more significant rising scores for developed provinces on the 'provincial: domestic demand' pillar are captured, reflecting the effects of their prioritized implementation of industrial restructuring strategies (figure 5(b)). The relatively stable scores for the 'spillover: international supply' pillar indicates that, in the socioeconomic transition, the scores of environmental impacts embodied in China's international imports are relatively stable and there is no significant trend of increase in China's outsourcing of environmental impacts to other countries/regions over time (figure 5(c)). However, the average 'provincial: international demand' scores of developing provinces continued to decline, indicating that the local environmental threat embodied in foreign export of China's developing provinces is increasing (figure 5(d)). Similar results were reached when comparing the top and bottom five developed province groups (see supplementary section 10).

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Figure 6 illustrates the unevenness in the environmental impact embodied in different types of trade across regions. Considering only the trade-related footprint, the environmental impacts embodied in exports stood out in the north (NT) region and the east coast of China, as well as other inland provinces such as Henan. For example, scarce water use is most notable in Xinjiang and Shandong; nitrogen surplus is highlighted in Hebei and Henan, and GHGs and BC are most highlighted in Hebei and Shandong. This reflects China's industrial layout, with scarce water use and nitrogen surplus mainly coming from agricultural cultivation, while GHG and BC emissions are mostly from various industrial combustion processes. Central and SC regions always have the most considerable impact embodied in import, related to China's development strategy of regional industrial transfer (Zheng et al 2019). Second, interprovincial imports (30%–40% of the total consumption-based impact) of the four crucial environmental variables accounted for are two to three times greater than the impact embodied in international imports (only 4%–17%); and interprovincial exports (30%–35% of the total direct impact) are two to seven times greater than the impact embodied in international exports (only 15%–18%). China is a net exporter of embodied environmental impacts as opposed to entities like the US, Europe, and Japan, which are net importers; a significant proportion of their environmental impact is embodied in China's domestic production and trade (Jun et al 2015, Zhou et al 2022). Our finding highlights that, compared to international trade, China's interprovincial trade poses the greatest environmental concerns (see table S2).

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Considering a prioritized restructuring of the impact patterns embodied in interprovincial trade would help to more effectively lessen China's absolute environmental impact on the GC. The main sources of net interprovincial environmental impact footprint are from three hinterlands (figure 7), namely the NW, Central (CT), and NT regions, together accounting for 80%–93% of total net exports. The net importing economic regions are the BT, SC, and CC regions, accounting for 52%–79% of total net imports. The SW and northeast regions have relatively balanced net trade deficits. This pattern implies that, in China's internal trade, the goods and services consumed in most regions are at the expense of environmental resources in the northern hinterland region, especially Xinjiang, Ningxia, and Inner Mongolia.

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The assessment framework further captures the environmental impacts of key industry sectors within each province. Figure 8 shows the embodied impact footprint of the four key environmental variables for the top five final products driven by global consumption in eight Chinese economic zones in 2015.

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Crop cultivation dominates the scarce water use and nitrogen surplus footprints (>90%), followed by other agriculture in second place. For the other two variables, the CT region has the largest share, followed by the SW. From a sectoral perspective, for GHG emissions, the highest emitting sector is petroleum (20%), followed by electrical and machinery (14%) and food (13%). For BC, the highest consuming sector is tourism (16%), followed by transport (15%) and agriculture (11%). Although the top-ranked sectors vary across the different variables, the negative impacts embodied by consumption in agriculture-related sectors are the most prominent, particularly in the environmental domains of 'nutrient-cycles disruption' and 'water-cycle disruption'.

Our assessment of impacts on the GC among provinces in China yields five major findings. First, we found that the average overall CS-GCS index score has improved over time. Second, CS-GCS index score patterns vary across provinces and have changed over time. Affluent provinces that achieved service-led industrial upgrading in recent years (such as Beijing and Tianjin) score higher in the two intermediary provincial pillars and lower in the two intermediary spillover pillars than northern and western provinces that rely on energy-intensive industries. In general, it follows that developing provinces have higher average scores than developed provinces, but the environmental impacts embodied in foreign exports from developing provinces are getting worse. Third, the environmental impacts embodied in China's international imports are relatively stable and there is no significant trend of increase in China's outsourcing of environmental impacts to other countries over time. Fourth, the origins of impacts matter: across all of China's provinces, the environmental threats embodied in interprovincial trade dominate when compared to international trade. And, relatedly, the largest flows of spillover impacts in China's internal trade are goods and services originating in the northern hinterland region, such as Xinjiang, Ningxia, and Inner Mongolia. Fifth, the sectoral composition of a province can explain some of the variations in scores. Across most of the key indicators analyzed here, agriculture is the crucial sectoral driver of all environmental domains (figure 8). Modern agriculture's heavy reliance on pesticide and fertilizer inputs, and inappropriate practices such as cultivated land abandoning, further increase threats such as soil contamination, water disruption, and eutrophication. There is a need to prioritize actions such as promoting advanced green technologies to transform industrial chains to reduce their embodied impact.

Stakeholders at the national and subnational levels in China benefit from this analysis in a number of ways.

First, this assessment establishes that there are important spillover effects related to Chinese production and consumption—not just internationally but between provinces. Harnessing the power of the metrics requires ongoing efforts to maintain and enhance data systems and then disseminate those data in a timely and convenient way. The government should further support, encourage, and invest in the collection, verification, and reporting of data relevant to environmental spillovers.

Second, on a more practical level, the CS-GCS index provides a more comprehensive perspective for investigating how environmental resources are appropriated, as well as comparisons between commodities and geographic regions, to help coordinate interprovincial cooperation in sharing resources and technologies, and to inform greener and more resilient trade patterns at the subnational level. The results from our analysis also yield lessons for decision-makers interested in balancing economic development with environmental sustainability. For example, we found that the following provinces had the worst performance in 2015 (the latest year with available data), in proportional terms: Tianjin, Shanghai, Beijing, Ningxia, and Inner Mongolia. Stakeholders should monitor performance gaps and trajectories; lagging indicators at the dashboard deserve priority implementation of action, such as technology-driven decoupling, net-import regions subsidizing the transitions cost for the mutual benefit of long-term economic growth, and environmental stewardship (see more discussion in supplementary section 12).

Finally, through the implementation of environmental policies, significant improvements occurred in some regions during the study period, while others struggled to reach the desired level. Making progress on environmental sustainability goals and reversing trends that transgress planetary boundaries requires coordinated attention to how economic development involves trade-offs between provinces. Our results suggest that more comprehensive monitoring of threatened sustainable development targets should be undertaken to identify conflicting and synergistic mechanisms between the impacts of different policies on multiple environmental domains, further minimizing multiple environmental impacts across provincial boundaries through restructuring trading patterns and redistribution of resources use structure. As a major positive attempt at joint governance, China formed a new 'Ministry of Natural Resources' and 'Ministry of Ecology and Environment' in 2018 (Wang 2018). The CS-GCS index can be used to help further monitor the practical effects of this action.

In general, the methods outlined in our paper are of value to China's monitoring efforts and might help local governments to achieve sustainable development goals through bottom–up approaches. Further research is necessary to address limitations in the current data and approach. Most significantly, analyses of subnational spillovers are hampered by a lack of data. At a broad level, there are data gaps in the kinds of environmental impacts that are being measured. More research is required to develop a more comprehensive view of impacts to the GC; for example, with threats to biodiversity. Likewise, even when these data are available at the national level, they are not always collected or disaggregated at the provincial level. In some cases, missing values can be imputed using, for example, population-weighted means, but this presents evident difficulty when attempting to create both absolute and proportional versions of the indictors. The paucity of good data also means that there are uncertainties in the MRIO-based accounting results. In order to test the robustness of our assumptions, choices, and methods, we include sensitivity analyses and a comparison of our results to other benchmarks in supplementary sections 5 and 6. The comparative results also highlight the unique contribution of this index in differentiating it from other index systems.