Sustainability Development - MCM 2015 Report

8/9/2019 Sustainability Development - MCM 2015 Report

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Institut Teknologi Bandung|Mathematics Department

Davin Kurnia Wangsa, Nicholas Leo, Yohans

For office use only

T1 ________________

T2 ________________

T3 ________________ T4 ________________

Team Control Number

37772

Problem Chosen

D

For office use only

F1 ________________

F2 ________________

F3 ________________ F4 ________________

Sustainability: Reconciliation between Human and NatureCambodia, Comprehensive Study Case

In order to measure sustainability of a country, we create Human Sustainability Index

(HSI) as well as Environment Sustainability Index (ESI) using composite indicators taken

from World Bank. Our main idea is to make a ranking system based on Analytical Hierarchy

Process between those two indices, thus it would represent the true sustainable condition:

togetherness in harmony between human and nature. Our model is quite flexible since it uses

proportion of HSI and ESI that will yields indifference curve. That kind of curve can

determine whether a country is sustainable and the comparisons between countries are

shown.

We also attempt to tackle sustainability problem on Cambodia, one of 48 Least

Developed Country issued by United Nation. By using our model, we can identify the most

severe sustainability problem occurring there which is relating to its energy production. By

doing so, we come up with strategies which produce the optimal solution within the given

time period and increase its sustainability both on environmental side as well as human

development side. Last but not least, we believe that we success to develop sustainability

model which not only can measure sustainability of any country but also can give an insight

about specific problem regarding to its sustainability.


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Contents

Contents .................................................................................................................................................. 1

1. Introduction ..................................................................................................................................... 2

2. Outline of Approach ........................................................................................................................ 2

3. Ranking System Theory ................................................................................................................... 3

4. Logistic Model with Inversion Method .......................................................................................... 3

5. Assumptions .................................................................................................................................... 5

6. Ranking System Model ................................................................................................................... 5

7. Cambodia: Brief Introduction ......................................................................................................... 7

8. Cambodia: Sustainable Development Strategy .............................................................................. 8

9. Cambodia: Evaluation of the Strategy ............................................................................................ 9

10. Sensitivity Analysis .................................................................................................................... 12

11. Conclusion ................................................................................................................................. 13

12. Strength and Weakness ............................................................................................................ 13

13. References................................................................................................................................. 14

Appendix ............................................................................................................................................... 15

List of Figures

Figure 1: graph of logistic model............................................................................................................. 4

Figure 2: Contour of a Misfit Function .................................................................................................... 5

Figure 3: model result ............................................................................................................................. 7

Figure 4: sustainability chart ................................................................................................................... 8

Figure 5: plan projection ....................................................................................................................... 10

Figure 6: plan projection for CO2 .......................................................................................................... 11

List of Tables

Table 1: composite indicators ................................................................................................................. 6

Table 2: weight of indicators................................................................................................................... 6

Table 3: sensitivity result ...................................................................................................................... 12

Table 4: weight data.............................................................................................................................. 13


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1. Introduction

Since its most widely accepted definition introduced in 1987, sustainable

development has been a central topic for humanity. Term of sustainable development

comes from the realization of the limitedness of earth natural resources. Therefore,humanity need to give a deep thought how to make the best use of the resources to

support humanity and, at the same time, need to preserve the resources for the future

generation.

Even though the definition has been affirmed by Brundtland as “development that

meets the needs of the present without compromising the ability of future generations to

meet their own needs”, such definition still holds certain degree of subjectivity. The

different interpretation of „without compromising the ability of future to meet their own

needs‟ may deliver different condition of sustainability of each country. Thus, it is

appropriate to create certain system which can determine the level of sustainability of

each country.

2. Outline of Approach

We do acknowledge that there are countries which are more sustainable than the

others. Therefore, we are motivated to create a ranking system that can determine level of

sustainability of each country. It is important to note that we use several indicators as our

input to the ranking system. Our selected indicators characterize the importance of the

human development aspect as well as environment sustainability, thus it would capture

the essence of sustainability development which focus on the harmony between human

and nature.

We also describe certain model derived from logistic model for our input indicator

which is able to predict the change of our ranking system if any intervention occurs on a

certain country. Based on that model, we can determine what kind of plan which is most

suitable to implement to our selected country, where in this case we choose Cambodia.

Later, we do some sensitivity analysis with regard to our sustainability strategy and our

ranking system.


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where,

From the equation above, it is quite clear that logistic model has several unknown

parameters. Therefore, this model is quite sensitive to the chosen parameters and

estimation of parameters which represent our data needs to be done. This estimation

problem is referred to inverse problem and can be solved by using inverse method.

Figure 1: graph of logistic model

Basically, the parameters chosen by inverse method should minimize the „closeness‟

between real data value and model value. The „closeness‟ can be quantified by using

misfit function which is usually written as sum square error,

( ) where in our model is a function of rate of exponential ( and time whenmaximum growth reached .


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Figure 2: Contour of a Misfit Function

The figure above is the contour of misfit function for certain indicators. The -axisdenotes the constant and the -axis denotes . It can be seen from figure above that weshould choose parameter from the darker blue area as it minimizes the misfit function.

For an exact result, we use Simulated Annealing Method to find paired (e.g. from the figure).

5. Assumptions

We use several assumptions to simplify our model, which are:

Indicators are independent to each other.

Economic development is increasing in some constant rate.

Indicator modeled with logistic should be monotone increasing each time.

Government of Least Developed Country (LDC) always implements our plan

without delay.

6. Ranking System Model

We derive a model to determine sustainability of a certain country based on 2 indices,

which are Human Sustainability Index (HSI) and Environment Sustainability Index (ESI).

We generate those two indices as a function of others common indicators. Thus, the

general form of our ranking system is

where is weight for -th indicator (.


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Indicators we use to generate either Human Sustainability Index or Environment

Sustainability Index are listed below,

HSI ESI

Indicators Unit measurement Indicators Unit measurementWater access % population CO2 Emission Particle per millionHuman Dev. Index Scale of 0 to 1 Terrestrial Area % total area

Nourishment Rate % population Renewablegenerated electricity

% total electricityEducation Index Scale of o to 1

Table 1: composite indicators

Indicators used to form HSI are mainly human centric. In this model, we decide to use

indices above as it primarily measures the welfare of humanity. It is worth to mention that

we define Nourishment Rate index as a percentage of population which is not

undernourished. On the other hand, environmental centric index derives the ESI and

mainly describe human effort to maintain and enhance environment condition.

As for the appropriate weight, we apply Analytical Hierarchy Process for each of

indicator and the result can be seen below. It is noted that total weight of each index, HSI

and ESI, should be one.

HSI ESI

Indicators

Weight Indicators

Weight

Water access 0.2405 CO2 Emission 0.5367Human Dev. Index 0.2414 Terrestrial Area 0.2317

Nourishment Rate 0.3215 Renewablegenerated electricity

0.2317Education Index 0.1967

Table 2: weight of indicators

From our model, we find that the most important component in HSI is nourishment

rate, followed by Human Dev. Index, water access, and then Education Index. The value

of each indicator is not too different. For ESI, we obtained that CO2 play the most part to

determine the index for country‟s environment, twice as more than other indicators.


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The result of our model will be presented as below.

Figure 3: model result

The graph above describes how each countr y‟s sustainability index is positioned. In

our model, we assume Human Indicator and Environment Indicator have equal proportion

to sustainability index. Therefore, our indifference curve will have -1 as its slope.

Generally, slope value can be changed depends on the importance index between

environment and human. Thus, our indifference curve has an equation,

In brief, countries which have the same sustainable index will be positioned on the

same indifference curve. From graph above there are 4 countries which are likely to have

the same sustainability index.

7. Cambodia: Brief Introduction

Cambodia is located in South East Asia and regarded as a developing country by

United Nation. Even though Cambodia has achieved impressive economic growth since

1990, the poverty is still a big issue there. As of 2013, Country Intelligence Agency of US

noted that the GDP PPP of Cambodia is just $39.64 billion, far behind of Thailand ($673

billion) or even Vietnam ($368.9 billion).

0

0.05

0.1

0.15

0.2

0.25

0.3

0.5 0.6 0.7 0.8 0.9 1

Indiffrence Curve

Sustain Index

ESI

HSI

Indifference curve


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In environment sector, Cambodia is facing with illegal logging activity which reduces

its biodiversity significantly. Indexmundi.com also has several notes of water access

problem with, approximately, only 75% of its population is able to use improved drinking

water source. On the other side, while Cambodia has experienced a significant progress

on its Human Development Index by UNDP, there are still persistent problems in human

development area, especially in literacy rate.

By using our sustainability model, it can be seen from chart below that the

sustainability proportion between environment and human is not balance. It can be

summarized from the chart that Cambodia needs certain strategy, especially in

environment sector, to improve its ecosystem sustainability as well as its human

development sustainability.

Figure 4: sustainability chart

8. Cambodia: Sustainable Development Strategy

Using our model, we find that the sustainability indices of Cambodia are 0.676 and

0.258 for human and environment index respectively. Those values can be justified

because Cambodia has just through a recent surge of economic improvement which

positively correlated to human development. However, it arguably has just little positive

impact on environment. A low environment sustainability score comes from the result of

high CO2 emission and a little electricity generated by renewable sources.

ArmeniaCosta Rica

Nicaragua

El Salvador

Jamaica

Guatemala

Panama

Cambodia

EcuadorSri Lanka

Honduras

Benin

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.6 0.65 0.7 0.75 0.8 0.85 0.9

E

S

I

HSI

Sustainability Chart


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In order to tackle the problem, we decide to focus on electricity production

management as it also would help to reduce CO2 emission in the future.

As it is stated on 2013 report titled Low Carbon Competitiveness in Cambodia, it is

known that more than 60% of power plants in Cambodia use diesel engine power plant

which contribute to more than 25% of CO2 emission. On the other hand, the usage of

renewable (excluding hydroelectric) power plant in Cambodia is still new which just

contribute to no more than 1.67% of total electricity.

Our proposed strategy is to increase the usage of renewable energy, especially the

ones who use biogas and biomass using rice husk and animal manure.

With its abundant of biogas resource which is estimated can produce energy

potentially 25.5 Peta Joule (Mustonen et al, 2013) equal to 40.7GW electricity, Cambodia

can fulfill its electricity needs by just using 1% efficiency of this renewable resource.

However, it is very unfortunate that electricity produced by renewable resources still have

not been fully explored by Cambodia government. Thus, we recommend several action

plans to develop its electricity produced by renewable resources:

Give several incentives to investors who are interested in constructing biogas

power plant as it takes a huge first capital investment to create this kind of power

plant.

Impose some regulation to hold coal exploration as it would reduce the incentive

to explore the renewable energy sector.

Enhance the awareness of Cambodia people to use biogas as their main energy

resources. This is still very rare even Mustonen stated that there is just one

household who use biogas for energy purpose.

Shift the main electricity power plant to the one who uses biogas.

Our strategy mainly aims to increase the capacity of electricity generation using

renewable resources which is also known as carrying capacity.

9. Cambodia: Evaluation of the Strategy

Usage of renewable energy in Cambodia is still low which around 1-2% of total

energy usage in 2010. This number grows slowly because of lack of fund and experience

as Cambodia usually depends on coal as its main energy resource. As we state before, one

way to increase environment index is to increase the usage of renewable resource,


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especially to generate electricity, by increasing its carrying capacity. The result projection

of our strategy can be seen below.

Figure 5: plan projection

The graph above describes the projected result in 2030 using logistic model if

Cambodia decides to increase its carrying capacity of electricity produced by renewable

resources. The blue line represents projected percentage of electricity which is generated

by renewable resources. On the other hand, green line depicts the increment of percentage

electricity generated by renewable resource. To project the result of our plan, we assume

that government of Cambodia is able to increase carrying capacity of renewable based

power plant by twice over time.

Furthermore, we also believe that our strategy can affect the concentration of CO2

emission in Cambodia. Here we present the effect of our plan on CO2 concentration in

Cambodia over time, assumed the other factor is constant. We believe that the

concentration of CO2 in the country is proportional to the country‟s economic growth. We

propose the carbon cycle model to model the CO2 concentration (De Lara, Doyen, 2008)

( ) Where denotes concentration of CO2 at year (in ppm), denotes baseline

of CO2 emission (around 7.2 GtC per year), denotes abatement rate of CO2, and denotes pre-industrial CO2 concentration (about 280 ppm), constant denotesconversion factor from emissions to concentration ( ppm/GtC), and constant denotes the natural rate of removal of atmospheric CO2 ( per year).


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The variable is the one that we would like to control as abating CO 2 emission.As from the World Bank data of CO2 emission from 1960 to 2010, we could take a

conclusion of how big the increasing of CO2 emission and then we give appropriate

control to

as later action. As the assumption of global economic development around

3% annum, we set to increase 3% per year too.

Figure 6: plan projection for CO2

With the plan on shifting coal to biogas fuel to yield electricity power, we directly cut

off some proportion of CO2 emission. As from the graph above, the abatement of CO2

emission would increase after implementing the plan so that the concentration of CO 2 is

lower than first estimated. This phenomenon can be noted by blue line as plan

implementation result is lower than the green line as without plan condition.


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10.

Sensitivity Analysis

To test the sensitivity of our model, we use an approach to check that the value

around our chosen weighted value gives the same country‟s ranking. Then we will test

our model by choosing the new value and analyze the result of our new index. There

are 3 new weighted values that we will test. The new result for HSI index, sorted form

of the largest to the smallest value is

AHP Equal Weight Random1 Random2

Armenia Armenia Armenia Armenia

Costa Rica Costa Rica Costa Rica Costa Rica

Panama Panama Panama Panama

Jamaica Jamaica Jamaica Thailand

Thailand Thailand Thailand Jamaica

Peru Sri Lanka Peru Peru

China Peru China China

Colombia Colombia Colombia Colombia

Sri Lanka China Sri Lanka Ecuador

Philippines Philippines Philippines Sri Lanka

Ecuador Ecuador Ecuador Philippines

Indonesia Indonesia Indonesia Indonesia

El Salvador El Salvador El Salvador El Salvador

DominicanRepublic

DominicanRepublic

DominicanRepublic

DominicanRepublic

Honduras Honduras Honduras Honduras

Guatemala Guatemala Guatemala Guatemala

India India India India

Nicaragua Nicaragua Nicaragua Nicaragua

Cameroon Cambodia Cameroon Cambodia

Cambodia Cameroon Cambodia CameroonBenin Benin Benin Benin

Togo Zimbabwe Togo Togo

Zimbabwe Kenya Zimbabwe Kenya

Kenya Togo Kenya Zimbabwe

Ethiopia Ethiopia Ethiopia Ethiopia

Table 3: sensitivity result


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The weighted value we use as our sensitivity analysis is summarized at table below

Water access HDI Nourishment rate Education Index

AHP 0.2405 0.2414 03215 0.1967

Equal Weight 0.25 0.25 0.25 0.25

Random1 0.25 0.25 0.3 0.2

Random2 0.2 0.3 0.35 0.15

Table 4: weight data

From the result above, we obtain, there is no significant change in country‟s ranking.

Therefore we can use our weighted value to determine country‟s ranking.

11. Conclusion

Our sustainability model place a great emphasize on CO2 emission as a determining

factor in our environmental index while in human development index, nourishment takes

the biggest effect. By doing sensitivity analysis, we also find out that our ranking system

is quite robust as the weight factor we use is valid.

Based our sustainability model, we come up with a strategy to tackle sustainability

problem in Cambodia. Our plan is to improve the carrying capacity of percentage of

electricity generated by renewable resources. Generally, sustainability problems in

Cambodia mostly are related to environment issues.

12.

Strength and Weakness

Our model is able to explain the effect of a lot of indicators.

Our model is also flexible to decide whether the country is sustainable or not by

determining which is between human and environment that contribute most to

sustainability condition.

Our model gives information about the indicator that need to be improved to achieve

sustainability.

The approach to evaluate the plan cannot be used for every indicator.

Indicators we used to evaluate sustainability are not complete.


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13. References

Mustonen, S. (2003) Bioenergy Consumption and Biogas Potential in Cambodian

Households. Sustainability 2013, 5, 1875-1892; doi:10.3390/su5051875

Saatl, T.(1990) Models, Methods, Concepts & Applications of the Analytic Hierarchy

Process. Springer

Freudenberg, M. (2003). Composite Indicators of Country Performance: A Critical

Assessment, OECD Science, Technology and Industry Working Papers, 2003/16,

OECD Publishing. http://dx.doi.org/10.1787/405566708255

Lara, M., Doyen, L. (2008). Sustainable Management of Natural Resources. Springer

Moldan, B. (2012) How to understand and measure environmental sustainability:

Indicators and targets. Ecological Indicators, Volume 17, June 2012, Pages 4 – 13

Ellis, K. et al. (2013) Low carbon Competitiveness in Cambodia. Overseas

Development Institut

http://www.thwink.org/sustain/glossary/ThreePillarsOfSustainability.htm .Web. Feb

09, 2015.

http://unstats.un.org/UNSD/environment/default.htm .Web. Feb 09, 2015.

https://www.cia.gov/library/publications/the-world-factbook/geos/cb.html .Web. Feb

09, 2015.

http://www.who.int/water_sanitation_health/mdg1/en/ .Web. Feb 08, 2015.

http://www.indexmundi.com/cambodia/environment_current_issues.html .Web. Feb

09, 2015.

http://www.un.org/esa/agenda21/natlinfo/countr/cambodia/energy.pdf .Web. Feb 09,2015.

http://hdr.undp.org/en/content/education-index .Web. Feb 08, 2015.

http://www.uncsd2012.org/history.html .Web. Feb 07, 2015.


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Appendix

Human Development Index by using our model

Country

Water

Access HDI Nourishment

Edu.

IndexArmenia 0.998 0.72 0.943 0.701367

Costa Rica 0.966 0.75 0.941 0.640489

Panama 0.943 0.759 0.894 0.662967

Thailand 0.958 0.715 0.932 0.607965

Jamaica 0.931 0.712 0.921 0.668356

Peru 0.868 0.722 0.913 0.656042

China 0.919 0.701 0.894 0.598556

Colombia 0.912 0.706 0.886 0.610556

Ecuador 0.864 0.701 0.888 0.593838

Sri Lanka 0.938 0.736 0.754 0.737778

Philippines 0.918 0.651 0.885 0.609984

Indonesia 0.849 0.671 0.913 0.594164

El Salvador 0.901 0.652 0.865 0.540887

Dominican Republic 0.809 0.691 0.853 0.583851

Honduras 0.896 0.612 0.879 0.501609

Guatemala 0.938 0.613 0.857 0.461648

India 0.926 0.57 0.848 0.456

Nicaragua 0.85 0.604 0.832 0.4839

Cambodia 0.713 0.571 0.839 0.495178

Cameroon 0.741 0.493 0.895 0.480233

Benin 0.761 0.467 0.903 0.402907

Togo 0.6 0.46 0.847 0.500533

Kenya 0.617 0.522 0.757 0.514556

Zimbabwe 0.799 0.459 0.682 0.499933

Ethiopia 0.515 0.409 0.65 0.300318


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Environment Sustainability Index by using our model

Country Emission Marine Terestial Area Electrity Generates…

Armenia 0.364900087 0.08102788 0.001078416

Costa Rica 0.198206701 0.226018539 0.175292154

Panama 0.159878188 0.1411041 0.002965359

Thailand 0.005215836 0.164118215 0.02136436

Jamaica 0.215163934 0.07061907 0.042918455

Peru 0.026748185 0.176529775 0.020424085

China 0.000185853 0.161215678 0.016715271

Colombia 0.020350809 0.208331105 0.009473165

Ecuador 0.047191011 0.370328027 0.025216624

Sri Lanka 0.121177149 0.154040381 0.009568045

Philippines 0.018876404 0.050590886 0.14779605

Indonesia 0.003548796 0.088171714 0.056037473

El Salvador 0.246478873 0.068717037 0.302088555

Dominican

Republic 0.073465104 0.208107223 0.002327074

Honduras 0.189959294 0.16216214 0.020641521

Guatemala 0.138522427 0.298192795 0.295969203

India 0.000766688 0.048491212 0.044444306

Nicaragua 0.338709677 0.324648418 0.232303908

Cambodia 0.368421053 0.237559969 0.023138833

Cameroon 0.212873796 0.109115135 0.010001695

Benin 0.296819788 0.25513736 0.006666667

Togo 1 0.24228579 0.015151515

Kenya 0.123930363 0.115617475 0.238234902

Zimbabwe 0.16336056 0.271725839 0.008578431

Ethiopia 0.23715415 0.184099355 0.003614458


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Sustainability Index (slope is -1)

Country

Human

Index

Environment

Index

Final

Rank

Armenia 0.850855 0.214866 1.065721

Costa Rica 0.843623 0.199361 1.042985Panama 0.828645 0.119188 0.947833

Thailand 0.823495 0.045776 0.86927

Jamaica 0.822403 0.141785 0.964188

Peru 0.808156 0.05999 0.868146

China 0.796783 0.041326 0.83811

Colombia 0.795883 0.061388 0.857271

Ecuador 0.782976 0.116975 0.899951

Sri Lanka 0.782967 0.102944 0.885911

Philippines 0.780148 0.056097 0.836245

Indonesia 0.779775 0.035318 0.815093El Salvador 0.759683 0.218201 0.977884

Dominican Republic 0.755228 0.088186 0.843414

Honduras 0.745691 0.144307 0.889998

Guatemala 0.740697 0.212012 0.952709

India 0.7214 0.021945 0.743345

Nicaragua 0.714985 0.310831 1.025816

Cambodia 0.681827 0.258135 0.939962

Cameroon 0.681385 0.141849 0.823234

Benin 0.668786 0.219963 0.888749

Togo 0.62953 0.596348 1.225878

Kenya 0.622133 0.148501 0.770634

Zimbabwe 0.61119 0.152622 0.763812

Ethiopia 0.498248 0.170774 0.669022


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Source Code

Logistic Model

%data y=[0.196463654 0.137362637 1.048492792

1.71990172 1.556016598 1.4455782311.20886501 1.284651792 1.671974522 2.3138833];%year according to data tint=2001; tlast=2010;t=tint:tlast;

%logistic model syms k t0 x f=30/(1+exp(-k*(x-t0)))

for i=1:length(y)ys(i)=subs(f,x,t(i));

end

for i=1:length(y)e(i)=ys(i)-y(i);

end

E=e*e';

%a priori grid xmin=0; xmax=1;ymin=1900; ymax=2030;

x0=0.1;y0=2010; %initial guess

plot(x0,y0,'ro','MarkerFaceColor','b')hold on

%% Simulated Annealing method x1=xmin+rand*(xmax-xmin);y1=ymin+rand*(ymax-ymin);

n=1; %number of iteration T=10; %initial temperature

[a,b]=meshgrid(xmin:0.01:xmax,ymin:0.01:ymax+50);X=subs(E,{k,t0},{a,b});

[xx,yy]=contourf(a,b,X,20)colorbaraxis([0 1 0 2080])hold on

while n

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Sustainability Development - MCM 2015 Report