UNU/IAS Working Paper No. 98

An environmentally based approach for industrial location decisions and its application in Punjab Province, Pakistan

Awais Latif Piracha

1 ABSTRACT

Analyses of decisions pertaining to the spatial distribution of industries have long been the domain of economists or economic geographers. “Location Theory” is considered to be a part of both economic geography and microeconomics. Location decisions of firms, including those of secondary industries, have mostly been looked at in terms of cost minimization, profit and market maximization, cost reduction and comparative advantage. A more recent concern has been the link between innovations and industrial locations. At times welfare concerns have entered into the study of industrial locations but again these issues have been taken from an economic viewpoint with income generation and employment creation being the main focus. There has been a noticeable absence of interest in environmental issues in planning methods pertaining to normative influences on industrial locations. This lack of emphasis, however, begs the question of how would public regulators decide on the optimal location of industries so as to safeguard natural environments as well as achieve the social equity goals.

In present climate of market-oriented reforms and privatization, it is unlikely, that in foreseeable future, governments will directly invest in the industrial sector. In most countries the future public sector’s role will be limited to safeguarding the natural environment and welfare of its people. Thus, economics based theories and concepts of industrial location will not be of much help for government officials. In this context, it makes sense for governments to look for new approaches to strategic industrial location policies that will help to both preserve the environment and achieve social goals. In this paper the author has attempted to formulate one such approach.

The approach formulated contains certain steps, guided by flexible principles to accommodate regional environmental variations. As environmental concerns vary from place to place, it was therefore imperative to provide an in-depth case study to highlight the salient features of the approach. Punjab Province in Pakistan was selected as it is likely to see rapid industrial development in the future and its environment is sensitive to perturbations.

The paper has two parts. The first part is subdivided into two sub-sections; the first discusses the existing approaches for industrial location and the second presents the new environmentally based approach. The second part presents the results from the application of the new approach to Punjab Province in Pakistan. The details of the research necessitate the subdivision of this section into four sub-sections, pertaining to different modules of analysis. The first three sub-sections present different scientific inputs needed to carry out the evaluation and the fourth sub-section discusses policy implications for industrial locations based upon this approach.

2 PART - A: ENVIRONMENT-BASED APPROACH

I - Existing approaches of industrial locations

The study of industrial location is considered a part of “location theory,” which in itself is a part of the field of Economic Geography or Spatial Economics. Location theory addresses the questions of what economic activities are located where and why? It uses economic analyses to study the distribution of economic activities. It focus is the microeconomics of space, with the goal of allocation and equilibrium achieved through the price mechanism. The German economist Beckman (1968) has provided a comprehensive overview of location theory.

The first major work in location theory was that of Von Thünen (1826). He explained the geographic distribution of activities in and around towns. He suggested a model that predicts agriculture development in concentric zones around cities, based upon the weight and shelf life of the products. In his model, heavy products, in proportion to value, and perishables are produced close to the town; lighter and durable goods, produced further away on the periphery. Land rent and transport costs are related such that, as transport costs to the city increased with distance, the returns to the land would diminish until, at a certain distance, land rent would become zero. Moreover, methods of cultivation would vary - with land cultivated more intensively near the city, where it was more valuable. Von Thünen’s model has provided fertile ground for geographers. Researchers have ever since been building on his work. A major extension of Von Thünen’s model was done by William Alonso (1964) in his work titled “Location and Land Use: Towards a General Theory of Land Rent”.

The next major contribution to location theory was related to predicting industrial locations. In 1909, the German location economist Alfred Weber formulated a theory of industrial location in his book entitled Über den Standort der Industrien (Theory of the Location of Industries, 1929). Weber's theory, called the location triangle, sought the optimum location for the production of a good based on the fixed locations of the market and two raw material sources, which geographically form a triangle. He sought to determine the least-cost production location within the triangle by calculating the total costs of transporting raw material from both sites to the production site and the product from the production site to the market. A number of variations to Weber’s theory have been attempted all striving at optimizing location for a production activity where profit is maximized.

Advancement to the theory of industrial location was achieved through several interdependent activities. The first and most important was the work of Walter Christaller (1933) in terms of his theory of Central Places. According to him the primary purpose of a town is to provide goods and services to surrounding market areas. Such towns therefore must be centrally located in terms of maximizing their market share and may be called central 3 places. Further, settlements could be characterized, in ascending order, by the amount and type of goods and services they provide. There are three such levels of towns in Christaller’s hierarchy that follow a hexagonal geometry. Hence, Christaller’s theory consists of two basic concepts: 1) threshold - the minimum market needed to bring a firm or city selling goods and services into existence and maintain business; and, 2) range - the average maximum distance people will travel to purchase goods and services. The German economist August Lösch (1940) expanded Christaller’s work in his book titled The Spatial Organization of the Economy.

Most work on the subject since that time until approximately 1960 extended the notions related to the above mentioned classical location theories. In the case of industries, location theory remained concerned primarily with profit maximization, through transport cost reduction and market share maximization. Thereafter, it was increasingly recognized that the location of individual factories could only be understood through the individual firm’s relation to wider corporate systems to which they belong. This understanding went hand in hand with the increase in importance of multinational corporate strategies, discussions or globalization and foreign direct investment. A recent contribution in this direction is from Paul Krugman (Krugman, 1993, 1997: Fujita and Krugman, 1999). Others, such as Chapman (1991) and Harrington (1995) have elaborated on two new themes, the role of multinationals and the role of governments. Benjamin Higgins’ (1995) work that reviews regional development policy includes interesting references to the role of location theory.

From the literature on industrial location six different approaches to industrial location decision-making can be identified.

1. Normative industrial location approach

The normative approach concerns minimizing cost or maximizing profits. This approach is based upon Alfred Weber’s work (1909) on least-cost industrial locations. Weber considered demand to be constant. Other researchers varied demand and emphasized the location strategy of competitors, but they kept cost constant. August Lösch (1944) combined the two in “The Economics of Location”. He argued that an entrepreneur should select a location at which profits are maximized. Lösch realized that, in reality neither demand nor costs are spatial constants as assumed by the least-cost and locational interdependence schools respectively. However, he maintained that such a model was impossible to construct. Despite his pessimism, however, other researchers have since been trying to build such model.

The normative approach is based upon the existence of an “Economic Man” (Chapman, 1991). An “Economic Man” is a rational decision-maker with the single-minded pursuit of

4 profit maximization. He also possesses complete knowledge of all relevant economic information, including the ability to predict the actions of competitors and future events. As such a person does not exist, it is necessary to see how decisions are actually made. This deficiency of the normative approach along with rapid economic growth of the 1960s and the associated construction of new manufacturing establishments in the developed countries provoked interest in so-called behavioral approach.

2. Behavioral approach

Researchers in the field of industrial location realized that decision-makers do not possess either the level of knowledge or the powers of reason ascribed to “Economic Man.” The empirical studies conducted in this area confirmed the importance of 'personal considerations' in location decisions.

Behavioral approach derived its inspiration from the work of Simon (1957a, 1957b) on administrative behavior. Simon observed that whereas economic man is an optimizer, his real-world equivalent is a “satisficer.” Decision-makers adopt courses of action, which are perceived to be satisfactory. Numerous empirical surveys in the 60s and 70s highlighted related and interesting points such as; new plant location decisions are taken with great reluctance and the kind of rational behavior upon which normative theory is based is more frequently associated with large organizations than with small businesses.

3. Geography of enterprise

The geography of enterprise approach is concerned with the spatial ramifications of decision- making within large firms. Starting from the 1960s, it has been increasingly realized that the geography of modern society is determined by strategies of large organizations. The principal objective of these business organizations is the minimization of uncertainties. Central to the geography of enterprise approach is 'that firms are able to manipulate or modify their environmental context' (Hayter and Watts, 1983).

The geography of enterprise approach produced a significant shift in emphasis away from the influence of economic considerations spatially dispersed (i.e. the location of raw materials or markets), exemplified by normative location theory, towards an interest in the impact of industrial enterprise activities upon the environment.

4. Structural approach

Whereas the principal concern during the 1960s was with the management and redirection of economic growth, the consequences of industrial stagnation and decline have been the

5 major preoccupation in most developed countries during the 1980s. Industrial stagnation in the 1980s led to changes in the distribution of industry.

To analyze these changes researchers reverted to the application of Marxist analysis. According to their analysis industrial stagnation was one aspect of a process of restructuring which represents a stage in the development of capitalism. The chief merit of the Marxian approach is its breadth, which permits industrial location to be analyzed as an integral part of the totality of economic, social and political processes (Smith, 1981).

In the normative, behavioral and geography of enterprise approaches the question of location is viewed from a managerial perspective. The structural approach, however, deals with the social implications of shifts in industrial activity, employment and inequalities. The structural approach is labor oriented. This approach has been used at the regional and community-scale and attempts to integrate events at these levels within a much broader context.

5. Industrial location under socialism

In theory, a socialist economy offers the best opportunity to account for welfare criteria at the earliest stage in planning new investment. The objectives of Soviet planners in locating industries were economic growth and social equality (Ye Probst, 1974). These planners emphasized economic criteria of minimization of transport costs, regional specialization and the division of labor. The other broader criteria that they took into account were national security and minimum spatial variations in living standards. These planners designed integrated systems of productive and infrastructure investment, conceived on a massive scale. Although in theory these were good planning practices, soviet industries suffered from waste and inefficiencies and damaged natural environment through excessive pollution discharge.

6. Manufacturing in regional development theory and planning

There is a tendency in the process of economic growth to polarize development. The location of manufacturing creates differences between prosperous and less prosperous regions. Regional development theory and planning is an interdisciplinary approach that attempts to further understanding of and to influence the forces responsible for the uneven spatial distribution of economic development. Apart from urban and regional planners this approach has links with other disciplines such as sociology and political science.

Location of manufacturing facilities has economic impacts - purchasing, labor-market, local wealth and infrastructure. Industries create agglomeration economies through backward and forward linkages of industry with other sectors. Industries such as chemicals, iron and steel

6 and metal products, characterized by extensive backward and forward linkages with other sectors, have the potential to transmit growth impulses widely throughout an economy. Regional development theory investigates considers industries to be the most important tool in bringing poorer regions at par with the well off ones.

While discussing regional development, Higgins (1995) has stressed the importance of taking regional/local conditions into account. According to him, the regional development theories alone cannot guarantee success in regional development work, as regional social and cultural conditions are also important.

Main features of the existing approaches

The approaches to study of industrial locations, discussed above, can be categorized into two: the first concerns firms and the other concerns places. Firms have their private interests with maximization of profit being the most important one. With their interests in mind, firms try to uncover why and when they should build another plant, what is the best location for the new plant and how best to use the existing plants at their particular locations. The normative, behavioral and geography of enterprise approaches are the ones that are of interest to them.

The place approach to industrial location falls in the arena of the public sector. The chief objectives of the public sector are social welfare and regional economic balance. The approaches of interest to public sector are structural, industrial location under socialism and regional development and planning. Out of these three, for governments in developing countries the approach of primary interest is regional development and planning approach as structural approach is mostly for developed countries only and socialistic approach is increasing irrelevant.

Governments can and do influencing the location of private investment through urban and regional policy. In any nation (federal or not), the central government may concern itself with the sub-national distribution of wealth, welfare, or growth. In a federal system, states or provinces establish programs that set them in competition for industrial investment, population migration, or technological advancement.

The set of policies used by the national government are called “centralized policies” where as those used by states of provinces are called “competitive policies”. These policies can be implicit or explicit. The objectives of these policies are equity, employment and income generation. The tools used by governments include supply of investment funds in certain areas (in the form of soft loans etc.), lowering of factor costs (cheaper state-owned transport

7 services, state-run utilities etc.), subsidies for employment or training of labor and provision of infrastructure including industrial estates and technology parks.

In summary the existing approaches to industrial location are economics based, as economists and economic geographers developed them, in large part. Lately urban and regional planners and social and political scientists have become concerned with industrial location decisions. These types promoted approaches for the public sector. While the approaches meant for private sector have aimed at maximizing profit, the ones developed for the public sector have tried to increase employment and income generation. In all cases, concern for the natural environment has been conspicuously missing.

II - Environment-based approach for industrial locations

The way industries are located in a region determines the level of harm to the natural environment. The public sector, therefore, needs an approach for influencing industrial locations, which minimizes the regional environmental damage and which fits well with the regional economic development plans. The new environmentally based approach must be proactive and strategic, appropriate for regional level, flexible - to handle regional variations, transparent, and not too restrictive for private entrepreneurs.

Developing an environment-based location concept has been facilitated by an increased awareness of the environment, which in turn has provided the public sector with political incentives and by the advancement of environmental modeling tools and expertise. The basis of this new approach has to be an understanding of the nature of pollutants industries discharge, the mechanisms of how industrial pollutants interact with the natural environment, damages that industrial pollutants can do to the natural environment / people and methods of treatment and modeling of industrial pollutants.

This new approach involves a thorough understanding of the environmental conditions in the region and possible damage that discharges from different categories of industries can do to the environment. Based on this understanding the next step in this approach would be zoning of the region for excluding certain industries from certain areas. Another important element of this new approach has to be determination of pollution assimilative capacities of water, air and land. This knowledge will help in limiting the discharges from industries or even the volume of industrial activity itself. The next component of this approach is finding the locations of maximum social benefit in backward areas of the region for luring industries there. Translating all findings into policy measures is an integral and concluding part of this new approach. This new approach is strategic in nature.

8 Historically public environmental concern over industrial discharges occurred many years after the beginning of the industrialization process itself. This may explain why early theories of industrial locations lacked an environmental focus. In developed countries, modern environmental concerns had their origins in the fifties, leading to activism and the promulgation of policies in the seventies. Among the new laws were limits on industrial air and water pollution discharges and the requirement for providing environmental impact assessments (EIAs) for new proposed industrial developments funded through public dollars. For developing countries that are beginning to industrialize there is an opportunity to deviate from the path taken by the developed world and strategically and pro-actively create environmental policies for industrial locations. The new environmentally based approach can therefore be especially valuable for the developing countries in this position.

A number of writers have addressed the role of environment in planning. McHarg (1967) in his book “Designing with Nature” for the first time emphasized incorporation of ecological principles in urban and regional design. Selman (2000) has introduced general principles of environmental planning, paying particular attention to the scientific and social scientific background to environmental planning. In between these two authors a number of others have attempted explaining general environmental principles in planning, land use zoning based on environmental principles or have highlighted the importance of EIAs in industrial development (Chapman, 1991). But none of these works have attempted to formulate a strategic, environment-based approach to locate industries. Although there exists something called Strategic Environment Assessment (SEA), but it is merely a glorified EIA process, as it uses same tools as EIA (IChemE, 1997) and therefore retains the reactive nature of assessment. SEA remains a general purpose and loosely defined tool. Also, it can only be applied to a proposed economic policy, plan or program. As opposed to SEA, the new approach for industrial location proposed in this paper is something that will be useful during the initial creation of policies, plans or programs.

The specifics of the new approach will be determined by the biophysical and socioeconomic conditions of the region under study. Higgins (1995) has repeatedly stressed the importance of social and cultural differences in regional development planning. In his view, the application of theories of regional development on a particular region can only be successful it they are tailored to the regional socio-cultural conditions. In this author’s view, the region’s natural environment is an even more significant variable than socio-cultural conditions when it comes to situating industries. In order to highlight the technique’s usefulness, an elaborate application of the new approach was performed using Punjab province, Pakistan as a case study.

9 Punjab province is a good region for application of the new approach as many more industries are likely to be setup there in future, which makes it an interesting region to study. Also, Punjab is a very important administrative district and hence is suitable for implementation of any policy recommendations coming out of the research. Lastly, Punjab province was selected as study area because of the author’s intimate knowledge of its environmental characteristics by virtue of his work experience.

While Punjab was picked for the first case study using this technique, other regions in developing countries are also appropriate for its application.

10 PART – B: APPLICATION OF ENVIRONMENT-BASED APPROACH IN PUNJAB, PAKISTAN

Pakistan, a country located in South Asia, has a population of 132 million and covers approximately 887,750 km2. Socioeconomic conditions in Pakistan are very poor. The per capita income and level of literacy are extremely low and both fertility and child mortality rates are high. Pakistan is poorly endowed with natural resources. It is a very dry country with less than 20% of its land having potential for agriculture and less than three percent forest coverage.

On the basis of geological and topographical conditions, Pakistan can be subdivided into two parts (eastern and western). Most of the eastern part of the country is flat and within the Indus river watershed and its five major tributary rivers. This region (also called the Indus plain) generally constitutes the provinces of Punjab and Sindh. The upper part of the Indus plain, that constitutes the Punjab Province (Figure 1), is the densest part of the country, as it has the most productive agricultural land and many of its major urban areas.

Punjab Province, with its population of more than 72 millions and area of 200,000 km2 is divided into eight divisions. The four divisions of Lahore, Gujranwala, Faisalabad, and Multan form the core of the province. These divisions cover only one third of the land but contain two third of the population of the province. These divisions retain most of the industry of the province. The remaining four divisions, Bhawalpur, Dera Ghazi Khan, Sargodha, and Rawalpindi, are backward, less densely populated, and have few industries. Figure 1 shows relative location of these divisions.

Punjab has a continental climate, being very hot in summer with temperatures of up to 46 or so degrees Celsius, and cold in winter with near zero temperatures. Punjab is a semi-arid with

11 very little rainfall. Figure 2 shows the rainfall distribution in the province. The province received its name from the five rivers (Indus, Jhelum, Chenab, Ravi and Sutlej) that run through it. These rivers originate from mountains in Kashmir and are mainly fed by water from snowmelt. These rivers are source of substantial surface and ground water resources and fertility. About 50% of Punjab’s land is cultivated using water from these rivers as well as the ground water. These water resources are also the source of potable water for people of the densely inhabited province. In short, water resources are the single most important natural resource of the province. An understanding of the system of natural streams and groundwater, therefore, is essential for an environmental assessment. Figure 3 provides a description of that system. The ground water quality varies with distance from the rivers, with ground water closer to rivers having better quality. This trend holds true for soil and vegetation types as well.

Punjab Province is densely populated and rapidly growing. To meet employment needs the government has encouraged the growth of an industrial sector, which is predicted to continue increasing with the population. A further stimulus is the continued political and ethnic violence in Karachi that is forcing many industries to shift to Punjab. Hence, the industrial sector is likely to grow rapidly in future.

One result of population and industrial growth has been serious environmental problems. The river water quality at places is so bad that all aquatic life has vanished. Given the current situation, there is an urgent need to wisely locate future industries as to reduce their environmental impact.

Considering the environment-based approach described earlier on, a methodology of the study was formulated. The methodology put forward, makes use of scientific methods and computer tools to produce rational and transparent policy recommendations. This methodology has four modules;

1. Zoning of the province for different categories of industries based on their compatibility with the biophysical conditions using GIS as a tool;

12 2. Determining the permissible magnitude of industries for different sub-areas so that the waste discharges are within assimilative capacity of the nature (for four core divisions); 3. Determining the locations for attracting industries for achievement of social and equity goals (for four peripheral divisions); and, 4. Elaborating the ways and means for interpreting, integrating and operationalizing the findings of modules 1,2, and 3.

Module I - Zoning for different industrial categories

The exercise under module I lead to land-use zoning recommendations that emphasize safeguarding the natural environment from industrial pollution. The zoning exercise was carried out using GIS as a tool to match certain biophysical conditions of the region and vulnerability of those conditions with industrial wastes from industries of different categories. An in depth understanding of the natural environment of the region, industrial pollution and the possible interaction between the two were used to come up with zoning.

The first step in this exercise was to identify the types of industries existing in the region and those likely to develop in future. The identified industries were then categorized into groups on the basis of similarity in pollution discharges. The next step was the identification of the most critical element of the natural environment. A GIS method was then developed (in the form of a flowchart) for determining permissible industrial categories for different mixes of critical elements. Data in the form of maps were collected for each of these elements from relevant agencies in Pakistan. These data were then digitized into GIS layers for further analysis. The final product was a map that showed zones for different industrial types.

The industries were subdivided into two groups. Group I was the agro-industries that mostly discharge organic carbonaceous waste. These industries do not produce much air pollution. Textile, paper, sugar, dairy, and oil industries can be included in this group (more than 60% industry existing in Punjab consists of textile mills). Group II included toxic waste and air pollution producing industries such as tanneries, paints, plastics, fertilizer, and chemical production etc. The analyses suggested that the province should be subdivided into three zones, A, B, and C. Zone “A” was designated as an area free from any industries. Only industries of the first group should be allowed in zone “B” and all industries should be allowed in zone “C”.

Land use, water bodies, groundwater and soil type were taken as the most critical elements of the natural environment. Population concentration was another criteria taken into consideration. A discussion is given in the following paragraphs as to how these individual parameters affect the zoning technique.

13 Population concentration: The industries of Group I, particularly the textile industry, discharges organic wastes devoid of nutrients like nitrogen and phosphorus. These wastes cannot be biologically treated unless nutrients are added. One way of adding nutrients is by mixing these wastes with domestic wastewater. This means locating these industries close to high-density areas is desirable or at least not objectionable. Also as Industries of Group I do not produce air pollution so their location in or around the urban centers should not pose any threat to the health of the people. Industries of Group I are labor intensive, another factor that shall advocate their location around urban areas.

Land use: Forests, wildlife areas, and wetlands are land uses that should not have any industries allowed. This precaution is very important as very little forest is remaining in the province.

Surface water bodies: It is desirable that surface water bodies, especially rivers, have buffers around them in which no industry is allowed. This precaution will help avoid undetected direct discharges of pollutants into the rivers.

Groundwater: Ground water is probably the most precious natural resource of the province. In case of groundwater, depth and water-quality (measured in terms of salinity) are of utmost interest. In general, higher water tables with good water quality are more critical. If the water table is high pollutants can easily contaminate it. Also if the ground water quality is good, it is more important to preserve its quality.

Soil type: The issue of soil type is related to groundwater. In general clay soils are better barriers against pollutant migration to groundwater than the sandy soils. The soil barrier is of greater importance when the groundwater is shallow and of good quality.

The GIS Process The GIS process for area categorisation is shown in Figure 4. This flowchart describes only the overall steps and does not show details like data acquisition, data input etc. ArcInfo and ArcView were the GIS software used to go implement this process.

14 Overlay & Dissolve

S1Q1D1 = A1 S2Q2D2 = C1 Others = B1 Overlay & Dissolve H.Permeability = S1 Soil Types L.Permeability = S AB = A, AC = A, AA = A 2 BC= B, BB = B

L V E CC = C

A1=No Industries Overlay & Dissolve Groundwater TDS<1000 = Q1 S O B1 = Industries - I AB = A, AC = A, AA = A Quality TDS>1000 =Q2 C1=Industries-I+II BC= B, BB = B CC = C A =No Industries 3 Overlay & Dissolve Groundwater D I S Depth > 3m = D B = Industries - I 1 3 AB = A, AC = A, AA = A Depth < 3m = D C =Industries-I+II Depth 2 3 BC= B, BB = B CC = C A5=No Industries Cities and Inside = B2 B5 = Industries - I Towns Outside = C2 C5=Industries-I+II

A7=No Industries Protected = A B = Industries - I R = (pop)Buffer Landuse 4 7 op >200000 Not Protected=C4 C7=Industries-I+II 1/3 /7

(forests,Dissolve wildlife Inside = A Rivers 6 protected) Outside=C6

Width Buffer= 10 km

Figure 4: GIS Analysis for Area Classification

The process started with a categorization of soil coverage into low permeability and high permeability (Figure 5), groundwater quality into low salinity and high salinity (Figure 6), and groundwater depth (Figure 7) into shallow and deep. A combination of high permeability, low salinity (good water quality) and shallow depth warrants the area to be protected from industries (category “A”). On the other hand a combination of low permeability, low groundwater quality and greater depth reflects a non-sensitive area and can be categorized as “C”. All other combinations of these parameters were allocated to the “B” category.

The next step involved using point coverage of cities and towns. All cities and towns of more than a hundred thousand population were included in this coverage. A buffer of radius equal to the cube root of population divided by seven was drawn around all cities and towns (Figure 8). The inside of these buffers was categorized as “B” where as outside was be categorized as “C”. This coverage was then overlaid on the previous coverage resulting from groundwater and soil. The new categorization was done in a way so as to emphasize the more critical land use when categories overlapped. In the next step, areas to be protected (Figure 9) were identified and categorized as “A”. The remaining areas were considered “C”. This coverage was then overlaid on the previous coverage and the new categorization was again performed so as to emphasize the more critical land use when categories again overlapped. The same process was then repeated with the surface waters (Figure 10).

15 16 The final output of this GIS analysis is a map (Figure 11) that shows the study area subdivided into three categories A, B, and C. Category “A” represents the area where no industries should be allowed, category “B” represents areas where industries with organic wastes only should be allowed and in area category “C” all types of industries can be permitted. The proposed zoning can be used for controlling new industrial development.

The zoning map can serve as an easy reference for several departments; such as Environmental Protection Department Punjab, Industries Department Punjab etc. The critical elements and other criteria can be further fine tuned, as the process can easily accommodate modifications.

Module II – Pollution assimilative capacity of nature

For module II, it was assumed that pollution assimilative capacity of a natural area was determined the permissible volume of industries in a region. Therefore it was necessary to estimate the permissible pollution assimilative capacity for water, air and soil for a variety of pollutants. Also was also necessary to sub-divide the region into smaller units based on the extent of spread of pollutants and the variation in biophysical conditions. When done properly these calculations were both difficult and time consuming, therefore the study limited the analysis to that of water. The assimilative capacity of the waters in the natural streams for the most common pollutant, organic matter, was calculated in terms of Biochemical Oxygen Demand (BOD) for the four core divisions.

The methodology envisaged by author involved dividing the study area into several sub-areas and then calculating the pollutant load that would not harm the natural environment. This waste has the industrial and domestic components. The industrial waste component was determined by subtracting the domestic waste component from the total assimilative capacity. The total industrial pollution for a region is a function of volume of industries and the level of treatment. The assimilative pollution capacity mentioned in above paragraph therefore can be translated in different combinations of level of industrialization and level of treatment.

17 First, present condition river DO levels were estimated. For estimating the domestic component of the pollution contribution demographic information along with level of sanitation was used. The World Health Organization’s (WHO) Rapid Source Inventory technique (WHO, 1993) was employed for estimating industrial pollution. Combining these two components the total amount of organic matter discharged to the river was estimated for each of the sub-areas.

Information on pollution, connectivity to the rivers, and river-system discharge values along with temperatures was then used to determine the DO levels in the rivers. For this purpose a Spreadsheet Model based on Streeter Phelp Equation was developed. Thereafter, an estimate of the amount of pollutants that each sub-area can discharge without severely affecting the river water quality was obtained. The water quality model was used to determine the amount of BOD each sub-area could be allowed to discharge so that river DO level does not drop below 4mg/L at any point. This analysis involved incrementally increasing the values of the current level of pollutants discharged from each sub-area till the sag curve goes below the critical line.

As mentioned earlier the first step in this methodology was to determine the pollution contribution to natural streams from the industries as well as from domestic sources. This effort in itself led to some interesting findings that are discussed in the following parts of this paper.

Estimation of Industrial Pollution

The data on quantities of pollutants discharged from industries was not available in the study area. In the absence of data on pollution discharges, the other possibility was to make estimates using industrial production figures. These figures were also not directly available for individual divisions in the core zone. Quantifying pollutants in industrial wastes for the study area, therefore, required an enormous effort.

Figures of Industrial Production Capacities (a proxy for industrial production) were summed from some thousands of industries listed in the industrial directory of the province (DIMD, 1988). District-wise industrial production was summed up for different types of industries. The Rapid Inventory Techniques of the World Health Organization (WHO, 1993) were then used to transform the production figures into pollution quantities.

The Rapid Assessment methodology provides a particularly effective way of assessing air, water and solid wastes generated by each source, or groups of similar sources, within the study area. In addition it permits convenient assessment of the effectiveness of alternative

18 pollution control options. This method is based on the documented experience of the nature and the quantities of pollutants generated from each kind of source, with and without associated control systems. The advantages offered by the Rapid Assessment approach include convenience of use, which makes it possible to conduct source inventories in highly complex situations with modest resources. Moreover, despite the simplicity of the method, the end result is often considered more reliable than that from direct source monitoring programs in cases where shortcuts have to be taken (WHO, 1993).

Table 1 (one of the four tables that were prepared for all four core divisions) shows effluent BOD estimates for one of the four core zone divisions of the province using Rapid Assessment Procedure. The first four columns in the table present the production capacities for different categories of industries for different districts. This group of columns was constructed using the Industrial Directory for the province (DIMD, 1988). The second set of columns was taken from the Rapid Inventory Techniques (RIT) manual (WHO, 1993). The second group of columns shows the conversion factors for different industrial production types. Note that the units for measuring quantities in this group of columns are different from the units in the first group. The quantities of industrial production had to be converted into units of Rapid Inventory Techniques in order to apply the factors for conversion of production into pollution discharge. The third group of columns that presents pollution estimates contains industrial production in RIT figures as well as the Biochemical Oxygen Demand (BOD) estimates of industrial discharges. These BOD estimates have been calculated by multiplying the RIT conversion factors with industrial production figures in RIT units.

In Table 1 the BOD estimates have been summed up for each of the districts and then projected for the years 1988 (the year of compilation of the industrial directory) to 1998 using a 3.3% annual growth rate in industrial production (according to the United Nations (1998), industrial manufacturing in Pakistan grew at 3.3% per annum from 1989 to 1997). Year 1998 was used as reference as population census took place in that year (Population Census Organization, 1998) and hence it was a good year to compare the industrial and domestic pollution contributions.

Industries Play a Minor Role in Degradation of Natural Bodies of Water

Table 2 shows a sum-up of the BOD production from all four core-zone divisions. For industries the values were taken from Table 1 and similar tables prepared for three other divisions. The population figures were taken from 1998 census data (Population Census Organization, 1998). BOD production from domestic sources was estimated using the population figures and a standard 70 grams BOD per person per day figure. Table 2 clearly demonstrates that the BOD contribution from industries in total waste load is very low. For

19 most districts it ranges from 1 to 3 percent. For two industrialized districts it is 5 and 8 percent. The average for all districts is 3 percent.

As the figures in Table 2 are of BOD generation and not that of what actually reaches the natural water bodies one may argue that a higher proportion of industrial waste may reach the natural streams than that of domestic wastes. The counter argument to this suggestion is that the percentage of population with sewer access has greatly increased over the last 20-30 years with 77% of urban population connected to wastewater sewers (WRI, 1999), meaning most domestic wastewater reaches the natural streams. Also the estimates of industrial pollution are very liberal as industrial production capacities have been taken as industrial production figures (actual industrial productions may be lower than installed capacities). On the other hand estimates of domestic pollution are very conservative, as contributions from commercial sector have not been taken into account. Thus industrial contribution to the BOD that actually reaches natural streams may not be less than that stipulated in Table 2.

These results clearly indicate that industrial effluents play a minor role in overall degradation of receiving water bodies. Industries seem to be responsible for very localized but probably sever environmental damage at a few locations that can be termed as hot spots. The extensive environmental degradation in the region stems primary from the extremely dense and fast growing population. As that finding was surprising for the author, he had a closer look at the scientific literature on state of environment in Punjab.

Table 1: Pollution from Industries in Gujranwala Division

Industrial Production Conversion Factors Pollution Estimation

District Industry Unit Production RIT Ref. Units BOD Production Pollution kg/Unit RIT units tons BOD

Gujranwala Bakery Tons/year 103 3117 tons 0.11 103.00 0.01 Beverages Crates/year 660,000 3134 cum 3.10 3960.00 12.28 Confectionery Tons/year 307 3117 tons 9 307.00 2.76 Corn Oil Expellers 2 3115 tons 0.3 2000.00 0.60 Cycle Tyres & Tubes Nos./year 512,000 3551 tons 0.4 512.00 0.20 Dying & Finishing Rs./year 46,545,000 3210 tons 155 930.90 144.29 Ice Cream Rs./year 710,000 3112 tons 10.9 35.50 0.39 Molasses Tons/year 2,500 3117 tons 2.9 2500.00 7.25 Oil Mills Expellers 34 3115 tons 24.9 3400.00 84.66 Poultry Farms Birds/year 833,000 3111 1000b 17 833.00 14.16 Soap Tons/year 11,725 3523 tons 6 11725.00 70.35 Starch Tons/year 1,200 3121 tons 13.4 1200.00 16.08 Sugar T.Cane/day 550 3118 tons 2.9 9900.00 28.71 Tanneries Tons/year 1,048 3231 tons 63.5 2289.77 145.40 sqm/year 620,885 year 1988 527.14 year 1998 729.34

Gujrat Confectionary Tons/year 69 3117 tons 9 69.00 0.62 Cycle Tyres & Tubes Nos./year 3,050,000 3551 tons 9 3050.00 27.45 Ice Cream Bars/year 65,000 3112 tons 10.9 6.50 0.07 Juices & Squashes Bottles/year 18,000 3113 tons(r) 9.4 18.00 0.17 Oil Mills Expellers 15 3115 tons 24.9 1500.00 37.35

20 Poultry Farms Birds/year 202,000 3111 1000b 17 202.00 3.43 Soap Tons/year 1,770 3523 tons 6 1770.00 10.62 Sugar T.Cane/day 3,000 3118 tons 2.9 54000.00 156.60 Tanneries Tons/year 5 3231 tons 63.5 1491.00 94.68 sqm/year 743,000 Vegetable Ghee Tons/year 9,000 3115 tons 24.9 9000.00 224.10 year 1988 555.09 year 1998 768.01

Sailkot Bakery Tons/year 9 3117 tons 0.11 103.00 0.01 Confectionery Tons/year 16 3117 tons 9 16.00 0.14 Cycle Tyres & Tubes Nos./year 366,000 3551 tons 9 366.00 3.29 Chemicals Rs./year 800,000 3511 tons 6 4000.00 24.00 Ice Cream Rs./year 980,000 3112 tons 10.9 49.00 0.53 Oil Mills Expellers 18 3115 Tons 24.9 1800.00 44.82 Poultry Farms Birds/year 57,000 3111 1000b 17 57.00 0.97 Sugar T.Cane/day 15,000 3118 Tons 2.9 270000.00 783.00 Soap Tons/year 11,130 3523 Tons 6 3523.00 21.14 Tanneries hides/year 62,100 3231 Tons 63.5 67.76 4.30 sqm/year 5,655 year 1988 882.21 year 1998 1220.61 Total 1998 2717.97

RIT=Rapid Inventory Technique, r = raw fruit, b = birds

Studies on State of Environment in Punjab

The World Bank financed a study called “Punjab Urban Environmental Project” in 1992-93 (World Bank, 93) that was mainly conducted by the Environmental Engineering Division of the National Engineering Services Pakistan. The Asian Development Bank funded a study at the national level called “Implementation of the National Conservation Strategy” in 1996-97 (ADB, 97), which discussed aspects of the state of environment in Punjab province. In addition to these studies conducted in the area, some minor research projects have been carried out by graduate students studying abroad (Maqsood, 1996).

Table 2: Contribution from Industries in Waste Generation in Four Core Divisions of Punjab

Population Pollution (BOD) Division District 1998 Domestic Industrial Industrial Tons/Year % Gujranwala Gujranwala 4,196,414 107,218 729 0.7 Gujrat 2,981,614 76,180 768 1.0 Sailkot 3,937,181 100,595 1,221 1.2

Multan Khanewal 2,040,441 52,133 569 1.1 Multan 4,244,547 108,448 3,504 3.1 Sahiwal 3,095,396 79,087 2,177 2.7 Vehari 2,047,771 52,321 572 1.1

Lahore Kasur 2,347,020 59,966 2,267 3.6 Lahore 6,212,715 158,735 5,374 3.3 Okara 2,195,698 56,100 454 0.8 Sheikhupura 3,229,998 82,526 7,283 8.1

Faisalabad Faisalabad 5,340,771 136,457 8,139 5.6

21 Jhang 2,804,397 71,652 778 1.1 Tobateksingh 1,589,740 40,618 829 2.0 Total 1,069,767 33,058 3.0

Most of these studies are location specific with the city of Lahore and the stretch of river Ravi near Lahore the center of focus. This is understandable, as Lahore is the focal point of all educational, administrative, commercial and industrial activities in Punjab Province. A number of industrial clusters are located within a distance of 50-60 km from the city. Other locations that have been studied are other major urban-industrial centers like Faisalabad and Multan. The broader studies also select a few locations as examples and describe the situation there.

The most common topic of these studies is a verbal description of extensive environmental degradation. Some of the studies scientifically examine the level of pollution at various other locations; for example quality of water in River Ravi near Lahore. Then there are few studies that have estimated industrial pollution in different industrial centers and from different types of industries.

The literature related to the state of environment in Punjab fails to clearly relate environmental degradation with its different causes and most seem to point finger at the industrial pollution. These studies tend to discuss individual causes and individual spots but do not explain the relative contributions of different sources of pollution and fail to recognize any pattern for the whole province. Some of these reports contain inaccurate information also. The report financed by the World Bank, while estimating pollution discharge from textile industry, estimated textile production of Punjab at 100 times the actual value (World Bank, 1993: Bureau of Statistics, 1998).

There is little effort to systematically link different sources of pollutants with their health impacts. Most of the studies describe what kind of pollutants are discharged from different sources and in theory what damage this kind of pollutants do. With the exception of reports on water logging and salinity, most studies are quiet on the pollution contribution from agricultural activities and livestock.

A survey of this literature suggests that it fails to clearly relate environmental degradation with its different causes and when this is done industrial pollution is targeted. These studies (ADB, 1997; Morishita, 1999; The World Bank, 1993; Maqsood, 1996; EPA Punjab, 1989; Tariq, 1990 and others) tend to discuss individual causes and individual spots but do not explain the relative contributions of different sources of pollution and fail to recognize any pattern for the whole province. It clearly came out that there is nothing in literature that could prove that industries are the main cause of environmental degradation in the Region.

22 Some more evidence that the industries are not the main culprit of environmental pollution

Apart from the analysis presented earlier, the following facts can further augment the findings of this study: • Contrary to the popular belief that Punjab is highly industrialized, industrial activity in the province is very small in volume. According to the UNIDO (1998a), there are mere 500,000 manufacturing jobs in the whole country. The per capita Manufacturing Value Added (MVA) in Pakistan is as low as 60$/year. The average value for developing countries is 300 and that for developed countries is 5000 (UNIDO, 1998b). • The environmental impact of industries depends on the industrial sectors. The most polluting industries are the fabricated metal machinery industry, the chemicals, petroleum and rubber and the non-metal industries in order. Textile and food processing industries are considered the least polluting ones (Hong, 1999). A vast majority of the industries in Punjab is that of least polluting type. • The main source of environmental degradation in Punjab is pollution from domestic sources arising from extremely dense population (in the core zone of the province 46 million people live in an area of about 70,000 km2). • The people have very limited ability to pay for pollution treatment measures (Pakistan is one of the poorest countries of the world). And the environmental awareness is low (only one-third population of the country is literate). • A number of health studies have indicated that a large proportion of deaths and illnesses stem from infectious diseases. A very high child mortality rate (more than 100) is also attributed to the diarrhea and other infectious ailments. Infectious diseases are closely related to the human waste contamination.

Conclusions from module II and implication for the study

The root cause of environmental degradation in Punjab Province is untreated wastes from densely populated residential areas with rapidly increasing population. Pollution from industries plays a minor role in regional environmental degradation. Pollution from industries is local in nature. It can be very acute and very damaging but its spatial expanse is limited. Pollution from industrial centers or hot spots affects the people working in them and those living in immediate vicinity. A source of concern can be gradual build up of toxic compounds in the fields that are irrigated by waters from streams that receive industrial effluents.

In Pakistan government policies and actions as well as media attention on pollution control is largely directed towards the industrial sector. In addition, there is positive pressure on the exporting industries from the advent of ISO14000 standards that require them to properly

23 treat their wastes. Larger industries are, therefore, quite likely to adopt some kind of treatment in coming years. There is no pressure, however, in local governments to treat their wastewater. In part, this is because it is not understood that untreated wastes from densely populated areas with fast growing populations can have devastating impacts on the regional environment.

From a societal point of view, the main problem faced by the region is not an excess of industries; rather it is a lack of them. Industries can create employment leading to socioeconomic development that in turn can help reduce population growth rate and increase environmental awareness.

In short Punjab is not industrialized but will move towards industrialization. The zoning based on biophysical conditions for different categories of industries, module 1 of this study, can serve as a good guide for minimizing environmental damage from any such move.

The module on determination of limiting industrial volume from assimilative capacity considerations did not proceed as intended. The pollution from industries forms a very small fraction of the total pollution. So the assimilative capacity considerations are mainly linked with pollution from sources other than industries, which is beyond the scope of this study.

Module III – Backward areas uplift

As mentioned earlier and shown in Figure 1, the province of Punjab is divided into 8 administrative units called divisions. The four core divisions are much more developed, densely populated and environmentally degraded than the four peripheral ones. As describe in methodology, the third module is on “Backward Areas Uplift”. This module is related to the social goal of equity and deals with the four backward peripheral divisions. The analysis in this module attempts to pinpoint the locations where creation of industries can maximize benefit to people living in these under-developed areas.

The agricultural land in the peripheral division is of low quality and the water for crops is scarce as these areas have lower rainfalls and have poor irrigation systems. The availability of infrastructure, like roads, water supply and telecommunications services, is also poor in these areas. At the same time, the natural population growth rate is higher than that in the core divisions. The lack of employment opportunities in these divisions is associated with migration to those of the core.

If the government could encourage industrial development in these peripheral divisions the local population can be provided with employment opportunities. Private entrepreneurs can be lured into building industries by providing infrastructure needed for industries in the form

24 of industrial estates as well as providing tax rebates. The first question in this regard is where should these new government initiatives be located? As access to these facilities/employment opportunities is the main issue the selection of theses locations can be based on the minimization of average travel distance or maximal coverage. The Locational Analysis Decision Support System (LADSS) is a computer tool that can find locations based on minimization of average travel distance and maximal coverage objective functions.

LADSS Locational Analysis Decision Support System

LADSS is a suite of programs for addressing location-allocation problems. LADSS was developed by Paul Densham of the Department of Geography of University College London. The suite has programs for generating and manipulating data sets, for formulating and solving location-allocation models, and for generating statistical reports about configurations of facilities. LADSS runs on most PCs or compatibles under PC/MS-DOS - this ensures that a very broad range of hardware can run this software.

Users can solve 6 different objective functions with three heuristic algorithms: the Teitz and Bart Vertex Substitution Heuristic, Goodchild and Noronha's variant of Teitz and Bart's algorithm, and Densham and Rushton's Global-Regional Interchange Algorithm.

Problems of 3,000 demand nodes - all of which may be candidates - and 12,000 network links with up to 1,500 facilities can be solved. The user interface on this system employs a hypertext shell which, with help and tutorial facilities, guides users through the process of solving problems using a flow-chart representation of the steps required. A data set and example are supplied with the model, which is available through the National Center for Geographic Information and Analysis (NCGIA) Software Series.

LADSS has a multi-layered Graphics User Interface. Figure 12 shows the first layer of the interface. This figure shows the steps to be followed for analysis in the form of a flow chart.

The Flow Chart Page uses a series of buttons and colored arrows to depict the ways in which LADSS' components can be sequenced. Arrows of two colors link the buttons: white and gray. 25 White arrows show the sequence required to formulate and solve a location-allocation model - the typical "goal-driven" application of a location-allocation model. In the literature, the most common use of location-allocation models is in "goal-driven" analyses to provide a normative solution to a problem. The white path on the Flow Chart Page corresponds directly to this mode of use, specifically: obtain a data set from a database, check it for common errors, build candidate and demand strings with the shortest path algorithm, select an objective function, solve the resulting model, evaluate the solution statistically, and, finally, display a map and/or graphics of the solution.

Gray arrows indicate alternative paths through the components, which correspond to other uses, including "what-if" types of analysis. The gray paths correspond to alternative paths through the system.

1) The path from "Shortest path algorithm" to "Evaluate a location set" enables the user to examine facility configurations without running a location-allocation model. This is useful for evaluating an existing set of facilities and for investigating the effects of making specific changes to a set of facilities. The following steps should be carried out: a) Generate a Demand Strings File using "Shortest path algorithm." b) Use these strings with "Evaluate a location set" and a list of facility sites (which may be in an Initial Solution File created by "select data" or "Editor" on the Utilities Page) to generate a set of statistics. c) By modifying the facility identifiers in the Initial Solution File, a range of "what-if" analyses can be undertaken. 2) The path between "Shortest path algorithm" and "Find locations (solve model)" exists because of the method used to select objective functions (see "Select Objective function" on Help Page for details). If the user wants to solve a model with a p- median objective function, they simply go directly from the shortest path algorithm to the location-allocation model solver. All other objective functions, in contrast, require Hillsman editing of the candidate and demand strings built by the shortest path algorithm. The text beside the gray and white paths is there to remind the user of this situation. 3) The path from "Find locations (solve model)" to "Display solution" is there to remind the user that information required to generate a map of the solution is output from the location-allocation model solver in file LA-SOL.DAT (see "File formats used in LADSS" and "Linking LADSS to other software", both on the Tutorial Page, for details). Thus, the user does not have to run the statistical evaluation option before they can generate a map of a solution.

26 The steps of LADSS analysis will be as follows:

1) Generate a lattice data set. a) Draw a network of locations linked with roads, determine weights of the nodes and road distances among them and make a coordinate system; a) Create Node and Random Link Files; and then, b) Convert a Random Links File, such as might be generated by a database program or a digitizing package, into an Ordered Links File used by other components of LADSS.

2) Run a links consistency check on the generated links file (Figure 13). The purpose of this step is to check the records in an Ordered Links File to determine: first, if node x is linked to node y, then is y is linked to x; and, second, if the distance from x to y is n miles, is that also the distance from y to x?

3) Run a network contiguity check on the links and nodes files (Figure 13). This utility checks that a path through the network can be found from node 1 to every other node on the network. By searching for all nodes on the network, this utility ensures that a network does not contain any islands or sub-networks.

4) Edit the candidacies of the demand nodes. Preparing an initial solution file can accomplish this task.

5) Run the shortest path algorithm. The Shortest Path Algorithm (SPA) generates the candidate and demand strings, which record potential interactions among demand nodes and candidate facility locations.

6) Select an objective function. To edit the weighted distances in the Candidate and Demand Strings Files that the location-allocation heuristics solve an objective function other than the p-median. The user chooses from among five objective functions to edit the contents of the distance strings: a) P-median with a maximum distance constraint 27 This objective function minimizes the total distance traveled from demand nodes to their allocated facilities, of which there are p, which is equivalent to minimizing the average distance traveled. b) Maximal covering problem The maximal covering problem locates facilities to maximize the amount of demand they can serve. c) Maximal covering problem with a maximum distance constraint This objective function locates facilities to maximize the amount of demand they can serve within distance S, subject to a maximum distance constraint, T. d) Maximize attendance subject to linear distance decay In this objective function, attendance is assumed to decline linearly with increasing distance from a facility; sites are chosen to maximize attendance. e) Maximize total powered distance This objective function simply places an exponent on the distance terms of the objective function, increasing the friction of distance.

7) Solve the problem by following the remaining steps given in the flow chart (Figure 3). The whole process of running the model will involve the following files; a) Links and Nodes files are used to record the topology of the network and attribute information for each node, respectively. b) Candidate and Demand Strings contain information about potential interactions among demand nodes and candidates. c) An Initial Solution File contains the node identifiers of the facilities in the initial solution supplied to the location-allocation algorithms. d) Output Files record the solution process of a location-allocation algorithm for a given problem. e) A Solution File contains the node identifiers of the final set of facilities identified by a location-algorithm. f) "LA-SOL.DAT" is a file, which can be used to transfer information about the solution to a location-allocation model to GIS, database or mapping software. g) An EVAL Output File contains a statistical analysis of the solution to a location- allocation model. The uses and formats of each of these files are described in the manual of the software. (N.B. except where shown otherwise, all files are free-format with the fields separated by spaces (ASCII 32) - commas and other forms of delimiters will result in error messages).

Locational Analysis using LADSS

28 Divisions are important administrative units in Punjab and therefore the locational analysis for the four back ward divisions was performed individually. Divisions in Punjab are subdivided into smaller administrative units of districts and sub-districts (or Tehsils). Every sub-district has a sub-district headquarter which is normally the biggest or the most important town/village in the sub-district area. Figure 14 shows the location of these sub-district headquarters in four peripheral divisions.

A total of 43 sub-districts headquarters were selected in four divisions. These headquarters were selected as nodes for supply and demand of services (provision of industrial infrastructure in this case). For the sake of simplicity the sub-district populations were collapsed at the nodes. This is a reasonable assumption as the sub-district headquarters are the towns/villages with biggest populations are normally centrally located.

The geographic coordinates (i.e., latitudes and longitudes of these nodes) were taken from the topographic maps of the province. Also the road network connecting these nodes was taken from those sheets. In order to prepare input files for the models the coordinates of the nodes were needed in linear coordinates. To do so transformation of geographic coordinates to UTM was done using GIS software. That was a complex process details of which have been omitted in this summary. Table 3 summarizes weights of nodes as well as their geographic and UTM coordinates while figure. The node and link files were prepared using information for each link as depicted in Figure 15.

29 As mentioned earlier two different objective functions (i.e., minimizing average travel distance and maximal coverage) were selected. The model was then run using these two objective functions for all four peripheral divisions. For each run an initial solution file was used that indicated to the model how many locations in each of the divisions was to be selected. For each division and for each of the objective functions model was run to find one, two, three and four locations. As there were four divisions, two objective functions, and four possibilities of number of locations to be selected, the model had to be run 32 times (and each run of the model involves a number of steps that have been described earlier in this report). Table 3: Weights and Co-ordinates of Nodes Division Sub-district Weight Geographic Co. UTM Coordinates (kms) Population Lat. Long. x-co. y-co. x - 500 y-3000

Rawalpindi Attock 497,787 33.77 72.35 810 3744 310 744 Hasan Abdal 135,248 33.82 72.72 844 3748 344 748 Fateh jang 212,213 33.57 72.65 838 3720 338 720 Pindi Gheb 194,640 33.23 72.27 804 3682 304 682 Jand 224,677 33.45 72.03 782 3706 282 706 Rawalpindi 1,923,383 33.6 73.08 878 3726 378 726 Gujar Khan 491,603 33.25 73.3 900 3688 400 688 Kahuta 310,710 33.6 73.3 899 3727 399 727 Taxila 370,802 33.73 72.82 854 3740 354 740 Muree 175,668 33.92 73.4 906 3762 406 762 Kotli Sttian 79,827 33.78 73.4 908 3747 408 747 Jhelum 511,555 32.92 73.7 939 3652 439 652 Pind Dadan K. 261,987 32.6 73.05 880 3615 380 615 Sohawa 156,311 33.12 73.43 913 3674 413 674 Sarai Alam G. 174,051 32.92 73.75 945 3653 445 653 Chakwal 586,978 32.93 72.85 859 3649 359 649 Choa Saidan 103,291 32.72 72.97 872 3628 372 628 Talagang 369,182 32.92 72.42 820 3648 320 648

Sargodha Sargodha 1,075,493 32.08 72.67 847 3556 347 556 Sillanwali 252,968 31.82 72.53 836 3526 336 526 Shahpur 272,852 32.28 72.47 825 3577 325 577 Sahiwal 235,481 31.97 72.32 814 3542 314 542 Bahawal 816,552 32.28 72.88 865 3579 365 579 Mankera 169,127 31.38 71.45 733 3475 233 475 Kalur Kot 222,419 32.15 71.27 714 3559 214 559 Bhakkar 419,954 31.62 71.03 692 3500 192 500 Darya Khan 232,086 31.78 71.12 701 3518 201 518 Khushab 716,554 32.3 72.3 811 3578 311 578 Noorpur 171,640 31.88 71.9 774 3532 274 532 Mianwali 528,331 32.57 71.53 737 3606 237 606 Piplan 259,881 32.3 71.37 723 3576 223 576 Isa Khel 249,028 32.5 70.98 714 3618 214 618

D.G.Khan D. G. Khan 1,140,895 30.07 70.63 656 3328 156 328 Taunsa 365,582 30.7 70.65 657 3397 157 397

30 Rajanpur 392,104 29.1 70.18 615 3220 115 220 Rojhan 205,478 28.68 69.93 590 3174 90 174 Jampur 473,120 29.65 70.6 655 3282 155 282 Leiah 582,047 30.95 70.95 686 3426 186 426 Karor 364,336 31.22 70.97 687 3456 187 456 Chaubara 157,483 30.92 71.5 739 3423 239 423 Muzaffargarh 969,533 30.07 71.18 710 3328 210 328 Alipur 389,487 29.38 70.92 686 3251 186 251 Jatoi 432,136 29.52 70.83 677 3268 177 268 Kot Addu 790,207 30.47 70.98 688 3371 188 371

Bahawalpur Hasilpur 312,132 29.7 72.55 843 3291 343 291 Khairpur 183,250 29.58 72.25 815 3278 315 278 Yazman 402,573 29.12 71.8 772 3226 272 226 Ahmadpur East 714,102 29.15 71.27 720 3227 220 227 Bahawalpur 798,509 29.4 71.7 762 3256 262 256 Bahawalnagar 536,501 29.98 73.27 912 3326 412 326 Chishtian 488,386 29.8 72.87 873 3303 373 303 Haroonabad 372,150 29.58 73.13 900 3281 400 281 Fort Abbas 289,749 29.2 72.85 874 3237 374 237 Minchinabad 346,996 30.17 73.58 941 3347 441 347 Rahim Yar K. 969,702 28.43 70.3 627 3146 127 146 Sadiqabad 760,279 28.3 70.15 613 3132 113 132 Liaqatpur 675,015 28.93 70.97 692 3203 192 203 Khanpur 668,373 28.65 70.65 661 3171 161 171

The locations determined by the model are given in Table 4 (for one of the divisions these results have been graphically presented in Figure 16). In this table P-Median refers to minimum average distance traveled objective functions where as the other objective function is maximal coverage. The top part of the table (just below the headings) gives the location IDs that were selected by the model for one, two, three or four number of locations. It must be explained here that same IDs for different refer to different locations. The lower half of the table is encoding of the IDs into name of the towns/villages selected.

Table 4: Locations Selected by LADSS (for two objective functions)

Rawalpindi Division Sargodha Division D.G.Khan Division Bahawalpur Division

P-Meidan Maximal P-Meidan Maximal P-Meidan Maximal P-Meidan Maximal 1 12 12 8 6 8 8 13 13 2 12,4 12,4 8, 2 6, 2 8, 4 8, 4 13, 2 13, 2 3 12, 4, 5 12, 4, 5 8, 2, 12 6, 2, 12 8, 4, 10 8, 4, 6 13, 2, 11 10, 11, 4 4 12, 4, 5, 15 12, 4, 5, 10 8, 2, 12, 14 6, 2, 12, 7 8, 4, 10, 6 8, 4, 6, 10 13, 2, 11, 8 10, 11, 4, 1

1 Rawalpindi Rawalpindi Sargodha Sahiwal Kot Addu Kot Addu Bahawalngr Bahawalngr

2 Rawalpindi Rawalpindi Sargodha Sahiwal Kot Addu Kot Addu Bahawalngr Bahawalngr Chakwal Chakwal Bakkar Bakkar Jatoi D.G. Khan RahimyarKn RahimyarKn

3 Rawalpindi Rawalpindi Sargodha Sahiwal Kot Addu Kot Addu Bahawalngr Haroonabad Chakwal Chakwal Bakkar Bakkar Jatoi D.G. Khan RahimyarKn Hasilpur Jhelum Jhelum Mianwali Mianwali Leiah Jatoi Hasilpur Liaqatpur

4 Rawalpindi Rawalpindi Sargodha Sahiwal Kot Addu Kot Addu Bahawalngr Haroonabad Chakwal Chakwal Bakkar Bakkar Jatoi D.G. Khan RahimyarKn Hasilpur Jhelum Jhelum Mianwali Mianwali Leiah Jatoi Hasilpur Liaqatpur Attock Jand Piplan Kalarkot D.G. Khan Leiah Bhawalpur Sadiqabad

31 The table indicates that the results are not markedly different for two different objective functions. This is understandable as both objective functions try to maximize access to facilities. A very important point here is the sequence of selection of locations. The results show that provision of facilities can be done in stages. For example in case of D.G. Khan Division the first choice has to be ID number 8 or Kot Addu, which was selected in all four cases of whether one, two, three or four locations are selected. In the case of same division number 4 or Jatoi appears three and hence should have second priority. In other words Kot Addu can be provided with facility in the first stage, Jatoi in second and so on. Table 4 presents choices to the decision-makers. The decision-makers can decide how many locations in each division can they afford to provide with industrial estates/infrastructure. They can also decide on if they want to do it in one or more stages.

Module IV: Operationalizing Findings

The output of first module was a zoning map describing what industries should be excluded from what areas to safeguard the environment of the region. The second module yielded the understanding that the main cause of environmental degradation in the region is untreated wastes from fast growing dense population and not that from industries. The output of the third module was systematic selection of the locations in the backward parts of region for promoting industrial development. As discussed in each of the first three modules, the findings from these modules are useful on their own. However, it still needs to be discussed, where they fit in regional development planning / environmental planning process.

The first step was an understanding of the administrative and planning system in Punjab as well a basic understanding of regional development and environmental plans, policies and tools through a review of literature. To keep this document concise this review is not included.

Punjab is one of the four provinces in Pakistan. Provinces are a very important administrative level in Pakistan. Provinces have their legislative assemblies (at times when democracy is in place), ministers, line departments and budgets. Provinces can levy taxes and have their own development policies. In Pakistan all these functions exists only at two levels, the federal level and the provincial level.

Despite being at an important administrative level and being the largest province, Punjab does not have a strategic regional/provincial development plan. Punjab, a province of 72 million population ought to have one. Provincial government in Punjab must prepare a development plan of 20 years perspective. This plan should envision where the province would like to be in future. It ought to include the development strategies for the province.

32 Industrial development will inevitably be an extremely important component in the development strategy. The findings of the first three modules can form the basis of molding an industrial development strategy that does not compromise on natural environment.

The policies of the government of Punjab can roughly be divided into two groups; the socioeconomic development policies and the environmental protection policies. These two policies are not linked and the development policies receive much more attention. This should change. The findings of the first three modules can be reflected in both policy types. Actually these findings can serve as bridge between the two.

The governmental institutions in Punjab have a similar division. While industries, housing and physical planning and public health engineering departments are responsible for development activities the environmental protection department is responsible for protecting the environment. The cooperation between these departments is minimal. The findings of earlier modules of this research are of relevance of both types of departments and can serve as a common basis of cooperation between the two. For industries department can utilize zoning as first criteria for issuing licensees for new industries. The same zoning can be useful for environmental protection department for compliance, in case an industry tries to produce something different.

For the zoning recommendations put forth in this study, the appropriate implementing tools must be those of command and control. For assimilative capacities different tools may be used. Pollution taxing and pollution trading are alternatives to command and control in this case. Theoretically, command and control is the least economically efficient while pollution trading is the most economically efficient tool. For a country like Pakistan, however, where there is corruption and administrative weakness, it might make sense to start with command and control and aim at pollution taxing in the medium run and at pollution trading in the long run. For backward area development, the experience of a number of researchers indicates that provision of vocational skills to labor and physical infrastructure are the most effective tools in attracting industries at particular location.

33 REFERENCES

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