Category Archives: Social Science

Millennium Development Goals (MDGs) and Quality Education Situation in Pakistan at Primary Level

Muhammad Sabil Farooq*, Yuan Tong Kai

ABSTRACT

The quality of education is a priority for every nation any educational institution or organization and their beneficiaries. This paper is concerned with methods and theories recently used in Quality Education research in Pakistan. It begins by looking at policies, practices and procedures implemented and their impact on quality of education in the light of MDGs. This study will explore the comparative difference of quality education against MDGs at primary level in Pakistan to identify the gaps and challenges in their policies, practices and procedures to suggest the possible measures for their quality improvement standards at proposed level.

In light of few international commitments has made by Pakistan to provide quality basic education to everyone as a basic right. As per the constitution of Pakistan, “The State shall provide free and compulsory education to all children of the age of five to sixteen years in such manner as may be determined by law” National Educational Assessment System (NEAS) reported shocking situation regarding the achievement of these obligations. The main focus of this article was to analyze the current situation of quality education in the light of MDGs and to understand what we can expect in near future regarding provision of quality primary education in Pakistan.

Key Words: Quality education situation, Comparative differences against MDGs, Primary education


INTRODUCTION

Situated on the western edge of South Asia, Pakistan has a population of about 184 million, with sex ratio of 105.6: 100. It is estimated that about 62% of the people are residing in rural and 38% in urban areas. GDP Per Capita Income is US$ 1,387 for 2014-15.

Pakistan is a developing country, gradually transforming from agriculture-based economy to an increasing share of industry and services sectors in the GDP. Country spends a major part of its budget to address challenges of national security and interest payments on its loans. This leaves a relatively smaller amount to be invested on infrastructure development to boost economic growth and enable social sectors to meet basic needs of the people like education, health, social services etc. Pakistan is confronted with a host of serious development issues.

The Gross Domestic Product (GDP) of Pakistan grew at a rate of 4.3% (FY 2014/15), but there are several challenges which are likely to restrict its future progress unless strict actions are implemented. Energy shortage is a major obstacle to raise production. Pakistan is a partner of on-going global war against terrorism. Resultantly, geo-political dynamics in the neighboring countries and on its borders have generated security and terrorism related threats for the local people, foreign investors, and development workers. This situation is restricting investment and emphasizing unemployment in the country. The deficit on trade balances is also adding to the fiscal pressures.

  Table1: Area and population by Province/Region

Figure1: Distribution of population in Regions/Provinces shown in percentage (%)

In the past, Pakistan has not been spending enough in terms of basic social services to the people. Another hindering factor has been rapid population growth, which was 3.1% or more during 1990s, and is still above 2% per annum. Continuous fast increase in population has eaten up or diluted benefits of the investment on development. Illiteracy, rapid population growth and slow economic development have increased unemployment, stuck evolution of socio-political institutions and democratic norms in the society. Due to illiteracy and poverty, health indicators are also low in Pakistan. One third children are born underweight and infant mortality rate is high.

Educational indicators of Pakistan are still miserably low, although steady progress has been noticed during last few decades. At present, about one third primary school age children are out of school, 42% population (age 10+) is illiterate. Wide discrepancies persist in education indicators pertaining to provinces/areas, location (urban vs. rural) and gender. At the national level, about two third women of age 15+ cannot read and write, and 35% girls remain out of school. Gender Parity Index in case of participation in primary education is 0.82. It is estimated that over 6.7 million children are out of school, and majority of them (62%) are girls.

Quality Education Importance:

Quality Education is a dominant instrument of socioeconomic and political change related to global, technological and democratic developments. So it is necessary to improve quality of education at different levels. Quality is one of the most important dimensions of an education system. There are probably as many different ideas about quality as there are schools. Quality is creating an environment where educators, parents, government officials, community representatives, and business leaders work together to provide students with the resources they need to meet current and future academic, business and changes. Strengthening the quality of education has become a global agenda at all educational levels and more so at the primary level. The quality of basic education is important not only for preparing individuals for the subsequent educational levels but to equip them with the requisite basic life skills and social norms too. Quality education also ensures increased access and equality and it is mainly due to these reasons that various international Forums and Declarations have pledged improvements in quality of education. It is important to mention that quality of education can be measured from three different viewpoints i.e. quality of inputs, quality of the process, and quality of output. Input reflects the resources committed by the government and society in general for the cause of providing education; these resources include infrastructure (including various physical facilities), teaching resources, curriculum and other support materials. Quality of the process reflects how good the delivery process is, and generally measures what goes on in the classroom as well as in the school in general. The quality of output reflects the conformance of the knowledge and skill levels of students to well established standards, e.g. exam systems and their results are a useful measure of output quality.

Improving and sustaining quality of education is ultimate importance in any society round the world. By ensuring quality education the nations can be able to economic, social, mental, psychological and emotional growth of individuals on the right direction. The Universal Declaration of Human Rights (1948) also declared quality primary education as the basic right of all people. According to (Hoy, et al, 2000), many developed and developing countries have attained or near to the goal of (UPE) universal primary education. Now the focus has been turned to the quality of students’ learning and it is quite justifiable not only for those countries which have attained quantitative targets, but it is also valid for those countries who are striving to achieve the target of EFA and MDGs like Pakistan. Quality of education requires standards set in order to develop assessment tools, compatibility of programs and propose someone as accountable for to meet the targets. Pakistan has made its commitments in all international forums of providing basic education with high quality and to make it accessible for all. Commitments of Pakistan with the international community are as under;

International Commitments

Pakistan was one of the 48 member states who voted in favor of the Universal Declaration of Human Rights on 10 December 1948. According to article 26 of this declaration, “Everyone has the right to education” and “Education shall be free, at least in the elementary and fundamental stages. Elementary education shall be compulsory”. The convention on the Elimination of All Forms of Discrimination against Women was adopted by the UN general assembly in 1974 but Pakistan acceded to the convention on March 1996. As a signatory to the Beijing Declaration and Platform for Action 1995, Pakistan is committed to promote “people-centered sustainable development through the provision of basic education, lifelong education, literacy and training for girls and women” (Article 27), and ensuring “equal access to and equal treatment of women and men in education” (Article 30).

Pakistan’s Commitment to Education-For-All 2000

This is largely because of the sorry state of Pakistan’s education system and the failure of successive governments to provide even basic education for all: according to the 2012 Global Monitoring Report, Pakistan continues to have the second-largest number of out-of-school children in the world. As a signatory of this declaration on Education for All (EFA), Pakistan agreed that every person should be able to benefit from educational opportunities designed to meet their basic learning needs, and called for an expanded vision of education, encompassing factors such as universalizing access to education and promoting equity including goal 2 “to achieve universal primary education” by 2015.

Dakar Framework for Action

Pakistan was among 164 countries who adopted the Dakar Framework for Action, Education for All: Meeting Our Collective Commitments at the World Education Forum. All 164 countries acknowledged education as a fundamental right for all people, regardless of gender or age, recognized the need to make comprehensive efforts to eliminate gender discrimination. The Dakar Framework is a collective commitment to achieve all EFA goals and one of these is “ensuring that by 2015 all children, particularly girls, children in difficult circumstances and those belonging to ethnic minorities, have access to and complete free and compulsory primary education of good quality” Unfortunately, Pakistan is among the countries which are not likely to achieve these goals by 2015.

After Dakar, the country took a number of initiatives to accelerate the pace and progress towards achievement of EFA Goals, including abolition of school fees, provision of free textbooks to students and legislation to declare free and compulsory access of children to education as their constitutional right. Article 25-A has been inserted in the Constitution through landmark 18th Constitutional Amendment. Although, the country has lagged behind the targets of EFA, nonetheless, a momentum has been built and required legal and institutional mechanisms are being created to sustain and accelerate the pace of progress towards EFA Goals.)

The 2013 EFA Global Monitoring Report showed that progress towards many of the targets is slowing down and that most EFA goals are unlikely to be met in Pakistan. After good progress in the initial years after Dakar, the number of children out of school aged 5-9 years has risen to 6.7 million in 2013. It is clear that the target of universal primary education will be missed by a considerable margin by 2015.

 

The Millennium Declaration and Millennium Development Goals (MDGs)  

Pakistan, including 192 members of United Nations countries has agreed to achieve the Millennium Development Goals developed/framed at the Millennium Summit in New York in 2000. All members States agreed to work towards achieving all the MDGs, including goal 2 “to achieve universal primary education” by 2015.

Constitution amendment # 18

Through a constitutional amendment # 18, free and compulsory education for the children aged 5 to 16 years has been declared a fundamental right. Article 25-A of the Constitutions provides that: “The state shall provide free and compulsory education to all children of the age of five to sixteen years in such manner as may be determined by the law.”

National Plan of Action (2001-2015)

To honor the international commitment made in signing the Education for All Dakar Framework for Action (April 2000), the Government of Pakistan developed the National Plan of Action (NPA) on Education for All 2001–2015. The objectives of the NPA are to ensure access to education for disadvantaged rural and urban population groups, particularly girls and women; to promote community participation and ownership of basic education programmes; and to improve the relevance and quality of basic education. The National Plan of Action 2013 estimates a total of 6.7 million primary-aged out of school children during 2013-16. Of these 5.06 million children are expected to be enrolled in the country. The total cost estimated to be Rs. 189 billion.

National Education Policy (2009)

As per nature of the current situation of gender and rural- urban disparities regarding access of education, the New Education Policy aims to revitalize the education system. Policy also aims to enable Pakistan to fulfill all its international commitments regarding education on different forums and summits in general and EFA and MDGs in particular.

 

Education System in Pakistan

In Pakistan, education is now a provincial subject as a result of the 18th Constitutional Amendment legislated by the parliament during April 2010. The provincial/area governments enjoy greater autonomy in several social and economic sectors, including education. The Ministry of Education and Trainings and Standards in Higher Education (MET&SHE) at the federal level coordinates with international development partners and provides a platform to the provincial/area departments of education for exchange of information and creating synergy, synchronization and harmony.

Public sector formal school system, which is largest service provider in Pakistan, consists of 12 academic years. It starts from Primary and ends at Intermediate level or Higher Secondary School Certificate (HSSC). Pre-primary classes (local name Katchi class, translation: Pre-Primary; premature or not ripe yet) can be found in schools, but this level is not recognized in terms of budgetary provision or examination. Private sector caters for educational needs of about one third enrolled children having diverse streams, some following public sector national curricula, while others opting for curricula of Cambridge International Examinations.

The children of upper-middle classes, residing in urban localities, mostly attend high cost private schools, offering foreign curricula and international examination systems (O and A levels) and are staffed with qualified and trained teachers, well-equipped classrooms, all essential facilities of good quality, and imported teaching-learning materials.

 

 

 

 

 

 

 

Figure2: Basic Structure of education in Pakistan

In addition to the public and private schools, there is another stream of ‘Deeni Madrassas'(Religious Schools) offering free religious education with free boarding and lodging. These Madrassas are usually managed by local communities and are financed through charity and donations. These parallel systems of education in Pakistan have perpetuated inequalities and economic stratifications, and are root cause for behavioral divisions and social conflict in the society.

Majority of the children, residing mainly in rural and semi-urban areas and belonging to the low income families, attend public schools which offer free education but are characterized by poor quality of education due to lack of physical facilities, shortage or absence of teachers, and non-availability of suitable learning materials.

Private Sector Contribution in Primary Education

Private Sector is playing an important role in the promotion of education in Pakistan. Private sector enrollment is increasing because of its overall better quality of education as compared to public sector.

NEMIS (National Education Management Information System) data indicated that in 2012/13, there were 17,093 private primary schools in the country. In addition, there were 25,658 middle/lower secondary schools and 17,696 high schools in the private sector. At the primary level, overall 4.8 million (34%) children of 5-9 years age group are enrolled in private sector schools. It is estimated that 34% of boys and 33% of girls are enrolled in private schools.

EARLY CHILDHOOD EDUCATION (ECE)

 In Pakistan, there are two types of pre-primary education: (i) poor quality “Katchi” classes in government primary schools, and (ii) good quality ECE usually in private sector commonly termed as Nursery, Kindergarten, and Montessori etc.  Pre-Primary/Katchi class neither has a separate classroom nor a specific trained and qualified teacher. The children are usually those who accompany their older siblings to school and simply “sit around” in school premises. Mostly, one teacher, following multi-grade approach, teaches them and grades I & II simultaneously. This part-time or shared teacher daily assigns pupils of Katchi class some simple activity and over the year they learn simple alphabets and numbers only, and are not able to cover full national curriculum of ECE. Whereas, the more proper and good quality Early Childhood Education (ECE), with separate classroom, trained teacher and specific teaching and learning aids, is available mostly in urban private sector schools, where children from privileged families are enrolled. Although there are no separate pre primary/ECE budgetary allocations in public sector, however there is a clear national policy, standards, curriculum and teacher training packages for pre-primary/ECE. In public sector schools, pre-primary is a part of primary school and follows prescribed syllabus while private sector follows child-centered teaching methodology. The government has approved national curriculum which is implemented in selected schools, mostly supported by donors.

There are wide variations across provinces in Gross Enrollment Rates (GER) of ECE/pre-primary, though gender differences do not appear pronounced (Table 2). Since 2000, for all Provinces and Areas there have been steadily increases in the gross enrollment rates for a decade while all rates reflect a decline in 2012-13 due to an upward adjustment in population.

The national average for ECE/Pre-primary GER was 66% in 2012/13. While Punjab and Khyber Pakhtun-khwa (KP) demonstrate highest rates of gross enrollment in ECE/Pre-Primary, the pace of progress has been remarkably high in Sindh (Table 2).

Table 2: ECE / Pre-Primary: Gross Enrolment Rate from 2001-02 to 2012-13 by Province

Source: NEMIS (2001-13) & NIPS Projections (2005-25) Govt. of Pakistan

Primary and Secondary Education

In Pakistan, there are 422 Pre-Primary Institutions and 145,491 formal primary, 42,920 middle level, High/Higher Sec./Inter Colleges 35,792 Degree Colleges are 1,086; 75% are public sector schools; 10% private sector schools and the remaining almost equally divided between non-formal basic education schools and ‘Deeni Madrassas’

Table 3: Education Statistics at every Level

Source: National Education Management Information System (NEMIS) Database 2014 Govt. of Pakistan

On world community forum Pakistan is one of signatory regarding making its effort to achieve universal primary education (UPE) which is still a dream yet to come true. By utilizing less than 2 per cent of GDP, how it can achieve its targets in education sector. For the achievements of set targets it needs to raise the share of GDP for education and then assures transparent mechanism of implementation strategies to provide access and quality education for all. Increases in grass enrollment shows sincere efforts are being made and Pakistan has to multiply its efforts to achieve target of UPE.

Education Expenditure in Pakistan as % of GDP

During the past decade, Pakistan’s education expenditure as percentage of GDP has varied between 1.5% and 2.1%

 

Table 4: Education Expenditure in Pakistan as % of GDP

Source: Ministry of Finance; Government of Pakistan (2001-13)

 

 

Distribution of National Education Expenditures by Sub-Sectors

On average at the national level, 89% of education expenditures comprise current expenses such as teachers’ salaries (Figure 3). Only 11% comprises development expenditures, which is not sufficient to raise quality of education. Across provinces, too, an overwhelming proportion of total actual education expenditures are spent on current heads, mainly teachers’ salaries, leaving a very small proportion for development expenditures. For 2012/13, except in KP where development expenditures are 22% of the total actual expenditures, these range between 5% (in Punjab), 6% (in Sindh); and 9% (in Baluchistan).

 

 

 

 

Figure 3: Distribution of National Education Expenditures by Sub-Sectors

Source: Office of the Controller General, Accounts (CGA). 2013, Govt. of Pakistan

 

The National Plan of Action (NPA) to Accelerate Education-Related MDGs (2013-16)

The National Plan of Action (NPA) is designed to accelerate progress towards education related goals and targets identified by MDG for 2015/16.The NPA to Accelerate Education Related MDGs is a consolidation of 8 Provincial and Area Plans, each specific to its local conditions, challenges and interventions. The National Plan envisages increasing the national net primary enrollment from 68% in 2011/12 to 91% by 2015/16. Given the stock of 6.7 million out-of-school primary-aged children, the Plan expects to enroll an additional 5.1 million (2.4 million boys and 2.7 million girls) by 2015/16.

GLOBAL PARTNERSHIP FOR EDUCATION 2015-18, REPLENISHMENT PLEDGING FRAMEWORK OF PAKISTAN

Education sector has faced myriad issues and challenges of access, equity and quality in the past. Current new political government has stoic resolve to enhance the allocation for education sector substantially in the next four years (FY 2014-15 to FY 2017-18). The political government in the election manifesto pledged to increase budgetary allocation from current 2% to 4% of the GDP by the year 2018. Right to education of every child of age 5-16 years is a constitutional obligation under Article 25-A. Immediately after taking over , the new government took stock of the situation and prepared a National Plan of Action to Accelerate education related MDGs and EFA targets.

All the governments, Federal and Provincial, through their manifesto are committed to people of Pakistan to gradually rise the spending on education to at least 4% of GDP by 2018. We pledge that Pakistan will increase the expenditure on education in public sector by an average of at least 1 percentage point per year from FY 2014-15 to FY 2017-18.

Government of Pakistan is fully committed to remove all types of disparities in the education service delivery in Pakistan as early as possible through making specific allocations for the education of disadvantaged and under-served groups especially girls, disabled and minorities.

Figure 4: Primary: Comparative territory wise enrollment

Source: ASER, Pakistan 2014

Situation Analysis

Primary Enrollment Rates

Table 5: Primary: Gross Enrollment Rate from 2001-02 to 2012-13 by Province

Source: NEMIS (2001-13) & NIPS Projections (2005-25) Govt. of Pakistan

Despite repeated policy commitments, primary education in Pakistan is lagging behind in achieving its target of universal primary education (UPE), 100% survival rates up to grade V, low/negligible dropout rates and good quality education. This is largely due to low budgetary allocations (2% of GDP) to education sector; shortage of schools especially for girls and also in remote and far flung areas; shortage and absenteeism of teachers; lack of trained teachers, especially female teachers; missing facilities such as water, toilets and boundary walls; weak supervision and mentoring; and a host of out-of-school factors such as conservative and tribal culture; insecurity and lawlessness; and poverty, compelling a large number of children to work rather than attend school.

Since 2005-06, for all Provinces and Areas there have been steady increases in the gross enrollment rates for a decade while all rates reflect a decline in 2012-13 due to an upward adjustment in population.

The overall gross primary enrollment rate in Pakistan is 86% (Table 5). ICT, KP and Punjab display higher than national average rate while Baluchistan, FATA, Sindh and AJ&K have lower than national average rate. It is encouraging to see that FATA and KP are showing progress despite years of uninterrupted conflict and militancy leading to aggression, insecurity and terrorism.

Table 6: Primary: Out of School Children 2012-13 by Province

Source: NEMIS (2001-13) & NIPS Projections (2005-25), Govt of Pakistan

 

Table 7: Net Primary Enrollment Rate of age 5-9 years (2013-14)

Source: National Education Management Information System (NEMIS) Database 2013-14, AEPAM, MET, Islamabad & Population Projection 2005-2025, NIPS, 2013

Primary School Survival Rates (2013-14) Grade 5

Also called Retention Rate, Survival Rate to Grade 5 is the proportion of a cohort of pupils who reached Grade 5 expressed as a percentage of pupils enrolled in the first grade of a given cycle in a given school year.

A Survival Rate approaching 100 percent indicates a high level of retention and low dropout incidence. Survival Rate may vary from grade to grade, giving indications of grades with relatively more or less dropouts. The distinction between survival rate with and without repetition is necessary to compare the extent of wastage due to dropout and repetition.

 

Table 8: Primary School Survival Rates Grade 5

Source: 1.National Education Management Information System (NEMIS) Database 2013-14, AEPAM, MET, Islamabad 2. Calculated through UNESCO Reconstructed Cohort Model

 

Figure 5: Survival Rate to Grade V

Source: Annual Report of Education Statistics. National Education Management Information System (NEMIS) Database 2013-14, AEPAM, MET, Islamabad Government of Pakistan

 

Pupil Teacher Ratio (PTR)

PTR is one of the most common indicators used in educational planning for improvement of quality education. A low number of pupils per teacher indicate pupils will have a better chance of contact with the teachers and hence a better teaching learning process. The PTR should normally be compared to established national norms on the number of pupils per teacher for each level or type of education. A high pupil-teacher ratio suggests that each teacher has to deal with a large number of pupils and that; conversely, pupils receive less attention from the teacher. The ratio of students to teaching staff is also an important indicator of the resource Level wise PTR in public sector of education is shown in table 9 and figure 6.

 

Table 9: Pupil-Teacher Ratio by Level

 

Figure 6: PTR (Pupil Teacher Ratio)

Source: Annual Report of Education Statistics. National Education Management Information System (NEMIS) Database 2013-14, AEPAM, MET, Islamabad Government of Pakistan

Table 10: Quality of Education in Pakistan at Primary Classes

Source: ASER, Pakistan 2014

 

Figure 7:Learning Levels Province wise grade 5

 Source: ASER, Pakistan 2014

Quality of education also depends on the physical environment and availability of facilities such as water and sanitation in educational institutions. In this context, statistics on public sector schools show that availability of drinking water is positively related with the level of educational institution e.g. upper secondary schools, in relation to lower secondary and primary schools are best provided with drinking water facility e.g. 64% primary, 80% middle and 91% upper secondary schools have water available (Table 11).

Table 11: Drinking Water Facility 2012-13

Source NIMS 2012-13, Govt. of Pakistan

Data for sanitation facilities in public sector schools, too, show better availability by levels of educational institutions e.g., 66% primary, 85% middle and 91% upper secondary girls’ schools have sanitation facilities while 54% primary, 76% middle and 85% upper secondary boys’ schools have access to sanitation facilities.

Table 12: Sanitation Facility 2012-13

Source NIMS 2012-13, Govt. of Pakistan

Education is considered as the cheapest defense of a nation. But the down trodden condition of education in Pakistan bears an ample testimony of the fact that it is unable to defend its own sector. Though 62 years have been passed and 23 policies and action plans have been introduced yet the educational sector is waiting for an arrival of a savior. The current government invested comparatively good in education sector and that era saw a visible positive educational change in Pakistani society. Now days, the economic situation in Pakistan is under stress and education is the worse effected sector in Pakistan.

Education Key Challenges in Pakistan

The key challenges to Pakistan’s education are: (i) lack of access to education; and (ii) poor quality of education; (iii) equity; and (iv) governance. Other influencing factors include budgetary constraints and weak management, which indirectly accentuate the lack of access and poor quality; and a set of external factors such as poverty, adverse law and order situation; and devastation due to natural disasters especially devastating floods of 2010 and annihilating earthquake of 2005.

These challenges are strongly interlinked with poor teaching quality, teacher absenteeism, truancy and/or lack of textbooks etc. As cumulative effect this generates lack of interest/motivation among students who dropout from school – adversely affecting every EFA goal and its corresponding targets.

Improving the quality of education is one of the key objectives of the National Plan of Action (2013) for education. For each strategy to be adopted for increasing enrollments, 15% of the total current and development costs have been additionally included for quality improvement measures. In this, the provinces and area governments will be free to select the most appropriate mix of investment e.g. in teachers’ training, distribution of free textbooks, provision of missing school facilities such as water, toilets, electricity, better supervision or any other facility.

Recently, minister of education announced a new Education policy for that next 10 years even the previous educational policy from 1998 to 2010 is still not expired. It is said in this policy that all the public schools will be raised up to the level of private schools because level of private schools considered good as public schools. Now a notice is issued to private schools to induct government course in 5th and 8th class and these classes will bound to take board exams.

Solutions for Educational System: 

Estimating the value of education, the Government should take solid steps on this issue. Implementation instead of projecting policies should be focused on. Allocation of funds should be made easy from provinces to districts and then to educational institutes. On their end, provinces will need to make higher financial allocations to education, both formal and non-formal and literacy; strengthen their capacities to design and implement education policy. Workshops must be arranged for teachers to enhance their professionalism, regular training of teachers, timely provision of textbooks, and effective monitoring and supervision is necessary for quality of education. Besides, undertaking more public-private partnerships, involvement of the community and participation of parents in school matters (through school management committees) should be encouraged. Lessons learned from public-private partnership experiences show that it produces better quality education at lower cost with improved management and greater coordination between parents and teachers. LSS (Learning Support Systems) Explanation: “Create systems of learning support to enable students to achieve extraordinary learning results in classrooms, laboratories and beyond.” should be inducted in Pakistani schools to improve the hidden qualities of children. Technical education must be given to all the classes. Promotion of the primary education is the need of time must have to work on UPE. Teachers, professors and educationists should be consulted while devising any plan, syllabus or policy; and develop a strong field force of supervisors and monitors for tracking progress (or lack of it) in the education sector. International development partners can assist Pakistan in its efforts to meet the international commitments. They can assist in:

  1. Development of a well-organized consultative process among different stakeholders in education;
  2. Establishment of a Consortium of Sponsors to Education in Pakistan;
  3. Simplify the procedures required for approval of project/programme.

Given the fast approaching deadline of 2015 for meeting the internationally agreed goals and commitments, the international development partners should come forward and generously support educational development in Pakistan, strictly in line with national priorities. Investment in the education sector will help improve quality of life of the people through improved awareness and lead to the creation of a literate, tolerant, and development oriented society in Pakistan.

Model of Quality Control in Education

Adams (1993) included six elements of quality, i.e. reputation of the institution, resources and inputs, process, content, output and outcomes, and value added. Since the concept of quality control and quality management have come from industrial and management sciences, the models of quality control are essentially based on the same philosophy. The industrial models were later on applied and adapted to the educational settings. The educational planners have been defining the quality out-put and have been searching for educational quality correlates. The quality out-put is defined in terms of learning achievement in three domains, i.e. cognitive, affective and psychomotor. Other indicators of quality output are decreasing rates of dropout and increasing rates of stay-ins, number who complete the program cycle and, gender and social equality.

Lockheed and Verspoor (1991) in a study of developing countries have identified various input and process determinants of educational output. These include orderly school environment, academic emphasis in the form of clearly defined learning outcomes and standards, curriculum, particularly the “implemented curriculum” (textbooks, other learning materials), time for learning, and effective use of school time, qualified teachers and healthy children. The developed countries show the similar results with a varying level of quality inputs. For example literature on Educational Reforms in the United States describes that standards of education can be improved through redefining basic curricula, and setting performance standards required from students at the completion of the program (Paliakoff and Schwartizbeck, 2001). Farguson, as cited in Paliakoff and Schwartzbeck (2001), after his examination of student achievement in 900 Texas school districts concluded that the quality of teachers is the most critical aspect of schooling and that it has a direct impact on student learning.

 

The study:

This study designed to achieve the following objectives;

  1. To analyze the current situation of primary education in terms of enrollment.
  2. To find out the quality of education regarding academic achievement in different school subjects at primary level through different documents and
  3. To find out the differences of quality education in light of MDGs and
  4. To devise a strategy of managing quality education at primary level in Pakistan.

 

 

Research questions:

Following research questions were constructed to guide the study;

  1. What is the enrollment rate at primary level in Pakistan?
  2. What is the completion / survival rate of primary education?
  3. What is the academic achievement of the students at primary level?
  4. How quality education can be controlled in order to achieve millennium development goals (MDGs), objectives of Education for All (EFA) and targets of Universal Primary Education (UPE).

 

RESEARCH METHODOLOGY:

Research design was mixed method. Qualitative objectives (Obj. # 1 & 2) and satisfactory answers of all four research questions were found through document analysis technique. Different national and international reports, online books, research articles and education policies were consulted. For quantitative part of the study a ASER Pakistan (Annual Status of Education Report) survey reports 2013/14 and NEMIS (National Education Management Information System) Database 2013-14, AEPAM, Government of Pakistan was used.

 

 

RESULTS OF QUALITATIVE ASPECT

Research question # 1: What is the enrollment rate at primary level in Pakistan?

On average, Pakistan’s gross primary enrollment rate (GER) is 86%, with 92% for boys and 119% for girls. KP displays the second highest GER of 104%, followed by ICT (89%), Punjab (88%), FATA (88%) and Sindh (76%).

In Pakistan, of all the primary-aged (5-9 years) children, 68% are enrolled in primary school (Table 7 on page 8). The highest net primary enrollment rate is in KP (81%) where 92% of all boys (aged 5-9 years) and 68% of all girls (aged 5-9 years) attend primary school. This is followed by Glt.B (76%); Punjab (70%); and ICT (70%). About two-thirds of children attend school in Sindh (63%) and FATA (62%) while only one-half children in Baluchistan (51%) are enrolled in primary schools. ICT is also the only area in the country where primary enrollment rate is higher for girls (72%) than boys (68%) while AJ&K has almost an equal enrollment rate (58%-59%) for boys and girls.

Research question # 2: What is the completion rate or survival rate of primary education?

For Pakistan, estimated information reveals that of all the children entering primary school, 70% reach Grade 5 (Table 8 on page 08). For boys this rate (71%) is slightly above than that for girls (68%). Among the provinces and areas, the highest rate of survival is for ICT (91%) while Glt.B (32%) is lowest. In Sindh, FATA and KP, almost two-thirds of the children reach grade 5 while in Baluchistan only one-half survive up to the final primary class.

Research question # 3: What is the academic achievement of the students at primary level?

According to ASER (2014), (Table 10 on page 9), analysis of reading ability in Urdu/Sindhi/Pashto shows that 49% of Class 5 students could not read Class 2 story compared to the 50% in  2013. 84% of Class 3 children and 30% of Class 1 children could not able to read letters in Urdu/Sindhi/Pashto as compared to 31% in 2013. 58% of Class 5 students could not able to read English sentences of level 2 compared to 57% of children in 2013. 86% of Class 3 children could not read class 2 level English sentences .38% children enrolled in class 1 cannot read capital letters as compared to 39% in 2013. 89% children enroll in class 3 could not do two digit division as compared to 88% in 2013. 30% of class 1 children could not do number recognition (1-9) as compared to 30% in 2013.

Research question # 4: How quality education can be controlled in order to achieve millennium development goals (MDGs), objectives of Education for All (EFA) and targets of Universal Primary Education (UPE)?

The following findings of quality inputs and quality processes were emerged from data gathered and analyzed by ASER survey 2014 and Pakistan EFA review report 2015.

  • In 2014, 21% of the children (age 6-16) were reported to be out of school which has almost remained the same as compared to the previous year (21%).15% children have never been enrolled in school and 6% have dropped out of the school for various reasons.
  • 46% of the boys as compared to the 39% of girls could read language sentences the other side 49% of the boys as compared to the 42% of girls could read at least English words. Similarly, 42% of boys as compared to 38% of girls were able to do at least subtraction.
  • In communities parents 24% of mothers and 48% of fathers in the sampled households have completed at least primary education.
  • In multi-grade teaching, 43% of surveyed Government schools and 25% of private schools had class two sitting with other classes where as 10% of Government and 17% of surveyed private schools had class 8 sitting with other classes.
  • 15% children in surveyed Government schools and 10% of private schools were absent where as 12% teachers in Government schools and 7% in private surveyed schools were absent too.
  • 33% teachers in surveyed Government schools have done graduation as compared to 39% teachers of private schools where as in term of professional qualifications 38% of Government teachers are professionally qualified as compared to 49% of private school teachers.
  • 41%of Government schools have computer labs as compared to the 36%in private surveyed schools where as 49% of Government schools did not have toilets in 2014 as compared to 53% in 2013. Similarly, 25% surveyed private primary schools were missing toilets facility in 2014 as compared to 24% in 2013.
  • 43% of Government primary schools did not have drinking water in 2014 as compared to 36% in 2013. Similarly, 21% of surveyed primary schools did not have drinking water facility in 2014 as compared to 17% in 2013.
  • 39% of surveyed Government primary schools and 27% of private primary schools were without complete boundary walls as compared to 28% in 2013 and 68% Government Primary schools and 62% private primary schools were without playgrounds.

CONCLUSIONS AND RECOMMENDATIONS

The Constitution of Islamic Republic of Pakistan says,

“The state of Pakistan shall remove illiteracy and provide free and compulsory secondary education within minimum possible period.”

In Human Development Report, Pakistan is placed at 136th position for having just 49.9% educated populace. The primary completion rate in Pakistan, given by Data Center of UNESCO, is 33.8% in females and 47.18% in males, which shows that people in the 6th largest country of the world are unable to get the basic education. Following conclusions and recommendations need to be discussed bellow.

Firstly, the educational system of Pakistan is based on unequal lines which directly effects on quality of education and especially at primary level. Medium of education is different in both, public and private sectors at every level. This creates a sort of disparity among people, dividing them into two segments. One division is on the basis of English medium language while the other is Urdu medium language. There should be better to standardize the medium of education in all over the country to maintained quality education.

Secondly, regional disparity is also a major cause which also affects the quality education. The schools in Baluchistan (The Largest Province of Pakistan by Area) are not that much groomed as that of Punjab (The Largest Province of Pakistan By Population). In FATA, the literacy rate is deplorable constituting 29.5% in males and 3% in females. Here it should be equal treatment to all the provinces to improve educational system and quality.

Thirdly, the ratio of gender discrimination is a cause which is projecting the primary school ratio of boys & girls which is 10:4 respectively. For the last few years there has been an increase in the growth of private schools. That not only harms the quality of education but create a gap among haves and have not’s. Here its need to work on gender harmony in order to achieve educational goals.

Fourthly, the allocation of funds for education is very low. It is only 1.5 to 2.0 percent of the total GDP. So, it’s very low budget to fulfill the basic necessities of the education sector at every level which definitely affect the quality of education.  It should be around 7% of the total GDP. Political government currently plans to increase budgetary allocation from current 2% to 4% of the GDP by the year 2018.

Fifthly, the teachers in government schools are not well trained. The education sector if fully influenced by the political parties so the teachers even not professionally equipped can get easy job in education sector without any tough criteria. They are not professionally trained teachers so they are unable to train a nation by delivering good quality education. However, professionally more trained people can educate the people to build a good nation.

Sixthly, irrelevant curriculum, non-availability of textbooks and shortages of other learning materials affect learning levels especially in primary level. Lack of regular supervision and monitoring has failed to check teacher absenteeism and misuse of resources. So, these problems should be tackling according to the proper needs to improve good quality.

Finally, Educational outcomes are one of the key areas influenced by family incomes which directly effects quality education. Children from low-income families often start school already behind their peers who come from more affluent families, as shown in measures of school readiness. The incidence, depth, duration and timing of poverty all influence a child’s educational attainment, along with community characteristics and social networks. However, it represents that the effects of poverty can be reduced using sustainable interventions to enhance quality education.

REFERENCES

Pakistan Education-For-All (EFA) National Review Report 2015 Islamabad: Ministry of Education (AEPAM),Government of Pakistan.

Dakar Framework of Action, Education-For-All (EFA), UNESCO, April 2000.

Pakistan Education Statistics 2013-2015, National Education Management Information System (NEMIS): Academy of Education Planning and Management (AEPAM), Government of Pakistan.

Govt. of Pakistan National Plan of Action to Accelerate Education-Related MDGs (2013/14-2015-16) Achieving Universal Quality Primary Education in Pakistan.

National Annual Status of Education Report ASER-Pakistan 2013-14,Alif-Ailaan and  (NEMIS) Academy of Educational Planning and Management Ministry of Federal Education and Professional Training Government of Pakistan.

Govt. of Pakistan. (1998). National Education Policy 1998-2010. Islamabad: Ministry of Education.

Govt. of Pakistan. (NCHD) National Commission for Human Development Annual report 2013-14.

Govt. of Punjab and UNICEF. (2003). Universal Primary Education: Guidelines for District Education Department, Punjab. UNICEF.

Lockheed, M.E. et al. (1991). Improving Primary Education Developing Countries. Oxford: Oxford University Press.

Paliakoff, A. and Schwartzbeck, T.D. (Edits). (2001). Eye of the Storm: Promising Practices for Improving Instruction. Washington D.C: CBE.

Alvi, N. A & Alam, A. (2004). Pakistan Institute of Quality Control. Quality Review, Vol. 1. J Ibrahim Publisher. Lahore.

Pakistan Education Roadmap for Universal Primary Education and Skills Education, A report of the world economic forum’s Global Agenda Council on Pakistan (2012-2014).

All EFA Global Monitoring Reports 2012-2015 on Educational Quality, UNESCO.

The Millennium Development Goals Report

2014 United Nations New York, 2014.

USAID Study of Education Research and Policy making in Pakistan (June 2013)

UNESCO. Education for All National EFA 2015 Reviews and Guidelines (2013).

UNESCO: Dakar Framework for Action – Education for All: Meeting our Collective Commitments.

Adams, D. (1993). Defining Education Quality. Improving educational Quality Project Publication # 1, Biennial Report Arlington, VA: Institute for International Research.

Govt. of Pakistan and UNESCO. (2001). Learning Achievement in Primary Schools of Pakistan: A Quest for Quality Education. Islamabad.

National Education Policy 1998-2010. Islamabad: Ministry of Education.

UNESCO. (1990). The World Declaration on Education for All.

UNICEF. (2000). Defining Quality. (A paper presented at the International Working Group on Education Meeting, Italy).

UNESCO. (2000). World Education Forum: Dakar Framework for Action 2000. Paris: UNESCO.

Schneider, Karl Heinz and Bergmann Herbert (2002). The Impact of PEP-ILE: Teacher In-service Training and Textbooks on Pupil Achievement and Teacher Behavior. Peshawar: GTZ.

 

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Econometric Models  to Water Use Estimation in Power Plants: An Experiential  Analysis

 PERINI PRAVEENA SRI

Department of Social Science, Faculty of Economics

 Ethiopia, Aksum University

ABSTRACT

The purpose of this paper is to examine water use estimation in hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. However there are very few studies of determinants of water use in hydel and thermal electric generation. Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary hydel and thermal electric water use within the system. The multivariate regression procedures were used to identify the significant determinants of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The estimated regression coefficients indicate that the best explanatory variables for the total quantity of hydel water withdrawals are storage capacity, tail water level and actual generation and thermal water withdrawals are condenser cooling and ash disposal. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing power plants.

Keywords:

Thermal water withdrawals, hydel water withdrawals, storage capacity, tail water level, actual generation, condenser cooling and ash disposal.

  • INTRODUCTION

Water has become a growing source of tension especially in power sector in many parts of the World. For India hydro and thermal power projects are vital to fill in the serious electric energy shortfalls that crimp its economy. About 40 percent of India’s population is off the power grid and due to this the welfare of the economy was badly affected. The main stumbling block for this kind of worse situation are a genuine water shortage problem in India and the country’s inability to properly manage large quantities of water during rainy season has made matters worse, exposing it to any small variation in rainfall or river flow. Though the country has invested heavily on nuclear power to generate 30,000 MW and $ 19 billion to produce factories of major thermal, hydro and nuclear power stations, the electric energy shortages were very much prevalent in most parts of the country. For this the first and foremost thing is to judiciously manage the vital resource “water”. The country also planned for setting up of 20,000 MW solar power by 2020. The Government of India has an ambitious mission of Power for All By 2012. This would require an installed generation capacity of atleast 20,000 MW by 2012 from the present level of 144,564.97 MU. However the power requirement will double by 2020 to 400,000 MW. How India is able to meet this target with the on-going water shortage plight in Electricity Generation Industry is a matter of great concern. However the Electricity Generation Industry strategy should primarily focus on this invisible culprit “Water” causing huge generation losses through better water efficiency techniques and lay emphasis on technology up gradation and massive utilization of renewable sources of energy.

The purpose of this paper was to examine water use estimation at hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. Water use at the power station level (by fuel type) can be estimated indirectly by using multiple regression analysis. In regression models, water use relationships are expressed in the form of mathematical equations, showing water use as a mathematical function of one or more independent (explanatory) variables. The mathematical form (eg. Linear, multiplicative and exponential) and the selection of the Right hand side (RHS) or independent variables depend on the category and on aggregation of water demand represented by Left Hand side (LHS) or dependent variable.

2.0  THEORETICAL AND CONCEPTUAL REVIEW OF LITERATURE: DIFFERENT APPROACHES OF WATER USE ESTIMATION

The various studies relating to water demand for thermal power plants and its significant determinants are reviewed for explicit understanding of thermal electric energy water use. Cootner, Paul and George O Golf (1965) have build upon a systematic model for estimating water demand in conventional steam electric utility industry. They have regarded   water as a common factor input along with fuel. Here

TWD= f (Qf, Cw, EHe, CWH )

Where in TWD = Thermal water withdrawal demand,    Qf = Quantity and cost of fuel,   Cw = Cost of water,  EHe = Economics of heat exchange and recycle and  CWH= other costs of thermal power plant associated with the disposal of waste heat.

In other words the quantity of the fresh water withdrawals depends upon the above mentioned factors. In another study Wollman and Bonem (1971) found that the quantity of fresh water withdrawals for steam electric power generation depends upon (1) Thermal efficiency (with higher thermal efficiency less heat will be dissipated. Due to this smaller amount of cooling water are needed) (2) The extent to which sea or brackish water can substitute for fresh water (3) The rate of recirculation. Recirculation is a function of price of water availability. Young and Thompson (1973) in their study identified three factors that affect water use   in thermal electric energy generation. They can be listed as water pricing, change in generation, technology, price of electricity, price of substitutes used in electricity i.e. oil and gas, population and level of general economic activity. The other factors include waste and heat discharge to water and the changes in cooling technologies.

Gleick (1993) in his study reviewed the water requirement of electric energy. Taking as base of earlier studies, he estimated the consumptive water use in Electricity Generation Industry using different technologies. The system efficiency for conventional coal combustion (Once through Cooling Towers), natural gas combustion (Once Through Cooling Towers) and nuclear generation (CTs) stood at 35 percent, 36 percent and 40 percent. The estimates specifies that with the help of Once Through Cooling Technologies, the average consumptive use ranges from  1.2 m3/MWH  in case of conventional coal, for oil and natural gas consumption the average consumption use is less by 1.1 m3/MWH  , where as with cooling towers it was 2.6 m3/MWH. For nuclear power generation the average consumptive use of water with the aid of CTs was more that stood at 3.2 m3/MWH. There is a need for use of high efficient technology in cooling towers for water conservation. Electric Power Research Institute 2002, estimated the evaporation water loss from recirculating towers i.e., roughly 480 gal/MWH for a coal fired power plant. Mortenson, 2006 in his study have provided a technological breakthrough i.e. small scale tests of one technology (that uses cross-currents of ambient air for condensation) as a counteracting measure for these evaporation losses. By this technology the evaporation losses can be reduced to about 60-140 gallons/MWH (that can be applied even to hotter climates). In value terms, EPRI 2004 notified that the savings from reduction of evaporation losses will be $870,000.

There are very few studies of determinants of water use in hydel and thermal electric generation. The literature available relating to water use estimations is very few. Water use experts have to opt for estimation methods for many of the water withdrawals classes i.e. domestic, agriculture and industry because of the true fact that many legal, financial and political constraints limit for getting the hard data. For instance water withdrawals in domestic and live stock water use are usually estimated by multiplying population figures by coefficient. In case of agricultural sector, the irrigation water withdrawals are often estimated by multiplying the acreage by assumed water requirements of the crop rather than by measuring actual water pumped and applied.

Snavely (1986), explicitly details the water use data collection programs and maintaining regional data base of the Great Lakes St. Lawrence River Basin States.  The results are very much appealing indicating as how broad the range of estimation coefficient for water use can be within a geographic area with similar water availability. Mostly the estimated coefficients used for agriculture and domestic use vary by a factor of 10. The econometric studies relating to water use estimation in public supply use and thermo electric power use have the potential to explain temporal and geographic variability across USA. The aggregated water use estimates were provided by the National water Use Information Programme. These estimates primarily focus on measuring total water withdrawals (that includes annual extraction of fresh  surface water and ground water) for the period 1980-1985 to 1990-1995 in each of 48 states of USA for public supply water withdrawals , domestic, commercial, irrigation and live stock. The saline water withdrawals were estimated for industrial, mining and thermal electric categories. The public supply water withdrawals are estimated within geographical area i during year t using a set of explanatory variables that includes air temperature, precipitation, price of water, median household income and others.

Cohn et.al (1989) and Christensen et.al (2000) have used examples of such kind by using statistical techniques. The shorter time period used has the advantage of highlighting the recent trend of declining water use since the 1980 compilation. The mean withdrawal for the period (1980-1995) clearly indicates that it was 183.7 gallons per capita per day. This average water withdrawals would decrease by 7.8 gpcd, if the state GDP per capita increased by $1000. The inclusion of this state GDP captures the effects of relative volume of non residential uses (along with their ability to pay for water). The model also indicates that US was able to withdraw 17.2 gpcd, because of its surface water rights in comparison with riparian law states. The inclusion of temperature and precipitation variables also clearly shows the effect of weather on water withdrawals and can be used in normalizing water use for weather. The model indicates that average per capita demand for water in the state decreases by 2.1gallons per day per one inch increase in precipitation and vice versa i.e. water demand increases during summer months. i.e. average temperature.

Billings and Jones, 1996 employed indirect estimation of water use in urban and municipal planning using coefficient based methods. It calculates water use for commercial, residential and industrial categories. They assume constant water use rates and ignores trends i.e. changes in water use due conservation, technological change or economic forces. Mullusky et.al (1995), Wood Well and Desjardin (1995) for Washington D.C. metropolitan area have employed this water use coefficients for three categories of water users i.e. simple family homes, multiple family homes and employment water use.   Another approach of estimating National Water Use in USA includes Stratified random sampling followed by Census of Agriculture. They employed various methods of collecting data such as telephone, mail survey instruments to develop detailed country level estimates of national agricultural activities. According to Hutson et.al 2004 the thermo electric power water use refers to water that is removed from the ground or diverted from surface water sources (that includes fresh water and saline water) for use in the process of generating electricity with steam driven turbine generators. In this paper the term water withdrawals is used more often precisely. The term designates the amount of water that is extracted from natural water sources. Again it is essential to demarcate between water withdrawals and discharge as consumptive use. Water consumption is the quantity of water with drawn that is evaporated, transpired, incorporated in to crops, consumed by human or live stock.

At the end it can be said that different authors have notified different methods for estimation of water use for various uses of the economy. This paper employs multivariate models of water use for estimation of significant determinants of thermal and hydel water withdrawals.

Objectives of the paper

The objective is to determine if multiple regression models of unit hydel and

thermo electric water use have the potential

To identify significant determinants of total hydel and thermo electric water withdrawals across selected region wise power plants in AP using aggregated category wise water use estimates.

To estimate the future water withdrawals for hydel and thermal electricity generation plants expressed as cubic meters per second. (Cumecs) and cubic meters using the growth rate phenomenon.

The types of data used for estimation are monthly water withdrawals data (For surface fresh water resources)

Region level models for hydro and thermo electric water withdrawals

The potential dependent and independent variables for water withdrawals are identified for estimation purpose. Regional level data for thermal and hydel water withdrawals are more accurate data. The underlying reason being water withdrawals are usually metered.

Dependent Variable: Total Hydel Water Withdrawals

     Total Thermal Water Withdrawals

Independent Variables of Hydel Power Plant:

(a) Reservoir levels, (b) Inflows, (c) Storage capacity, (d) Evaporation losses, (e) Tail water level and (f) Gross Head

Independent Variables of Thermal Power Plant:

(a) Demineralised water, (b) Boiler Feedback, (c) Condenser Cooling (d) Ash disposal, (e) Others include colony domestic, drinking, sanitation, fire fighting, back wash filter, (f) Installed generation capacity, (g) Actual electric energy production (h) Total no. of cooling towers, (i) Water temperatures in summer, rainy and winter.

Multiple Regression analyses were performed using the data related to category wise water use in power plant, generating facility and weather conditions from month wise 1995-96 to 2008-09 data in respective thermal and hydel power plants. The effect of variables such as quantities of water used exclusively for the production of electricity i.e. Boiler feed, Demineralised water, Condenser cooling, Ash Disposal, colony domestic (Drinking, Sanitation, Fire Fighting, Back wash filter ), installed capacity generation, number of cooling towers, cooling temperature and electric energy generation on total water withdrawals of thermal power plants are explicitly analyzed. In addition to this, the effect of variables such as reservoir elevation, storage capacity, tail water level, evaporation losses, inflows, gross head, actual generation on total hydel withdrawals have also been looked in to. This paper explores the structure of power plant level aggregated water use data based on corresponding and routinely collected economic and climatic data. The purpose of this enquiry is to determine if multiple regression models have the potential to explain the temporal and climatic variability across various thermal and hydel power plants in Andhra Pradesh using aggregated water use estimates and most importantly to identify significant determinants of total water withdrawals of thermal and hydel power plants. The statistical models examined here are derived using data estimates of total water withdrawals for hydel and thermo electric power use.

Specification of Mathematical Model

WHEim = a +∑ bj Xj

                    j

Where WHEim  = Fresh water withdrawals for Hydel Electric Energy within region wise i during particular months m in a year.

     Xj is a set of explanatory variables. (Mentioned above)

WTEim = a +∑ bj Xj

                    j

WTEim = Fresh water withdrawals for Thermal Electric Energy within region wise i during particular months m in a year.

      Xj is a set of explanatory variables. (Mentioned above Coefficients a and bj can be estimated using multiple regression model.

Specification of the Econometric Model:

In Linear forms, these equations can be estimated as follows

Yt = B1+B2X2+B3X3+B4X4+B5X5+B6X6+B7X7+ µ

Model: 1 WTEim = B1+B2 CT+B3DB+B4CD+B5AS+B6WT+B7AG+µ ……… (1)

Where, WTEim = Water withdrawals for thermal electric energy in region i for particular months m.

CT = Condenser cooling (with Cooling Towers), DB = Demineralized water and Boiler Feed

CD = Colony Domestic, AS = Ash Slurries, WT= Water Temperature, AG= Actual generation

µ= random error term

Condenser Cooling: Water required for cooling hot turbines and condensers

Demineralized Water:  Water that is, free of minerals and salts. Water runs through active resin beds to remove metallic ions and filtered through sub micron filter to remove suspended impurities.

Colony Domestic: Water that is used for the purpose of colony maintenance, drinking purpose and plantation.

Ash Slurries: As coal burns, it produces carbon –di-oxide, sulphur –di-oxide and nitrogen oxides. These gases together with lighter ash are called fly ash. The electro static precipitators remove all the fly ash and are mixed with water to make in to ash slurries.

Water temperature: Recording the temperature of water during summer, rainy and winter seasons.

Actual Generation: The generation of electricity that is actually generated apart from installed generation.

Model 2: WHEim = B1+B2 RE+B3SC+B4 TW+B5GH+B6WT+B7AG+µ ……. (2)

Where WHEim= Water withdrawals for hydel electric energy in region i for particular months m.

RE = Reservoir Elevation, SC= Storage Capacity ,TW= Tail water level, El= Evaporation losses, GH= Gross Head, WT= Water Temperature, AG= Actual Generation,µ= random error term

Reservoir Elevation: This is defined as the foot of the dam. i.e. the level from which the reservoir storage level and the height of the dam are measured.

Storage Capacity: This corresponds to the flood level usually designated as the upper limit of the normal operational range, above which the spill gates come in to operation

Tail water Level:  Water immediately below the power plant. Tail water elevation refers to the level that water which can rise as discharges increase. It is measured in the feet above sea level.  1 foot = 0.305 meters.

Inflows: The inflow may be monsoonal rains or lakes, rivers. The average volume of incoming water, in unit period of time.

Evaporation Losses: Conversion of liquid to vapor state by latent heat. Water gets saturated in the form of vapor due to rise in water temperature.

Discharge: Volume of water released from power dam at a given time measured as cubic feet per second.

Gross Head: A dam’s maximum allowed vertical distance between upper stream’s surface water fore bay elevation and the down stream’s surface water (tail water) elevation at the tail race for reaction wheel dams.

Actual Generation: The amount of electricity actually generated apart from installed generation.

Selected power plants in three regions of Andhra Pradesh

Power Plant by

Fuel Type

Rayalaseema Region Telangana Region Coastal Region
Thermal Rayalaseema Thermal Power Plant .Kothagudaem Thermal Power Station  O & M

 

.Kothagudaem Thermal Power Station Stage V

 Narla Tata Rao Thermal Power Plant
Hydel Nagarjuna Sagar Main Power House

 

Nagarjuna Sagar Left Canal Power House

 

Nagarjuna Sagar Right Canal Power House

Srisailam Left canal power house

 

Srisailam right Canal Power House

 

 

The collection of data includes a monthly time series data analysis during the period (1995-96 to 2008-09). Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary thermal and hydel electric water use within the system. The multi- variate regression  procedures were used to identify the significant determinants  of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing hydel and thermal electric power plants.

3.0 Approach and Methodology

 The study includes three main components. (a) A series of site visits and interviews with power plant personnel. (b) Field surveys of selected hydel and thermal power plants of Andhra Pradesh (c) The multiple regression analysis of power generation data and other associated information.

Summary of site visits: Site visits for selected five hydel namely Nagarjuna Sagar Main Power House, Nagarjuna Sagar Left Canal Power House, Nagarjuna Sagar Right Canal Power House, Srisailam Left canal power house and Srisailam right Canal Power House and four thermal namely Rayalaseema Thermal Power Plant, Kothagudaem Thermal Power Station O & M, Kothagudaem Thermal Power Station Stage V and Narla Tata Rao Thermal Power Plant have been made to assess the overall performance scenario of power plants and also to examine the extent of water irregularities .Appraisal of Power Plant Survey:  The research estimates of hydel and thermal Electric Energy water withdrawals are based upon the authenticated sources of data provided by respective hydel and thermal power plants of Andhra Pradesh Generation Corporation of India Limited. In order to transparently clarify the way that power generation officials responded to this kind of field survey in practice and to solicit information from them on factors responsible for water use at power generation facilities, site visits have been taken up.  At various Power plants several personal interviews with power plant officers helped to identify the types of onsite water uses, the measurement of these uses and provision of information on various types of cooling systems and water use procedures employed by hydel and thermal electric energy generation facilities.

The purpose of conducting a series of personal interviews with power plant officials can be listed as follows:

(a)    Scrutinize and examine the power generation water use and water withdrawals from intake (surface water) to discharge in various types of facilities.

(b)   Observing the fact that all the water with drawals (hydel and thermal) are metered.

(c)    Detailed analysis about important onsite uses of water and its significant determinants

(d)      To obtain feedback on the cooling system level of water use in power stations.

Multiple Regression Models of Water Use

The principal sources of data used in the multi variate analyses of thermal and hydel power plants are most accurate and provides a fairly comprehensive review of plant characteristics, power generation and water withdrawal details. The data in electronic format and in official records was available for the years 1996-97 to 2008-09. The weather data i.e. especially related to water temperatures during summer, rainy and winter was collected in order to examine the influence of it on total thermal and hydel water withdrawals.

At the end it can be concluded that the site visits and field surveys helped to identify important concerns about water measurement and use at thermal and hydel electric power plants. Added to this, these factors have received attention in the development of models to describe hydro and thermal electric water use. All the above mentioned information proved very much useful in the design of data analysis that was used to develop water use bench marks.

4.0 RESULTS AND DISCUSSION: ESTIMATION AND INTERPRETATION OF MODEL SPECIFICATIONS

Hydel based Electric Energy Power Plants

Model Specification I Nagarjuna Sagar Main Power House

 (Appendix table: A1)

In model 1 the estimated regression equation for total hydel water withdrawals is in the linear form as follows:

*              * *                          *

WHE = -146.238-0.080RE-0.258SC+0.350TW+0.133GH+50.67AG

                                               (-3.96)         (3.144)                      (119.87)

N= 154, R2 =0.99, f= 5543.05

  • The estimated equation indicates that the total hydel water withdrawals are inelastic with respect to storage capacity. This kind of negative relationship indicates that the hydel water withdrawals are somewhat in responsive to changes in the storage capacity. The coefficients are statistically significant at 1 % level.
  • The total hydel water withdrawals are elastic with tail water level and actual generation that hold a positive relationship. The coefficients are statistically significant at 5 % and 1 % level.
  • The t-ratio of regression coefficients is highly significant for three independent variables namely SC, TW and AG. As the t ratio value is greater than 2.58 indicates that the relation between dependent variable and independent variables observed in the sample holds good.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as reservoir elevation and gross feet, as the t- value is very small.
  • The R2 (coefficient of determination) is 0.99. It means that the independent variables tail water level, actual generation and storage capacity can explain 99 percent of variation in the dependent variable (WD) and remaining 1 percent variation is unexplained by the model. As R2 is very high, the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher and more significant at 1% level. This clearly indicates that the regressors are significantly associated with dependent variable.

Model SpecificationII Nagarjuna Sagar Left Canal Power House

         (Appendix Table: A2)

*                                 *            *                    *

WHE = 1660.770-3.516RE-21.705SC+9.653TW+491.286AG+0.130EL

            (3.314)                       (4.16)        (3.84)         (15.67)

 N= 166, R2= 0.78, f = 116.22

  • The estimated regression coefficients indicate that the best independent that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. As t ratio value is greater than 2.58, it indicates that the relation between Hydel Water withdrawal and independent (SC) and (AG) observed in the sample holds good.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 percent variation in the dependent variable and the remaining 22 % variation is unexplained by the model. The estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with dependent variable.

Model Specification III Nagarjuna Sagar Right Canal Power House 

         (Appendix Table: A3)

             *                                      *                                                     *

WHE = 6133.252+0.628 RL-58.029 SC+0.414EL+37.493TW+486.057 AG

          (7.314)                        (6.063)                                          (16.232)

N= 166, R2= 0.78, f value = 116.22

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The relation between water withdrawals and Storage capacity and actual generation in the sample holds good as the t-value is greater than 2.58.
  • The t-ratio of regression coefficients is not at all significant for other independent variables such as reservoir level, storage capacity and evaporation losses.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 % variation in the dependent variable and remaining 22 % variation is unexplained by the model. The estimated equation is considered as the equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors are significantly associated with dependent variable (WD)

Model Specification IV Srisailam Left Bank Power House

                  (Appendix Table: A4)

                                                                *                          *

WHE = -2243.501-0.766RE+1.195SC+57.47AG+0.592EL+4.24TW+0.000IF

                              (-2.27)                         (18.81)                     (2.69)

N= 58   , R2= 0.96, f value = 221.872

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are actual generation and tail water level with significant levels at 1 % and 10 % for independent variables.
  • The t-ratio of regression coefficients is highly significant with three independent variables namely reservoir elevation, actual generation and tail water level. The t-ratio value is greater than 1.96 value for reservoir level and greater than 2.58 value for actual generation and tail water level. This indicates that the relation between WD and independent variables AG and reservoir elevation observed in the sample holds good.
  • The t- ratio of regression coefficients is not at all significant for other independent variables such as evaporation losses and inflows.
  • The R2 is 0.96. It means that the independent variables reservoir level, actual generation and tail water level can explain 96 % of variation in the dependent variable and remaining 4% is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (221.872) and more significant at 1 % level. This indicates that the regressors AG and TW are significantly associated with dependent variable. (WD)

 

Model Specification V Srisailam Right Bank Power House

                   (Appendix Table: A5)

                 *                        *        *

Y = -7630.380-1.78RE+0SC+56AG+0.051EL+0.627TW+0.289GH

              (-4.199)             (-4.3)  (122.65)

  N= 138    , R2    = 0.99 and f value = 4.59

  • The estimated regression coefficients indicate that the best independent variables that have a significant effect are storage capacity and actual generation with significant levels at 1 % level each of independent variable.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The t- ratio value is greater than 2.58 for SC and AG that indicates that the relation between WD and independent variables SC and AG holds good.
  • The t- ratios of regression coefficients is not at all significant for other independent variables such as evaporation losses, tail water level and gross head.
  • The R2 is 0.99. It means that the independent variables such as storage capacity and actual generation can explain 99 % variation in the dependent variable and remaining 1 % is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall relationship was statistically significant as f value is 4.59 and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with WD.

Thermal based Electric Energy Power Plants

Model Specification VI Kothagudaem Thermal Power Plant O &M

      (Appendix Table: A6)

                                                     *                                                     *   

Y= -787978.047 + 1.021CC-2.130DB-12.190CD+146.699 OT +1.152 AD+4616.497 CT-817.112AG

                              (3.259)                                                        (3.841)

N= 84, R2 = 0.55, f value = 13.710

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers (Natural Draft cooling system) and ash disposal with significant levels of 5 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Natural Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with natural draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as DM and Boiler feedback, colony domestic, others (Drinking, Sanitation, Fire fighting, Back Wash Filter), cooling temperature and actual  electric energy generation.
  • The R2 is 0.55. It means that the independent variables such as condenser cooling and ash disposal can explain 55 % of variation in the dependent variable and remaining 45 % variation is unexplained by the model. The estimated equation is considered as good fit.
  • The overall model is statistically significant as f value is higher (13.710) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Natural Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

Model Specification VII Kothagudaem Thermal Power Station Stage V

          (Appendix Table: A7)

                                   *                *

Y= 98233.879+0.873 CC+1.186AD+0.111 DB-1688.373CT+32.019 AG

                               (20.91)       (15.247)

              N= 83, R2= 0.97, f value = 706.164

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of WD per million tonnes are Condenser cooling and ash disposal with significant levels at 1% level each.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely Condenser cooling with natural draft CT’s and Actual Generation. The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s. Next comes ash disposal as second good determinant.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, cooling temperature and Energy Generation.
  • The R2 is 0.97. It means that independent variables such as CC and AD can explain 97 % of variation in the dependent variable (Water withdrawal) and remaining 3 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (706.164) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s and Ash Disposal are significantly associated with Water withdrawal’s (Dependent Variable)

Model Specification VIII Rayalaseema Thermal Power Plant

          (Appendix Table: A8)

                           *

Y = 10334.674+0.745 CC+8.725 BF+0.847 AS-4.143 AG-145.408 CT

     (2.677)                (3.007)

N= 35, R2 = 0.87 and f value = 33.145

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of Water Withdrawal Condenser cooling with significant levels at 5%.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for one independent variables namely Condenser cooling with natural draft CT’s .The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, Ash Disposal cooling temperature and Energy Generation.
  • The R2 is 0.87. It means that independent variables such as CC can explain 87 % of variation in the dependent variable (WD) and remaining 13 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The over all model is statistically significant as f value is higher (33.145) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s are significantly associated with WD’s (Dependent Variable)

Model Specification IX Narla Tata Rao Thermal Power Plant

                     (Appendix Table: A9)

                          *                               *   

Y = 139993.709 + 1.002CC -0.863CD + 1.031 AS- 373.483 CT- 56.843 AG

                                    (1277.966)                 (19.88)

N=      R2 = 1.00, f value = 907849.564

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers ( Induced l Draft cooling system) and ash disposal with significant levels of 1 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Induced Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with Induced draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as, colony domestic, cooling temperature and actual electric energy generation.
  • The R2 is 1.00. It means that the independent variables such as condenser cooling and ash disposal can explain 100 % of variation in the dependent variable. This shows that we have accounted for almost all the variability with the variables specified in the model. The estimated equation is considered as very good fit.
  • The overall model is statistically significant as f value is higher (907849.564) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Induced Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

The pertinent conclusion of this study is there may be significant potential for water conservation after having identified the significant determinants of total thermal water withdrawals i.e. condenser cooling and ash disposal. The choice of explanatory variable for eg: Induced draft and natural draft technological innovation was able to address the significant changes of water withdrawals.

5.0  CONCLUSION AND RECOMMENDATION

The thermal and hydel power plants sustenance is very much under stake due to major reason of fresh water shortages in power generation. The most sophisticated technology followed in advanced countries namely Concentrated solar thermal power integrated with combined system of conventional steam plant, Fresnel Solar Collector and  Solar Flower Tower can be used as a replica even in developing countries like India though not cost effective in order to counteract the water shortage problem

REFERENCES

Benedy Kt Dziegielewski, Thomas Bik (August 2006), “ Water Use Bench Marks for Thermo Electric Power Generation” Project report, Southern Illinois University, United States

Geological Survey, 2004, USGS National Competitive Grants Program.

Gbadebo Oladosu, Stan Hadley, Vogt D.P. and Wilbanks J.J. (September, 2006), “Electricity

Generation and Water Related Constraints: An Empirical Analysis of Four South Eastern

States”, Oak Ridge National Laboratory, Oak Ridge Tennessee.

Sitanon Jesdapipat and Siriporon Kiratikarnkul, “ Surrogate pricing of water: The Case of micro Hydro –Electricity Co-operatives in Northern Thailand”.

 Xiaoying Yang & Benedy Kt Dziegielewski (February,2007), “ Water Use by Thermo Electric power plants in the United states” Journal of the American Water Resources Association, Vol 43, No.1.

“Estimating Water Use in United States: A new Paradigm for National Use Water Use Information Programme”(2002),

http://books.nap.edu/openbook.php?record_id=10484&page=95

 

Data Sources

Annual Report on the Working of SEBs and Electricity Departments, Planning Commission, Various Issues

Administrative Reports of Andhra Pradesh Generation Corporation of India Limited (APGENCO),Various Issues. Field Level data of selected thermal and hydel power stations authenticated  by APGENCO.

APPENDIX TABLES

Table: A1: Nagarjuna Sagar Main Power House

Variables Entered/Removed  
Model Variables Entered Variables Removed Method  
1 acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage_capacitya . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_discharge_cums  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .997a .995 .995 512.92868  
a. Predictors: (Constant), acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage capacity  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 7291771208.745 5 1458354241.749 5543.053 .000a
Residual 38675087.446 147 263095.833    
Total 7330446296.191 152      
a. Predictors: (Constant), acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage capacity
b. Dependent Variable: water_discharge_cums      
 

Coefficientsa

 
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) -146.238 1555.816   -.094 .925  
Reser_elevation -.080 .093 -.012 -.865 .389  
Storage capacity -.258 .065 -.091 -3.966 .000  
tail_water_level .350 .111 .031 3.144 .002  
Gross_feet .133 .094 .026 1.419 .158  
acutal_generation 50.669 .423 1.041 119.869 .000  
a. Dependent Variable: water_discharge_cums        

Table: A 2 Nagarjuna Sagar Left Canal Power House

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 evaporation, energe_bus, twl_ft, storage capacity, reservior_levela . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_drawals  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .864a .747 .739 2350.84646  
a. Predictors: (Constant), evaporation, energe_bus, twl_ft, storage capacity, reservior_level  
 

ANOVAb

Model Sum of Squares df Mean Square F Sig.
1 Regression 2626964399.664 5 525392879.933 95.068 .000a
Residual 889763133.646 161 5526479.091    
Total 3516727533.310 166      
a. Predictors: (Constant), evaporation, energy bus, twl_ft, storage capacity, reservior_level  
b. Dependent Variable: water_drawals        
Coefficientsa  
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) 1660.770 501.102   3.314 .001  
reservior_level -3.516 3.411 -.157 -1.031 .304  
storage capacity -21.705 5.219 -.538 -4.159 .000  
twl_ft 9.653 2.510 .394 3.846 .000  
energy bus 491.286 30.765 .987 15.969 .000  
evaporation .130 .508 .015 .255 .799  
a. Dependent Variable: water_drawals        

Table: A3 Nagarjuna Sagar Right Canal Power House

Model Variables Entered      
1 generation bus, reservior_level, evaporation, storage capacity, tailwaterlevela      
b. Dependent Variable: water_drawals

Model Summary

 
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .885a .784 .777 3767.05581  
a. Predictors: (Constant), generation bus, reservior_level, evaporation, storage capacity, tailwaterlevel  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 8246365913.182 5 1649273182.636 116.222 .000a
Residual 2270513515.133 160 14190709.470    
Total 10516879428.315 165      
a. Predictors: (Constant), generation bus, reservior_level, evaporation, storage capacity, tailwaterlevel  
b. Dependent Variable: water_drawals        
Coefficientsa  
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) 6133.252 838.604   7.314 .000  
reservior_level .628 7.571 .016 .083 .934  
storage capacity -58.029 9.570 -.832 -6.063 .000  
Evaporation .414 .810 .027 .511 .610  
Tailwaterlevel 37.493 21.598 .263 1.736 .084  
generation bus 486.057 29.945 1.045 16.232 .000  
a. Dependent Variable: water_drawals        

Table:  A4 Srisailam Left Canal Power House

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 inflow, Reservoir, evaporat, Actual generation, Tail water, storage_capacitya . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_withdra  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .981a .963 .959 1454.18057  
a. Predictors: (Constant), inflow, Reservoir, evaporat, Actual generation, Tail water, storage capacity  
                                                                                   ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 2815082375.894 6 4.692E8 221.872 .000a  
Residual 107846697.597 51 2114641.129      
Total 2922929073.491 57        
a. Predictors: (Constant), inflow, Reservoir, evaporat, Actual generation, Tail water, storage capacity    
b. Dependent Variable: water_withdra          
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -2243.501 2527.275   -.888 .379
Reservoir -.766 .337 -.239 -2.272 .027
storage capacity 1.195E-6 .000 .000 .004 .997
Actual generation 57.476 3.055 .953 18.814 .000
evaporat .592 .939 .081 .631 .531
Tail water 4.237 1.572 .248 2.695 .010
inflow .000 .002 -.017 -.339 .736
a. Dependent Variable: water_withdra        

Table: A5 Srisailam Right Canal Power House

Model Variables Entered Variables Removed Method  
1 Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir . Enter  
a. All requested variables entered.    
b. Dependent Variable: water withdrawals  
 

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate
1 .998a .995 .995 631.39218
a. Predictors: (Constant), Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir

ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 1.099E10 6 1.832E9 4.596E3 .000a  
Residual 5.222E7 131 398656.090      
Total 1.105E10 137        
a. Predictors: (Constant), Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir  
b. Dependent Variable: water withdrawals        
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -7630.380 1817.341   -4.199 .000
Reservoir -.178 .322 -.027 -.553 .581
storage .000 .000 -.068 -4.288 .000
actual generation 56.314 .459 1.022 122.651 .000
Evaporation .051 .139 .005 .365 .716
Tailwaterlevel .627 .334 .059 1.874 .063
Gross head .289 .320 .036 .904 .368
a. Dependent Variable: water withdrawals      

Table: A6 Kothagudaem Thermal Power Plant O &M

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler) a . Enter  
a. All requested variables entered.    
b. Dependent Variable: Total water consumption  
 

Model Summary

 
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .747a .558 .517 289298.132  
a. Predictors: (Constant), energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler)  
ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 8.032E12 7 1.147E12 13.710 .000a  
Residual 6.361E12 76 8.369E10      
Total 1.439E13 83        
a.     Predictors: (Constant), energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler)  
       
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -787978.047 1.334E6   -.591 .557
Condenser Cooling 1.021 .313 .551 3.259 .002
DM Water & Boiler Feed back -2.130 5.717 -.038 -.373 .710
Colony domestic -12.190 15.642 -.250 -.779 .438
(Drin, Sani, Firefigh, Backwarhfiler) 146.699 201.477 .467 .728 .469
Ash Disposal 1.152 .300 .409 3.841 .000
cooling temp 4616.497 10000.955 .039 .462 .646
energy generation -817.112 1096.318 -.295 -.745 .458
a. Dependent Variable: Total water consumption      

Table:  A7 Kothagudaem Thermal Power Plant Stage V

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)a . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: TOTAL CONS.  (MT)  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 .989a .979 .977 64726.513  
  a. Predictors: (Constant), Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)  
 

ANOVAb

Model Sum of Squares df Mean Square F Sig.
1 Regression 14792454121098.932 5 2958490824219.786 706.164 .000a
Residual 322593153570.889 77 4189521474.947    
Total 15115047274669.820 82      
a. Predictors: (Constant), Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)
b. Dependent Variable: TOTAL CONS.  (MT)      
  Coefficientsa  
  Model Unstandardized Coefficients Standardized Coefficients t Sig.  
  B Std. Error Beta  
  1 (Constant) 98233.879 76676.230   1.281 .204  
  COOLING TOWER MAKEUP        (MT) .873 .042 .577 20.912 .000  
  ASH DIS-POSAL (MT) 1.186 .078 .484 15.247 .000  
  Boiled Feed and DM plant Regeneration .111 .978 .003 .114 .910  
  Cooling Temperature -1688.373 2158.260 -.014 -.782 .436  
  Energy Generation 32.019 115.619 .005 .277 .783  
  a. Dependent Variable: TOTAL CONS.  (MT)          

Table: A 8 Rayalaseema Thermal Power Plant

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCWa . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: Water consumption  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 .934a .873 .846 1324.085  
  a. Predictors: (Constant), Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCW  
  ANOVAb  
  Model Sum of Squares df Mean Square F Sig.  
  1 Regression 3.487E8 6 5.811E7 33.145 .000a  
  Residual 5.084E7 29 1753200.788      
  Total 3.995E8 35        
  a. Predictors: (Constant), Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCW  
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 10334.674 3861.078   2.677 .012
Condenser cooling, BCW .745 .248 .432 3.007 .005
Boiler feed 8.725 4.628 .244 1.885 .069
Ash slurry .847 .501 .230 1.692 .101
Power Generation -.595 .388 -.138 -1.532 .136
Actual Generation -4.143 5.478 -.077 -.756 .456
Cooling Temp -145.408 94.141 -.109 -1.545 .133
a. Dependent Variable: Water consumption        

Table : A 9 Narla Tata Rao Thermal Power Plant

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)a . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: Totalwaterconsumption  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 1.000a 1.000 1.000 50290.302  
  a. Predictors: (Constant), Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 11480277367590772.000 5 2296055473518154.000 907849.564 .000a
Residual 42994946072.977 17 2529114474.881    
Total 11480320362536844.000 22      
a. Predictors: (Constant), Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)
b. Dependent Variable: Totalwaterconsumption      
  Coefficientsa  
  Model Unstandardized Coefficients Standardized Coefficients t Sig.  
  B Std. Error Beta  
  1 (Constant) 139993.709 137540.088   1.018 .323  
  Condenser cooling & BCW (KL) 1.002 .001 .987 1277.966 .000  
  Colony Domestic (KL) -.863 .584 -.001 -1.476 .158  
  Ash slurry water (KL) 1.031 .052 .018 19.879 .000  
  Cooling Temperature -373.483 3763.081 .000 -.099 .922  
  Energy Generation -56.843 138.469 .000 -.411 .687  
  a. Dependent Variable: Totalwaterconsumption