Impact of Population, Urbanisation and Consumption Pattern on Environmental Degradation: A State-level Analysis of India
Lopamudra Ray Saraswati & Protap Mukherjee
Ph.D. Scholars
Centre for the Study of Regional Development
School of Social Sciences
Jawaharlal Nehru University
New Delhi - 110 067, India
Industrialisation
Water pollution
Deforestation
Desertification
Air pollution
Soil erosion
World Environment Scenario
 Severe environmental damage caused by rapid growth of world’s population and economic activity
 A striking phenomenon – increased visibility of environmental degradation in developing countries
 Likelihood of further environmental deterioration because of:
 Continuous growth of world population and productions
 Increasing use of natural resources to satisfy demand for a better living standard in third world and the desire for higher incomes in industrialized countries
 Use of natural resources such as raw materials and energy will result in increasing emissions of polluting and toxic substances
 As a result – environmental problems will play a more important role in political decision-making
 The challenge – protection of world’s natural environment in the coming decades
The Present Study
Present study examines the macro-level links between the environmental outcomes, and population parameters, level of development and economic variables, using state-level data of India. Analysis has been based on the
IPAT model, i.e. I = P * A * T , where I is the environmental impact, P is a set of population parameters, A represents affluence and T is technological advancement. Environmental impact has been measured by percentage of
forest cover to the total area of a state, and also from the average annual level (gm/m3) of SO2, NO2 and RSPM (respirable suspended particulate matter) in the air. With these variables, a state level environmental impact index
has been constructed using Principal Component Analysis (PCA). OLS estimators have been predicted for environmental impact taking the parameters for population, affluence and technology as explanatory variables. A
composite index has been constructed for population parameter using PCA with the variables population size, density and proportion in the working age-group. Affluence has been measured by the composite index
constructed by state-level GDP, urban MPCE and electricity consumption. Technological advancement has been replaced by the urbanisation proxy.
Variables used in the Analysis
Description
Variables measuring environmental impact
Percentage of forest in total
Forest cover
geographic area
Average annual RSPM (respirable
RSPM level
suspended particulate matter)
emission (gm/m3)
Average annual SO2 emission
SO2 level
(gm/m3)
Average annual NO2 emission
NO2 level
(gm/m3)
Explanatory variables
Total projected population (in
Population size
thousands)
Percentage of urban population in
Urbanisation
the total
Proportion in
Percentage of population in ageworking age
group 15-59
Population
Number of persons per sq. km. of
Density
area
Data-source
India State of Forest
Report 2009
Compendium of
Environment Statistics
India 2008/2009
Correlation Matrix
Year
2007
2008
Same as above
2008
Same as above
2008
Projections made by
Census of India
2006
Same as above
2006
Same as above
2006
Census of India
2006
Urban MPCE
Average monthly consumption
expenditure (Rs) per person for
Urban areas
Ministry of Statistics
2005and Programme
06
Implementation
National Sample Survey
2005Organisation of India
06
(62nd Round)
Electricity
consumption
Gross annual per capita
consumption of electricity
Central Electricity
Authority
State GDP
Gross State Domestic Product at
Current Prices (Rupees in crore)
200506
Independent variables
Population
Proportion Population State
Urbanisation
size
aged 15-69 Density
GDP
Urban
Electricity
MPCE consumption
Independent variables
Population size
Urbanisation
Proportion aged 15-69
Population Density
State GDP
Urban MPCE
1
-0.239
-0.507
-0.195
0.700
-0.615
1
0.550
0.765
0.103
0.421
1
0.352
0.030
0.651
1
-0.238
0.243
1
-0.170
1
Electricity consumption
-0.513
0.785
0.502
0.413
-0.069
0.588
Dependent variables
Forest cover
RSPM level
SO2 level
NO2 level
-0.438
0.205
0.643
0.321
-0.013
0.351
0.094
0.350
0.225
-0.191
-0.060
0.104
-0.134
0.585
-0.194
0.541
-0.259 0.510
-0.064 -0.110
0.824 -0.386
0.224 -0.102
1
0.109
0.198
-0.098
-0.056
Estimation Results for Different Environmental Indicators from
OLS Regression Analysis
Dependent variables
Forest cover RSPM level
Independent variables
Population parameter (composite index including
population size, density and proportion aged 15-59)
0.086
- 0.320
SO2 level
NO2 level
- 0.463
0.253
Affluence (composite index constructed by state-level
0.586
- 0.065
- 0.509 *
GDP, urban MPCE and electricity consumption)
- 0.430
0.603 **
0.714***
Urbanisation
Level of significance: * p < .15, ** p < .10, *** p < .05, **** p < .01
- 0.717 **
0.628 **
Findings
From Principal Component Analysis
The first component explains 57% of the total variation in the data. Since the eigen-value of the first component is greater than one, in the present study only the first component has been used to calculate component score for
each state to determine its ranking in environmental degradation. From the ranking of the states based on this score, Delhi, Punjab and Uttar Pradesh found to be the worst sufferers of environmental degradation.
From Correlation Analysis
1. Population size seems to be an important player for environmental degradation – it has negative correlation with forest cover and positive correlation with the level of green house gases.
2. Population density has positive correlation coefficients with the levels of RSPM and NO2.
3. There is a strong correlation between the state-level GDP and the level of SO2 in the air.
4. Correlation coefficients between other dependent and independent variables, although not very high, are mostly in the expected direction.
From Regression Analysis
1. Among all the variables, urbanisation seems to have the most significant impact on environment. It significantly raises the level of RSPM, SO2 and NO2 gases in the air and causes to decline the forest cover.
2. Affluence was found to be good for environment, as it has positive impact on forest cover and negative impact on the level of green house gases. In case of SO2 and NO2 levels, the results are statistically significant.
3. The composite index constructed for population parameters did not show to have a significant impact with any of the environmental indicators.
Conclusion and the way forward
Present study finds that not all the highly
industrialised states are among the worst sufferers
of
environmental
degradation.
Although
urbanisation showed a negative environmental
impact, economic development of the states found
to be helpful for sustaining the environment. Hence
it can be concluded that a vigilant urban
governance may lead to sustainable development.
However, there is a massive gap in information
for the sub-national level analysis. India still lags
behind in maintaining the appropriate data-set
measuring environmental impacts at the statelevel, making a causal analysis of environmental
degradation almost impossible. Hence the
government and other stakeholders immediately
need to consider collection and compilation of
pertinent data-base to measure the state of
environment in India.