Space-Time Dynamics of Regional Income and
Air Pollution in China
Alvaro Domínguez

Asian Growth Research InstituteJiaqi Li
Graduate School of International Development, Nagoya University
Carlos Mendez
Graduate School of International Development, Nagoya University

Working Paper Series Vol. 2024-03
March 2024

The view expressed in this publication are those of the author(s) and do
not necessarily reflect those of the Institute.

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Asian Growth Research InstituteSpace-Time Dynamics of Regional Income and
Air Pollution in China
Alvaro Domı́nguez1 , Jiaqi Li2 , and Carlos Mendez3
1

Asian Growth Research Institute, Kitakyushu, Fukuoka, 803-0814, Japan ∗2 Graduate School of International Development, Nagoya University, Aichi
464-8601, Japan †3 Graduate School of International Development, Nagoya University, Aichi
464-8601, Japan ‡March 25, 2024

Abstract
Relying on a novel satellite dataset, we examine the spatial distribution of air pollution, specifically PM2.5 , and income across 285 Chinese
prefectural and above-level cities. A static spatial dependence analysis
reveals the locations of high-value clusters (hot spots) and low-value
clusters (cold spots), highlighting a strong negative assosiation between income and air pollution. Then, through dynamic spatial clustering techniques, we study the intertemporal relationship between air
pollution and income and find a polarization effect between different
regions. Our integrated approach demonstrates how these analyses
complement each other in identifying regions where policies to enhance air standards can improve the population’s quality of life.
Keywords: Air Pollution, China, Local Indicators of Spatial Association.
JEL classification: C15, O33, P11, P18, Q42∗

E-mail: alvdom123@gmail.com
E-mail: li.jiaqi.bonny@gmail.com‡
E-mail: carlos@gsid.nagoya-u.ac.jp
†

1

1

Introduction

In the past few decades, China has experienced a rapid increase in pollution
and environmental deterioration as it underwent dramatic economic growth.
According to Brauer et al. (2016), most of the population in the country has
been exposed to levels of PM2.5 air pollution higher than what the World
Health Organization (WHO) considers healthy. This issue poses a threat
since the Chinese economy is expected to continue growing rapidly, and thedeterioration of the environment and air quality will contribute to worsening
the general population’s health.
We aim to study the relationship between income and air pollution froma spatial distribution point of view. For this, we rely on a novel dataset by
Wu et al. (2022) of 285 Chinese prefectural and above-level cities spanning
2000-2019. We differentiate our work by relying on methods from spatial
data analysis to inquire about the evolution of the variables under study
along with their spatial dependence, both static and dynamic. Therefore,
we obtain results that support Wu et al. (2022) while expanding on theseand contributing to the literature by performing additional analyses. These
analyses are only possible due to the spatial nature of the methodology employed.
Being a major industrial and economic powerhouse, China has had dramatic rapid urbanization and industrialization in recent decades. These developments have contributed to substantial increases in greenhouse gas emissions (GHG), making China the world’s largest emitter of carbon dioxide andother GHGs. Most of the population has been exposed to levels of PM2.5 air
pollution higher than the WHO considers healthy.
In 2013, the Chinese government introduced the Air Pollution Prevention
and Control Action Plan (APPCA program) to mitigate urban air pollution.
The main objective of this initiative was to achieve a minimum 10% reduction in PM levels by 2017 compared to the 2012 baseline across all Chinese
cities. Notably, the government set a more ambitious reduction target of
25% in heavily polluted regions like Beijing, Tianjin, and Hebei. By 2017,
the program demonstrated success by effectively lowering PM10 levels in all
APPCA program cities by 22.7% from the 2013 baseline.
Even though the APPCA program successfully reduced PM10 concentrations, a potential issue is that this policy could shift the distribution of PM2.5
concentrations among different income groups in the long run. As shown in
Figure 1, the median for PM2.5 concentrations before and after 2013 slightly
increased. To test for this, we perform a dynamic analysis of spatial dependence and inspect whether there was an actual shift in the distribution of
PM2.5 amongst various regions and income groups.
2Figure 1: Distribution of relative PM2.5 across cities in 2013 and 2018

This paper is organized as follows. In section 2 we provide a review ofthe literature. In section 3 we discuss the different methods and the data
employed throughout this work. In section 4 we describe the results obtained.
In section 5 we conclude and provide some policy implications.

2

Related literature

Many studies exist regarding the relationship between GDP and pollution.
One line of research has focused on the so-called “Environmental Kuznets
Curve,” or EKC, which shows the connection between economic growth andthe environment. The classic work by Grossman and Krueger (1991) analyzes
the environmental implications of the North American Free Trade Agreement
by introducing the EKC. Since then, numerous studies have researched thisrelationship. In more recent times, Al-Mulali et al. (2015) investigate thelinkage between the ecological footprint and GDP of 93 countries and findan inverted U-shaped relationship between these. Churchill et al. (2018) relyon panel data from the late 19th century to 2014 to study the EKC for 20
OECD countries. The authors find that the EKC hypothesis holds for thepanel taken as a whole, but when explicitly considered by country, the results
are instead mixed.
In the case of China, different authors have also analyzed this association.
Relying on a dataset of 73 Chinese cities, Hao and Liu (2016) explore the EKC
hypothesis in PM2.5 for 2013 and observe that it exists. Jalil and Mahmud(2009) examine the EKC relationship between CO2 emissions and per capita
real GDP. They encounter a one way causality via economic growth to CO2emissions, relying on Granger causality tests. In the same vein, Yin et al.
(2015) look into the EKC hypothesis for the case of CO2 emissions by utilizing
panel data for the years 1999-2011, finding evidence of its existence. Wanget al. (2016) examine the impacts of economic growth and urbanization on
sulfur dioxide emissions through the EKC hypothesis and confirm that there
is a relationship between economic growth and sulfur dioxide emissions, but
not for urbanization and the latter.
3

A different strand of the literature concerns the convergence of emissionsof different pollutants. These works are influenced by the economic convergence hypothesis, which assumes that countries with low-income tend to growat a higher rate than their high-income counterparts. Due to this, income
levels converge in the long-run. In a similar fashion, it is assumed that pollution declines faster in high-pollution countries relative to low-pollution ones.
Therefore, in the long-run, countries converge in their environmental quality.
Bulte et al. (2007) and Brock and Taylor (2010) lay the foundations for thetheory of environmental convergence. Herrerias (2013) studies the environmental convergence of CO2 emissions for a set of developing and developed
countries, for the period going from 1980 to 2009. By considering the sourceof emissions, she is able to find the pattern of convergence of emissions moreaccurately. Although a large number of countries show club convergence,some still diverge. This implies the necessity of implementing environmental
policies tuned to each of the clubs, since countries may converge to particular
clubs.
A third line of research, where our work is positioned, consists of using
spatial analysis to study the economy, pollution, and the interlink between
these two. He et al. (2017) perform a geospatial analysis of inequality acrossvarious Chinese counties, prefectures and provinces for the period 1997-2010.
Utilizing local indicators of spatial autocorrelation, they discern a northward
movement of hot spots of economic growth. This shift was influenced by the
movement of foreign investors towards the northern regions and the spatial
agglomeration, alongside the impact of institutional forces in China. We
distinguish our work by performing both static and dynamic analyzes ofspatial dependence for a more extended period (2000-2018). Through thesemethods and data, we can pinpoint to regions and locations where policies
to improve local populations’ quality of life can be implemented.
Han et al. (2021) explore the interplay between the socioeconomic statusof Chinese counties and their prolonged exposure to PM2.5 concentrations.
The study reveals that populations residing in economically disadvantaged
counties are disproportionately vulnerable to the adverse impacts of such
exposure. Consequently, this disparity in environmental risk exposure exacerbates socioeconomic inequality and health disparities within these places.
By relying on the Directional LISA method, our study finds that regionswith similar levels of relative air pollution and income tend to “move” (i.e.,cluster) together throughout time.
The current body of literature has yet to yield definitive results concerning
the relationship between income and pollution. There remains potential for
creating more sophisticated dynamic models that capture the intertwined
evolution of economic growth and pollution emissions. Furthermore, there4

is an opportunity for empirical research to examine alternative theoreticalmodels and uncover new stylized facts in this context.

3

Methods and Data

In this study, we rely on a panel dataset containing data on relative PM2.5
levels along with relative income from Wu et al. (2022) for the period spanning 2000-2018. We then georeference the data, incorporating latitude andlongitude variables, allowing us to perform the spatial analysis. All thesedata are for 285 Chinese prefectural and above-level cities.
We employ spatial dependence analysis to check the existence (or lackthereof) of clusters in the spatial distribution of a given characteristic or
attribute. A global dependence test seeks if a pattern of clusters in thespatial distribution for an attribute is present or not. In this test, the null
hypothesis consists in detecting if there is randomness in the spatial location
we are studying (i.e., are the cities under study independent from each otherand do they provide no significant information). If the null hypothesis isrejected, this implies that we can find clusters that are important for ourstudy. The “gold standard” for testing global spatial dependence is Moran’sI (Cliff and Ord, 1981). This test can be expressed as:XXXI=wij (xi − µ) (xj − µ) /(xi − µ)2
(1)
i

j

i

where wij is the row-standardized element of the weighted matrix, outlining
the spatial structure of the data we analyze. Additionally, xi indicates thelevel of air pollution in city i, and µ shows the average level of air pollution.
Figure 2 below shows a graphical depiction of spatial dependence.

5

Figure 2: Spatial Dependence

Source: Adapted from Grekousis (2020).

When the spatial weights wij of the weighted matrix W take positive
values (i.e., wij > 0), it denotes a relationship among neighbors in a geographical zone. Meanwhile, if wij = 0, there is a lack of such a relationship.
These weights can be specified in various manners. One such way is the socalled “Queen contiguity” (referring to the movement of the queen piece inchess), in which two regions are considered neighbors if they share a common
border or vertex (shown in Figure 3 below). In this work, we rely on thisinterpretation due to its ease of implementation and interpretation.
Figure 3: Queen Contiguity

In certain instances, we may encounter isolated locations or uneven distributions of data points. This situation poses a problem since it is not clear
what the neighborhood of each data point is, complicating the computationof the spatial weights. In order to avoid these issues, we rely on the so-called6

“Thiessen polygons.”1 These polygons allow us to work around these issues
by dividing the areas into regular subareas. Additionally, it serves by generalizing the concept of contiguity, providing clear delineations of neighborhoodboundaries on maps. Figure 4 below provides a visual explanation of theconstruction of Thiessen Polygons.
Figure 4: Construction of Thiessen Polygons

A method of spatial association at the local level proposed by Anselin(1995) is that of Local Indicators of Spatial Association (LISA). In it, theLocal Moran statistic assesses local spatial patterns through “hot spots”(displaying relatively high patterns), “cold spots” (relatively low values),
and spatial outliers (high values surrounded by low values or vice-versa).
The Local Moran’s I is given by the expression:(xi − µ) X
wij (xj − µ)Ii = P(xi − µ)2 j(2)the notation and variables in this expression are the same as those given byequation 1.

3.1

Directional LISA

To analyze the spatial co-evolution of income and air pollution distribution
throughout time (i.e., whether these attributes form clusters or not), we makeuse of the method by Rey et al. (2011). This indicator connects two different
periods of cross-sectional LISA, showing moving vectors and allowing us to1

These are also known as Voronoi diagrams or tessellations in the literature.

7

observe the joint movement of cities and their neighbors’ income and air
pollution. The Directional LISA2 for city i at time t is given by:(xi,t − µ) X
Ii,t = P
wij (xj,t − µ)(xi,t − µ)2 j(3)where xi,t is the variable of interest for city i at time t and wi,j has the sameinterpretation as in equation 1.
The hypothesis testing is given by the following criteria:
H0 : hi,t+k = hip,t+k
H1 : hi,t+k ̸= hip,t+k
where hi,t+k refers to the height and direction of the vector from year t toyear t+k, and hip+k alludes to the expected direction and height of the vector
based on the null hypothesis of random spatial permutations, implying spacetime independence. Consequently, the null hypothesis affirms that there isindependence in the movements between cities and their neighbors. In Figure
5 we present a graphic comparing the static and dynamic LISA.
Figure 5: Comparison Between Static and Dynamic LISA

Source: Adapted from Aginta (2022).

4

Results

We begin by constructing Thiessen polygons in the map (Figure 6) to clearly
delimit each city’s neighbors. From this, we observe a “ring-like” pattern
2

Throughout this work, we will use the terms ”Directional” and ”Dynamic” LISA interchangeably.

8where the cities in the center tend to have higher concentration levels of
PM2.5 , and the outer-ring cities have lower concentrations than the rest. Thissituation naturally begs the question of whether spatial patterns of dependence play a role in this apparent clustering of cities with similar pollution
levels.
Figure 6: Spatial distribution of PM2.5 in 2000 using Thiessen Polygon

To identify the presence of local clusters and local spatial outliers for2000 and 2017, we rely on the Local Moran statistic. The results for the year
2000 are shown in Figure 7. In part (a), we see that there is a high spatial
dependence (Moran’s I is 0.79), with most of the observations concentratingin the High-High (HH) and Low-Low (LL) quadrants. In part (b) of Figure
7, we observe that the concentration of hot spots (cities with high levels ofpollution, surrounded by cities also with high pollution levels) are locatedtowards the center of the Thiessen polygon.
Meanwhile, the cold spots are located towards the outer parts, following
the “ring-like” pattern encountered before. These clusters, highlighted in
red and blue, are statistically significant (p-value < 0.1). Finally, in part(c) of Figure 7, we show the geographic position of the cities on the Chinese map, with their respective PM2.5 levels.3 We see that, just as with theThiessen polygons, most of the highly polluted cities are concentrated towards the center/middle-to-east of China, with less polluted cities outside ofthis cluster.
3

It is worth noting that parts (a) and (b) are not directly correlated or matched in a
one-to-one manner with the map in part (c). We plot them together to understand theresults and their geographical positioning visually.

9

Figure 7: PM2.5 Spatial Dependence (2000)

Figure 8 shows the same results but for the year 2017. Once again, we
find a high level of spatial dependence (Moran’s I is 0.83) and the statistically significant ring structure (p-value < 0.15) from before. The map in part(c) suggests that in the period spanning from 2000, many cities in the centerof China with high concentrations of PM2.5 have improved. In the meantime, those to the center-east have slightly worsened (those at the tail-end
increasing from 1.859 to 1.915 relative PM2.5 ).
Figure 8: PM2.5 Spatial Dependence (2017)

From the results above, we ask ourselves, what is the income for thesecities? Do we observe cities with high income having lower or higher pollution
levels? To answer this, we do the same local spatial dependence analysis but
for relative income. The results are presented in Figures 9 and 10. Figure
9 shows that Moran’s I is still positive but lower than before, at 0.34. Thisimplies that despite spatial dependence being present, it is not as strong as
in the case of PM2.5 . In part (b) of this Figure, we see that contrary to thePM2.5 case, the hot spots are located in the outer ring, while the cold spots
are in the center.
Figure 10 once again paints a similar picture. Although spatial dependence is slightly higher than before (Moran’s I is 0.37), it is not as strong as
10

for PM2.5 . Nevertheless, for 2017, we still find cold spots in the center andhot spots on the outer sides of the ring. This suggests that cities with higher
income levels (with respect to the mean) tend to have lower concentrationsof PM2.5 and vice versa.
Figure 9: Spatial Dependence for Relative Income (2000)

Figure 10: Spatial Dependence for Relative Income (2017)

4.1

Directional LISAIn the previous section, we found a negative relationship between income
levels and concentrations of PM2.5 . This relationship, though, was in a static
sense. We would like to know whether this relationship also holds intertemporally and, if so, where in China. Therefore, we rely on the Directional
LISA method discussed in section 3.1. Doing this will allow us to analyze
the spatial co-evolution between PM2.5 and income throughout time.

11

Figure 11: Standardized Directional LISA for PM2.5 (2000-2017)

In Figure 11, we plot the directional LISA for PM2.5 comprising the years
2000-2017. We proceed to divide the cities in China into three regions: East,
West, and Central. The arrows point to the quadrant to which each city ismoving, with the color representing the region where each belongs. Additionally, we standardize the plot so that all arrows depart from the origin. For
this period, there is a clear divide between East and West (with the central
region being more ambivalent).
In Figure 12, we observe the results for relative income. Similar to thecase of PM2.5 , a polarization-like effect occurs, in which the East and Westregions move in opposite directions (cities with high levels of income move
upwards, surrounded by cities moving upwards and vice-versa). Just as in
the static case, this suggests that cities with high (low) income levels tend
to have lower (higher) air pollution levels and neighbors similar to them.

12Figure 12: Standardized Directional LISA for rel. income (2000-2018)

Additionally, we analyze the evolution of PM2.5 before and after the 2013
Air Pollution Prevention and Control Action Plan (APPCA program) implemented by the government. Doing so lets us see if said program altered theclustering tendency we observed for the whole period. The left panel of Figure 13 presented below shows that the polarization effect between the Eastand West regions, previously shown in Figure 12, persisted until 2013. In theright panel, we can see that from 2013 onward, this trend mostly reversed:
the West region started to improve while the East got worse. The Central
region also, in its majority, moved downwards.

13

Figure 13: Standardized Directional LISA before and after 2013

5

Discussion

By “spatializing” the data and making use of spatial analysis methods, we
can identify clusters that present both similarities in location and attributeswith respect to air pollution and income. This is in contrast to the approachby Wu et al. (2022), who rely on a distribution dynamics approach to findlong-run relationships between these variables.
The dynamic clustering analysis in the previous section reveals a growing
polarization between the eastern and western regions of China, indicating distinct patterns of development and environmental dynamics. However, whensplitting the period into two (before and after 2013), we see a shift post-2013,
suggesting unintended consequences possibly stemming from implementing
the Atmospheric Pollution Prevention and Control Action (APPCA) program. These findings highlight the complexity of addressing environmental
challenges, particularly in relation to reducing PM2.5 pollution levels. Moreover, the analysis of diffusion patterns suggests a negative correlation between
pollution levels and income distribution, emphasizing the need for targetedinterventions to mitigate environmental inequalities while promoting sustainable development.

14

The significance of finding these clusters relies on the fact that it allows
for policy coordination at that level. The static approach, through the Local
Moran statistic, provides information on spatial dependence and whether
there is a diffusion process taking place amongst neighboring cities or regions.
Complementing this, the dynamic approach shows whether these cities andregions, along with their neighbors, are converging towards similar levels ofpollution and income. When considered together, these approaches help to
pinpoint the locations where more effort needs to be taken by providing ananalytical and visual representation of the data.
On the policy side, collaborating with Japan and other neighboring countries can be beneficial for China. Japan’s successful experience in addressing
air pollution during its high economic growth period in the 1950s and 1960spresents valuable lessons for China. Given their geographical proximity andshared concerns about air quality, fostering joint cooperation is important.
By working together, these nations can effectively tackle environmental challenges while also encouraging regional economic growth.

6Conclusions

In this work, we analyzed the pollution-income relationship for cities in China
using data on relative income and PM2.5 . After georeferencing this data, werelied on spatial analysis methods and found a “ring-like” pattern where
cities at the center tended to have higher concentration levels of PM2.5 , andthe outer-ring ones had lower concentrations than the rest. This linkage isinverted when considering relative income, suggesting that spatial patternsof dependence may be causing this clustering of cities with similar incomepollution levels. We then proceeded to detect hot spot and cold spot clusters,with a very high (> 0.5) and significant (p-value < 0.1) spatial dependence.
All this further confirmed the ring pattern of clusters found before.
Finally, through a dynamic LISA analysis, we studied the intertemporal
association between pollution and income for the whole period and before andafter the APPCA program in 2013. The former showed that for the years
2000-2017, there was a polarization between different regions: the westernregion increased its income while improving its air pollution, while the east
region headed in the opposite direction. The latter, instead, painted a different picture. While presenting a similar behavior up to 2013, afterward, thetendency reverted.
Future research could focus on analyzing the relationship between income and air pollution for a longer time frame. Additionally, using a larger
dataset that includes more cities from the western region would allow us to15

corroborate the present results. Furthermore, a more detailed analysis andunderstanding of the impact of the APPCA program on the PM2.5 levels
would allow the development of programs that better address the issue of air
pollution.

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