Conflict analysis and compromise strategies in integrated spatial systems

Regional

Science

and Urban

CONFLICT

Economics

13 (1983)

ANALYSIS AND INTEGRATED Wim University

of Amsterdam,

University,

North-Holland

COMPROMISE STRATEGIES SPATIAL SYSTEMS

IN

HAFKAMP 1011 NK,

Peter Free

115-140.

Amsterdam,

Amsterdam,

The Netherlands

NIJKAMP 1007 MC,

Amsterdam,

The NetherEands

This paper aims at showing. the potential of multiple objective analysis for analyzing confficts in a spatial-economic-environmental system. After a brief introduction to interactive approaches to multiple objective decision models, a conceptual framework for an interregional system will be designed, while next an operational application will be given. Various empirical resuIts of an interregional model for the Netherlands will be presented.

1. Introduction Conflict analysis has been the subject of intensive research over the past decade. One of the well-known examples in this area is the linkage between economic activity, land use, energy consumption and environmental effects. Various policies m&y be pursued to m inimize conflicts between various options or interests, but the problem of reconciling different policy objectives is a far from easy task. At a conceptual level, multiple objective -decision analysis may be a very helpful tool for clarifying tradeoffs among successive policy choices, for identifying efficient solutions and for reaching compromise strategies and solutions (by means of, e.g., ‘satisficer’ principles). At an operational level of actual regional and sectoral policy-making, decision analysis may provide empirical tools for multiple objective characterizing the precise nature of conflicts among different targets, for testing the sensitivity of compromise solutions with regard to alternative policy controls and for generating quantitative information regarding the outcomes of different policy scenarios. In the present paper, the potential of multiple objective decision analysis for conflict analysis in a spatial-economic-environmental system will be demonstrated. After some general remarks on multiple objective decisionmaking and more specifically on interactive approaches, a conceptual framework for a complex spatial system will be designed, followed by an At the end of this paper, several empirical operational specification. applications will also be presented. 01660462/83/0000-0000/$03.00

0

1983 North-Holland

WT Hafkamp

and P. Nijkamp,

2, A multidimensional

conceptual

116

Conflict

analysis

framework

and compromise

for conflict

strategies

analysis

Ideally, models used in policy analysis should be able to indicate the boundaries within which policy decisions can be made, the tradeoffs inherent in choosing alternative solutions, the impacts of policy measures on a set of relevant policy objectives, the possibilities for a communication between analysts (or planners) and decision-makers, and the sensitivity for changes in the spatial scale, the time horizon or the level of measurement of variables. In practice, however, such methodological prerequisites are hardly fulfilled, so that the determination and the judgement of the optimal state of the system is often an illusion. Consequently, many conventional programming approaches have only a limited validity in practical policy analysis. That is also the reason why instead of optimality analyses impact analyses, effectiveness analyses and strategic decision analyses have received increasing In such analyses, much more emphasis is placed on the attention. formulation of policy objectives, the use of policy instruments, the management of conflicts and the development of compromise principles. An additional reason for the limited relevance of conventional programming models in planning is the fact that such models are usually based on a set of stringent assumptions, such as: the existence of a single and identifiable decision-maker, complete information on all relevant objectives and instruments, perfect insight into the impacts of policy measures on socioeconomic objectives, the absence of equity problems and of spatial or social spillover effects, a stable structure of the economy and so forth. Clearly, such aspects are especially relevant in an integrated economic, environmental, energy and regional policy analysis marked by a wide variety of conflicts. Hence, such a policy analysis should necessarily take account of the multidimensional nature of choice problems [see Nijkamp (1979, 1980)]. It is clear, that a broader view of policy analysis requires an integrative framework for judging alternative policy options. This may imply that instead of optimization of the systems outcomes the attention has to be focussed on providing a logical framework for policy decisions regarding the system by revealing, among other things, conflicts among objectives or groups, by assessing tradeoffs among different choice options, by gauging the distributive aspects of policy measures, by identifying efficient solutions, and by designing appropriate and relevant methods for policy evaluation and compromise strategies. The current interest in interactive multiobjective decision models shows clearly such new trends in designing and employing modern tools for environmental policy-making [see also Hafkamp and Nijkamp (1982a, b)]. In the field of mathematical programming and mathematical economics, several efforts have been made to formulate optimization procedures for problems with multiple objectives [see among others, Keeney and Raiffa (1976), Cohon (1978), Rietveld (1980), and

W Hafiamp

and P. Nijkamp,

Conflict

analysis

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117

Nijkamp and Spronk (1981)]. At present, there is a whole spectrum of different multiobjective methods available, both in the field of continuous programming anaIysis [see, e.g., Nijkamp (1979)J and in the field of discrete plan and project evaluation methods [see, e.g., Voogd (1982)]. The general conclusion from many experiences is that choice problems in an integrated multidimensional policy ansilysis usually do not require a single unambiguous soIution that represents once and for all the optimal state of the system concerned. In the light of the process character of many decision policy analysis is, therefore, an appropriate problems, an interactive approach. This approach is usually based on 8 systematic‘ exchange of information between decision-making agencies and analysts. These interactive approaches are normahy characterized by two steps: I -the

analysts propose a meaningful and feasible tentative solutions on the basis of a we&defined compromise procedure, -the decision-makers respond to each (trial) solution by indicating in what respect (i.e., in regard to which effects) the proposed compromise soIution is still unsatisfactory. These steps can be successively repeated until, after a series of steps, a final satisfactory compromise has been identified. Recently, a large number of interactive models has beeen developed [see among others, Rietveld (1980) and Spronk (1981)]. Interactive policy analysis based on .multiobjective programming methods have already demonstrated their meaning in various policy problems and they appear to have significant advantages compared to traditional optimization methods. In the present paper, only one specific interactive‘ choice method will be dealt with, viz. the so-called reference point method or the method of displaced ideals [see Zeleny (1976)]. It is a method which needs no explicit information on the weights attached by the decision-makers to the relevant objectives. If a feasible and efficient solution to the multiobjective probIem is presented to them, they only need to indicate which objective has to be impr0ve.d in the next itera.tion of the interactive procedure. Fig. 1 gives a concise representation of such an interactive optimization procedure. More details regarding this m -ethod can be found in Hafkamp and Nij,kamp (1982b). The abovementioned procedure can also be directly related to scenario analysis. A scenario is a consistent set of prospective. vaIues of plans, goals, instruments and exogenous circumstances. Both single and compound ,scenarios can be linked to the abovementioned approaches. In the next sections of this paper both a conceptual and an, operational version of an integrated regionaI-economic+nvironmental-energy~ model will be presented on the basis of ‘the abovementioned interactive -multiple objective. framework. This model wil1 be called a Triple Layer -Model.

WC Hujkamp

118

and P. Nijkamp,

ConjZict

analysis

and compromise

strategies

Initiate

Calculate pay-off matrix by means of separate optimization of objectives

c

'

Add/change constraints to initial problem A

Generate compromise solution reference method

1

trialvia point i ,

Solution

Fig.

3. The Triple

Layer

Identify value

1. Stages

of an interactive

optimization

of

unsatisfactory objective

procedure.

Mode1

3.1. Design The Triple Layer Model (TLM) is a model of a spatial system where the pertaining economic, environmental and employment aspects are integrated by projection on three parallel layers. The presence of spatial aspects implies that the system is analyzed at both the regional and the national levels. Hence, it is a National-Regional Economic-Environmental Model [see also Issaev et al. (1982)]. The model is constructed in three stages [see Hafkamp (1979) and Hafkamp and Nijkamp (1979)]: (1)

In the first stage a simple model of the constructed. It contains a ‘naive’ or Relationships and problem definition. defined nor exactly measured. A more model is given in Hafkamp (1979), and

spatial system to be described is ‘common sense’ version of the variables need not be formally detailed description of this simple Hafkamp and Nijkamp (1979).

W

(2)

(3)

Hajkamp

and

P. Nijkamp,

Conflict

analysis

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compromise

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119

In the second stage, a conceptuaE model is constructed by combining the simple model with a suitable formal model (e.g., a general equilibriumtype. model). In this stage, theoretical aspects are put forward, while problems of data collection, measurement, computational difficulty, etc., may be disregarded for the time being. This model will be further discussed in section 3.2. In the third stage, an operationaE model is built. This model allows for the analysis of actual tradeoffs, policy decisions and choices. It is an empirical version of the conceptual model, in which elegance and robustness have to be traded off against data problems and problems of model estimation and validation. The optimal version will be treated in section 3.3.

Once these three versions of the model have been constructed, an iterative feedback process starts so as to improve the model with results that are obtained in each successive stage. For instance, data collected during the construction of the operational model or test results may be used to reformulate the simple model, and so forth, until after a series of experiments a satisfactory and convergent result has been obtained. 3.2.

The

Triple

Layer

Model:

Conceptual

The model has the following set of regions: R=(I,2 Different regions:

version

structure.

The

regional

system

is made

,..., R).

sets of individuals

up of a

(1) (e.g., households)

are associated

with

each of these

(2)

Feasible ‘states’ of the spatial system in. terms of employment and environmental quality are described by

regional

income,

where s is an R-tuple of vectors describing the state of the entire spatial system; s, is a vector describing the state of the system for region T. The variables y,, 1, and z, denote regional income levels, employment levels and environmental quality indicators. S is supposed to be a closed, compact and convex set. Various policy mixes (combinations of regional economic policy, environmental policy and Iabour market policy) may be ‘assumed enabling a

120

W Hajkamp

(central) government any situation which Next, individuals denoted by Xr={+,=k

and P. Nijkamp,

Conflict

analysis

and compromise

strategies

to control the spatial system so as to reach in principle is reflected by an element of S. are assumed to have an identical ‘consumption set’, X,,

Lz,)~R:).

(4)

This does, however, not imply that all individuals have identical preferences, as one may distinguish different interest groups. There are various ways of incorporating such interests in individual welfare functions. Here we suppose the existence of twice differentiable, concave individual welfare functions:

so that individual welfare in region Y is only determined by income, employment and environmental quality in region r. In addition, a choice set C is defined that serves as a basis for individ.uals to” decide which objective deserves the highest priority and hence should be raised first: C=

A aj, 5

~i,~R~(ci,=(a,,a,,a,)

ajE(O,

j=l

l>

.

(6)

>

(6) means that during each stage of the choice process not all objectives can be improved simultaneously, but that only one objective can be increased in value (in other words, aj is a zero-one variable). The spatial system composed of individuals can now in a concise way be characterized by

EE = (R, c, s, (X,, WQC)}.

PI

from the point of view of individuals be Spatial system EE can regarded as an economy with external effects only. The set of Pareto-optimaE states PO in this system can now be defined as PO = (S E S I3r E IT, 3i, E E:

Wi,(S:)

>

Wi,(Sr)*S’

+ S V

A state of the system is called Pareto-optimal, if an improvement of any individual welfare position is only possible (i.e., within S) through affecting at least one other individual’s welfare position. It should be noted here that an efficiency criterion is defined rather than an equilibrium criterion. The set of Pareto-optimal solutions is in multiple objective decision theory also known as the set of efficient or’non-dominated solutions.

W Hajkamp

and P. Nijkamp,

Conflict

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and compromise

s.l.ategies

121

The presence of different interest groups aiming at maximizing respectively regional income, employment and environmental quality makes it impossible to identify one single overall best solution. Consequently, formally a multidecision-maker, multiobjective problem has to be solved so as to achieve a state of the system that is a compromise for the conflicting interests among groups of individuals. This can be done by using the abovementioned method based on the reference point .method. However, this method does not specify a decision rule for identifying a most preferred objective if there are many decisionmakers involved. This lack may inter alia be overcome by using a voting procedure based on a majority rule. The preference relationships (5) should which decision-makers be interpreted as ‘tacit preferences’ (preferences themselves are not explicitly and entirely aware of). Clearly, if all individuals would have known welfare functions, an interactive compromise procedure would be superfluous in selecting an optimal (compromise) state for system EE, since in that case a straightforward traditional optimization approach could be used. We make the assumption, however, that individuals are not explicitly aware of welfare functions describing their preferences. We also assume that they are unable to give precise information on their preferences in terms of weights (tradeand attached to the various objectives (income, employment offs) environmental quality). Otherwise, it would be easy for a central government to. simply optimize a social welfare function of the type:

or to optimize a weighted sum tradeoffs given by the individuals. 3.3.

The

Triple

Layer

Model:

of the

Operational

objectives,

using

information

on

version

As mentioned above, TLM is a model of a spatial system where economic, environmental and labour market aspects are integrated. The spatial element implies that the system is analyzed at the level of regions interacting with the national level. Consequently, the operational version of TLM requires a more accurate look at the interactions of national-regional economic, environmental and employment dimensions of such a complex system. As TLM projects such a complex reality on three mutually interacting parallel layers, viz. an economic layer, an environmental layer and an employment layer, several dimensions of human (individual or collective) behaviour can thus be depicted in three submodels, according to their respective different Thus, in the operational version of TLM the aspects and consequences.

122

W Hafkamp

following

sub-models

and P. Nijkamp,

Contict

analysis

and compromise

strategies

have to be calibrated:

----an economic sub-model; this is a national-regional economic model of the (Dutch) economy. It is the result of coupling the so-called Secmon-model with a multi-regional input-output model of five Dutch regions. The Secmon-model has been developed by Driehuis (1978). It is primarily developed as a simulation model of the Dutch economy that analyzes long-term effects of various alternative economic policies. Main goal variables included in this model are: inflation, unemployment, economic growth and current accounts. Main policy instruments are: taxes and public expenditure, monetary instruments, exchange rate, wages and price control, and labour market policy. -a labour market sub-model; this describes employment (supply. and demand) in all regions and sectors of the economy. The demand for labour is analyzed through the production structure. Hence, gross production, production capacity and capacity use in capital-intensive sectors, as well as import substitution constitute major elements in this model. The supply of labour is analyzed by means of normal market factors, while it is also based on demographic data. -an environmental sub-model; this contains a description of emission and diffusion of air pollutants. In addition, anti-pollution technology is also included in this sub-model. Furthermore, since a major part of air pollution is caused by combustion of fossil fuels, it is important to take into account the availability of several types of energy sources and to introduce an energy saving policy. An illustration interrelations greater detail 4. Three

of TLM with its sub-models, its modular is given in fig. 2. The three sub-models will in section 4.

sub-models

of the Triple

In this section, the economic, employment detail (see also fig. 2). 4.1.

The economic

Layer

three important and environmental

design and be described

its in

Model components model will

of the TLM, viz. the be described in greater

sub-model

The economic sub-model consists of a national-regional model of the national economy. Within this sub-model four groups of economic actors are distinguished: households, firms, government and other agents. For a further description of this sub-model, we use a subdivision into nine modules, aiming at analyzing successively the following components:

W Hafkamp

and P. NVkamp,

Conflict

analysis

and compromise

ECONOMIC <

I

I

I

I

2’.

-

L&&&+&y&l L!L?e!+L-

t 3----------,--I

nvestmentsby

I’

:

J I

Monetary Relations ----a~n-te-i~si rate7 Balance o I- - - .- - - ;pcLyfients - .- -

t

‘lczcpotis.

cstccks

1 Government Budget and Social Insurances ! EMPLOYMENT

SUB-MODEL f I

(I) (11) (III) (IV) 09 WI)

production, final demand, imports, production capacity, wages and prices, income,

T

L-mL~~l---I---

I I

Fig.

123

SUB-MODEL

I

f. Wages +--h ~

strategies

2. The TLM

structure.

124

W! Hajlzamp

(VII)

and P. Nijkamp,

Conflict

analysis

and compromise

strategies

government budget, social insurances, monetary relations.

(VIII) (IX)

From these nine modules, only the production module and the final demand module are followed by a regional disaggregation. The other modules analyze economic relationships only at the national level. This is evidently a serious disadvantage of the present economic sub-model, especially as far as the imports and the production capacity modules are concerned. Furthermore, a regionalization of the income module would be of primary interest for a better description of spatial (consumer) expenditure behaviour. For this purpose, the integration of a multi-regional demographic model into the present model would be a prerequisite. However, given the lim itations emerging from the available data, the final demand module only allocates national final demand from 4 sectors to 23 sectors (top-down ‘structure), while the production module does not specify the spatial origin of deliveries. In the light of these constraints, a regionalized income module would be superfluous. It should also be added that as far as government expenditure policy is concerned, the model is hampered by the top-down allocation of final demand categories, so that here a regional specification of regional economic policy can hardly be integrated into the model. For the remaining modules: wages and prices, social insurances and monetary relations, an entirely national approach is evidently more justified. Since the Secmon-model is originally a non-Zinear model, a large number of equations have been linearized. The various modules will now successively be described. Special attention will be paid to spatial and sectoral (dis)aggregation.

(I)

The

production

moduk

The basis of the production module is a multi-regional input-output model, in which 23 economic sectors located in 5 main regions in The Netherlands are distinguished (see appendix). The data used is mainly based on as yet unpublished regional input-output tables for The Netherlands (1970). They give partial information on spatial flows of goods and services by specifying deliveries from national sectors to regional sectors and to they reflect essentially lim ited regional categories of final demand. Hence, information input-output data. Since 1973 is the base year of the model, the regional input-output data has first been updated from 1970 to 1973 by assuming a time-constant regional-sectoral activity pattern and by imposing that the updated data is summing up to the known national input-output data for 1973. An improvement of this updating procedure is currently being made; this includes the partial regional accounts for 1973 which give exact

W Nafkamp

sectoral, equations

and P. Nijkamp,

regional values of this module xN-

2

A,x,-f

Conflict

analysis

and compromise

strategies

for gross production and value added. are the 23 sectoral production equations:

Zf”“‘,

125

The

basic

(10)

r=1

where =vector of national production volumes in 23 sectors, production volumes in 23 sectors; r = i, . _ ., 5, x* = vector of regional f =final demand vector in 23 sectors being endogenous in the economic sub-model (see final demand module), fau’=final demand vector in 23 sectors being exogenous in the economic,submodel. XN

The disadvantage of insufficient data for these production equations, is of regional production uniquely evidently, that the vectors are not Therefore we set constraints on regional determined within the model. production variables, allowing a ten percent variation around the starting values: r-=1,2

XL,rs%5-ICcJ,.,

,...,

5,

(12)

where = vector = vector Xr-J,t XL,r

of lower of upper

lim its lim its

on production on production

in region in region

r in 23 sectors, r in 23 sectors.

Since the national part of the economic sub-model has only four sectors, the 23 gross production values of eq. (10) are aggregated to 4 production values as far as the national level is concerned. The aggregation scheme is given in the appendix. The production values determined in this way play a role in the modules for imports, production capacity, income and government budget. (II)

The ,finaZ

demand

module

Since there is a great number in very different ways, the final RSUE

- E

of final demand

demand module

categories that are analyzed consists of a number of sub-

126

W Haflcamp

modules,

in which

and P. Nijkamp,

Conflict

the following

types

analysis

of final

and cornp+omise

demand

strategies

are described:

- consumption by households, - investments by firms, - material government consumption, - government investments, -export to other countries. We shall first give a description link between the final demand

of these sub-modules and then deal with module and the production module.

the

Consumption by households is calculated as a fixed proportion of real (wage and non-wage) income available after taxes. The sectoral expenditure pattern depends on relative prices in four sectors. Trade margins in the retail sector, sector calculated as fixed proportions from the agricultural and manufacturing industries, are assigned to services provided by the service sectors. Thus, final demand exerted by households is reformulated as final production to be delivered by the various sectors. Consumption by households is essential in the production module but it also appears in the income module and in the government expenditure module. Investments

by firms

- investments -investments investments and service -depreciation calculated. The (1)

basic

make

up a relatively

large

sub-module

since:

in equipment and buildings are calculated separately, by destination are transformed into investments by origin; in housing are allocated to delivering sectors (construction sectors), of capital stock and changes of investments in inventories are

elements

of this sub-module

can be described

Investments in equipment by firms (of manufacturing service sectors) are assessed. They depend on:

in four

steps:

industries

-real non-wage income available for investments (i.e., after m inus the ascribed income of self-employed persons), -the liquidity rate, -the occupancy rate of capital stocks. For

the manufacturing

industries,

the basic

pi+y

ib,qal(Z-AT,-w-Q i

equation

a,dL,/Y”-aa, (

and

taxes

the

and

is

(~)++)+id,,,,,,

(13)

W

Hajkamp

and

P. Nijkamp,

Conjlict

analysis

and

compromise

strategies

127

where =investments by firms in equipment, = non-wage income, =change in direct taxes on non-wage income, = wage-sum per employee, T = number of self-employed, = prices of investment goods, Pi’ = national income, Y A L4/ Y” = liquidity rate, = occupancy rate of capital goods, 4 *b = autonomous investments by. firms in equipment. zoaut ii

Z A T,

Investment levels in the variables in the model. (2)

(3)

(4)

sectors

agriculture

and

construction

are exogenous

The relative changes of investments in equipment by firms are used to assess the relative changes of investments in buildings by firms. The relative changes in. overall investments by firms are then calculated as a weighted sum of both investment categories. Then the investment levels of firms are assessed by using the relative changes in the levels of investments from a previous period. These are all investments by destination. Investments by origin are calculated by allocating the investments by destination to sectors according to a certain transformation scheme (which is assumed to be constant); investments in housing (which is a. policy instrument within the model) are allocated to the construction and service sectors. Depreciation of capital depends on gross investments by firms and is partially exogenous in the model. Changes in inventories depend on changes in gross production of a sector; they also have an exogenous component.

Government expenditure is divided into consumption and investments. Expenditure on military and other equipment is exogenous in the model. These categories of consumption, together with consumption depending on the number of government employees, are allocated to the various sectors. Investments

-

by the government

are divided

into

several

categories:

and buildings; schools these are exogenous variables allocated to construction and service sectors, road construction, water control and other infrastructural projects; these variables, depending on interest rate, investments in are endogenous houses and an exogenous part, allocated to construction and service sectors,

128

-

W

Hajkamp

other facilities; industries.

and

P. Nijkamp,

these

Conjlict

are exogenous

analysis

and

variables

compromise

strategies

supplied

by

manufacturing

Exports of goods and services are exogenous in the sectors agriculture and construction and in the service sectors. They are only endogenous in the manufacturing industries. The endogenous variables occupancy rate of capital and export prices have in addition to exogenous variables for the volume of world trade and world market prices an important influence. This can be reflected, by a basic equation: pj=Pl~j+PZ(tiej-~iwj>+P,4j,

(14)

where

ej

= relative r+~~= relative Qej = relative relative P'wj= L& = relative

Imports

change change change change change

of of of of of

exports in sector j, world market trade volume for sector j, price level for exports of sector j, price level on the wbrld market, occupancy rate of capital stock.

are subdivided

into

4 main

categories:

-non-competing imports like raw materials and primary energy that cannot be replaced by a national supply (crude oil and other raw materials). They are calculated as a constant proportion of sectoral production levels. -competing imports like half-products; their amounts do not only depend on sectoral production, but also on relative prices. A basic equation for this type of imports is

[ 1 Y2

mkj=yl-xj-

5

,

where

(15)

mj

=competing imports delivered by 3 foreign sector k imported sector j, =production volume of sector j, xj = domestic price level of goods delivered by sector k, Pk pmkj = world market prices for goods imported by sector j from sector

by

m,kj

-

investment goods; these amounts depend on firms, -consumer goods and services; they depend consumption and relative prices.

the

level

on

the

of

investment

levels

of

k.

national

by

W. Hafkamp

(IV)

and

The production

P. Nijkamp,

capacity

Conflict

analysis

and

compromise

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129

module

Key variables in this module are production capacity and occupancy rate. The latter variable is defined as the ratio of actual value added (which is also found in this module) and production capacity. Important factors in production capacity are the investment rate and the number of working hours in firms. A representative typical equation is

s;j*+j,

[

1‘b

1

“.@

+6,AAjf6,hb+6,,

Y2

where = relative change in production =number of working hours, =change in economic life-time sector j.

97 hb AA,

capacity

of sector

of the oldest

j,

vintage

of capital

goods

in

Value added is simply calculated as total primary costs minus imports and of labour productivity depends on the depreciation. Relative change difference between the relative change in value added and a weighted sum of relative changes in the number of employed and the number of self-employed persons. (V)

The

wage

and

prices

module

depend on relative changes in measured in relative changes, Wages, labour/productivity (see previous module), consumer prices, the relative rate for social insurances, tax structure and the percentage premium Manufacturing industries are considered as ‘wage-leaders’. employment. These relative changes in ‘contract wages’ and ‘incidental wages’ are calculated separately. The basic equations are:

where

6, k

=relative = relative

change change

of consumer prices, of labour productivity,

130

S, td

S,

W

Hajkamp

and

=social insurance income, =direct taxes paid =social insurance income.

P. Nijkamp,

Conflict

premiums

paid

analysis

by

and

compromise

employer

as

strategies

a share

by households as a share of labour income, premiums paid by households as a share

of

labour

of labour

In regard to-prices, a distinction is made between producer prices, prices of final demand categories and prices of capital services. Producer prices depend on other producer prices (P,), the difference between relative change of wages and labour productivity, import prices, occupancy rate of capital, investment prices and an autonomous price change. Prices for several final demand categories depend in turn on producer prices, import prices and autonomous factors. (VI)

The income

module

Main categories of income distinguished here are wage income and nonwage income. Available wage income (IV”) is calculated by adding up total wages paid by firms and government (w), social insurance payments to households (US..), pension funds payments to households (Up. & and income transfers paid by the government to households (Tr,.,), and by subtracting in&me taxes (Tw), premiums paid by households to social insurances (P, - P, . ,), and pension funds {Pp .J, WB=

W-t- Us.,+

U,.,+

Trs+-

Tw-(P,-PP,.,)-PP,.,.

cw

Non-wage income is calculated from gross production value by subtracting all other elements that appear in a column of an input-output table; this makes it a residual variable in the model, which may be inaccurate, as it is calculated as the sum of inaccuracies in all other variables (such as intermediary deliveries, imports, depreciation, wages and prices for these categories). (VII)

The government

budget

module

Government income is calculated as the sum of a number of tax-categories: direct taxes on final demand categories, direct taxes on wage and non-wage income, natural gas receipts and an exogenous category of other government income. Government expenditure is the sum of a number of payments, such as: wages (paid to employees), interest (paid on government debts), transfers (to households and firms), government consumption and investments, plus two exogenous variables for autonomous expenditures in general and specifically for social insurances.

W Hajkamp

and P. Nijkamp,

Conflict

The deficit on the government between expenditure and income, been accounted for elsewhere. (VIII)

The sociaE insurance

analysis

and compromise

strategies

131

budget is finally calculated as the difference with a correction for income, that has not

module

This module stems from the VINTAF-XI-model, which is a macroeconomic model of the Dutch economy, developed by the Central Planning Bureau (CPB). Several categories of insurance payments to households are assumed to depend inter aEia on the number of individuals entitled to payments and wage-levels. Finally, the resulting premiums to be paid are allocated to households and firms. (XI)

The monetary

mod&

Monetary variables play an important role throughout the model. In this module some relations between the monetary and the real sphere are depicted. Three major sources of money creation are distinguished: -monetary finance measures by the government; current account, capital account and the deficit of the government budget are the most influential variables here, -credit facilities, created by banks; important influences are exerted by the interest rate and the unemployment rate, -changes in gold and currency stock; the deficit on the capital account is explained via the deficit on the current account and the difference between world and national interest rate. The interest rate itself is assumed to rate (exogenous), the depend on the national ‘liquidity rate’, the discount percentage of unemployment and consumer prices. 4.2.

The employment

sub-model

The employment sub-model analyzes primarily the demand for labour at both a regional and a sectoral level. For the time being, the supply-side of the labour market is considered as exogenously determined by demographic and social developments. It would be worthwhile however, when more detailed information on detailed information on demographic developments, education and training were available to incorporate an endogenous supply.m side of the labour market. Labour

demand

E,=A,T-X,,

is linked

with

the production

system

through:

(21)

132

W Hafkamp

and P. Nijkamp,

Conflict

analysis

and compromise

strategies

where I, = total demand for labour I, = vector of sectoral labour

in region r (r = 1,:. ., 5) in 23 sectors, coefficients.

This simple linear relationship between sectoral output and labour demand is adjusted for the effects of changes in labour productivity, occupation rate and wage levels; these are influences from the economic sub-model. Regional labour supply is assumed to have an upper lim it, while for the moment no interregional m igration and commuting is assumed:

I!,*, is the vector

where 4.3.

of upper

The environmental

The environmental

lim its

on regional

employment.

sub-model sub-model

describes

3 aspects

of environmental

quality:

(1)

emission of air pollutants caused by: combustion of fossil fuels, (4 (b) process emissions, etc., (2) concentration of air pollutants (via diffusion), (3) reduction of emission by: saving energy, selective growth, etc., (4 alternative choices of energy sources, (b) anti-pollution technology. (cl Pollution of water and soil is not taken into account here, while no attention is paid to the phenomenon of synergetic effects. The following pollution categories are taken into account: sulphur dioxide, nitrogen oxides and dust particles. The choice of energy source also has an important influence on the emission of air pollutants. For example, SO, emissions in The Netherlands decreased ‘drastically after a large-scale introduction of natural gas, but since a switch back to coal or oil is presently taking place, a drastic increase is expected. Especially the shift of electricity producers from natural gas to coal or nuclear energy and the further exploration and introduction of alternative energy sources (solar energy, wind, etc.) are of great importance to environmental quality. The way in which the components of these 3 modules are linked is represented in fig. 2. More details are to be found in Hafkamp (1982). Energy

demand e *.J

=

ij

is linked ’ x,,

with

the production

system

through: (23)

W Hajkamp

and P. Nijkamp,

Conflict

analysis

and compromise

strategies

133

where e,,j = vector j=l,..., sj = vector matrix Pollution

of demand for energy of type j in region Y in 23 sectors; 8; r=l,..., 5, of energy coefficients in 23 sectors; j = 1,. . ., 8 (~j is a diagonalized representation).

emission

in the spatial

system at hand can be written

as

where pr,k = vector caused =vector 4k.j energy

of emission by the input of emission type j.

of pollutant of type. k in region I in 23 sectors, of energy type j (j = 1,. . ., 8), k = 1,2,3, coefficients for pollutant type k, caused by using

In eqs. (23) and’ (24) policy instruments are not yet introduced. This may be done by including the impact on energy demand from fuel saving techniques, the influence on emission of pollutants from energy saving, a changing energy mix, and anti-pollution techniques. ways of controlling W ithin the model presented above, two possible emission levels may be distinguished: -by -by

controlling the general level of economic activity, adopting the possibility of a selective growth policy; relatively non-polluting sectors are favoured in their detriment of polluting sectors.

5. Empirical

this means development

that in

results

The implementation of the abovementioned model has led to severe computational problems, which are very hard to overcome. Solving vector optimization problems of this size requires powerful optimization routines. Some tentative results are given in tables l-3. These results are only valuable as a illustrative example because they refer to the period from which the data is taken (1970-1977) and because the interactive compromise procedure of fig. 2 was slightly adjusted to the computational problem. The model is characterized by two sorts of conflicts. First, there is a conflict between objectives. A high national income, a high level of employment and a high environmental quality cannot be obtained simultaneously. Secondly, there is a conflict between regions. This conflict may be a pure spatial distribution conflict; if maximization of income in one RSUE-F

134

W! Hafkamp

and

P. Nijkamp,

Conflict

analysis

Table Results

of the interactive

Pay

compromise

strategies

1

optimization

Optimization

and

procedure:

Iteration

no. 1.

of

off matrix

Variable

Income (log Dfl/Y)

Employment ( lo6 man y.)

Environ. quality

First compromise solution

National income National employment Environmental qual.

134.600 3.360 77.000

133.300 3.390 76.200

130.400 3.330 72.700

134.200 3.380 74.900

14.200 22.300 29.000 32.600 36.500

14.100 21.400 29.500 3~.100 ‘36.200

12.600 20.400 28.600 34.100 34.800

14.200 21.900 28.600 32.800 36.700

0.309 0.582 0.827 0.775 0.869

0.313 0.587 0.833 0.782 0.874

0.306 0.575 0.818 0.767 0.859

0.312 0.585 0.831 0.781 0.873

6.460 10.720 23.110 16.230 20.530

6.570 9.630 25.830 15.270 18.890

6.410 9.780 23.300 14.120 19.130.

6.240 9.630 22.420 16.070 20.540

Income, Income, Income, Income, Income,

Region Region Region Region Region

Employment, Employment, Employment, Employment, Employment, Environm. Environm. Environm. Environm. Environm.

1 2 3 4 5 1 2 3 4 5

Qual. Qual. Qual. Qual. Qual.

I 2 3 4 5

region leads to low’ incomes for other regions. The conflict may alsq exist in conjunction with the first sort of conflict: maximization of income in one region may lead to high pollution levels in adjacent regions. In the present version of the Triple Layer Model, an analysis of the actual impacts of combinations of the abovementioned conflicts is not yet included. The assumptions made on consumer/voter behaviour in section 3 are rather restrictive; they exclude, for example, ‘altruistic’ behaviour which may exist when someone (a voter, a consumer) is also interested in environmental quality in a region other than his own or in the spatial distribution of income. Consequently, a less complete version of the interactive optimization’ procedure from fig. 2 has been carried out. The information given for each iteration in the tables has been lim ited to the values of objectives on a national and a regional level. In addition, for the sake of simplicity, the procedure has been modified for our illustrative case study by optimizing objectives at a nationaE level (which reduced the number of optimizations per iteration) and after identification of an

W Hafkamp

and P. Nijkamp,

Conflict

analysis

and compromise

strategies

135

unsatisfactory objective value adding (or changing) constraints on the national objectives. Results of various experiments are included in tables 1-3. The variables represent income (in prices of 1973), employment (in industry), and environmental quality (as a weighted average of the three pollutants considered). After the first iteration, environmental quality was taken to be the objective which needed to be raised most urgently. Therefore, the compromise value of the environmental quality indicator was added to the constraint set as a lower bound. As a result, environmental quality showed a slight improvement in the next iteration, though apparently not all regional variables were necessarily increased in value (see table 1). For the third iteration, the compromise values of the national environmental quality indicators were again added to the constraint set as lower bounds on environmental quality. From table 3, it can be seen that the values of objectives showed very little variation across the columns of the in this way the consequences of many policy pay-off matrix. Clearly, decisions or of various policy scenarios can easily be identified. Table Results

of the interactive

optimization

Optimisation Pay-off

2 procedure:

Iteration

no. 2.

of

matrix

Variable

Income (10’ DWY)

Employment (lo6 man y.)

Environm. quality

Second compromise solution

National income National employment Environmental qual.

134.500 3.360 74.900

132.400 3.390 74.900

130.400 3.330 72.700

134.000 3.360 73.400

14.200 22.300 28.800 32.700 36.500

12.700 21.600 28.600 33.700 35.800

12.600 20.400 28.600 34.100 34.800

14.200 22.000 28.800 32.300 36.800

0.310 0.582 0.827 0.775 0.869

0.313 0.586 0.832 Or782 0.874

0.306 0.575 0.818 0.767 0.860

0.310 0.581 0.826 0.774 0.868

6.460 10.720 22.640 16.170 18.900

6.520 10.670 23.310 15.050 19.350

6.410 9.780 23.300 14.120 19.130

6.430 9.630 ‘22.620 14.800 19.940

Income, Income, Income, Income, Income,

Region Region Region Region Region

Employment, Employment, Employment, Employment, Employment, Environm. Environm. Environm. Environm. Environm.

1 2 3 4 5 1 2 3 4 5

Qual. Qual. Qual. Qual. Qual.

1 2 3 4 5

‘_

136

W

Hajkamp

and

P. Nijkamp,

Conflict

analysis

Table Results

of the interactive

Pay-off

compromise

strategies

3

optimization

Optimisation

and

procedure:

Iteration

no. 3.

of

matrix

Variable

Income (10’ DWY)

Employment ( lo6 man y.)

Environm. quality

Third compromise soIution

National income National employment Environmental qua].

134.000 3.360 73.400

132.000 3.360 73.400

130.400 3.330 72.800

133.300 3.360 73.100

13.600 21.300 29.200 33.300 36.600

12.600 21.200 28.700 33.700 35.800

12.600 20.400 28.600 34.100 34.800

13.500 21.500 28.300 34.400 35.700

0.310 0.582 0.828 0.776 0.869

0.306 0.575 0.818 0.767 0.859

0.309 0.581 0.826 0.774 0.867

6.500 10.110 23.310 14.160 19.330

6.410 9.780 23.300 14.120 19.130

6.510 10.100 23.300 14.120 19.110

Income, Income, Income, Income, Income,

Region Region Region Region Region

Employment, Employment, Employment, Employment, Employment, Environm. Environm. Environm. Environm, Environm.

1 2 3 4 5

0.310, 0.58 1 0.826 0.774 0.868

1 2 3 4 5 Qual. Qual. Qual. Qual. Qual.

1 2 3 4 5

6.420 9.620 23.280 14.150 19.930

In fact, variations in the levels of objective variables are small in all these iterations. This is mainly due to the fact that regional sectoral production and economic policy instruments are allowed to vary approx. 10% around their initial value (for production variables), and between 0 and 0.5 billion guilders per year (for instrument variables). Simulation experiments with wider variations and more policy instruments are currently being carried out. The results in tables l-3 are surprising because they show that even gradual changes at the national level imply also trade-offs at the regional level. For instance, environmental quality at a national level increases during the successive iterations (because of modified constraints on environmental quality). At the regional level, we find that three regions show a decline of the environmental quality (these are peripheral regions in the North-Eastern and Southern part of the country), while the remaining regions show an improvement of environmental quality. The net effect of both changes appears to be positive. The employment situation at the national level shows a gradual (but m inimal) decrease during the three iterations, which is equally spread over all regions; this is primarily due to the fact that overall economic activities are

W HaJkamp

and P. Nijkamp,

Conflict

analysis

and compromise

strategies

I37

decreasing slightly due to a demand for a higher environmental quality in our model where no abatement industry has been taken into account. For the income variable, almost the same remarks can be made, except for region 4. This is the only region that ends up in the compromise procedure with both a better environmental quality and a higher regional income. 6. Concluding

remarks

There are two major obstacles in the use of iterative multiobjective programming methods focussing on future policy choices. The first one is the lack of reliable data. The previous experiments have demonstrated that generated regional I-O tables may be meaningful in assessing the spatial spillover effects of various policies, but it is also evident that uncertainty is still a prevailing feature in multiregional modelling [see also Issaev et al. (1982)]. It should be added, however, that this uncertainty is not a specific feature of multiobjective programming methods, but is inherent in the use of regional economic models, in the use of economic models in general. W ith regard to this, it has to be emphasized that the development of TIM is still continuing. For the operational version of the model, the major points for extensions and revisions are: -the improvement of the multi-regional part of the economic sub-model by using estimated full-information input-output tables, updated from 1970 to an appropriate recent period, -regionalization of the final demand module in the economic sub-model, -linking the model with a multi-regional demographic model, - expanding the environmental sub-model by introducing more economicenvironmental impacts, describing ecological process and environmentaleconomic feedback relationships. A second major obstacle is made up by uncertainties in the decision-making area. In many cases, the identification of decision-making bodies is fraught with difficulties, as many interest groups and individual power dimensions enter the social choice problems. In this respect, interactive choice methods do not guarantee an unambiguous solution, but may provide a platform for a rationalization of choices in severe conflicts. The preliminary results of the previous section show that conflicting objectives exist between different kinds of variables as well as between regions. Therefore, a closer analysis of decision-making procedures deserves a lot of attention, not only to recognize these categories of conflicts, but also to model the behaviour of groups of decision-makers, national and local authorities, interest groups, and in general, multi-actor systems. In regard to this, the abovementioned interactive multiobjective

138

approaches framework

W H&

and P. Nijkamp,

complex policy to have several advantages:

Conflict

analysis

and compromise

based

problems

on

strategies

an

input-output

-They are in agreement with the process character of complex policy problems and are abIe to use learning principles for policy-makers. -They emphasize the active role of policy-makers in specifying and solving inter alia by making policy objectives and trade-offs choice problems, more explicit. -They are able to take account of the variety and the conflicting nature of policy options or criteria without requiring a prior specification of weights. -They provide an integrative framework for eliminating less relevant alternatives and for choosing consistent compromise solutions. Appendix:

Regions

and sectors

Regions

Provinces

1 Friesland 2 Groningen

i

North

3 Drenthe

ssel

4 Overij

2 East

5 Gelderland 6

Utrecht

3 West

I-

4 West

II

7 Noord-Holland 8

Zuid-Holland

9 Zeeland 5 South

10 Noord-Brabant 11 Limburg

Fig. A.l.

Eleven

Sectors in the regional part The aggregation scheme:

provinces

and five regions

of the model

of The Netherlands.

and in the national

Multiregional (1) Agriculture, forestry, fishing. (2) M ining (oil and natural gas excluded).

part

of the model.

W Nafkamp

(3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23)

and P. Nijkamp,

Conflict

analysis

and compromise

strategies

Food industry, stock farm products. Food industry, other productsBeverages and tobacco industry. Textile industry. Clothing, leather and shoe manufacturing. Timber and furniture industry. Paper and graphical industry, publishing. Oil and chemical industry. Construction materials, pottery and glass. Metal(ware) and machine industry. Electrotechnical, means of transportation, and machine manufacturing industry. Public utilities. Construction industry. Trade, hotel and catering, repair services. Transportation and communication services. Banks and insurances. Housing property and business service. . Medical and veterinary services. Culture and recreationOther services. Not mentioned elsewhere.

industry;

139

other

National

(1) (2) (3) (4

Agriculture (1). Manufacturing industries Construction (15). Service (16-23).

(2-14).

Cohon, J.L., 1978, Multiobjective programming and planning (Academic Press, New York). Driehuis, W., 1978, Een sectoraal model t.b.v. de analyse van de nederlandse economic op lange termijn (SECMON), Mimeo. (Dept. of Economics, University of Amsterdam, Amsterdam). Hafkamp, W., 1979, Elements of science policy in environmental economics, Working paper, Mimeo. (Dept. of Economics, University of Amsterdam, Amsterdam). Hafkamp, W., 1982, Linearisering van SECMON-A, Working paper, Mimeo. (Dept. of Economics, University of Amsterdam, Amsterdam). Hafkamp, W. and P. Nijkamp, 1979, Dilemmas in environmental economics, Canadian Journal of Regional Science 11, no. 2, l-21. Hafkamp, W. and P. Nijkamp, 1982a, Towards an integrated national-regional environmentafeconomic model, in; S. Rinaldi et al., eds., Environmental systems and management (NorthHolland, Amsterdam) 653-663. 1982b, Conflict analysis in environmental-economic regional Hafkamp, W. and P. Nijkamp, systems, in: S. Rinaldi et al., eds., Environmental systems and management (North-Holland, Amsterdam) 639-651.

140

W Hafkamp

and P. Nijkamp,

Confict

analysis

and compromise

strategies

Issaev, B., P. Nijkamp, P. Rietveld and F. Snickars,1982, Multiregional economic modeling: Theory and practice (North-Holland, Amsterdam). Keeney, R. and H. Raiffa, 1976, Decisions with multiple objectives (Wiley, New York). Nijkamp, P., 1979, Multidimensional spatial data and decision analysis (Wiley, Chichester). Nijkamp, P., 1980, Environmental policy analysis (Wiley, Chichester). Operational methods (Gower, Nijkamp, P. and J. Spronk, 1981, Multicriteria analysis: Aldershot). Rietveld, P., 1980, Multiple objective decision methods and regional planning (North-Holland, Amsterdam). Spronk, J., 1981, Interactive multiple goal programming (Martinus Nijhoff, Boston, MA). Voogd, II:, 1982, Multicriteria evaluation for urban and regional planning (Pion, London). Zeleny, M., 1976, The theory of displaced ideal, in: Multiple criteria decision making (Springer, Berlin) 153-206.