BigEssays Logo

ECON2213 china and India

Hi, this question: "ECON2213 china and India" has been answered but we do not resell delivered works. Order your custom solution today. Get 30% discount.

Hi, the question: "ECON2213 china and India" has been answered previously by our writers. The full homework question is provided below for confirmation. However, the full answer delivered is not provided since, at BigEssays, we never resell answers. We maintain 100% Privacy. For a customized solution to this question, place your order now. Start with checking how much it'll cost. Get a 30% discount for this question.


"At BigEssays, Your homework assignment is always in good hands."

Price Checker

Know how much it'll cost upfront.

Full Question

Assignment 4 Grading Rubric
Student Name:________________________________ B00:_________________
Title and
(maximum 5
Data and
(maximum 15
Analysis and
(maximum 15
(maximum 15
The student
provides an
entertaining title
and a clear
The student finds
all of the required
data, attributes all
information, and
uses appropriate
figures and charts
The student
provides a
analysis, provides
convincing support
for their
information fully
The student
provides an
introduction and a
clear conclusion,
definitions of
relevant terms, and
writes without
punctuation, and
grammatical errors
The student
provides a
descriptive title and
a hypothesis
The student lacks
either a title or a
The student lacks a
title and a
The student finds
the required data,
attributes some
incorporates some
information, and
uses some figures
and charts
The student may
lack some of the
required data,
attributions of
information, or
figures and charts
The student lacks
the required data,
does not attribute
sources, provides
little background
information, and
does not use figures
or charts
The student
provides a clear
analysis, provides
some support for
their hypothesis,
information to
some extent
The student may
lack a correct
analysis, support
for their
hypothesis, or
integration of
The student does
not provide a
correct analysis,
support for their
hypothesis, or
integration of
The student
provides an
introduction and
conclusion, defines
some relevant
terms, and writes
with few spelling,
punctuation, and
grammatical errors
The student does
not provide an
introduction or
conclusion, lacks
definitions of
relevant terms, and
writes with some
punctuation, and
grammatical errors
The student lacks
an introduction or
conclusion, does
not define relevant
terms, and writes
with many spelling,
punctuation, and
grammatical errors
Total (maximum 50 points)
Dalhousie University
Fall 2022
ECON 2213 / CHIN 2290
Professor T. Cyrus
Emerging Giants: The Economic Rise of China and India
Assignment 4: Climate and Environmental Policy
Due: Thursday, November 17, 11:59 p.m.
In this assignment, you will formulate and evaluate a hypothesis regarding the reasons behind
pollution emissions in China and India. To do this, you will find and analyze data on the carbon
and particulate emissions of China and India. You will then try to explain changes over time in
World Bank World Development Indicators Database
Background data collection and analysis:
1. Download data on total CO2 emissions for China, India, and the United States starting in the
year of the 1990s corresponding to the last digit of your B00 number. For example, if your B00
number ends in 5, collect data starting in 1995. Find data through the year 2019. Use the series
CO2 emissions (kt).
2. For China and India (separately), calculate the correlation coefficient between CO2 emissions
and GDP (use the series GDP (constant 2015 US$)).
3. Construct a figure showing the CO2 emissions of China, India, and the United States on the
same chart.
4. Download data on CO2 emissions per person for China, India, and the United States starting in
the year of the 1990s corresponding to the last digit of your B00 number. For example, if your
B00 number ends in 5, collect data starting in 1995. Find data through the year 2019. Use the
series CO2 emissions (metric tons per capita).
5. For China and India (separately), calculate the correlation coefficient between CO2 emissions
per person and GDP per capita (use the series GDP per capita (constant 2015 US$)).
6. Construct a figure showing the CO2 emissions per person of China, India, and the United
States on the same chart.
7. Download data on energy intensity for China, India, and the United States starting in the year
of the 1990s corresponding to the last digit of your B00 number. For example, if your B00
number ends in 5, collect data starting in 1995. Find data through the year 2018. Use the series
CO2 emissions (kg per 2015 US$ of GDP).
8. Construct a figure showing the energy intensity of China, India, and the United States on the
same chart.
9. Download data on PM2.5 air pollution between 1990 and 2017 for China, India, and the
United States. Use the series PM2.5 air pollution, mean annual exposure (micrograms per cubic
In your essay, clearly explain your hypothesis regarding why pollution emissions have changed
over time in China and India. Describe how carbon and particulate emissions have changed over
time; compare the two countries to each other and to the United States, and discuss whether GDP
or GDP per capita are related to carbon emissions. Why does Greenstone et al. (2015) say that
particulate matter is so detrimental to health? Describe one government policy that each country
is currently undertaking in order to improve its environmental quality.
Detailed Instructions:
Your essay must have a title, an introduction, and a conclusion. Make sure to integrate all
relevant definitions into your essay, and provide appropriate background information. A
suggested essay structure contains six paragraphs:
• a first paragraph that briefly introduces the hypothesis you propose;
• a second paragraph describing how total CO2 has changed over time and explaining how
well it is correlated with total GDP;
• a third paragraph describing how per-capita CO2 has changed over time and explaining
how well it is correlated with per-capita GDP;
• a fourth paragraph describing how energy intensity has changed over time;
• a fifth paragraph describing how PM2.5 has changed over time, and explaining why
Greenstone et al. consider it to be detrimental to health;
• and a sixth, concluding, paragraph that briefly summarizes your hypothesis and
The maximum length is 500 words; include a word count at the end of your submission. Include
the grading rubric as the first page of your submission. Write in 12-point font, double-spaced,
with one-inch margins. Provide a bibliography listing the references you use, including your data
sources and, if relevant, the class PowerPoints. Please note that your assignment will be
submitted to anti-plagiarism software.
Include your Excel spreadsheet as an attachment in your submission. Your Excel spreadsheet
should include all of your data and calculations. Required charts and figures should be placed
into the body of your essay; any supplemental charts and figures may be placed in an appendix at
the end of your essay.
• Late submissions: -10 points out of 50 points per day, starting immediately after 11:59
p.m. (i.e., an assignment submitted at midnight will be docked 10 points).

Remember that you are allowed to submit one assignment up to 48 hours late without
being assessed a late penalty, as long as you email your instructor before the due date,
stating your name and B00 number and requesting the extended deadline.
No rubric: -1 point.
No Excel spreadsheet: -2 points.
No word count: -2 points.
Over 500 words: -1 point for each additional 50 words (e.g., -1 for 501 words, -2 for 551
words, etc).
Lower Pollution, Longer Lives
Life Expectancy Gains if India Reduced Particulate
Matter Pollution
Michael Greenstone, Janhavi Nilekani, Rohini Pande, Nicholas Ryan, Anant Sudarshan, Anish Sugathan
India’s population is exposed to dangerously high levels
of air pollution. Using a combination of ground-level in
situ measurements and satellite-based remote sensing
data, this paper estimates that 660 million people, over
half of India’s population, live in areas that exceed the
Indian National Ambient Air Quality Standard for fine
particulate pollution. Reducing pollution in these areas
to achieve the standard would, we estimate, increase life
expectancy for these Indians by 3.2 years on average for
a total of 2.1 billion life years. We outline directions for
environmental policy to start achieving these gains.
We thank Harvard’s Sustainability Science Program for hosting the India
Initiative and funding. We also thank Susanna Berkouwer for research
assistance and an anonymous referee for helpful comments. All views
and errors are solely ours.
Michael Greenstone ([email protected]) is the Milton Friedman
Professor of Economics at the University of Chicago and the Director of
the Energy Policy Institute at Chicago; Janhavi Nilekani (janhavi.
[email protected]) is a Giorgio Ruffolo Doctoral Research Fellow in
the Sustainability Science Program, Harvard University; Rohini Pande
(rohini_ [email protected]) is the Mohammed Kamal Professor of
Public Policy at Harvard University; Nicholas Ryan ([email protected]
edu) teaches Economics at Yale University; Anant Sudarshan ([email protected] is the Executive Director of Energy Policy Institute at
Chicago’s Office in Delhi; and Anish Sugathan ([email protected])
is a Giorgio Ruffolo postdoctoral research fellow in the Sustainability
Science Program, Harvard University.
1 Introduction
ir pollution is a global public health problem. The
World Health Organization (WHO) declared air pollution
the world’s single largest environmental health risk
and attributed around seven million deaths globally to air pollution in 2012.1 The Global Burden of Disease 2010 report estimated that ambient particulate matter (PM) air pollution accounts
for about 6% of global deaths (IHME 2013; Lim et al 2012).
Air pollution in India is severe. Data from the country’s apex
environmental regulator, the Central Pollution Control Board
(CPCB), reveals that 77% of Indian urban agglomerations exceeded National Ambient Air Quality Standard (NAAQS) for
respirable suspended particulate matter (PM10) in 2010 (CPCB
2012).2 Estimates from the WHO suggest that 13 of the 20 cities
in the world with the worst fine particulate (PM2.5) air pollution are in India, including Delhi, the worst-ranked city.3 India
has the highest rate of death caused by chronic respiratory
diseases anywhere in the world.4
In this paper we estimate the life expectancy loss from fine
particulate air pollution in India, and in doing so highlight air
pollution as an urgent public health problem that deserves
policy attention. These estimates provide one measure of
“benefits” which can be used to conduct cost–benefit analyses
of potential air pollution control policies in India.
Our analysis has three steps: (1) construction of a fine particulate
air pollution data set at the district and city level; (2) identifying an
appropriate estimate of the effect of particulate pollution on longterm mortality; and (3) applying the estimated relationship between air pollution and mortality to the air pollution data to calculate the life expectancy gains from reducing air pollution in all
parts of the country to India’s national air pollution standard.
We use the best available information on air pollution levels
across India to construct our data set. We use ground monitoring data for the predominantly urban areas covered by the
CPCB’s air quality monitoring network. Where monitoring data
is unavailable, we use new satellite-based estimates (Dey et al
(2012) create satellite measures of fine particulates for the
whole of India). The data show that both urban and rural
populations are exposed to dangerously high levels of fine
particulates (PM2.5). Six hundred and sixty million people
(54.5% of the population) live in regions that do not meet the
Indian NAAQS for fine particulate matter, and nearly every
Indian (1,204 million people, or 99.5% of the population)
febrUARY 21, 2015
vol l no 8
Economic & Political Weekly
lives in a region with air pollution levels above the stricter health advisory system and as a means of increasing pressure
on polluters to comply with existing regulations. Second, we
guidelines of the WHO. .
Within a given district, individuals may vary in their exposure recommend a greater reliance on civil penalties in environto air pollution. However, as many air pollution action plans mental law; this is a natural extension of the polluter pays
target specific districts, industrial clusters, or metropolitan principle, widely recognised in Indian law, and would provide
areas, a focus on district and city averages is suitable for evalu- an incentive to reduce pollution rather than evade regulation.
ating the benefits of a policy that is able to target and bring Third, market-based mechanisms for environmental regulation
can build on the first two policies, monitoring and the penalties
down district-average or city-average ambient air pollution.
Next, we identify an appropriate estimate of the effect of aver- for violations, to reduce pollution at the lowest possible cost.
age ambient air pollution concentrations on life expectancy. The Market-based instruments have been used successfully to adkey concerns in estimating this relationship are twofold. First, dress a range of pollution problems in many other countries,
air pollution and life expectancy may co-vary with a host of un- and have been discussed, but never tried, in India.
The remainder of this paper is organised as follows. Section 2
observed factors. This implies that the extent of correlation between these two variables may not provide an estimate of the describes how we estimate particulate concentrations and
causal impact of air pollution on life expectancy. Second, an indi- discusses pollution levels across the country. Section 3 briefly
vidual’s ability to limit his or her exposure to dirty air may vary describes the scientific evidence relating fine particulates to
with socio-economic status and available public amenities. Thus, mortality and carries out a calculation of the impact of high air
a causal estimate would be applicable to India only if citizen’s pollution on life expectancy in India. Finally Section 4 consocio-economic status and available public amenities are compa- cludes by discussing policy responses.
rable. Our preferred estimate is from a recent study using data
from Chinese cities (Chen et al 2013). This paper, using a research 2 Estimating Particulate Air Pollution across India
design that separates the effect of pollution from other factors First things first — what is particulate matter? PM is a type of
that also affect mortality, finds that an additional 100 micro- air pollution, consisting of numerous tiny particles suspended
grams per cubic metre (µg/m3) of total suspended particulates in air. PM affects the cardiovascular and respiratory systems
and has consistently been shown to be dangerous to human
(TSPs) reduces life expectancy at birth by roughly three years.5
Finally, we apply this estimate to the Indian data to conduct health. PM air pollution is called by different names, dependa detailed health accounting exercise that estimates the loss ing on the size of the particles. Box 1 lays out these various
of life expectancy in India from outdoor particulate matter names, their relation to one another, and the standards that
pollution. We find that the 660 million people exposed to have been set by India and the WHO to designate maximum
PM2.5 pollution above the Indian NAAQS
Box 1: What Is Particulate Matter Air Pollution and What Air Quality Standards Apply to It?
would gain an average of 3.2 years of life ex“Particulate Matter” (PM) refers to small particles suspended in air, either solid or liquid droplets,
pectancy if air quality in these areas were imand originating from various sources that pollute ambient air. Particulate matter is made up of
proved to meet the national standards. Put
different organic and inorganic components; the major constituents include acids (sulphate and
another way, compliance with Indian air
nitrates), ammonia, sodium chloride, black carbon, water, and mineral dust. PM is widespread
quality standards would save 2.1 billion life
and affects more people than any other ambient air pollutant.6
years. We show that these numbers remain
Particulate matter adversely affects the cardiovascular and respiratory systems. The health
broadly similar when using the estimated
impact of PM increases as particle size decreases. Thus PM is generally classified based on the size
pollution–mortality relationship from other
or coarseness of particles and that forms the basis for setting ambient air quality standards. The
classification is presented below.
studies that are designed to investigate the
PM Classification
impacts of sustained high ambient pollution
“suspended particulate matter”/
particles of size 90
WHO for India (Note 3).
No Data
For areas not covered by the CPCB
monitoring network we use satellite * 2001 district boundaries are used in this map.
measurements of air pollution. Recent advances in satellite- long distances from where they are originally emitted, imposbased remote sensing technologies have allowed scientists to ing health costs on people living far from major sources
construct credible measures of fine particulate concentrations in (Guttikunda and Jawahar 2014). In addition to transport of
the air. Dey et al (2012) construct a district-level measure of fine emissions from urban industrial and transport sources, rural
particulate air pollution using data from 2000 to 2010 from the India also directly faces particulate air pollution from local
NASA MODIS mission (calibrated against ground monitoring sources, such as biomass combustion (Hanna et al 2012).
Given the widespread and severe nature of India’s PM pollumeasurements). We use this data set to calculate district-level
tion, many Indians are exposed to dangerously high levels of
average PM2.5 measures using 2011 district boundaries.
We combine these two data sets, using monitoring data for fine particulates (PM2.5). Using 2011 Census of India population
2010 where available and satellite estimates otherwise, to numbers, we estimate that 660 million people (54.5% of the
create a unified estimate of fine particulate matter (PM2.5) con- population) live in regions that do not meet the 40 µg/m3
centration levels for every district and urban agglomeration in NAAQS (Note 8) and 262 million people (21.7% of the populathe country.
tion) live in regions with air pollution levels at more than twice
this standard. Nearly every Indian (1,204 million people, or
2.1 PM Concentrations and Exposure Levels
99.5% of the population) lives in an area with PM2.5 pollution
Figure 1 shows the PM2.5 concentrations for every district and above WHO’s 10 µg/m3 guideline (Note 6).
urban agglomeration in the country. Average PM2.5 concentraWithin any single district or city, individual exposure to air
tions for urban agglomerations, derived from CPCB monitoring pollution will depend on an individual’s socio-economic status
data, are represented with small circles. For regions of the and ability to avoid high-pollution zones. Thus, traffic policecountry where monitoring data is unavailable, remote sensing men will face relatively high pollution levels and richer indidata (Dey et al 2012) is used. The figure depicts increasing lev- viduals with access to air purifiers at home will have lower
els of pollution with darker shades of grey in accordance with exposures. Equally, individuals who commute by autorickshaw
the inset guide. Broad areas of the country, particularly in may face greater concentrations than those with access to cars
north India, are well out of compliance with the standard. This with improved ventilation systems (Apte et al 2011). Put differnon-compliance holds in rural as well as urban areas.
ently, the pollutant concentrations in Figure 1 do not represent
These geographical patterns are consistent with evidence the actual exposure to air pollution for any single individual.
from dispersion models that show how fine particles can travel That said, district-level pollution averages are the relevant
febrUARY 21, 2015
vol l no 8
Economic & Political Weekly
Figure 2: Comparing PM2.5 Annual Average Concentration
(124 cities)
(112 cities)
(565 cities)
(379 cities)
0 10 12
Annual average PM2.5 (µ g/m3)
Sources: (1) City-level annual average PM2.5 concentrations for India, China, Europe, and the
US from WHO’s 2014 “Ambient Air Pollution Database (Note 3).
(2) Ambient air quality standards for annual PM2.5 concentrations from the respective
agencies (Notes 6 and 8)9, 10, 11.
Particulate pollution in India is high, but has it improved over
time? In Figure 3 we use historic CPCB data to document trends
in the overall average SPM concentration in urban agglomerations covered by the CPCB network (“monitored urban agglomerations”). At no point in the last quarter century have average
urban SPM concentrations in India met the 140 µg/m3 SPM standard (Note 7).Despite a very slight downward trend in average
pollution over the last 20-plus years, the last five years show no
trend towards either improvement or deterioration in air quality.
The shaded bands show the percentile distribution of monitored urban agglomerations based on annual average SPM
Economic & Political Weekly
febrUARY 21, 2015
vol l no 8
Figure 3: Average Annual SPM Concentration across Monitored Urban
Annual average SPM (micro-g/metre-cube)
parameter for policy action when policymakers cannot control
individual exposure to ambient air pollution. For this reason,
regulators set policy to affect ambient pollution levels, and the
correspondingly relevant variable for policy purposes is average exposure in the population.
In Figure 2 we compare the full distributions of average
PM2.5 concentrations across major cities in India, China,
Europe and the United States (US), using the WHO Ambient Air
Pollution Database. Indian cities, with an average PM2.5 concentration of 46.0 µg/m3, are far more polluted than those in
Europe (21.7 µg/m3) or the US (9.6 µg/m3), and polluted even
in comparison to China, where cities average 40.4 µg/m3. A
number of Indian cities have very high fine particulate levels,
above 75 µg/m3. We also compare the ambient air quality
standards for annual average PM2.5 concentrations. There is
substantial variation in the levels of stringency adopted by
countries while setting national air quality standards. At the
current prescription of 40 µg/m3 for annual PM2.5, the Indian
NAAQS is four times the WHO guideline and is the least stringent of the four regions.
concentrations. The plot shows that during the 1987–2010 period only about 25% of the monitored urban agglomerations
complied with the NAAQ standard of 140 µg/m3. Similarly,
about 25% of urban agglomerations experienced pollution
levels exceeding 300 µg/m3 or more than twice the NAAQ’s
prescribed limits.
3 Impact of Fine Particulates on Life Expectancy
In this section we use the unified measure of fine particulates
from Section 2 to calculate the life expectancy gains from
reducing PM2.5 pollution to national standards. We begin by
reviewing the scientific literature that seeks to relate ambient
particulate air pollution over a geographical region to population health outcomes. This relationship differs from an individual
exposure-response curve and is an average over a population
of individuals — each with unique exposures and responses.
However, as mentioned above it is this average response that
rightly underlies policy decisions because it is what governments can target.
Several studies from different parts of the world demonstrate a strong positive association between particulate air
pollution and mortality rates. But correlation is not causation,
and a key research challenge in attributing a causal role to air
pollution is isolating the effects of air pollution from other
factors that co-vary with air quality. Estimates that use quasiexperimental approaches to identify the causal impact of air
pollution are an important way of achieving this (Dominici
et al 2014) and we focus our discussion on such studies.
A second consideration in choosing estimates is to measure
the effects of sustained exposure to particulate pollution.
Arguably, long-run exposure does more harm than contemporaneous impacts from a short-term increase in exposure. In the
case of India, several studies of the link between health
impacts and ambient pollution (Cropper et al 1997) have
primarily focused on the short-term impacts. This limits our
ability to use a study from India.
To the best of our knowledge, the only study in a comparable
country context that uses quasi-experimental methods and estimates the impact of sustained high air pollution is a recent study
gathering data from a number of cities in China (Chen et al 2013).
This paper compares Chinese cities north and south of the
River Huai to estimate that long-term exposure to an additional 100 µg/m3 of TSPs reduces life expectancy at birth by
roughly three years. We draw on this study to estimate the
impacts of India’s air pollution on life expectancy. This study
measures particulate air pollution in units of TSP, a measure
that also includes particles other than PM2.5, but that are nevertheless small enough to stay suspended. To apply these TSP
estimates to our PM2.5 measures of particulate air pollution, we
follow Pope and Dockery (2013) and assume the ratio of PM2.5/
TSPs for the China study is 0.30 (see Box 1).12
A useful feature of the estimates from Chen et al (2013) is
that they were estimated at high levels of pollution similar to
those in India. The SPM levels Chen et al study in China are
around 300–600 µg/m3, above the average for Indian cities
but similar to the more polluted ones such as Delhi. In contrast, most of the other dose-response estimates are derived in
the US at PM2.5 levels of 10–25 µg/m3, which are well below the
Indian NAAQS (Correia et al 2013; Laden et al 2006; Pope et al
2002; Pope et al 2009). If the relationship between pollution
and mortality depends on the level of pollution, these estimates would not apply well to India. In addition, our choice
accounts for the fact that socioe-conomic circumstances and
healthcare systems, which affect the relationship between
pollution and health impacts, may differ significantly across
developed and developing countries (Arceo-Gomez et al 2012;
Jayachandran 2009). The main limitation of using the Chen
et al (2013) estimate is that it was derived in terms of TSPs,
necessitating a conversion to PM2.5. An online appendix provides further details on the estimation method, as well as estimates of life expectancy gains based on Chen et al (2013) with
several different PM2.5/TSPs ratios.
Despite the appeal of estimates based on Chen et al (2013),
we also report alternative estimates of the long-term effect of PM
on life expectancy. These alternatives are based on research
primarily from the US and are shown in Table 1. Laden et al
(2006) and Pope et al (2002) are prospective cohort studies and
Hoek et al (2013) is a Table 1: Summary of Estimates of Marginal
meta-estimate of cohort Impacts of PM2.5 on Life Expectancy
Increase in Life Expectancy
studies. These papers Source
per 10 µg/m3 Decrease in PM2.5
estimate the increase in
mortality risk due to PM2.5. Chen et al (2013)
Pope and Dockery (2013) Pope et al (2009)
use life-table analysis to
convert these to life ex- Pope et al (2002) *
Laden et al (2006)*
pectancy estimates. The
Hoek et al (2013)*
estimates of Pope et al * Life expectancy interpretations from Pope and
(2009) and Correia et al Dockery (2013).
(2013) are based first on difference analysis of the US countylevel changes in PM2.5 and life expectancy. Pope and Dockery
(2013) review this literature and find estimates of life expectancy reductions between 0.35 and 1.8 years per 10 µg/m3 in air
pollution; our primary estimate from Chen et al (2013) is
roughly equivalent to a figure of one year, i e, in the middle of this
spectrum. This suggests our choice of a preferred estimate from
among these appropriate estimates is not critical in this context.
Long-term studies find that a 10 µg/m3 increase in PM2.5
increases mortality risk about 4%–6% (Pope et al 2002; Hoek
et al 2013), while in short-term studies a 10 µg/m3 increase in
two-day averaged or previous day’s PM2.5 is associated with
an increased daily mortality risk in the range of 0.98%–1.21%
(Franklinet al 2007; Zanobetti and Schwartz 2009). Thus
using long-term estimates, which can measure the costs of
sustained exposure, is important so as not to understate the
benefits of reducing pollution.
3.1 Estimating Life Expectancy Loss in India
We combine our district and urban agglomeration-level PM2.5
concentration data with our preferred estimates of the mortality
response to air pollution to estimate the total life-expectancy
loss due to non-compliance with India’s air quality standards.
We begin by estimating potential gains in life expectancy if
PM2.513 concentrations in the areas that exceed India’s NAAQS
were brought down to the standard. For this exercise, we
further assume that PM2.5 concentrations remain unchanged in
places currently below the standard. For each population
region (district or urban agglomeration), we estimate potential
gains by multiplying different candidate estimates of the
marginal effects of PM2.5 on life expectancy with the difference
between the measured PM2.5 concentration levels and the
NAAQS of 40 µg/m3 annual mean PM2.5 — this produces regionspecific estimates of the per person gain in life expectancy. For
instance, for a district with an annual PM2.5 concentration of
50 µg/m3, we estimate the gain in life expectancy when its
concentration declines exactly to the standard of 40 µg/m3
(i e, a decrease of 10 µg/m3). We then take the weighted
average of these region-specific gains in life expectancy across
regions that exceed the PM2.5 NAAQS, where the weight is the
relevant region’s population, which gives the average gain in
life expectancy in regions that exceed the standard.
Our preferred estimate based on Chen et al (2013) is that
bringing all regions of the country into compliance with the PM2.5
NAAQS would increase the life expectancy of the 660 million
people living in these areas by 3.2 years on average or a total of
about 2.1 billion life years. As Table 2 documents, estimates of
potential average life expectancy gains range from 1.1 to 5.7
years, depending on the assumed figure for the marginal
Table 2: Estimates of the Effect of Above-Standard Particulate Pollution
on Life Expectancy
Summary Statistics on National Average PM2.5 Concentration Levels
Average PM2.5 background concentration for the entire
Indian population
50.5 µg/m3
Average PM2.5 background concentration for the population
living in localities that exceed the 40 µg/m3 NAAQS
71.7 µg/m3
Number of people living in above-standard localities
Increase in average life expectancy for affected population if average
ambient PM2.5 concentrations were reduced to NAAQS (Years)
Chen et al 2013
Pope et al 2009
Correia et al 2013
Pope et al 2002
Laden et al 2006
Hoek et al 2013
febrUARY 21, 2015
vol l no 8
Economic & Political Weekly
effect of particulates on life expectancy, or from 0.73 to 3.76
billion life years in total. The estimated increases in life expectancy would naturally be larger if these areas achieved the
more stringent air quality standards that have been set in
other parts of the world.
4 Conclusion and Directions for Policy Response
Is cleaner air incompatible with India’s urgent need for economic growth? No. While cleaner air comes with costs, this
paper has made plain that there are substantial benefits in
terms of longer lives. The people who live longer would be
available to contribute to India’s economy for more years, beyond the meaningfulness to them and their families of a longer
life. Further, it hardly seems far-fetched to assume that cleaner
air makes all the more productive due to reduced rates of sickness. In this section, we outline policy reforms that all have the
promise of substantial benefits at relatively small costs.
First, improve the accuracy and coverage of pollution monitoring, both in ambient air and at source. As one point of comparison Beijing has 35 monitoring stations,14 while Kolkata,
the Indian city with the most monitoring stations, has only 20.
More monitoring stations built in more locations, and in a collaborative manner with independent scientists, will allow for
continual improvement in monitoring and the wider use of
monitoring data for source apportionment and other scientific
purposes. Moreover, regulators should ensure that the monitors
are calibrated well and functional and that the data are accessible to the public through traditional and new media outlets.
Wide public release can both play an important role as a health
advisory system and increase pressure on polluters to comply
with regulatory standards (García et al 2007; Tietenberg 1998;
Wang et al 2004).
Similarly at source, monitoring of industrial point sources
should leverage advances in Continuous Emissions Monitoring
Systems (CEMS) technology to produce complete and accurate
records of air pollution from every chimney of significant
enough size. It is simply not possible to produce a complete
record of air pollution sources through intermittent manual
samplings taken once or twice in a year. The efforts of the
CPCB to adopt standards for PM CEMS monitoring is an important first step in this direction (CPCB 2013). Beyond just setting
standards, the CPCB has also recently notified an order to
expand CEMS monitoring, for a range of air pollutants, to all
industrial plants in the 17 sectors with the highest pollution
potential.15 An expansion of the accuracy and breadth of
monitoring will enable smarter policy and greater public
awareness of pollution.
Second, restructure environmental law and regulation
around civil, rather than criminal, penalties. India’s flagship
environmental laws, the air and water Acts, are built on an
outdated criminal system where draconian penalties such as
imprisonment or industry closure are the main recourse available to regulators. These penalties are so severe that they are
seldom used, and typically reserved for the very worst polluters (Duflo et al 2013). It would be better to set civil penalties,
in accord with the widely-recognised polluter pays principle,
so that all industries and other pollution sources have steady,
uniformly applied and significant incentives to reduce their
pollution output. A pollution tax such as the coal cess, which
was levied starting in 2010 at a modest rate of INR 50 per
tonne,16 is a clear example of the application of this principle.
Third, building on the first two, implement market-based
environmental regulation, such as emissions trading systems
(ETS) (Duflo et al 2010). ETS is based on rigorous monitoring of
pollution from all sources. It uses civil and financial penalties
rather than criminal sanction to ensure compliance. International experience makes clear that market-based approaches
to regulation, like ETS, deliver the least cost way to reduce
pollution, making them compatible with the continued economic growth that is vital for India’s future.
Today, too many Indians are exposed to dangerous levels of
air pollution that are shortening lives and holding back the
Indian economy. A variety of effective policy solutions are
available that would efficiently reduce this scourge. There is
an opportunity to choose longer, healthier, and more productive lives for hundreds of millions of Indians.
1 “Seven Million Premature Deaths Annually
Linked to Air Pollution”, World Health Organization, 25 March 2014, viewed on 1 June 2014,
2 The standards in question – the NAAQS prescribe a maximum allowable concentration of
60 micrograms/cubic metre for annual average
concentrations of the pollutant PM10 (particulate matter of less than 10 micrometres in diameter). “National Ambient Air Quality Standards”, Central Pollution Control Board, India,
18 November 2009, viewed on 20 January
2014. http://cpcb.nic. in/National_ Ambient_
3 “Ambient Air Pollution Database”, World
Health Organization, May 2014, viewed on
1 June 2014,
4 “NCD Mortality, 2008, Chronic Respiratory
Diseases, Death Rates Per 100 000 Population,
Age Standardised: Female” and “NCD Mortality,
2008: Chronic Respiratory Diseases, Death
Rates Per 100, 000 population, Age Standardised:
Male”, World Health Organization 2011, viewed
on 8 February 2014, http://gamapserver.who.
5 This study measures particulate air pollution
in units of TSP, a measure that also includes
particles other than PM2.5 but that are nevertheless small enough to stay suspended. To apply
their TSP estimates to our PM2.5 measures of
particulate air pollution, we follow Pope and
Dockery (2013) and assume the ratio of PM2.5/
TSPs for the China study is 0.30 (see Box 1).
6 “Ambient (Outdoor) Air Quality and Health”,
World Health Organization. March 2014,
viewed on 7 April 2014,
7 “National Ambient Air Quality Standards, 1994”
India Environmental Portal. December 1994,
viewed on 20 January 2014,
Economic & Political Weekly
vol l no 8
febrUARY 21, 2015
8 “National Ambient Air Quality Standards” Central Pollution Control Board, India, 18 November 2009, viewed on 20 January 2014.
9 “Ambient Air Quality Standards”, Ministry of
Environmental Protection of the People’s Republic of China, 2012, viewed on 6 June 2014, bzwb/dqhjbh/
dqhjzlbz/ 201203/t20120302_224165.htm
10 “National Ambient Air Quality Standards
(NAAQS)”, US Environmental Protection
Agency, December 2012, viewed on 6 June
11 “Air Quality Standards”, European Commission: Environment, March 2014, viewed on 6
June 2014,
12 This is an approximation and to the extent that
the true ratio of PM2.5 to TSP in China differs
from 0.3, our results on life expectancy will
also vary. However, a 10%–20% error in this
approximation would not change the thrust of
our conclusions. Furthermore, estimates from
other studies expressed in PM2.5 suggest similar
13 Fine particles (PM2.5) can get deep into the
lungs and are thus the most dangerous. Prospective cohort studies have usually found “the
most robust mortality associations with PM2.5”
(Pope and Dockery 2013: 12861), so this is the
appropriate PM measure.
14 Angel Hsu and Jason Schwartz, “China-India
Smog Rivalry a Sign of Global Menace”, Guest
Blog, Scientific American, 26 March 2014,
viewed on 1 2014,
15 Susheel Kumar, chairman, CPCB, Direction to
the State Pollution Control Boards, 5 February
2014, viewed on 20 June 2014, http://www.
16 Vivek Johri, Joint Secretary, Department of
Revenue, Ministry of Finance, Circular with
Subject “Levy of Clean Energy Cess-regarding”,
24 June 2010, viewed on 20 June 2014, http://
Apte, Joshua S, Thomas W Kirchstetter, Alexander
H Reich, Shyam J Deshpande, Geetanjali
Kaushik, Arvind Chel, Julian D Marshall and
William W Nazaroff (2011): “Concentrations
of Fine, Ultrafine, and Black Carbon Particles
in Auto-Rickshaws in New Delhi, India”,
Atmospheric Environment, 45 (26): 4470–80.
Arceo-Gomez, Eva O, Rema Hanna and Paulina
Oliva (2012): “Does the Effect of Pollution on
Infant Mortality Differ between Developing
and Developed Countries? Evidence from Mexico City”, National Bureau of Economic Research Working Paper No 18349, available at:
http://www. papers/w18349 .
Chen, Yuyu, Avraham Ebenstein, Michael Greenstone
and Hongbin Li (2013): “Evidence on the
Impact of Sustained Exposure to Air Pollution
on Life Expectancy from China’s Huai River
Policy”, Proceedings of the National Academy of
Sciences of the United States of America, 110 (32):
12936-41. doi:10.1073/pnas.1300018110.
Correia, Andrew W, C Arden Pope, Douglas W
Dockery, Yun Wang, Majid Ezzati and Francesca
Dominici (2013): “Effect of Air Pollution
Control on Life Expectancy in the United
States: An Analysis of 545 US Counties for the
Period from 2000 to 2007”, Epidemiology,
Cambridge, Massachusetts, 24 (1): 23–31.
CPCB (2009): National Ambient Air Quality Status
2008 (New Delhi: Central Pollution Control
Board, Ministry of Environment and Forests,
Government of India),
— (2011): National Ambient Air Quality Status
2009, New Delhi: Central Pollution Control
Board, Ministry of Environment and Forests,
Government of India,
— (2012): National Ambient Air Quality Status
and Trends in India-2010, New Delhi: Central
Pollution Control Board, Ministry of Environment and Forests, Government of India, http:// 192_NAAQSTI.pdf
— (2013): Specifications and Guidelines for Continuous Emissions Monitoring Systems (CEMS)
for PM Measurement with Special Reference to
Emission Trading Programmes, New Delhi:
Central Pollution Control Board, Ministry of
Environment & Forests, Government of India,
Cropper, Maureen L, Nathalie B Simon, Anna
Alberini, Seema Arora and P K Sharma (1997):
“The Health Benefits of Air Pollution Control in
Delhi”, American Journal of Agricultural Economics, 79 (5): 1625–29,
Dey, Sagnik, Larry Di Girolamo, Aaron van Donkelaar, S N Tripathi, Tarun Gupta and Manju
Mohan (2012): “Variability of Outdoor Fine
Particulate (PM2.5) Concentration in the Indian
Subcontinent: A Remote Sensing Approach”,
Remote Sensing of Environment, 127 (December):
153–61, doi:10.1016/j.rse.2012.08.021
Dominici, Francesca, Michael Greenstone and Cass
R Sunstein (2014): “Science and Regulation:
Particulate Matter Matters”, Science, New York,
344 (6181): 257–59, doi:10.1126/science.1247348
Duflo, Esther, Michael Greenstone, Rohini Pande
and Nicholas Ryan (2010): “Towards an Emissions Trading Scheme for Air Pollutants in
India”, Ministry of Environment and Forests
Discusssion Paper, available at: http://www.
— (2013): “Truth-Telling by Third-Party Auditors
and the Response of Polluting Firms: Experimental Evidence from India”, The Quarterly
Journal of Economics, 128 (4): 1499–1545,
Franklin, Meredith, Ariana Zeka and Joel Schwartz
(2007): “Association between PM2.5 and
All-Cause and Specific-Cause Mortality in 27
US Communities”, Journal of Exposure Science
& Environmental Epidemiology, 17 (3): 279–87,
García, Jorge H, Thomas Sterner and Shakeb Afsah
(2007): “Public Disclosure of Industrial Pollution: The Proper Approach for Indonesia?”,
Environment and Development Economics, 12 (06):
739–56, doi:10.1017/ S1355770X07003920.
Greenstone, Michael and Rema Hanna (2014):
“Environmental Regulations, Air and Water
Pollution, and Infant Mortality in India”, American
Economic Review, 104 (10): 3038–72, doi:10.1257/
Guttikunda, Sarath K and Puja Jawahar (2014):
“Atmospheric Emissions and Pollution from the
Coal-Fired Thermal Power Plants in India”,
Atmospheric Environment, 92 (August), Elsevier:
449–60, doi:10. 1016/j.atmosenv. 2014.04.057.
Hanna, Rema, Esther Duflo and Michael Greenstone (2012): “Up in Smoke: The Influence of
Household Behavior on the Long-Run Impact
of Improved Cooking Stoves”, National Bureau
of Economic Research Working Paper, No WP
18033 (May), Cambridge, MA, doi:10.3386/
Hoek, Gerard, Ranjini M Krishnan, Rob Beelen,
Annette Peters, Bart Ostro, Bert Brunekreef,
and Joel D Kaufman (2013): “Long-Term Air
Pollution Exposure and Cardio-Respiratory
Mortality: A Review”, Environmental Health: A
Global Access Science Source, 12 (1): 43,
IHME (2013): The Global Burden of Disease: Generating
Evidence, Guiding Policy, Seattle, Washington:
Institute for Health Metrics and Evaluation,
Jayachandran, Seema (2009): “Air Quality and
Early-Life Mortality: Evidence from Indonesia’s
Wildfires”, Journal of Human Resources, 44 (4):
916–54. doi:10.1353/jhr.2009.0001
Laden, Francine, Joel Schwartz, Frank E Speizer
and Douglas W Dockery (2006): “Reduction in
Fine Particulate Air Pollution and Mortality:
Extended Follow-up of the Harvard Six Cities
Study”, American Journal of Respiratory and
Critical Care Medicine, 173 (6): 667–72,
Lim, Stephen S, Theo Vos, Abraham D Flaxman,
Goodarz Danaei, Kenji Shibuya, Heather AdairRohani, Markus Amann ( 2012): “A Comparative Risk Assessment of Burden of Disease and
Injury Attributable to 67 Risk Factors and Risk
Factor Clusters in 21 Regions, 1990-2010: A
Systematic Analysis for the Global Burden
of Disease Study 2010”, Lancet, 380 (9859):
2224–60, doi:10.1016/S0140-6736(12)61766-8
Pope, C Arden and Douglas W Dockery ( 2013):
“Air Pollution and Life Expectancy in China
and Beyond”, Proceedings of the National Academy of Sciences of the United States of America,
110 (32): 12861–62, doi:10.1073/ pnas.1310925110
Pope, C Arden, Majid Ezzati and Douglas W Dockery
(2009): “Fine-Particulate Air Pollution and Life
Expectancy in the United States”, The New
England Journal of Medicine, 360 (4): 376–86,
Pope, C Arden, Richard T Burnett, Michael J Thun
and (2002): “Lung Cancer, Cardiopulmonary
Mortality, and Long-Term Exposure to Fine
Particulate Air Pollution”, JAMA 287 (9): 1132–41, 10.1001/ jama. 287.9.1132
Tietenberg, Tom (1998): “Disclosure Strategies for
Pollution Control”, Environmental and Resource
Economics, 11 (3-4): 587–602, http://link.
Wang, Hua, Jun Bi, David Wheeler, Jinnan Wang,
Dong Cao, Genfa Lu and Yuan Wang (2004):
“Environmental Performance Rating and
Disclosure: China’s Green Watch Program”,
Journal of Environmental Management, 71 (2):
123–33, doi:10.1016/j.jenvman.2004.01.007.
Zanobetti, Antonella and Joel Schwartz (2009):
“The Effect of Fine and Coarse Particulate Air
Pollution on Mortality: A National Analysis”,
Environmental Health Perspectives, 117 (6):
898–903, doi:10.1289/ehp.0800108.
Sameeksha Trust Books
Village Society
The village is an important idea in the history of post-Independence India. A collection
of articles that covers various features of village society: caste and community, land
and labour, migration, discrimination and use of common property resources.
Pp x + 252 Rs 325
ISBN 978-81-250-4603-5
Orient Blackswan Pvt Ltd
Mumbai • Chennai • New Delhi • Kolkata • Bangalore • Bhubaneshwar • Ernakulam • Guwahati • Jaipur • Lucknow
• Patna • Chandigarh • Hyderabad
Contact: [email protected]
febrUARY 21, 2015
vol l no 8
Economic & Political Weekly
ECON 2213
4. Climate and Environmental
1. Introduction to the module
2. Background: environmental indicators
* + ,
3. Article: Chen et al.
4. Article: Greenstone et al.
5. Article: Zheng et al.
6. Article: Greenstone and Hanna
7. Conclusion to the module

The environment

The environment includes all aspects of nature
and the ecosystem that affect the quality of
human life.
Effects of environmental changes can be local
(e.g., particulate matter), regional (e.g., sulfur
dioxide, acid rain, polluted rivers, melting
glaciers) or global (e.g., CO2).
The environment
• The Environmental Performance Index
(computed by the Yale University Center for
Environmental Law and Policy) gives China a
rank of 160 out of 180 countries in 2022 and
gives India a rank of 180 out of 180.
The environment

The most immediate concerns are air pollution and
Bardhan (2010) cites a World Bank study estimating
the total cost of air and water pollution in China as
5.8% of GDP; in India, the cost of damage to human
health from water pollution has been estimated at
almost 4% of GDP.
The World Health Organization has estimated that air
pollution is the cause of more than half a million
premature deaths each year in India, and probably
more in China.
Particulate matter arises from burning fossil fuels in
cars, power plants, and for home heating, and from
burning biomass.
The environment

Surface water and groundwater are polluted by
untreated industrial waste, municipal sewage,
and fertilizer and pesticide runoffs.
In northern China, water scarcity is also an issue.
The South-to-North Water Diversion Project
transfers water from southern to northern China
and is the most expensive infrastructure project
in the world.
Desertification is a problem in both western
China and western India.
• Three questions:
– What is each country’s energy intensity?
– What is the energy demand by sector?
– How much are each country’s CO2 emissions?
• “Energy intensity” refers to the amount of energy (e.g.,
CO2) consumed per unit of economic output (GDP).
– Ideally we want this to be as low as possible.
• Energy intensity is higher in China and India than in the
U.S. when market exchange rates are used to calculate
GDP, but is lower when PPPs are used.
– Energy intensity fell in all three countries from 1980 to
2003, particularly in China.
– China’s energy intensity rose from 2002 to 2005, then
began to fall again, but more slowly.
– India’s has fallen consistently.
;[email protected]
Source: Shalizi (2007)
Source: Bergsten et al. (2008)
5..&7($) 757
Source: U.S. Energy Information Administration

Source: International Energy Agency

By sector, energy demand is highest in industry for
both China and India.
The energy needs of consumers (for air conditioners,
automobiles, etc.) are rising but are not yet that high
compared to industry.
According to Bergsten et al. (2008), “Every new steel
mill is more efficient than the last one; but…a new
steel plant—no matter how much more efficient than
its predecessor—uses substantially more energy than
a garment factory.”
India’s GDP relies more on services than on heavy
industry, which explains its lower demand for energy.
Source: Bergsten et al. (2008)
• The majority of electricity in China is
produced in power plants that use coal,
which is very harmful to health.
• India’s CO2 emissions are ¼ those of China,
but are also based on the use of fossil fuels
for the production of energy.
Source: Shalizi (2007)
BP Statistical Review of World Energy
2022 (data for 2021)
• Consumption of energy (in exajoules):
– U.S. 92.97
– China 157.65
– India 35.43
• Consumption per capita (in gigajoules per capita):
– U.S. 279.9
– China 109.1
– India 25.4
• Carbon dioxide emissions (in millions of tonnes):
– U.S. 4701.1
– China 10523.0
– India 2552.8
• It is important to recognize that, especially for
China, high energy usage is partially caused by
high production of goods that will be exported
and consumed in other countries.
– Who should therefore be held responsible for that
energy usage?
– See Davis and Caldeira (2010): “A ConsumptionBased Accounting of CO2 Emissions”
Environmental policy
• The Kyoto Protocol is an international agreement that came
out of the United Nations Framework Convention on
Climate Change (UNFCCC) and that sets binding targets for
reducing GHG emissions (an average of 5% of 1990 levels
over the period 2008-2012) for 37 countries and the EU.
– There are 192 Parties to the Convention, but all of the Kyoto
signatories (a.k.a. Annex I countries) are industrialized countries.
– There are no formal targets for developing countries.
• The Paris Agreement entered into force in 2016 and has
been signed by 195 UNFCCC members, including China and
• The goal to prevent the global temperature from rising
more than 1.5 degrees Celsius above pre-industrial levels.
– Each country sets its own targets.
Environmental policy
• China’s overall rating is “Highly Insufficient.”
• “NDCs with this rating fall outside of a
country’s ‘fair share’ range and are not at all
consistent with holding warming to below 2°C
let alone with the Paris Agreement’s stronger
1.5°C limit. If all government NDCs were in
this range, warming would reach between 3°C
and 4°C.”
Environmental policy
• “China’s updated NDC target remains “Highly
insufficient” and if all countries followed the level of
ambition implicit in this development, it would lead
to a warming of 3°C degrees globally. We rate China’s
current policies as “Insufficient” to meet the Paris
agreement’s 1.5°C limit, and are more consistent
with a global warming of 3°C.”
• “We estimate China’s emissions have risen 3.4% to
14.1 GtCO2e in 2021 due to a large spike in energy
demand as the country’s pandemic recovery
continues—this is concerning as power consumption
has been projected to rise 5–6% in the upcoming
Environmental policy
• “In 2020–2021, China began toning down its outlook on coal,
highlighted by President Xi Jinping when he announced that
China will strictly control coal consumption until 2025 and start
to gradually phase it down thereafter. By the end of 2021,
however, China had seemingly completely reneged on this
strategy to focus on shoring up coal (and other fossil fuels)
supply off the back of energy security and shortage concerns. In
2021, China produced its highest-ever annual output in coal
• “However, renewable energy will also continue to be a national
priority in parallel; installed capacity for renewables surpassed
1,000 GW in 2021. Energy from non-fossil sources in China
needs to grow by around 13% by 2025 and 52% by 2030 (from
2020 levels) to achieve its FYP and NDC targets.”
Environmental policy
• “China’s President Xi Jinping first announced China’s
commitment to reach ‘carbon neutrality before 2060’
in a declaration at the UN General Assembly in
September 2020. China has since officially submitted
a long-term strategy (LTS) to the UNFCCC in October
2021. As the LTS submission does not meet the
majority of our criteria for a best-practice approach
in LTS formulation, we keep China’s net-zero target
evaluation as ‘Poor’.”
Environmental policy
• India’s overall rating is “Highly Insufficient.”
• “NDCs with this rating fall outside of a
country’s ‘fair share’ range and are not at all
consistent with holding warming to below 2°C
let alone with the Paris Agreement’s stronger
1.5°C limit. If all government NDCs were in
this range, warming would reach between 3°C
and 4°C.”
Environmental policy
• “India has been severely impacted by COVID 19 during the
second wave in the first half of 2021, which has further
reduced the resilience of climate change vulnerable
populations already at risk of displacement by storms, floods,
droughts and other climate disasters.”
• “The Indian government has responded to the economic crisis
by unveiling one of the largest stimulus packages in the world,
equating to a share of around 11% of the country’s GDP in
2019. India’s overall COVID recovery stimulus package mainly
supports activities related to industries likely to have a large
negative impact on the environment by, for example,
increasing the use of fossil fuels, and unsustainable land use.”
Environmental policy
• “India’s most recent stimulus (2021) is more climate-friendly,
with two-thirds of the resources targeted towards a green
recovery, including roughly USD 3bn in battery development
and solar PV. While the additional stimulus is a positive step,
India continues to support coal, with fresh loans to a number
of thermal power projects, undermining a green recovery.”
• “CAT analysis shows emissions to 2030 will rise less than in
pre-COVID 19 projections, mainly because of the pandemic’s
impact on the economy. India is continuing to expand its coal
capacity, as a number of projects are under construction and
several others have been announced, despite the utilisation
rate of coal power plants falling. This risks profitability and
stranded assets.”
Environmental policy
• “Based on current coal expansion plans, India’s coal capacity
would increase from current levels of over 200 GW to almost
266 GW by 2029-2030, with 35 GW expected to come online
in the next five years: an increase of 17.5% in coal capacity.
India’s coal-fired power plant pipeline is the second largest in
the world and is one of the few to have increased since 2015.
A recent move to increase domestic coal production has
opened coal mining to private investors, risking a fossil fuel
lock-in as well as harm to areas of ecological significance. To
get on a 1.5°C emissions pathway, it is important for India to
phase out old, high-capacity power plants with lower
efficiency and higher emissions, and stop any new coal
capacity additions.”
Chen, Yuyu, Avraham Ebenstein, Michael
Greenstone, and Hongbin Li, “Evidence on the
Impact of Sustained Exposure to Air Pollution on
Life Expectancy from China’s Huai River Policy,”
Proceedings of the National Academy of
Sciences 110:32 (2013), 12936-12941.
* %5′(‘&H1


*E :7

D&7–`[email protected]
@ *%1

*E :7
* bIW
Chen et al.
key takeaways
Greenstone, Michael, Janhavi Nilekani, Rohini
Pande, Nicholas Ryan, Anant Sudarshan, and
Sugathan, “Lower Pollution, Longer Lives,”
Economic & Political Weekly L:8 (2015), 40-46.
@)H(5 Y7
%(.1 A1537
%(.1 1G#1L’5)LA
n %&)6 j

=(6= #$%%&'($)

@)H(5 =&=



Greenstone et al. (2015)
• Research published in Lancet Planet Health
(2021) estimates the economic impact of air
pollution in India in 2019.
• In 2019:
– There were 67 million deaths due to air pollution
in India.
– Lost output (GDP) from these premature deaths
amounted to US$28.8 billion.
– This was 1.36% of India’s GDP.


Greenstone et al.


.$3 1)2(3$)41)’5%



#3$63177 + #3$4$'($)
; #&S%(L


Purchase answer to see full


BigEssays Ad

Related Questions

Big Essays Order

Reveron Questions


Full Question
Big Essays Order

Organizational Behavior

MAT 510 Strayer University The Mortgage Approval and Time Study Case study Case Study: Mortgage Approval Time StudyRead the following case study:A major financial services

Full Question
Big Essays Order

costco Assigment

Strategy Features That Differentiated BJ’s BJ’s had developed a strategy and operating model that management believed differentiated the company from Costco and Sam’s Club: Offering

Full Question

Just a Sec,

Where Should we Send your 30% Discount Code?

Just a Sec,
Not sure we are the best?

We'll Send you a 30% Discount Code to Get Started.