Outsourced carbon mitigation efforts of Chinese cities from 2012 to 2017

Abstract

Outsourced carbon mitigation between cities means that some cities benefit from the carbon mitigation efforts of other cities more than their own. This problem conceals the recognition of cities’ mitigation contributions. Here we quantify local and outsourced carbon mitigation levels from 2012 to 2017 and identified ‘outsourced mitigation beneficiaries’ relying on outsourced efforts more than their own among 309 Chinese cities by using a city-level input–output model. It found that the share of outsourced emissions rose from 78.6% to 81.9% during this period. In particular, 240 cities (77.7%) were outsourced mitigation beneficiaries, of which 65 were strong beneficiaries (their local carbon emissions still grew) and 175 cities were weak beneficiaries (with larger outsourced mitigation efforts than local mitigation efforts). Strong beneficiaries were often industrializing cities with more agriculture and light manufacturing, focusing on local economic growth. In contrast, weak beneficiaries were mainly at the downstream of supply chains with services and high-tech manufacturing, which have stronger connections with upstream heavy industry cities. The findings suggest the need for policies to manage outsourced mitigation of supply chains and encourage transformation, improving the fair acknowledgment of cities’ carbon mitigation efforts.

Main

As the hubs for economic activities, cities play an important role in generating carbon emissions through production and consumption activities^1,2,3. The success of their carbon mitigation largely determines the deliverable of China carbon neutrality commitments and global decarbonization initiative^4,5. However, no cities stand alone, where their demands are increasingly outsourced via supply chains^40,41, energy consumption^42,43, forest landscape⁴⁴ and land use^45,46. In this study, we utilized the MRIO table data for the years 2012 and 2017 to estimate the carbon footprint of cities in China.

This study used the Leontief inverse model to calculate the carbon footprint caused by final demand⁴⁷. Mathematically,

$${{\mathbf{X}}}={\left({{I}}-{{A}}\right)}^{-{\bf{1}}}{{Y}}={{LY}},$$

(1)

where X is the vector of total output, I is the identity matrix and (I − A)⁻¹ is the Leontief inverse matrix, A is the technical coefficient matrix and Y is the final demand matrix. On the basis of the carbon intensity E (that is, CO₂ emissions per unit of output), the carbon footprint is calculated as:

$${{\mathbf{CF}}}={{ELY}},$$

(2)

where CF is a vector of carbon footprint, referring total CO₂ emissions in goods and services used for final demand.

CO₂ emission inventory construction

Equations (3) and (4) are used to calculate the fossil fuel-related and process-related emissions, respectively, as:

$${{\mathrm{CE}}}_{{ij}}=\mathop{\sum }\limits_{i}\mathop{\sum }\limits_{j}{{\mathrm{AD}}}_{{ij}}\times {{\mathrm{NCV}}}_{i}\times {{\mathrm{CC}}}_{i}\times {\mathrm{O}}_{{ij}}$$

(3)

$${{\mathrm{CE}}}_{t}={{\mathrm{AD}}}_{t}\times {{\mathrm{EF}}}_{t},$$

(4)

where CE_ij is the CO₂ emissions caused by the sector j using the fossil fuel i; AD_ij refers to activity data (that is, consumption of corresponding fossil fuel types and sectors); NCV_i (net calorific value of fossil fuel), CC_i (carbon content of fossil fuel) and ${\mathrm{O}}_{{ij}}$ (oxygenation efficiency of fossil fuel) are emission factors for fuel. CE_t is CO₂ emissions induced in the industrial processes t, AD_t is the production amount of processes t and EF_t is emission factor of processes t.

Structural decomposition analysis

To understand the socioeconomic driving forces, we employed structural decomposition analysis to decompose carbon footprint into carbon intensity (E), production structure ($L={\left({{I}}-{{A}}\right)}^{-1}$), final demand (F) in equation (2). We used the average of two polar decompositions⁴⁸ to solve numerical values as follows:

$$\begin{array}{c}\Delta {CF}=\Delta {C}_{E}+\Delta {C}_{L}+\Delta {C}_{Y}\\ =\frac{1}{2}\left(\Delta E{L}_{1}{Y}_{1}+\Delta E{L}_{0}{Y}_{0}\right)+\frac{1}{2}\left({E}_{0}\Delta L{Y}_{1}+{E}_{1}\Delta L{Y}_{0}\right)+\frac{1}{2}\left({E}_{0}{L}_{0}\Delta Y+{E}_{1}{L}_{1}\Delta Y\right),\end{array}$$

(5)

where 0 refers to base year (2012 year), and 1 refers to target year (2017 year). ∆ represents the change in a factor.

Mitigation efforts on technological progress

The total impact of change in production-side factors (that is, carbon intensity per output and production structure) reflects the technology progress as follows:

$$\Delta {C}_{\mathrm{T}}=\Delta {C}_{E}+\Delta {C}_{L},$$

(9)

where ∆C_T represents the change in emissions due to technological progress. However, ∆C_T cannot truly reflect the real mitigation efforts of technological progress in different cities due to different final demand benchmarks. With the same magnitude of mitigation efforts on technological progress, cities with higher demand benchmarks will have a greater contribution to emission reduction. Therefore, we calculated units to remove the effect of this amplifier:

$${\mathrm{TP}}=\frac{\Delta {C}_{\mathrm{T}}}{\frac{{{\mathrm{final}}\;{\mathrm{demand}}}_{0}+{{\mathrm{final}}\; {\mathrm{demand}}}_{1}}{2}},$$

(10)

where TP represents the change in emissions per final demand caused by mitigation efforts.

Data source

According to our previous study³⁰, we constructed a Chinese MRIO table consisting of 313 regions and 42 socioeconomic sectors for the years 2012 and 2017, by using a feasible nonsurvey methodology⁴⁹. We collected economic statistics for 309 cities, including output, value-added, GDP and trade data from city statistics books and the China customs database. Using calibrated city-level output and trade data, we estimated supply and demand by sector for cities in each province. The maximum entropy model was applied to disaggregate estimated demand and supply into self-supplied and externally supplied categories. We then used the cross-entropy model to estimate single regional input–output (SRIO) tables for each city based on these estimates and the provincial SRIO table. Using the maximum entropy model again, we estimated intercity trade flows by sector, linking all city-level SRIO tables and trade flows to create a city-level MRIO table for each province. These city-level MRIO tables were then nested into the China provincial MRIO table, excluding data for Hong Kong, Macau and Taiwan due to data unavailability. The 313 regions covered include 309 cities and Tibet, Yunnan, Qinghai and Hainan provinces, treated at the same level as cities due to missing data. Both the 2012 and 2017 MRIO tables were compiled using current year prices, with 2012 as the benchmark year, and 2017 prices were converted to 2012 prices using deflators.

For the CO₂ emission inventory of Chinese cities, we adopted the methods developed by Shan^50,51. This inventory includes scope 1 emissions from 17 types of fossil fuels and industrial processes as defined by the Intergovernmental Panel on Climate Change⁵². The emissions inventory is organized using 47 socioeconomic sectors, aligned with China’s national and provincial emission accounts. To estimate city-level emissions, we applied a systematic approach that downscaled provincial energy balances and sectoral energy consumption to the city level, using auxiliary socioeconomic data such as industrial output, population and GDP. To address inconsistencies in the statistical calibration of energy consumption in China, the sum of city-level energy consumption was constrained to match the sum of provincial energy consumption statistics.

We segregated cities into five distinct industry dominated type: agriculture cities, light-industry city, heavy-industry city, energy city and high-tech city (Supplementary Table 2). This classification was based on the percentage of each city’s GDP contributed by these sectors. Initially, we condensed 42 different economic sectors into the five mentioned categories and computed the proportion of value added by each sector. Subsequently, we utilized the K-means algorithm with the Euclidean distance measure, taking into account the value-added percentages of the GDP attributable to each of the five sectoral groups in the year 2017.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Source data

Source Data Fig. 1

Carbon footprint of Chinese cities.

Source Data Fig. 2

Contributions of driving factors to the carbon footprint change.

Source Data Fig. 3

City roles in terms of outsourced carbon mitigation beneficiaries.