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# Impacts of poverty alleviation on national and global carbon emissions

## Abstract

Wealth and income are disproportionately distributed among the global population. This has direct consequences on consumption patterns and consumption-based carbon footprints, resulting in carbon inequality. Due to persistent inequality, millions of people still live in poverty today. On the basis of global expenditure data, we compute country- and expenditure-specific per capita carbon footprints with unprecedented details. We show that they can reach several hundred tons of CO2 per year, while the majority of people living below poverty lines have yearly carbon footprints of less than 1 tCO2. Reaching targets under United Nations Sustainable Development Goal 1, lifting more than one billion people out of poverty, leads to only small relative increases in global carbon emissions of 1.6–2.1% or less. Nevertheless, carbon emissions in low- and lower-middle-income countries in sub-Saharan Africa can more than double as an effect of poverty alleviation. To ensure global progress on poverty alleviation without overshooting climate targets, high-emitting countries need to reduce their emissions substantially.

## Methods

To analyse global carbon inequality, we computed country- and expenditure-specific carbon footprints using detailed expenditure data linked with an EEMRIO analysis. Subsequently, we apply multiple poverty alleviation scenarios to determine impacts on carbon emissions.

### EEMRIO analysis

The input–output approach has been widely used for economic, environmental and societal analysis of economic structures48. Country- and expenditure-specific per capita carbon footprints along the supply chain of the consumed goods and services can be calculated using the MRIO framework, despite highly aggregated and imperfect datasets49. EEMRIO analysis has been applied in numerous studies to analyse environmental impacts of consumption and trade. A particularly common application is the computation of environmental footprints, such as ecological footprints and water footprints50, greenhouse gas and biodiversity footprints51, or carbon emissions and carbon footprints13,14,34,52. In addition, multiple studies have looked at the interconnections of income, inequality and carbon emissions by using the EEMRIO approach8,15,53,54.

The EEMRIO approach uses an MRIO table, which consists of the inter-regional trade between m sectors in n countries. The data are collected in matrix $$Z\left( {\left( {mn} \right) \times \left( {mn} \right)} \right)$$, consisting of elements $$z_{ij}^{rs}$$ as the inter-regional trade of sector i in region r into sector j in region s. Furthermore, it contains country-specific final demand vectors in matrix $$F\left( {\left( {mn} \right) \times \left( {tn} \right)} \right.$$ for t different categories.

The elements on the final demand matrix F are $$f\thinspace_i^{rs,\tau }$$ for final demand in region s for sector i of country r in final demand category τ. First, the total output of each sector in each region is computed and stored in a column vector $$x\left( {\left( {mn} \right) \times 1} \right)$$, with elements $$x_j^S$$ as the total output of sector j in region s. Subsequently, the $$A\left( {\left( {mn} \right) \times \left( {mn} \right)} \right)$$ matrix is calculated with equation (1). It consists of elements $$a_{ij}^{rs}$$, representing the technological production mix and efficiency.

$$\begin{array}{*{20}{c}} {a_{ij}^{rs} = \frac{{z_{ij}^{rs}}}{{x_j^s}}} \end{array}$$
(1)

The underlying formula of the MRIO framework can be simplified into:

$$\begin{array}{*{20}{c}} {\left( {I - A} \right)^{ - 1}\bf{f} = x} \end{array}$$
(2)

Here, the Leontief inverse (I – A)−1 consists of the identity matrix I and the A matrix. Moreover, an aggregated final demand vector $$\bf{f}\left( {\left( {mn} \right) \times 1} \right) = \left( {\bf{f}\thinspace_i^r} \right)$$ is used:

$$\begin{array}{*{20}{c}} {\bf{f}\thinspace_i^r = \mathop {\sum }\limits_{s = 1}^n \mathop {\sum }\limits_{\tau = 1}^t f\thinspace_i^{rs,\tau }} \end{array}$$
(3)

For multiple final demand vectors gathered in a final demand matrix F, equation (2) turns into equation (4).

$$\begin{array}{*{20}{c}} {\left( {I - A} \right)^{ - 1}F = X} \end{array}$$
(4)

Here, $$X\left( {\left( {mn} \right) \times \left( {tn} \right)} \right)$$ represents a matrix of total output vectors $$\bf{\chi} ^{s,\tau }$$ induced by the final demand vectors fs,τ in F.

To account for environmental impacts such as consumption-based CO2 emissions in this research, the MRIO framework can be extended by pre-multiplying the total output x with CO2 coefficients. CO2 emission data are stored in a vector $$\bf{\gamma} \left( {\left( {mn} \right) \times 1} \right)$$ with elements $$\gamma _j^s$$, which represent total CO2 emissions from sector s in region j. CO2 coefficients $$c_j^s$$, in a column vector $$\bf{c}\left( {\left( {mn} \right) \times 1} \right)$$, are created by dividing total CO2 emissions by the total output x:

$$\begin{array}{*{20}{c}} {c_j^s = \frac{{\gamma _j^s}}{{x_j^s}}} \end{array}$$
(5)

Vector c is diagonalized into a matrix $$\hat c\left( {\left( {mn} \right) \times \left( {mn} \right)} \right)$$. By pre-multiplying the total output matrix X with the diagonalized matrix $$\hat c$$, a matrix of consumption-based CO2 emissions $$E\left( {\left( {mn} \right) \times \left( {tn} \right)} \right)$$ can be computed (see equation (6)). The elements of matrix E, $$e_i^{rs,\tau }$$, represent the consumption-based CO2 emissions induced by final demand fs,τ in sector r in region i.

$$\begin{array}{*{20}{c}} {E = \hat cX = \hat c\left( {I - A} \right)^{ - 1}F} \end{array}$$
(6)

### Data

This research is based on a detailed expenditure dataset for the year 2011 from the WBCD14. The dataset was constructed by the World Bank from expenditure survey raw data and contains 116 countries and almost 90% of the global population. Multiple countries are missing from the dataset and, thus, are not included in the analysis. For every country in the WBCD, 201 expenditure bins and the corresponding population share are provided. The lowest expenditure bin represents expenditure of less than US$201150 PPP (in the following, US$2011 PPP is indicated by US$) per year, while people in the highest expenditure bin spend more than US$951,689 per year. For each bin, the expenditure for 33 different sectors of goods and services is listed. This detailed dataset provides the opportunity to compute per capita carbon footprints for each expenditure bin in each country represented in the WBCD. However, in some countries, the WBCD did not register people in every bin, resulting in empty bins at the bottom and top end of the expenditure spectrum. This can lead to underestimating national carbon inequalities (for example, in China) as extremes are missing. The most recent database of the Global Trade Analysis Project (GTAP 10)55 is used in addition to the WBCD to perform the EEMRIO analysis for computing the carbon footprints. As the most detailed and most recent version available, the GTAP 10 dataset in purchaser’s price on the world economy of 2014 was chosen, and WBCD data from 2011 were adjusted to prices of 2014. GTAP 10 contains information about 121 individual countries and 20 aggregate regions. For every country and region, the economy is divided into 65 economic sectors. The MRIO table, thus, shows the interconnection of 9,165 sectors, covering the world’s economy55. The GTAP 10 dataset has been chosen for the EEMRIO due to its high resolution on developing countries all over the world, matching the large number of developing countries in the WBCD.

The database contains the MRIO table. The essential part of the MRIO table is a square matrix of 9,165 times 9,165 entries with rows and columns for each economic sector in each region represented in GTAP 10. Each row in the input–output matrix represents the sales in million US$2014 of one economic sector in a region to all the other economic sectors in every region of the database. A column of the matrix, by comparison, corresponds to all the purchases in million US$2014 of an economic sector from every other sector of every region. As a result, the matrix combines all the intersectoral transactions of the world economy. In addition to the matrix of intersectoral transactions, the MRIO table of GTAP 10 shows 423 final demand vectors. The final demand vectors in GTAP 10 represent the purchases undertaken as investments, the purchases by households and the purchases of the government sector of every region in the database. Moreover, GTAP 10 contains a dataset of region-specific MtCO2 emissions for every economic sector as well as for direct household emissions in every region.

### Matching the WBCD to GTAP 10

To compute country- and expenditure-bin-specific per capita carbon footprints, we matched the expenditure represented in the WBCD with GTAP 10 household final demand vectors. Most countries in the WBCD are also present as individual countries in GTAP 10. Hence, the WBCD data can easily be matched with the household final demand vector of the country. However, some countries of the WBCD are part of aggregated regions in the GTAP 10 dataset. To match WBCD data of these countries with household final demand vectors of GTAP 10, we scaled down the aggregated regions to country level. As the dataset does not contain detailed economic data on the individual countries in the aggregated regions, we used population data from the year 201421 to scale down household final demand vectors, assuming that the economies of countries in one aggregate region are similar in their purchases and sales. This is done by dividing the population of the country by the overall population of the aggregate region, resulting in population ratios of the country in relation to the region. Subsequently, the household final demand vector of the aggregate region is multiplied with the calculated population ratio and thereby scaled down to the country level.

Moreover, the population data in the WBCD are updated to 2014 by using UN population statistics. The UN population data for every country in the WBCD are attributed to the different expenditure bins on the basis of the population shares of each bin in the original dataset. Furthermore, to match the most recent MRIO table provided by GTAP 10, for the year 2014, the WBCD expenditure data are inflated to 2014 using aggregated consumer price indices from the World Bank56.

### Reporting Summary

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

## Data availability

Global MRIO tables and carbon emissions were retrieved from the GTAP database, version 10 (https://www.gtap.agecon.purdue.edu/). Poverty lines can be obtained from ref. 5. Expenditure data were collected from the World Bank (https://data.worldbank.org/). Please contact the corresponding authors for more details.

## Code availability

Code developed for data processing in MATLAB, Python and R is available in the Supplementary Information.

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## Acknowledgements

We thank O. Dupriez for providing the raw dataset of the WBCD. This study was supported by the National Natural Science Foundation of China (72174111, H.Z.), the Shandong Natural Science Foundation of China (ZR2021MG013, H.Z.) and the Major Program of National Social Science Foundation of China (no. 21ZDA065, H.Z.).

## Author information

Authors

### Contributions

K.H., Y.S. and B.B. conceptualized and designed the study with crucial inputs from K.F. and H.Z. K.F. and H.Z. provided the expenditure and MRIO databases. B.B. performed the calculations and prepared the manuscript. B.B., K.H. and Y.S. contributed to writing the manuscript.

### Corresponding authors

Correspondence to Klaus Hubacek or Yuli Shan.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

## Peer review

### Peer review information

Nature Sustainability thanks Gilang Hardadi, Joel Millward-Hopkins and Victor Yakovenko for their contribution to the peer review of this work.

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## Extended data

### Extended Data Fig. 1 Per capita carbon footprints (CF) for each decile of national populations.

Per capita carbon footprints (CF) for each decile of national populations. The colouring indicates the average expenditure of the country.

## Supplementary information

### Supplementary Information

Supplementary Information and Figs. 1–12.

### Supplementary Table 1

Supplementary tables for national, regional and global carbon footprints, as well as poverty analysis and poverty alleviation scenarios

### Supplementary Software 1

Supplementary Python code for bridging the WBCD to GTAP 10, steps 1 and 3.

### Supplementary Software 2

Supplementary MATLAB code for bridging the WBCD to GTAP 10, step 2.

### Supplementary Software 3

Supplementary MATLAB code for running the EEMRIO analysis.

### Supplementary Software 4

Supplementary MATLAB function for running the EEMRIO analysis.

### Supplementary Software 5

Supplementary R code for the poverty alleviation scenario analysis.

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### Cite this article

Bruckner, B., Hubacek, K., Shan, Y. et al. Impacts of poverty alleviation on national and global carbon emissions. Nat Sustain 5, 311–320 (2022). https://doi.org/10.1038/s41893-021-00842-z

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